After feature selection, internal validation and external testing, a model of T1 radiomics had good diagnostic performance (AUC 0.753) for multinomial classification of disease phenotype . A subset of six radiomic features outperformed mean native T1 values for classification between myocardial health vs. disease and HCM phenocopies . We show that myocardial texture assessed by native T1 maps is linked to features of cardiac disease. Myocardial radiomic phenotyping could enhance the diagnostic yield of T1 mapping for myocardial disease detection and classification.We explored whether radiomic features from T1 maps by cardiac magnetic resonance (CMR) could enhance the diagnostic value of T1 mapping in distinguishing health from disease and classifying cardiac disease phenotypes. A total of 149 patients  and n = 28 with cardiac amyloidosis) undergoing a CMR scan were included in this study. We extracted a total of 850 radiomic features and explored their value in disease classification. We applied principal component analysis and unsupervised clustering in exploratory analysis, and then machine learning for feature selection of the best radiomic features that maximized the diagnostic value for cardiac disease classification. The first three principal components of the T1 radiomics were distinctively correlated with cardiac disease type. Unsupervised hierarchical clustering of the population by myocardial T1 radiomics was significantly associated with myocardial disease type (chi Myocardial T1 mapping is also helpful across a spectrum of disease conditions, including diffuse interstitial or replacement fibrosis, water, or infiltrative disorders3. However, a major problem with T1 mapping is that there is a significant overlap in myocardial native T1 values between health and disease; thus, while in-patient regional or even temporal variations in native T1 may be informative of disease development or progression, between-patient comparisons are less useful. A method that could harness the richness of information contained in myocardial T1 maps, would enhance the diagnostic value of T1 mapping for rapid detection of myocardial disease without the need of contrast agents.Cardiac magnetic resonance (CMR) is considered the state-of-the-art imaging approach for assessing myocardial disease4. The segmented anatomical volumes are rendered into quantitative radiomic features that provide information on tissue volume, shape, and texture by analyzing the spatial relationship of (dis)similar voxels 8. In other medical fields, such as in clinical oncology, radiomic phenotyping has been successfully used to characterize the distinct biological phenotypes of tumors and provide relevant prognostic information8. More recently, we have also shown how pericoronary tissue CT radiomics can enhance cardiovascular risk stratification4. Since T1 mapping is a standardized CMR acquisition, extraction of radiomic features by myocardial T1 maps is feasible9 and recent evidence supports their value for classification between hypertensive heart disease and hypertrophic cardiomyopathy (HCM)10. However more evidence is needed to fully establish the clinical value of T1 radiomics in reliably discriminating between various cardiac phenotypes.Radiomics is a rapidly evolving field, which uses data-characterization algorithms to extract data from medical imagesThe aim of this proof-of-concept study was to extract and validate radiomic features from T1 maps using machine learning in a wide spectrum of conditions ranging from normal to various myocardial diseases with particular emphasis on distinguishing healthy from diseased myocardium and classifying LVH phenotypes.Study Arm 1 comprised of 20 CMR scans with available native T1 mapping randomly selected by our archive . These imaging datasets served for the purpose of stability assessment  of extracted radiomic features from T1 maps, which were included in further analysis in Study Arm 2.Study Arm 2 included a total of 152 consecutive patients undergoing a CMR scan (period 2019–2020) and native T1 mapping as follows: individuals without evidence of structural heart disease on CMR (n = 30), patients with left ventricular hypertrophy  of various causes , patients with known HCM (n = 61) and patients with known cardiac amyloidosis (n = 28). Three patients (n = 3) with suboptimal image quality of T1 maps  were excluded from analysis. The study was approved by the Institutional Research Ethics Committee  with waiver of consent since the study involved only the use of anonymized imaging datasets; no individual patient data or human tissue samples were collected. All research was performed in accordance with relevant guidelines/regulations and in accordance with the Declaration of Helsinki.A schematic of the study flowchart is provided in Fig. All CMR scans were performed on an Ingenia 1.5 T MR system . Optimized single breath hold T1 Modified Look -Locker Inversion Recovery (MOLLI) sequence using the native 5 s(3 s)3 s scheme was used for T1 mapping with the following acquisition settings: preset FOV 320 × 320 mm, slice thickness 10 mm, Relative SNR 1.0, TR/TE 2.3/1.08, TFE factor 80 ACQ Matrix 160 × 160. As per our local clinical practice T1 mapping was routinely used in all patients with well controlled resting heart rate i.e., < 80–85 bpm. Also, patients with T1 mapping of suboptimal quality  were not included in the final study population (n = 3).http://www.slicer.org)11. Operators were blinded to the disease status of patients and were trained and supervised in T1 mapping analysis by an experienced CMR Level 3 accredited individual. Segmentation of LV myocardium was performed on basal LV short axis slice. LV myocardium was manually segmented with manual offset of epicardial and endocardial layers to avoid contamination by epicardial fat or blood pool, respectively. The segmented volume of interest was subsequently used to calculate and extract a series of radiomic features, using the SlicerRadiomics extension of 3D Slicer, which incorporates the Pyradiomics library into 3D Slicer12. Shape-related and first-order radiomic features were calculated using the native T1 values of the segmented myocardium. For calculation of texture features i.e., Gray Level Co-occurrence Matrix (GLCM), Gray Level Dependence Matrix (GLDM), Gray Level Run-Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM), voxels of LV myocardium were discretized into 16 bins of equal width, to reduce noise while allowing a sufficient resolution to detect biologically significant spatial changes in native T1 myocardial values13. To enforce symmetrical, rotationally-invariant results, texture statistics (GLCM etc.) were calculated in all four directions and then averaged, as previously described6.Extraction of radiomic features from left ventricular (LV) myocardium of native T1 maps was done using the 3D Slicer software , the wavelets can capture discontinuities, ruptures and singularities in the original data. At low frequency , the wavelet characterizes the coarse structure of the data to identify the long-term trends. Thus, the wavelet analysis allows extraction of hidden and significant temporal features of the original data, while improving the signal-to-noise ratio of imaging studies7.First-order and texture-based statistics were also calculated for wavelet transformations of the original image resulting in eight additional sets of radiomic featuresIn order to limit our analysis to radiomic features that could be of value as imaging biomarkers, we performed a stability assessment of all extracted radiomic features. For this purpose, we used 20 scans from Study Arm 1 to assess the coefficient of variation (CV) of each radiomic feature in multiple delineations by two independent operators. Only those radiomic features with multiple delineation CV < 10% were included in further analysis  that describe most of the phenotypic variation in the study population. The first two components PC1, PC2) were used to explore associations with disease background in relevant cluster plots . The variation of each of the 628 different radiomic features across the n = 149 observations of the Study Arm 2 cohort was represented in a relevant heat map with a row dendrogram indicating the clustering of patients  searches through a correlation matrix and returns a vector of integers corresponding to columns to remove and reduce pair-wise correlations. The absolute values of pair-wise correlations are considered. If two variables have a high correlation, the function looks at the mean absolute correlation of each variable and removes the variable with the largest mean absolute correlation  using the Ward method and the squared Euclidean distance  with fivefold cross-validation repeated 3 times was used to select the top features able to classify cardiac disease phenotype. Finally, the diagnostic performance of the top radiomic features identified from this validation process for classification of each cardiac disease phenotype was assessed by the c-index and compared to that of the native T1 values. R statistical package version 3.6.0 (https://www.R-project.org/)16 was used for all statistical analysis.We then attempted to select certain radiomic features as imaging biomarkers to describe heart disease phenotype and the presence of cardiac amyloidosis. The original population of Study Arm 2 was split into a training (67%) and a validation (n = 33%) dataset. Then the final 84 T1 radiomic features were included in multinomial logistic regression models (nnet::multinom) to seek independent associations with the cardiac disease phenotype . Machine learning was used for the internal cross-validation of the filtered radiomic features and final feature selection. A random forest algorithm  between patient subgroups are shown in Fig. Initially, before looking into associations of specific radiomic features with cardiac disease, we performed exploratory data analysis by reducing the original radiomic dataset of possibly correlated features to its principal components. A total of 32 components accounted for the 99.5% of variation in the study population  that removed highly correlated features in a stepwise manner. The final set of filtered 84 stable and non-highly correlated radiomic features is presented on a relevant correlation matrix Fig. .To explore whether this set of radiomic features could be used to classify cardiac phenotype, we split the cohort into a training (67%) and a validation dataset 20 technique or the shortened MOLLI sequence (ShMOLLI)21, and they all allow a standardized acquisition of the T1 relaxation times of the heart and the reconstruction of an image with a well-defined and repeatable relationship between voxel signal intensity and the T1 time19. T1 mapping allows the detection of subclinical myocardial disease before the development of replacement fibrosis and any LGE presence22. Also T1 mapping is useful for the detection of myocardial oedema24 or even iron overload25 and sphingolipid storage disease26. The degree of diffuse fibrosis detected by T1 mapping provides not only diagnostic information but also prognostic information as in non-ischaemic cardiomyopathy27. Despite this useful clinical information derived from T1 mapping significant overlap exists in native T1 values between health and disease19. Therefore, unless there is a specific clinical context in which the value of T1 mapping is well-established, T1 mapping is not appropriate as a screening tool for detecting subclinical disease in every patient undergoing a CMR scan. However, our approach provides unique evidence that could enrich the clinical information derived from myocardial T1 maps and further establish the value of T1 mapping in the field of CMR.The findings of this study expand and re-enforce those of previous studies in the field8. This approach allows the identification of shape, intensity, and texture patterns in the imaging voxels of interest by application of first, second or higher order statistics8. To date radiomics have been successfully applied in the field of clinical oncology, whereby radiomic signatures improve the diagnostic accuracy, staging and grading of cancer, response to treatment and prediction of clinical outcomes7. Radiomics have been also applied for the first time in the field of cardiovascular medicine to improve discrimination of high-risk coronary plaques5. CMR radiomics from T1 maps has been previously employed to discriminate between hypertensive heart disease and HCM10 or even between subtypes of sarcomeric HCM28. Our study further expands these findings and shows that radiomics analysis of T1 maps, enhances the value of T1 mapping to discriminate health from disease and classify the various LVH phenotypes. Such evidence suggests that radiomics could contribute to the improvement of clinical diagnostics or to assess the individualised disease risk and response to treatment, and be the key to tailored, personalized medicine8.Recent advances in biomedical technology and computational power have rendered feasible the application of high-throughput screening of human genome, transcriptome, proteome or metabolome and the acquisition of high-dimensional datasets. Along these domains, radiomics is a rapidly evolving field of medical imaging that relies on high-throughput exploitation of the rich data stored in medical images to extract a series of quantifiable radiomic features. In contrast to other omics technologies that are characterised by high measurement cost and limited temporal applicability, radiomics can be easily applied to detect patterns and extract quantifiable features from standard-of-care medical imaging stacks9, we followed a rigorous stability assessment approach and only those highly reproducible and consistent radiomic features in multiple delineation were used in further exploratory analysis. To facilitate feature selection, we also applied an automated algorithm to remove highly correlated features. Then we explored the value of the 84 retained radiomic features to classify the cardiac phenotype and, ultimately, we demonstrated that a radiomic signature provides incremental value for detection of cardiac disease phenotypes, beyond and above native T1 values.Our study has certain methodological strengths that lie within the stepwise, rigorous approach we followed. Initially, we demonstrated the proof-of-concept that T1 mapping images can be decomposed to several independent components that describe different aspects of cardiac phenotype and are distinctively associated with the patient profile. Then we focused on 850 different calculated radiomic features to generate radiomic signatures of myocardial health and disease. Since previous evidence suggests that not all T1 radiomics are stable and reproducibleCertain limitations of our study should be acknowledged. Specifically, this was a proof-of-concept study from a single study centre in which we used only basal T1 map slice for texture analysis, and with a relatively small study population sample. We used the whole cohort to remove highly correlated radiomic features, before training and testing our model. Thus, the generalizability of our findings remains to be seen, and therefore true external validation of our observations from third independent groups, in other scanner types/acquisition settings would be welcome. Given the reproducibility issues of radiomics, further research on the field is needed to harmonize the readouts between different scanners/vendors and acquisition sequences.In conclusion, we have presented a novel approach for myocardial texture phenotyping using T1 radiomics analysis. We demonstrated and validated by a machine learning approach that radiomic features provide added diagnostic value for distinction between healthy and diseased myocardium, as well as for differentiation between HCM and relevant phenocopies, such as amyloidosis, on top of native T1. The application of radiomics to standard native T1 mapping is a promising approach for the texture characterization of the human myocardium and for further enhancing the diagnostic value of CMR.Supplementary Information.


Premature ventricular beats (PVBs) in athletes are not rare. The risk of PVBs depends on the presence of an underlying pathological myocardial substrate predisposing the subject to sudden cardiac death. The standard diagnostic work-up of athletes with PVBs includes an examination of family and personal history, resting electrocardiogram (ECG), 24 h ambulatory ECG (possibly with a 12-lead configuration and including a training session), maximal exercise testing and echocardiography. Despite its fundamental role in the diagnostic assessment of athletes with PVBs, echocardiography has very limited sensitivity in detecting the presence of non-ischemic left ventricular scars, which can be revealed only through more in-depth studies, particularly with the use of contrast-enhanced cardiac magnetic resonance (CMR) imaging. The morphology, complexity and exercise inducibility of PVBs can help estimate the probability of an underlying heart disease. Based on these features, CMR imaging may be indicated even when echocardiography is normal. This review focuses on interpreting PVBs, and on the indication and role of CMR imaging in the diagnostic evaluation of athletes, with a special focus on non-ischemic left ventricular scars that are an emerging substrate of cardiac arrest during sport. Adolescents and young adults engaged in competitive sports are at increased risk of sudden cardiac death (SCD) compared with their sedentary counterparts. Sport activity can act as a trigger for life-threatening ventricular arrhythmias (VAs) in athletes with an underlying cardiovascular disease, predisposing them to adrenergic-induced ventricular fibrillation ,2,3,4. SDocumentation of premature ventricular beats (PVBs) on 12-lead electrocardiography (ECG), ambulatory ECG monitoring or ECG stress tests should raise the suspicion of an underlying cardiomyopathy. PVBs with common morphologies such as infundibular or fascicular ectopic beats are often not associated with structural cardiac abnormalities, whereas other PVBs may represent a red flag for the presence of a pathological substrate and must be properly investigated ,9. StandThis review focuses on interpreting PVBs, and on the indication and role of CMR imaging in the diagnostic evaluation of athletes, with a special focus on non-ischemic left ventricular scars that are an emerging substrate of cardiac arrest during sport.Malignant arrhythmic events may occur in athletes with a structurally normal heart as a result of genetic channelopathies , some ofIn master athletes (age >35 years), the most important arrhythmic substrate is ischemic heart disease. Male master endurance athletes, even with a low atherosclerotic risk profile, are more likely to have a higher CAC score compared with sedentary males with a similar risk profile ; the meaBeyond channelopathies and ischemic heart disease, different structural cardiomyopathies can be associated with PVBs, including genetic and acquired heart muscle diseases. Hypertrophic cardiomyopathy (HCM) has been reported to cause more than one third of SCDs in the USA ,15, whilNLVSs have been associated with cardiac arrest occurring during effort ,18. In 2Non-ischemic myocardial fibrosis, in contrast to the ischemic one, involves the mid-mural and subepicardial myocardial layers but spares the endocardium. Since the latter contributes most to myocardial contractility, echocardiographic wall motion evaluation has limited sensitivity in detecting the presence of a NLVS . ECG abnCardiac magnetic resonance is the gold standard for the assessment of biventricular volume, mass and global and regional function ,28; LV fRecently, myocardial strain analysis on feature-tracking CMR, following the footsteps of speckle-tracking echocardiography, has emerged as a surrogate marker of LV systolic function, analyzing the deformation of a fixed myocardial point through the cardiac cycle; it is of prognostic value in different cardiomyopathies such as HCM, dilated cardiomyopathy, ischemic cardiomyopathy or myocarditis ,36,37,38Beyond the role of functional indices, CMR offers the unique ability to identify myocardial tissue abnormalities that may have been missed by other imaging techniques. Tissue abnormalities typical of cardiomyopathies reflect chronic injury, inflammation, infiltration or abnormal storage that can be disclosed by specific CMR sequences. Standard CMR techniques for tissue characterization include T2-weighted CMR images, with an increased signal intensity reflecting inflammation-related myocardial edema ,40; T1-wOver the last two decades, it has been demonstrated that a higher scar volume quantified with late gadolinium enhancement (LGE) predicts the risk of life-threatening VA recurrence in patients with aborted SCD , and theMyocardial mapping, a technique that has been recently introduced in clinical practice, allows measuring the different magnetic properties of each myocardial volume as expressed by magnetic relaxation times  and the extracellular volume (ECV) derived from post-contrast T1. Changes in magnetic properties are typical of diseases characterized by intracellular cardiomyocyte disturbances , extracellular disturbances in the myocardial interstitial tissue  or both  ,51. ConvFinally, arrhythmogenic substrates characterized by inflammation, such as cardiac sarcoidosis, may be better disclosed by integrating CMR with nuclear imaging, particularly positron emission tomography (PET) ,57. CardAccording to the current criteria for ECG interpretation in athletes, abnormalities are labeled as either “common” or “uncommon” ,60. The The modern classification of PVBs follows a similar approach: “common” PVBs are usually idiopathic , whereas “uncommon” PVBs carry a higher probability of an underlying myocardial disease .PVB morphology is the most relevant feature. Athletes with frequent PVBs in the absence of cardiac disease most often show a morphology suggesting a right or left ventricular (LV) outflow tract origin -like with a vertical axis pattern) or from the posterior fascicle of the left bundle branch  with a right bundle branch block (RBBB)-like pattern and superior axis). These PVBs usually decrease during effort ,62.PVBs with an LBBB-like morphology and intermediate or superior axis (denoting a right ventricular (RV) or septal origin), and those with an RBBB-like morphology, wide QRS and superior axis , are classified as “uncommon” because they carry a higher risk of being associated with an underlying disease. The suspect is reinforced when arrhythmias increase in number or complexity during effort ,67,68,69The traditional concept that arrhythmic risk is predicted by “PVB burden”  has been disproved by recent evidence. Indeed, it has been demonstrated that benign extrasystolic foci (more often located in the outflow tract) can give rise to a very high number of PVBs on 24 h Holter ECG in the absence of a pathological substrate ,62. SeveThe evaluation of athletes with PVBs should take into consideration other clinical data: in particular, a positive family history of SCD or cardiomyopathy, history of unexplained syncope or the presence of other major ECG abnormalities (such as T wave inversion) on resting ECG.In the last year, CMR imaging has been increasingly prescribed to investigate athletes with PVBs even when other clinical investigations (particularly echocardiography) are normal. This examination has led to the discovery of isolated NLVSs as the origin of PVBs in some cases that would otherwise have been considered idiopathic.The first systematic study on athletes with VAs in association with an isolated NLVS was reported by Zorzi et al., in 2016 , althougSeveral studies have demonstrated that PVB morphology and behavior during exercise can predict the presence of an underlying NLVS in athletes with apparently idiopathic VAs. Cipriani et al. reported CMR findings in a series of athletes with PVBs who were referred to a tertiary center . FindingAnother investigation aimed to evaluate the PVB burden in apparently healthy young athletes who volunteered to undergo a 12-lead 24 h Holter ECG, including a training session. Seventeen subjects were subsequently referred to CMR for frequent (>500/day), repetitive or training-induced PVBs . In threIn a multicenter study reporting on 251 competitive athletes with a negative family history and unremarkable ECG and normal echocardiography findings who underwent CMR for evaluation of VAs, LV LGE was found in 28 (11%). The scar often showed a subepicardial/midmyocardial distribution (non-ischemic) and was more likely when PVBs depicted an RBBB-like pattern or multiple morphologies, or when non-sustained VT was recorded during exercise testing. Conversely, a high number of PVBs (>3300/day) predicted a normal CMR  23]..23].The systematic evaluation of PVBs occurring during exercise testing increases the diagnostic sensitivity of preparticipation screening compared to traditional first-line investigations . In a population of 10,985 young athletes, adding exercise testing for VAs led to a 75% increase in the diagnosis of pathological cardiac substrates, mainly NLVSs . The morFinally, the correlation between the characteristics of PVBs and the probability of an isolated NLVS as well as the prognostic significance of this CMR finding has also been documented in the general population. In a study by Muser et al. on 518 subjects with frequent PVBs, LGE was diagnosed by CMR in 16%. Polymorphic PVBs and PVBs with a non-infundibular morphology were predictors of pathological myocardial substrates. Nearly one third of the subjects with LGE suffered major arrhythmic events during a median follow-up of 67 months (three died suddenly during effort), underscoring, once again, the precipitating role of adrenergic stimulation as an arrhythmic trigger in patients with a NLVS . The samIn summary, NLVSs are a relevant substrate for malignant VAs, mostly for those occurring during sport activity. The two main PVB characteristics that should trigger CMR prescription to rule out an underlying scar in athletes with otherwise normal clinical findings are the morphology  and the increase in number and complexity during exercise.In athletes with PVBs, a careful clinical examination is required to rule out an underlying myocardial disease that could potentially cause SCD. Although more recent recommendations suggest performing additional testing only when two or more PVBs are recorded on the resting ECG, we believe that even a single “uncommon” PVB should be considered as a red flag .First-line investigations in athletes with PVBs should include history, physical examination, exercise testing, 24 h ambulatory ECG including a training session  and echocardiography . The preExercise testing is a key tool for unmasking electrocardiographic abnormalities or arrhythmias absent at baseline. The effort should be maximal, and the test should be carried out until muscular exhaustion, not just limited to 85% of the predicted maximal heart rate. This is important not only for the documentation of ischemic ST-T changes, but also for eliciting adrenergic-dependent PVBs that may occur only at the peak of effort. Echocardiography is essential to rule out valvular defects, cardiomyopathies, congenital disorders and the anomalous origin of coronary arteries ,83.In case one of the above investigations is abnormal, additional tests may be needed based on clinical suspicion . If first-line investigations are normal, indications for CMR depend on the characteristics of the PVBs  that have been discussed above. When analyzing CMR imaging, attention must be paid to some pitfalls. It is known that CMR shows systematically larger chamber dimensions and volumes, but a smaller wall thickness and mass, compared with echocardiography ; thus, cNot all non-ischemic LGE patterns should be considered markers of disease. Isolated right ventricular insertion point  LGE, i.e., LGE confined to the insertion points of the right ventricle to the anterior and/or posterior ventricular septum, has been described in many pathological conditions such as pulmonary hypertension  or hyperIn both the European Society of Cardiology and the American Heart Association guidelines on competitive athletes, NLVSs are addressed in the myocarditis chapter ,93. The Systematic investigations of athletes with PVBs through CMR imaging in recent years have served to clarify that substrates of SCD, particularly NLVSs, may be missed in routine clinical tests. In the presence of at-risk PVB characteristics, based on their morphology, complexity and relation to effort, CMR should be performed even when echocardiography and baseline ECG are normal. Besides standard CMR scans and post-contrast images, novel techniques such as mapping and feature tracking are also emerging in the subset of athletes’ evaluation.Beyond NLVS, ischemic heart disease represents an important substrate of VAs, especially in master athletes. In the evaluation of older athletes with PVBs, stress CMR in addition to the standard CMR protocol might be useful for ruling out coronary artery stenosis, although further studies are needed to evaluate its diagnostic value in this peculiar clinical setting.


R2 > 0.996) and excellent agreement [intraclass correlation coefficients (ICC) ≥ 0.999)] with reference measurements. In healthy volunteers, Multitasking-SMS T1/T2 maps reported similar myocardial T1/T2 values  to the reference myocardial T1/T2 values , with P = 0.347 and P = 0.296, respectively. Bland–Altman analyses also demonstrated good in vivo repeatability in both the multitasking and references, with segment-wise coefficients of variation of 4.7% (multitasking T1), 8.9% (multitasking T2), 2.4% [modified look-locker inversion recovery (MOLLI)], and 4.6% (T2-prep FLASH), respectively. In summary, multitasking-SMS is feasible for free-breathing, non-ECG, myocardial T1/T2 quantification in 16 AHA segments over 3 short-axis slices in 3 min. The method shows the great potential for reducing exam time for quantitative CMR without ECG or breath-holds.The aim of this study is to simultaneously quantify T1/T2 across three slices of the left-ventricular myocardium without breath-holds or ECG monitoring, all within a 3 min scan. Radial simultaneous multi-slice (SMS) encoding, self-gating, and image reconstruction was incorporated into the cardiovascular magnetic resonance (CMR) Multitasking framework to simultaneously image three short-axis slices. A T2prep-IR FLASH sequence with two flip angles was designed and implemented to allow B1+-robust T1 and T2 mapping. The proposed Multitasking-SMS method was validated in a standardized phantom and 10 healthy volunteers, comparing T1 and T2 measurements and scan-rescan repeatability against corresponding reference methods in one layer of phantom vials and in 16 American Heart Association (AHA) myocardial segments. In phantom, Multitasking-SMS T1/T2 measurements showed substantial correlation ( Cardiac magnetic resonance (CMR) imaging is rapidly evolving toward quantitative multiparameter measurement for myocardial tissue characterization –6. Quantvia ECG triggering) to avoid respiratory and cardiac motion; and (2) are performed in series  instead of simultaneously. This approach becomes impractical for patients having difficulty holding their breath or for whom ECG triggering fails. Respiratory gating  imaging , 33 has +–and through-plane-motion–robust T1 mapping  technique , mimicking the discrete Fourier transform and defining a discrete echnique . SMS encThe images acquired in the Multitasking framework can be represented as a 5-way tensor A . MultitaN time dimensions and diagonally throughout the multidimensional temporal space, reducing the images to the product of a small core tensor and five factor matrices:We model Ux contains spatial basis functions with voxel location index r = , UT1 contains basis functions for the T1 relaxation, Uτ, α contains basis functions that index the 10 different recovery modules with varying T2prep-IR duration τ and flip angle α combinations, Uc contains cardiac motion basis functions, Ur contains respiratory motion basis functions, and U matrices. The factor matrices and core tensor have far fewer elements than the full image tensor where UT1 and Uτ, α from a training dictionary of signal curves; (3) cardiac and respiratory binning of the real-time images; (4) determining the motion bases and core tensor from the training data; and finally, (5) solving for the spatial coefficients Ux from the imaging data.Image reconstruction in the CMR Multitasking framework is divided into the following steps: (1) preliminary “real-time” (ungated) image reconstruction; (2) predetermining the temporal basis functions in “Real-time”  image reconstruction generates ungated images with a low-rank matrix imaging strategy , to faciUT1 and Uτ, α are estimated from the SVD of this training dictionary.We generated a training dictionary of feasible T2-IR-FLASH signal curves governed by the Bloch equations, with a range of variable T1/T2 values, B1 inhomogeneities, and inversion efficiencies , 31. We UT1 and Uτ, α) to address the variable contrast weighting of the training data. We used 6 respiratory bins and 20 cardiac bins in the binning procedure.The respiratory and cardiac motion binning algorithm is derived from the methods described in the original MR Multitasking work . BrieflyOnce the motion states have been identified, the training data can be reorganized as a 5-way tensor dtr is the collected training data, Ωtr(·) is the sampling operator for the training dataset, Dtr, (i) is the mode-i unfolding of the training tensor, ||·||* denotes the matrix nuclear norm, and Rt(·) is a temporal regularizer, which was chosen as temporal total variation (TV) along the respiratory and cardiac dimensions in this work (where his work . Rt(Dtr)c and λr are the two regularization parameters that control the TV smoothing along the cardiac and respiratory dimensions.where λUc and Ur are extracted from the higher-order SVD (HOSVD) (Once the training data tensor  (HOSVD)  of Dtr. Ux was then recovered by fitting the known Φ to the acquired imaging data d, using the following optimization problem:The spatial factor F is the Fourier transform operator comprising non-uniform in-plane Fourier encoding and Fourier slice encoding, S is the coil sensitivity operator, and R(·) is an optional regularization functional to promote transform sparsity (chosen as a wavelet transform in this implementation). R(Ux) in Eq. (4) can be expressed aswhere Ω is the undersampling operator, W is the wavelet operator and λw is the regularization parameter that controls the wavelet sparsity. Once Φ and Ux have both been determined, the final reconstructed image can be calculated as where kth recovery period of the Multitasking-SMS pulse sequence is:The signal equation at the A, IR/T2prep-IR pulse efficiency B, FLASH readout interval TR, flip angle for the kth recovery period αk, B1+ field weights β (to account for B1+ inhomogeneity), and recovery time points n = 1, 2, …, N . The Qk absorbs the effects of having inverted the magnetization from the steady-state for the previous recovery period's excitation flip angle. Assuming a steady-state established at the final readout of each recovery period, Qk is expressed aswith amplitude factor The native T1 and T2 measurements can be estimated from the signal model in Eqs. (6) and (7). Our previous work  showed tAn International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) phantom   5(3)3 , T2-prep2 ×1.4 mm; slice thickness = 8 mm; T2 preparation times = 0, 35, and 55 ms. The IR-SE T1 protocol parameters were: FOV = 280 mm ×192 mm; in-plane resolution = 1.4 mm ×1.4 mm; slice thickness = 5 mm; TI = 150, 300, 500, 800, 1,200, 1,600, 2,000, and 4,500 ms. The SE-T2 protocol parameters were: FOV = 280 mm ×192 mm; in-plane resolution = 1.4 mm ×1.4 mm; slice thickness = 5 mm; TE = 15, 25, 45, 70, 100, 140, 180, 250, and 350 ms.The following scan parameters were used for the proposed 2D Multitasking-SMS sequence: Field of View (FOV) = 270 mm ×270 mm ; spatial resolution = 1.7 mm ×1.7 mm ×8 mm; 3 slices with a multiband factor of 3; TR/TE = 3.5/1.6 ms; flip angle = 3 and 10; T2 preparation times = 0, 30, 40, 50, and 60 ms (with 0 corresponding to a standard IR pulse); recovery period = 2.5 s; scan time = 3 min 3 s. The 2D MOLLI imaging parameters were: Repetition Time/Echo Time (TR/TE) = 2.7/1.1 ms; flip angle = 35; FOV = 220 mm ×220 mm; in-plane resolution = 1.4 mm ×1.4 mm; slice thickness = 8 mm. The 2D T2-prep FLASH imaging parameters were: TR/TE = 3.3/1.4 ms; flip angle = 12; FOV = 220 mm ×220 mm; in-plane resolution = 1.4 mmt-tests were also performed to evaluate measurement biases, with a significance level of 0.05.Linear regression, the Bland–Altman analyses, and intraclass correlation coefficients (ICC) with a two-way mixed model were performed on the vials with relevant T1 and T2 values  to evaluate the quantitative agreement between Multitasking and reference measurements. Pairwise N = 10 human volunteers  were imaged on a 3T scanner  with an 18-channel body coil.Healthy volunteer studies were approved by the institutional review board of Cedars-Sinai Medical Center. All subjects gave written informed consent before MRI. The 2D Multitasking-SMS pulse sequence imaged three short-axis slices over the left-ventricle, base, mid, and apex. It was applied twice to test scan-rescan repeatability. The scan parameters were the same as used in the phantom study. A 2-step fitting procedure was used to determine parameter maps. Step 1 estimates β and T2 from Eq. (6), and Step 2 uses the known β to fit T1 from the 3° recovery curve only, for which the Look–Locker effect is reduced.The 2D single-slice multitasking  pulse sequence was also applied to sequentially image the same three short-axis slices. The scan parameters were: FOV = 270 ×270 mm ; spatial resolution = 1.7 mm ×1.7 mm ×8 mm; TR/TE = 3.5 ms/1.6 ms; flip angle = 5; T2 preparation times = 0, 30, 40, 50, and 60 ms; recovery period = 2.5 s; scan time per slice = 1 min 31 s (4 min 33 s for 3 slices).Reference 2D T1 maps with MOLLI and 2D T2 maps with T2-prep FLASH (1.4 mm ×1.4 mm ×8.0 mm) were acquired at both systole and diastole during end-expiration breath-holds and were also collected twice. The scan parameters of the reference sequences were the same as used in the phantom study.T1 and T2 maps were segmented at the end-expiration respiratory and end-diastolic cardiac phases, using the AHA 16-segment model by drawing epi- and endocardial contours in commercially available software   between the first and second scans of the proposed method and of reference methods. Global and segment-wise CoVs were calculated to assess repeatability. Global CoVs were calculated as the standard deviation of average Left Ventricle (LV) myocardial T1/T2 between two scans, divided by the mean T1/T2 for each subject, and were root-mean-square (RMS)-aggregated over all 10 subjects to provide an overall summary of global repeatability. Segment-wise CoVs were calculated as the standard deviation between two scans for each LV myocardial segment, and were first RMS-aggregated over segments to calculate the segment-wise CoV and divided by the mean T1/T2 for each individual subject; segment-wise CoVs between methods, with a significance level of 0.05. Segment-wise CoVs were then RMS-aggregated over all 10 subjects to provide an overall summary of segment-wise repeatability.Measurement of global and segmental myocardial T1/T2 in all healthy volunteers were compared between the Multitasking-SMS approach and the reference approaches. The pairwise t-tests were used to compare the segmental T1/T2 values, segment-wise Signal-to-noise ratio (SNR), and segment-wise SNR efficiency between Multitasking-SS and Multitasking-SMS. The segment-wise SNR was calculated as the mean T1/T2 within each segment divided by the voxelwise standard deviation of T1/T2 within that segment. SNR values were transformed into 3-slice SNR efficiency values by dividing by the square root of the total scan time required to collect 3 slices (4.5 min for Multitasking-SS and 3 min for Multitasking-SMS).To determine the impact of dual-flip-angle SMS imaging on the accuracy and precision of multitasking measurements, pairwise All Multitasking image reconstructions were performed on a Linux workstation with a 2.90 GHz Intel Xeon processor in MATLAB 2018a . Statistical analyses were performed using IBM SPSS Statistics .t-test results between Multitasking-SMS/MOLLI/T2-prep FLASH and gold standard IRSE/SE measurements. Multitasking-SMS measurements and IRSE/SE measurements showed excellent agreement with ICC = 0.999 for both T1 and T2. All pairwise method comparisons showed statistically significant biases.Phantom T1 and T2 maps obtained from the 2D MOLLI, T2-prep FLASH, IRSE-T1, SE-T2, and the Multitasking-SMS approaches are shown in R2 > 0.996) with the reference 2D IRSE-T1 acquisition. The 95% limits of agreement of the T1 values were 46.2 ± 74.1 ms for Multitasking-SMS and IRSE-T1, and −61.7 ± 41.9 ms for MOLLI and IRSE-T1. T2 measurements from Multitasking-SMS and T2-prep FLASH were also each highly correlated (R2 > 0.998) with the reference 2D SE-T2 acquisition. The 95% limits of agreement of the T2 measurements were −5.1 ± 4.1ms for Multitasking-SMS and SE-T2, and 3.9 ± 3.7ms for T2-prep FLASH and SE-T2.Scatter plots and Bland–Altman plots of the T1 and T2 values in the relevant vials are shown in B) obtained from Multitasking-SMS are given in P = 0.347 and P = 0.296, respectively.P = 0.323) and higher T2  values compared to Multitasking-SMS T1/T2. The significant bias in T2 also exists between Multitasking-SS T2 and reference T2-prep FLASH measurements (P <0.001).Additionally, Multitasking-SS T1/T2 mapping results were compared to the Multitasking-SMS measurements in P = 0.002), and significantly larger for multitasking T2 than T2-prep FLASH (P = 0.001).The Bland–Altman plots in −1/2 (multitasking-SMS T1), 3.6 min−1/2 (multitasking-SMS T2), 2.8 min−1/2 (multitasking-SS T1), and 2.9 min−1/2 (multitasking-SS T2). The pairwise t-tests showed that multitasking-SMS had significantly higher T1 SNR and T1/T2 SNR efficiency than multitasking-SS (P <0.001) and similar T2 SNR (P = 0.692).The average SNR of multitasking measurements were 11.9 (multitasking-SMS T1), 6.2 (multitasking-SMS T2), 6.0 (multitasking-SS T1), and 6.1 (multitasking-SS T2). The average 3-slice SNR efficiencies were 6.9 minIn this study, a 2D SMS-accelerated, free-breathing, non-ECG, motion-resolved cardiac imaging method  was introduced for simultaneous 2D myocardial T1/T2 mapping over three short-axis slices in 3 min. It represents several new developments that have not previously been a part of T1-T2 multitasking: (1) this is the first SMS acceleration with multitasking framework; and (2) the first use of a dual flip angle scheme interleaved with T2prep-IR blocks for B1+-robust T1-T2 mapping.In the phantom study, the multitasking-SMS T1/T2 measurements and the typical cardiac mapping sequences MOLLI and T2-prep FLASH all showed statistically significant biases against the gold standard IRSE-T1 and SE-T2. For T1, multitasking-SMS and MOLLI both had small negative biases; for T2, multitasking-SMS and T2prep-FLASH had small biases in opposite directions (underestimation by multitasking-SMS and overestimation by T2prep-FLASH). All the comparisons showed ICC ≥ 0.999, reflecting high agreement with gold standard references.in vivo. This may be related to the difference in T2-prep modules between multitasking-SMS and T2prep-FLASH . These different modules may have different responses to motion, inhomogeneity, and flow that are present in the in vivo scans, which could change their behavior relative to the phantom scans.In healthy volunteers, multitasking-SMS T1/T2 measurements reported similar myocardial T1/T2 values compared to the reference T1/T2 measurements in healthy volunteers. The T1/T2 estimations from all methods were in the normal range of many 3T MRI studies –45. Multin vivo. T1 values were not significantly different, suggesting similar accuracy in T1. Regarding precision, the combination of SMS and dual-flip angle excitation significantly increased 3-slice SNR efficiency for both T1 and T2 vs. multitasking-SS. When traded for a 1.5 × reduction in 3-slice scan time (4.5 min to 3 min), this translated to the maintenance of T2 SNR and a 2.0 × boost in T1 SNR.2D multitasking-SS scans from our original work  were alsMultitasking-SMS could be a potential alternative to the conventional series of multiple T1 and T2 mapping scans in clinical studies. Conventionally, each quantitative parameter  is typically mapped using one breath-hold per 2D slice. As a result, 3 slices  of native T1, and T2 at diastole phase would require 6 breath holds. In a typical scenario, 3-slice T1 and T2 mapping could take ~3 min assuming a ~20 s gap between each scan for the patient to recover from the breath-hold while the technologist sets up the next scan. With an experienced MR operator, the gap can often be reduced to ~10 s and would take a total scan time of 2 min. However, shorter breath-hold recovery times may increase the likelihood of a repeat scan due to patients' difficulty in complying with breath-holds, which could then extend exam time. In our experiments, six breath-hold scans required 4–6 min. The proposed 2D CMR Multitasking-SMS removes this variability by offering a fixed 3-min scan, with the added benefits of push-button simplicity (no trigger delay times or cardiac acquisition windows to set up), free-breathing acquisition, and no ECG dependence. Further, Multitasking-SMS may have the opportunity to be extended to collect more slices in the heart with a multiband factor of 3 or more in the future.SMS acceleration techniques have been adopted in other quantitative cardiac MRI studies , 46, butin vivo, and were repeatable in vivo. This new method improved T2 accuracy and T1 precision over the original Multitasking T1/T2 mapping method while maintaining T1 accuracy and T2 precision. The method shows potential for reducing exam time and setup time for quantitative CMR.In summary, SMS Multitasking provides co-registered T1 and T2 maps at the base, mid, and apex short-axis slices without ECG or breath-holding, all in one 3-min scan. T1 and T2 values agreed with reference measurements in a phantom and The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Cedars-Sinai Medical Center. The patients/participants provided their written informed consent to participate in this study.XM, AC, and DL: study conception and design. XM, H-LL, and FH: pulse sequence development. XM: data collection. XM and AC: analysis and interpretation of results. XM, H-LL, ZH, TC, FH, SM, FS, ZF, YX, DL, and AC: draft manuscript preparation. All authors reviewed the results and approved the final version of the manuscript.This work was supported by the National Institutes of Health Grant R01 EB028146.FH is a full-time employee of Siemens Medical Solutions, Inc., USA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This study compared abnormal innervation and perfusion measured by [3 positron emission tomography (PET) scans, regional defect scores (%LV extent×severity) were calculated using HED and NH3 uptake, as well as HED distribution volume and NH3 myocardial blood flow by kinetic modelling.This is a sub-study analysis of the prospective PAREPET trial, which followed ischemic cardiomyopathy patients with reduced left ventricular ejection fraction (LVEF≤35%) for events of SCA. Using n=174 paired dynamic HED and NH3, especially in the lowest tertile of perfusion abnormality (p<0.001). Parametric defects were larger than their respective tracer uptake defects (p<0.001). SCA risk discrimination was not significantly improved with parametric or uptake mismatch (AUC=0.73 or 0.70) compared to HED uptake defect scores (AUC=0.67).During 4.1 years follow-up, there were 27 SCA events. HED defects were larger than NH3 myocardial blood flow produced larger defects than their respective measures of tracer uptake, but did not lead to improved SCA risk stratification vs. HED uptake alone.Quantification of HED distribution volume and NH The ECAT EXACT HR+  PET scanner  was used to perform PET imaging for this study, with 3 rotating 68Ge rod sources used to perform attenuation correction. Sympathetic innervation was assessed with 740 MBq of HED, and perfusion was assessed with 740 MBq of NH3. HED and NH3 were administered by bolus injection and dynamic imaging was captured for HED (60 minutes) and NH3 (19 minutes). The following frame sequence was used for HED dynamic imaging: 6 x 30 s, 2 x 60 s, 2 x 150 s, 2 x 300 s, 2 x 600 s, 1 x 1200 s, and for NH3 dynamic imaging: 12×5 s, 4 × 15 s, 2 × 30 s, 2 × 60 s, 1 × 240 s, and 2 × 300 s. The HED images were decay corrected and reconstructed using filtered back-projection and 12 mm Gaussian post-filtering, with the final images at 13 mm full-width-at-half-maximum resolution.Study participants underwent dynamic cardiac PET to quantify resting perfusion using [3-HED mismatch analysis techniques were incorporated into the software as follows.The quality of all scans was reviewed, based on factors such as image contrast and noise, as well as adequate spatial and temporal sampling. mC) to the tracer concentration in arterial plasma (pC) at equilibrium (TE).[The distribution volume (DV) is defined as the ratio of the tracer concentration in the myocardium (ium (TE).max/Kd), where Bmax is the NET-1 transporter density and 1/Kd is the affinity of the tracer (HED) for the transporter.[3) as it has been shown to provide more robust estimates of DV with optimal clinical reproducibility compared to the two-tissue compartment model.[1) and the efflux from the myocardium (k2) through the following equation:[The DV (mL/g) represents the tracer-transporter binding affinity  was obtained from a 20 mm3 region-of-interest positioned automatically in the LV cavity. Radiolabelled metabolites that accumulate in the blood plasma, not present in the myocardium, were corrected to ensure accuracy of the quantitative HED and NH3 analyses.[p(t), is related to the arterial whole-blood tracer concentration, CWB(t) using the parent fraction in plasma function, pfp(t) as determined previously in human subjects for HED and NH3:[The arterial whole blood time-activity curve Canalyses. The foll and NH3:,19LV(t)C due to contractile and respiratory motion leads to underestimation of myocardial tissue concentrations, and there is also a fraction of blood volume (FBV) within normal myocardial tissue. As such, partial volume correction was utilized as part of the 1TCM to correct for these effects.[Blurring of the acquired PET images in the LV myocardium  effects.,21 The g effects.1, is equal to the product of blood flow and unidirectional extraction fraction (E ’) during first pass of the tracer. The following equation was utilized to measure myocardial blood flow (MBF) with NH3 through its standard relationship with the permeability surface area product (PS) and extraction fraction:[The delivery rate constant, Kfraction:3 uptake, parametric, and mismatch measures were all expressed as regional defect scores which reflect the extent × severity of relative defects, as we have previously established in the literature.[3 images from 3 to 19 minutes post-injection. The LV myocardium activity was sampled automatically into 496 polar-map sectors (P) and normalized (0-100%) relative to the peak activity. The defect scores were then calculated as the extent × severity of polar-map sectors <75% of the maximum and expressed as a percentage of the left ventricle (%LV). 3 regional denervation, perfusion, and mismatch polar-map scores.HED and NHterature. Steady-s3 minus HED polar-map using the following equation:Perfusion-innervation mismatch was calculated as %LV by averaging positive values of the NHA threshold of 75% was chosen based upon the methodology of the original PAREPET trial, which is consistent with previous PET myocardial perfusion imaging methodology reported in the literature.,25 Diffe3 uptake scores. Time-to-SCA was analyzed using the Kaplan-Meier method, and differences between tertiles were assessed by the Log-Rank test. A cox-proportional hazards regression was also used to assess the association between the HED and NH3 PET parameters and SCA. Area under the receiver operator characteristic curve (AUC) was used to assess the ability of the individual HED and NH3 PET parameters to discriminate between patients who developed SCA vs. those who did not. Statistical analysis and graphical representation were performed using SAS 9.4 , GraphPad Prism 6.0 , SPSS (IBM), and Excel 2018 (Microsoft). For all analyses, statistical significance was defined as a p-value < 0.05.The comparisons of innervation vs perfusion as well as uptake vs parametric defect scores were conducted with linear regression, Bland-Altman analysis, and Wilcoxon Signed-Rank tests. The limits-of-agreement of repeated measures was calculated as the mean difference ± coefficient-of-repeatability (RPC=1.96×SD). To evaluate flow-dependence of HED uptake and clearance, the %LV defect scores were analyzed within tertiles of NHThe patient demographics are listed in 3, as depicted in 3 defect score (p<0.001). A similar trend was noted for the parametric measures. The HED LV distribution volume defect score was 9.4±12.4% larger than the NH3 myocardial blood flow defect score (p<0.001).Sympathetic denervation measured by HED PET imaging detected larger defects on average than myocardial perfusion imaging with NH3 uptake defect scores. The average HED scores were compared with NH3 scores in each tertile . However, in the highest tertile of perfusion defects, the HED defect score was only 6.8±8.7% larger than the NH3 defect scores (p<0.001).To assess the flow dependence of HED, the results were grouped into tertiles of NHtile see . The lar3 myocardial blood flow scores. In the lowest tertile of perfusion defects, the difference between the average defect size measured by the HED distribution volume scores and NH3 myocardial blood flow scores was 11.9±12.9% (p<0.001) compared to a non-significant difference of 3.4±9.2% (p=0.08) in the highest tertile of perfusion defects.A similar trend was seen in the parametric comparison of HED distribution volume defect scores vs NH3 myocardial blood flow scores) were larger than their respective uptake scores, as shown in 3 myocardial blood flow score was 8.2±7.4% of the LV greater than the NH3 uptake score (p<0.001). Parametric NH3–HED mismatch scores were significantly larger than their respective uptake mismatch scores .Defects assessed using parametric measures of tracer kinetics (i.e HED distribution volume and NH3 uptake and parametric scores were not (P>0.05). This is also reflected in the continuous survival curves shown in 3 uptake and parametric (MBF) scores were not statistically significant predictors. These survival curves have fluctuations in the NH3 uptake and parametric mismatch curves near the extremes of defect sizes, likely due to a small sample size of patients with very large defects.The Kaplan Meier curves in 3 and HED parameter, the receiver operator characteristic (ROC) curves and AUC values for SCA risk discrimination are found in 3-HED uptake mismatch and NH3-HED parametric mismatch were strong predictors of SCA using the cox-proportional hazards model (P=0.001-0.003), however NH3 parameters were not predictive (P=0.174-0.994), as shown in For each NH3 PET tracers, respectively. A head-to-head comparison of HED sympathetic denervation with NH3 myocardial blood flow measurements was also a novel aspect of this study, which we anticipate will have important clinical implications. Four key findings were derived from this study in patients with ischemic cardiomyopathy: (1) HED scores, both uptake and parametric, detected larger defects than their respective NH3 scores. This provides additional data supporting the paradigm of sympathetic neurons being exquisitely sensitive to ischemia, possibly more so than cardiomyocytes. (2) The largest differences between HED and NH3 defect size occurred in the lowest tertile of perfusion defects where resting perfusion was relatively preserved. This suggests that denervation may occur in the absence of myocardial scar. (3) The parametric measures of HED distribution volume, NH3 myocardial blood flow, and NH3-HED mismatch detected significantly larger defects than their respective counterparts, HED uptake, NH3 uptake, and NH3-HED uptake mismatch defect. Nevertheless, parametric defects did not significantly improve SCA risk discrimination compared to uptake defect scores. (4) NH3 uptake and parametric MBF scores were both poor predictors of SCA. This corroborates previous findings of the PAREPET study suggesting that HED is the strongest predictor of SCA. Taken together, these data provide credence to HED cardiac PET yielding biological information on sympathetic integrity, and independent of myocardial blood flow.This present study represents the first comparison of parametric and uptake score measures of cardiac sympathetic denervation and perfusion using HED and NH3 PET. The averages scores differed by approximately 9-10%LV (p<0.001) when comparing parametric vs. uptake measures. NH3 MBF vs uptake defects appeared to have greater variability as shown in 3 images that was further amplified by the MBF kinetic modeling. These findings are consistent with previous studies demonstrating C-1 1-hydroxyephedrine abnormalities are often more extensive than blood flow defects assessed by uptake polar map analysis.[Defects measured by HED PET imaging were larger than those detected using myocardial perfusion imaging with NHanalysis.,26 They analysis.3 parameters. SCA risk discrimination was poor with ammonia, as Kaplan Meier and Cox Proportional Hazards analysis demonstrated that tertiles of NH3 uptake and MBF were not predictive of SCA. The AUC values were low for both resting NH3 uptake (AUC=0.60) and NH3 MBF (AUC=0.50). This supports previous conclusions that resting NH3 is a poor predictor of SCA risk in comparison to HED.[3-HED mismatch zones could be important substrates for developing arrythmias, this present analysis did not demonstrate improved risk stratification with mismatch compared to sympathetic denervation alone.[Furthermore, HED parameters demonstrated improved SCA risk discrimination compared to NHn to HED.,28 Whileon alone.,133 MBF, and NH3-HED mismatch scores detected larger defects than their respective uptake scores by approximately 6-8%LV (p<0.001). Nevertheless, this did not impact the prognostic analysis, as SCA risk discrimination was not significantly improved by parametric vs. simple uptake analysis. Multivariate and interaction analyses were explored, shown in HED DV, NH3 myocardial blood flow require full tracer dynamic analysis, thus necessitating longer PET imaging times compared to simple static uptake. This analysis also requires high scan quality for appropriate model fit and analysis, as reflected by the N=14 patients excluded from analysis for quality assurance purposes. Furthermore, HED defect scores have been established as a highly reliable and reproducible measure, while previous literature demonstrates slightly lower inter-rater reliability with HED parametric analysis compared to simpler uptake defects.[Although absolute quantification methods yielded larger defect sizes, they ultimately did not improve SCA risk stratification and there are some limitations to this method of analysis to consider. Quantification techniques such as HED distribution volumes and NH defects. The diff3 defect size occur when the perfusion defects are the smallest, i.e. in the lowest tertile of perfusion uptake defects. This finding was consistent for both the parametric and uptake measure comparisons. Taken together, these observations argue against a significant impact of flow limited delivery on HED defect size.It has been previously hypothesized that HED is a relatively flow-limited tracer due to its rapid extraction from the blood by NET transporters, resulting in tracer uptake that is more dependent on blood flow rather than neuronal uptake.,30 HED rLimitations of this study include the retrospective nature of the analysis, limited sample size, and under-representation of female patients in this study. These factors preclude the comparative assessment of sympathetic imaging markers against other clinical parameters which are associated with SCA. There were also N=14 HED studies excluded from analysis. Due to these excluded studies, the number of SCA events was reduced in this study and thus multivariable modelling was not performed. Reasons that rendered the scans non-diagnostic include a severe motion artifact (n=5), truncated inferior wall (n=5), low-count statistics (n=l), and late scan start-time (n=2). Incomplete measurement of the LV heart volume due to a truncated inferior wall is a common limitation of older PET scanners without x-ray CT available to guide patient positioning. Currently available PET/CT scanners have a larger axial field-of-view and also facilitate attenuation correction and patient positioning to overcome these limitations in contemporary clinical practice. It should be noted that the cardiac perfusion and sympathetic imaging techniques are currently limited to centres with an on-site cyclotron and expertise in cardiac PET innervation imaging analysis.3, and NH3-HED mismatch detects larger defect sizes, our analyses demonstrated that there was no improved SCA risk stratification associated with these measures compared to simple uptake analysis. This simplifies the imaging protocol and analysis methods required for SCA discrimination in future clinical applications, as extensive tracer quantification measures may not be required to provide SCA risk prediction which is adequately captured in simple tracer uptake analysis. Furthermore, radiation ablation of VT is a growing field which utilizes non-invasive myocardial imaging to identify target substrates.[This study provides novel insights into cardiac physiology which supports further study of SNS PET imaging in risk stratifying patients who are the most likely to benefit from a primary prevention ICD to prevent SCA. While parametric analysis of HED, NHbstrates. There ma3 PET imaging. The largest differences between HED and NH3 defect sizes occurred when perfusion defects were low, suggesting higher sensitivity to detect denervation early during the course of ischemic heart disease progression. Parametric and uptake defects of HED and NH3-HED mismatch were associated with SCA in the univariate analysis, while NH3 alone was not predictive of SCA. Quantification of HED and NH3 cardiac PET imaging markers detected numerically larger defects but there was no apparent prognostic advantage from parametric imaging compared to conventional uptake defect scores.Cardiac innervation defects measured by HED PET imaging are larger on average than perfusion defects measured by NH1752083_Sup_tab-11752083_Sup_file-11752083_Sup_file-21752083_Sup_Vdo-1


E-wave to E′ ratio (E/E′) using transthoracic echocardiography (TTE), and (2) whether E/E′ contributed to BNP improvement independent of bodyweight reduction (UMIN000005565).  A single-arm prospective study was conducted among Japanese patients with type 2 diabetes having preserved ejection fraction. The aim was to investigate (1) whether liraglutide therapy could improve B-type natriuretic peptide (BNP) levels and diastolic cardiac function assessed by the E/E′ ≥ 13.0.  Patients with type 2 diabetes and left ventricular ejection fraction (LVEF) ≥ 40% without heart failure symptoms were enrolled, and daily injection with liraglutide (0.9 mg) was introduced. Cardiac functions were assessed by TTE before and after 26 weeks of liraglutide treatment. Diastolic cardiac function was defined as septal E/E′ improved, with BNP levels declining from 36.8 ± 30.5 pg/mL to 26.3 ± 25.9 pg/mL (p = 0.0014) and E/E′ dropping from 12.7 ± 4.7 to 11.0 ± 3.3 (p = 0.0376). The LVEF showed no significant changes. E/E′ improved only in patients with E/E′ ≥ 13.0. Favorable changes in E/E′ were canceled when adjusted for body mass index (BMI). Multivariate linear regression analysis revealed that the left ventricular diastolic diameter and ∆E/E′/∆BMI contributed to ∆BNP/baseline BNP .  Thirty-one patients were analyzed. BNP and E/E′ but not on LVEF. E/E′ improvement was only seen in patients with diastolic cardiac function. Body weight reduction affected the change of E/E′. The BMI-adjusted E/E′ significantly contributed to the relative change of BNP. GLP-1 analog treatment could be considered a therapeutic option against diabetic diastolic cardiac dysfunction regardless of body weight. This trial is registered with the University Hospital Medical Information Network in Japan, with clinical trial registration number: UMIN000005565. Liraglutide had favorable effects on BNP and  Heart failure is the leading cause of diabetes-related morbidity and mortality . In patiOne potential class of medications that may be of value is the glucagon-like peptide 1 (GLP-1) analogs, shown in prospective studies to suppress cardiovascular death from causes including heart failure . The AmeOne GLP-1 analog, liraglutide, was shown to suppress cardiovascular death with a hazard ratio of 0.78 in the LEADER study . LiragluThe favorable effects of liraglutide on diastolic function appear to be well established at present. By contrast, none of the medications typically used in the field of cardiovascular medicine, including diuretics , digoxinWe hypothesized that liraglutide would improve diastolic cardiac function mainly through body weight reduction. No previous clinical studies on GLP-1 analogs have investigated in detail the echocardiographic parameters associated with body weight reduction. Therefore, the primary aim of this study was to examine the contribution of body weight reduction to previously reported findings of heart failure risk reduction by liraglutide. The second aim was to confirm the previously reported findings of the improvement of B-type natriuretic peptide (BNP) levels and of diastolic function assessed by TTE in patients with diastolic dysfunction, and to evaluate the contribution of the diastolic function to BNP.UMIN000005565). The primary outcome was the changes in serum BNP levels, and the secondary outcomes were the TTE parameters, blood pressure, body weight, and laboratory data following liraglutide treatment for 26 weeks.This single-center study was registered beforehand ). All procedures followed the ethical standards of the Helsinki Declaration. Informed consent was obtained from all patients prior to the study. The patients were recruited with web notification and were informed that they could opt out at any time.The inclusion criteria were as follows: (1) type 2 diabetes, aged >30 years old, and treated with insulin for more than five years; (2) no use of incretin-based therapy at baseline; (3) tolerable to daily injection of liraglutide; (4) preserved left ventricular ejection fraction (LVEF ≥ 40%); (5) no symptomatic heart failure; (6) no history of heart failure admission; (7) no coronary intervention within three years; and (8) no critical primary heart disease, including ventricular arrhythmias, persistent atrial fibrillation, complete atrioventricular block, cardiomyopathy, and valvular disease. The diagnoses of type 2 diabetes were based on the ADA diagnostic criteria.The exclusion criteria were as follows: (1) endocrine disorders, including type-1 diabetes; (2) refractory malignant tumors; (3) dependency on hemodialysis; and (4) severe hepatic dysfunction (Child − Pugh Score ≥ 10).All the patients were administered liraglutide injections at the maximum dose of 0.9 mg per day following the protocol provided by Novo Nordisk, Japan . The diabetologists adjusted the participants' insulin doses to achieve fair HbA1c levels of <7.0% and to avoid hypoglycemia . The pharmacists educated the participants on the use of liraglutide during the admission period. All patients continued their daily liraglutide injections throughout the study period. The patients' records ensured adherence and persistence.All patients had undergone repeated TTE at a clinically stable condition. The TTE recordings and measurements were performed following the American Society of Echocardiography guidelines, using a standard imaging transducer  with a linear probe frequency of 5 MHz.E) and the peak atrial systolic (A) transmitral flow velocities (E/A) and deceleration time were calculated. The E-wave to E′ ratio (E/E′) was calculated using tissue Doppler imaging. Routine echocardiographic evaluations included the left ventricular diastolic diameter (LVDd), the left ventricular systolic diameter (LVEF), the left atrial dimension, and E/E′.The initial TTE was performed within three months before the liraglutide introduction. The second TTE was conducted after 26 ± 4 weeks following the liraglutide introduction. Two cardiologists independently evaluated the data. Standard echocardiographic parameters were obtained from M-mode imaging. The ratio of the peak early diastolic . Fasting glucose, HbA1c, serum creatinine, lipid levels, urine albumin-to-creatinine ratio, and BNP were measured at the same time as the TTE evaluations, using standard laboratory procedures. The plasma levels of BNP were measured using a commercially available kit . The use of medications was collected from medical records.E/E′  and preserved LVEF (LVEF ≥ 40%) [We defined diastolic cardiac dysfunction as satisfying both increased F ≥ 40%) , 32.α of 0.05, would then determine the requisite number of the patients as 32.The number of study patients needed was calculated to detect BNP's significant changes based on our preliminary examination and the advice from our former colleagues. They later published a liraglutide study on diastolic cardiac dysfunction . With tht-test for parametric data. Categorical variables were compared using the Chi-square test or Fisher's exact test. The paired data were compared using the paired t-test for continuous data and the Wilcoxon signed-rank test for categorical data. The association was evaluated by univariate analysis. Multivariate linear regression analysis was used to assess independent contributors. The relative change was evaluated by adjusting for the baseline value. The correlated parameters were adjusted by division. A p value < 0.05 was considered statistically significant, and all tests were two-tailed. Statistical analyses were performed with JMP Pro ver. 15.1.Continuous variables are expressed as means ± standard deviations, and categorical variables are expressed as numbers and percentages. Differences in the baseline characteristics were evaluated using Student's Participants were enrolled between May 2011 and December 2013, with the last patient visit occurring in June 2014. A total of 42 patients were screened. Two patients withdrew because of recurrent gastrointestinal symptoms. Four missed TTE at 26 ± 4 weeks from the liraglutide introduction and were excluded. Five others were excluded due to newly recognized paroxysmal atrial fibrillation (Paf). Ultimately, 31 patients were analyzed in this study.The baseline characteristics of the 31 patients  are shown in p = 0.0003), BMI dropped from 26.2 ± 4.9 kg/m2 to 25.0 ± 4.8 kg/m2 (p < 0.0001), BNP levels improved from 36.8 ± 30.5 pg/mL to 26.3 ± 25.9 pg/mL  improvement of E/E′ occurred only in patients with diastolic cardiac dysfunction; (3) improvement of E/E′ was canceled when adjusted for bodyweight reduction; and (4) correlation and multivariate linear regression analyses showed that changes in BMI-adjusted E/E′ and LVDd significantly contributed to the relative change in BNP.This prospective single-arm study showed that a 26-week treatment of liraglutide improved diastolic cardiac function in patients with type 2 diabetes with preserved ejection fraction. The main findings of this study were as follows: (1) liraglutide injection at 0.9 mg per day improved BNP and Liraglutide improved diastolic cardiac function mainly in a bodyweight reduction-dependent manner, but it significantly contributed to BNP reduction in a bodyweight reduction-independent manner in patients with type 2 diabetes with preserved ejection fraction. The results were compatible with previous papers that showed an improved diastolic cardiac function and no improvement in systolic cardiac function with liraglutide administration , 33. AddE/E′ was canceled when adjusted for body weight reduction; therefore, the previous papers would have overestimated the extent of diastolic cardiac function improvement by GLP-1 analogs because the contribution of body weight reduction was not factored into the estimates. Furthermore, weight reduction should receive more attention as a therapeutic option for diastolic cardiac dysfunction in type 2 diabetes. Interestingly, the extent of E/E′ improvement in the current study was very similar to that reported in the LIVE study [E/E′ could reach a plateau in different dose levels between Asians and Europeans, mainly due to the tolerability against the medication [This study is the first to examine the contribution of body weight reduction to the change in diastolic function by GLP-1 analogs. The improvement in VE study , althougdication .E/E′.The improvement in LVDd was difficult to interpret because BNP and LVDd interact, so an improvement in LVDd would result in an improvement in BNP, and vice versa. We could view the improvement in both BNP and LVDd as reflecting the reduction in LV filling pressure. LVDd did not show any significant change following the liraglutide introduction as a whole. LVDd in each patient might have paralleled with the change of BNP levels independently from that of One safety concern that should be mentioned is Paf. Five patients were newly identified to have Paf following the introduction of liraglutide. We supposed that some cases had unidentified Paf before the study, and this became obvious after liraglutide introduction. GLP-1 analogs can increase the heart rate through sympathetic nervous stimulation , althougE/E′ and the contribution of cardiac morphological changes reflected by LVDd. The vascular and myocardial relaxation would reduce end-diastolic filling pressure, thereby improving diastolic cardiac function. A recent report has shown that the vasodilatory actions of GLP-1 analogs are preserved in the myocardial microvasculature but not in obese subjects with vascular insulin resistance [The GLP-1 analog liraglutide has a beneficial effect on the cardiovascular system , since Gsistance . Body weHyperglycemia could affect the prognosis of diabetic patients with diastolic cardiac dysfunction . PatientE/E′. Considering the negative consequences of the prospective randomized studies, the FIGHT study, and the LIVE study, patients with reduced EF would not be good candidates for GLP-1 analogs. These medications could be one of the therapeutic options against diastolic cardiac dysfunction, although the extent of improvement might be clinically small. We believe that GLP-1 analogs could be a therapeutic option for HFpEF in patients with type 2 diabetes, but a substantial-scale clinical study would be needed to obtain positive results.Our results show that the primary candidates for the use of GLP-1 analogs would be the subjects with elevated BNP and E/E′ by body weight reduction adjustment suggested an appropriate sample size calculation in the present study. Another limitation was the absence of a control group and the high heterogeneity in the participants. Another limitation was the enrollment of asymptomatic patients, so the implication of GLP-1 analogs on symptomatic cohorts remains uninvestigated. The applicability of our findings to patients with symptomatic diastolic cardiac dysfunction requires further investigation.This study has several limitations. One limitation of the present study was its relatively small sample size cohort. However, too large a sample size would be unsuitable for evaluating clinically significant differences in diastolic function improvement. The cancelation of the statistical significance of Liraglutide improves diastolic cardiac function, mainly in a body weight reduction-dependent manner. However, it contributes to the reduction in heart failure risk in patients with type 2 diabetes with preserved ejection fraction independent of body weight reduction. GLP-1 analog treatment should, therefore, be considered as a therapeutic option against diabetic diastolic cardiac dysfunction regardless of a patient's body weight.Further randomized controlled trials with larger sample sizes that consider HFpEF are needed to establish this pleiotropic effect of GLP-1 analogs. The results would be meaningful as they could widen the therapeutic options for treatment of high heart failure risk in patients with diabetes.


Level of Difficulty: Beginner.)Arrhythmia-induced cardiomyopathy secondary to frequent ventricular premature contractions is a well-studied phenomenon; however, there is a paucity of data showing a similar association with frequent atrial premature contractions (APCs). Early recognition and successful APC ablation can reverse left ventricular dysfunction in these patients. (  He did not have any significant symptoms except occasional palpitations.2 . The rest of the examination was unremarkable.He had history of obesity and frequent APCs; however, he did not report obstructive sleep apnea or excessive intake of caffeinated beverages. His family history was unremarkable. On physical examination, he was normotensive, with a body mass index of 39.8 kg/mThe differential diagnosis of left ventricular dysfunction included tachycardia-induced cardiomyopathy secondary to frequent APCs versus primary dilated cardiomyopathy.His 48-h Holter monitor in February 2019 showed an excessive APC burden of 37% (average heart rate 92 beats/min [range 56 to 161 beats/min]). His previous 24-h Holter monitor in September 2015 showed a 36% APC burden (average heart rate 87 beats/min [range 54 to 171 beats/min]). He had an echocardiogram in February 2019 that showed a dilated left ventricle  , with an1 to V3; a negative P-wave in leads V3 to V6; and isoelectric in lead I. On the basis of the configuration, the APC appeared to be originating from the coronary sinus (CS) body on vector electrocardiography.The vector analysis showed the following APC wave configuration on his 12-lead electrocardiogram : a negatHe was previously prescribed metoprolol; however, it was discontinued because of intolerance. After shared decision making, the patient elected to proceed with an electrophysiology study in May 2019 to rule out tachycardia-induced cardiomyopathy secondary to frequent APCs. On mapping, the earliest activation timing site was initially localized to the left atrial floor near the CS os . However, after application of a few ablation lesions using a Thermocool SmartTouch SF contact force-guided radiofrequency ablation catheter  (maximum power 35 W with a target ablation index of 400), the left atrium was no longer the earliest site, and the change in configuration was consistent with a shift to the right atrium. Additional mapping was done in the right atrium as well as in the CS. The earliest activation timing site was ultimately localized near the CS os  . After bAPCs are usually considered benign; however, if APCs occur in high frequency, they can potentially lead to dilated cardiomyopathy. We present the case of a young patient with frequent APCs leading to cardiomyopathy that reversed after successful radiofrequency ablation and elimination of the APCs. The pathophysiology of APC-induced cardiomyopathy is not well understood. In an animal study by Pacchia et al. , atrial The patient had a follow-up 48-h Holter monitor in July 2019  that showed sinus rhythm with only 1 isolated APC (average heart rate 81 beats/min [range 48 to 144 beats/min]). A follow-up echocardiogram in August 2019 showed that right ventricular systolic function was normal and left ventricular function had recovered . APCs were not evident at the time of the study. Vervueren et al. (APC-induced cardiomyopathy can occur in patients with a high APC burden. Therefore, we recommend regular monitoring of cardiac function with an echocardiogram even in asymptomatic patients. If left ventricular dilation or dysfunction is noticed, catheter-based ablation therapy can offer excellent results.The authors have reported that they have no relationships relevant to the contents of this paper to disclose.


BackgroundThe tricuspid annular plane systolic excursion (TAPSE) / pulmonary artery systolic pressure (PASP) ratio has been a useful marker of right ventricular (RV)-pulmonary artery coupling. However, given the intricate functional and mechanical interdependence of the right and left ventricles, we believe this ratio would be less useful when assessing reduced left ventricular (LV) systolic function. Instead, we proposed using the tricuspid annular tissue Doppler imaging systolic velocity to LV outflow tract velocity time integral ratio (TA TDI s’ / LVOT VTI r) for this purpose. MethodsFor this proof-of-concept study, a retrospective analysis was conducted on 60 patients with complete echocardiographic studies while in sinus rhythm. The population was divided as follows; Group 1 included 20 individuals with normal left ventricular ejection fraction (LVEF) as well as normal RV and PASP. Group 2 was composed of 20 patients known to have been evaluated or treated for pulmonary hypertension, while group 3 was comprised of 20 patients treated for heart failure (HF). Results#; p < 0.001).TAPSE/PASP ratios were no different from any of the studied groups. However, the proposed TA TDI s' /LVOT VTI r was statistically different among all three groups (Group 1: 0.6 ± 0.1*; Group 2: 0.5 ± 0.1°; and Group 3: 0.8 ± 0.3ConclusionsBased on these results, there is now a need for additional prospective studies to explore the overall utility of using this TA TDI s' / LVOT VTI r in day-to-day routine assessments not only for diagnostic purposes but also to determine how this ratio correlates with symptoms and changes with therapy. Though invasive hemodynamic data is the gold standard to diagnose and characterize elevated pulmonary artery systolic pressures (PASP), echocardiography remains the most useful noninvasive tool for initial identification and routine follow-up .Previously, PASP was mainly determined using continuous-wave Doppler determination of pressures using the maximal tricuspid regurgitation velocity -4. HowevIn addition to these anatomical changes, there has been interest in a recently derived noninvasive echocardiographic which measures tricuspid annular plane systolic excursion (TAPSE) / PASP rate . This deHowever, given the intricate functional and mechanical interdependence of the right and left ventricles , we beliFrom this mechanistic point of view, we propose to use the tricuspid annular tissue Doppler imaging systolic velocity to LV outflow tract velocity time integral ratio (TA TDI s’ / LVOT VTI r). This novel echocardiographic parameter not only takes into account a more useful and utilized variable of RV systolic function (tissue Doppler imaging (TDI) velocity of annular excursion) but also a more direct and efficient measure of LV performance commonly used to assess stroke volume - 13.Therefore, in this proof of concept study, we tested our hypothesis that the TA TDI s’ / LVOT VTI r would be more useful in discriminating patients presenting with heart failure (HF) symptoms rather than the well-described TAPSE/PASP ratio. Therefore, to accomplish our goal, we would use the ratio of the RV outflow tract velocity time integral (RVOT VTI) to the left ventricle outflow tract velocity time integral (LVOT VTI) for our discriminatory assessment based on the intricate functional and mechanical interdependence between the right and left ventricles , 13-16.Patient and data collectionPatients who were referred to our university hospital, the University of Arkansas for Medical Center (UAMS) in Little Rock, AR, for a regular transthoracic echocardiogram were considered as potential candidates for inclusion in the final analysis. All patients must have had a complete echocardiogram that included pulsed Doppler spectral signals across the RV outflow tract (RVOT), discernible tricuspid regurgitation signal, good endocardial border resolution of both the right and left ventricular chambers, as well as M-mode and TDI of the tricuspid annulus with a good TDI signal of the mitral annulus (MA). For this proof of concept study, we conducted a retrospective analysis of a selected group of 60 patients from our echocardiographic database. All patients were in sinus rhythm with heart rates below 100 beats per minute without any ectopy at the time of the study. Finally, studies with varying degrees of PASP and LVEF were included in the final analysis. Our Institutional UAMS Review Board approved this study. No consent form was required as this was a retrospective study.EchocardiographyTwo-dimensional echocardiographic studies were performed using commercially available systems . Images were obtained in the parasternal and apical views with the patient in the left lateral decubitus position and in the subcostal view with the patient in the supine position using a 3.5-MHz transducer. Standard two-dimensional, color, pulsed, and continuous-wave Doppler data were digitally acquired in gently held end-expiration and saved in regular cine loop format for subsequent offline analysis . First, TAPSE was determined by measuring total excursion of the tricuspid annulus, from its highest position after atrial ascent to its maximal descent during ventricular systole, from the apical four-chamber view by aligning the M-mode cursor along with the movement of the lateral tricuspid annulus -11. MeasSecond, continuous-wave Doppler was utilized to record the tricuspid regurgitation jet from multiple windows, and the highest velocity was then used to estimate PASP using the modified Bernoulli equation and an estimate of mean right atrial pressure using the diameter and collapse index of the inferior vena cava and the hepatic venous flow pattern -11.Calculation of the echocardiography-derived TAPSE / PASP ratio was then performed as previously described , 8.Third, interrogation of the LV outflow tract (LVOT) was performed by placing a 1 to 2 mm pulsed-wave Doppler sample volume just within the LVOT from the five-chamber apical view so that the closing but not opening click of the aortic valve was visualized -18. The Fourth, measurements of MA systolic (s') and early diastolic (e') velocities were obtained using tissue Doppler imaging using the four-chamber apical view using only the lateral portion of the MA for assessment of LV systolic as well as diastolic function as previously described following guidelines published by the American Society of Echocardiography , 17.Finally, interrogation of the RV outflow tract (RVOT) was performed by placing a 1 to 2 mm pulsed-wave Doppler sample volume just within the pulmonary valve from the parasternal short-axis view. The sample volume was placed so that the closing but not opening click of the pulmonary valve was visualized , 14-16. Statistical analysisIn terms of the statistical analysis used for this study, the following steps were taken. A total of three measurements were obtained of each echocardiographic variable, and data is reported as individual mean ±standard deviation values. Baseline characteristics were compared between groups using analysis of variance (ANOVA) for continuous variables. Levene's test was first used to test to confirm the homogeneity of variances across groups, and this was followed by the Student-Newman-Keuls test that was then performed following the one-way ANOVA to compare all pairwise comparisons of the means. A stepwise multiple regression analysis was then used to identify the independent relationship between the ratio RVOT VTI / LVOT VTI and the most significant echocardiographic and Doppler variables noted in our analysis that are commonly used in the assessment of both left and right ventricular performance to determine which of these studied variables was the best discriminating predictors. Receiver operating characteristic (ROC) curves were then produced to test the ability of TA TDI s’/LVOT VTI r to identify patients with an RVOT VTI / LVOT VTI r of 0.8. A similar ROC analysis was also performed to assess the ability of the TAPSE / PASP r to identify patients using the same RVOT VTI / LVOT VTI r of 0.8. A p-value of <0.05 was considered significant. The data analysis was generated using SPSS version 17.0 .The total population of 60 patients included for analysis was obtained from a selection of consecutive studies from patients from our echocardiographic database according to their clinical profile as follows: group 1 included 20 individuals with normal LVEF as well as normal RV and PASP. Group 2 was composed of 20 patients known to have been evaluated or treated for pulmonary hypertension, while group 3 was comprised of 20 patients treated for HF. There was no difference in terms of either mean age  or body surface  between the three groups. Table Before analyzing the potential discriminatory ability of the TAPSE/PASP ratio, we verified that our patient population had a normally expected correlation between TAPSE and TA TDI values to avoid cases of decoupling. We accomplished this by performing a correlation analysis between the two echocardiographic variables. As shown in Figure Even when the TAPSE/PASP ratio values were significantly different from group 1 when compared to both group 2 and 3; TAPSE/PASP ratios were no different from each other. Furthermore, group 3 TAPSE/PASP ratios were not even considered abnormal, based on published data .In contrast, when we examined our proposed TA TDI s’ / LVOT VTI ratio it was the only calculated variable found to be statistically different among all three studied groups as shown in Figure Based on the intricate functional and mechanical interdependence between the right and left ventricles, the ratio of the RV outflow tract velocity time integral (RVOT VTI) to the left ventricle outflow tract velocity time integral (LVOT VTI) was used as the basis for our discriminatory assessment as (RVOT VTI / LVOT VTI r). On stepwise multiple regression analysis, as shown in Table Finally, as seen in Figure The TAPSE/PASP echocardiographic ratio not only has become a meaningful prognostic parameter in patients with pulmonary artery hypertension , 8 but aHowever, the utility of this ratio in trying to discriminate between patients treated for PH from those affected by HF with concomitant elevation in PASP has not been previously assessed. Furthermore, the presence of elevated PASP in HF is not only clinically relevant and common but more importantly, associated with worse clinical outcomes , 21. ConIn our patient population, we found that this TAPSE/PASP r was not useful in distinguishing patients with PH from those patients with HF and elevated PASP. However, we found that of the studied variables, the TA TDI s’ / LVOT VTI r was statistically different among all three studied patient groups. Surely, it can be argued that although this ratio differed in all three groups, the absolute difference in the mean value was not much, and the standard deviation was large-the latter suggesting the possibility that a significant overlap might exist among all three groups. Therefore, we conducted a ROC curve analysis to comment on the overall diagnostic accuracy of this ratio. Based on this ROC data, the TAPSE/PASP r can as previously published, 8, diffMechanistically speaking, the intricate functional and mechanical interdependence of the right and left ventricles, the RVOT VTI / LVOT VTI r was used as the basis for our discriminatory assessment. We have already described the utility of this ratio to explain mechanical differences in terms of both interventricular dyssynchronies and in cases of PH -16. In aThe inability of the TAPSE/PASP r to discriminate patients as intended by our analysis might be due to a combination of factors not entirely attributed to the degree and acuity in PASP elevation of PASP but rather to the extent of RV stiffness. The latter could be in part mediated by underlying cardiomyopathic process affecting both ventricles, independent of pulmonary pressures, thus reducing its utility. Conversely, the TA TDI s’ / LVOT VTI r could be intuitively a much better echocardiographic parameter as this ratio takes into consideration LV ejection that the TAPSE/PASP r does not. Consequently, our proposed TA TDI s’ / LVOT VTI r not only utilizes a more robust variable of RV systolic function (TDI velocity of annular excursion) but also, a more direct and efficient measure of LV performance commonly used to assess stroke volume. Aside from the utility of these novel findings found from this pilot analysis, we recognize the following study limitations. First, the retrospective nature of the analysis, small sample size and lack of concomitant invasive hemodynamic; however, this was a proof of concept study to determine the overall utility of the easily obtained echo-Doppler variable. In addition, our results may not be generalizable to patients with arrhythmias and/or stenotic valvular disease, as they were not included in the analysis. Finally, we had no speckle tracking imaging information, which could further improve patient discrimination. However, these preliminary results using this proposed ratio in terms of patient discrimination might set a benchmark to further speckle tracking studies. Based on these results, there is now a need for additional prospective studies to explore the overall utility of using this TA TDI s’ / LVOT VTI r in day-to-day routine assessments not only for diagnostic purposes but also to determine how this ration changes with therapy. As elegantly stated by many researchers, there is a need for additional hemodynamic variables to improve our characterization of pre-capillary PH with LV dysfunction. Our results might just be one of those variables. The opportunity for a validation study would certainly follow this proof of concept study. We foresee using this newly proposed variable not only when diagnosing or following HF patients with either reduced or preserved LVEF but also patients with either idiopathic or secondary pulmonary hypertension. We believe that the utility of this proposed echo variable would certainly be of great clinical utility on the routine, day-to-day evaluation of patients as this variable is easily obtainable and it does not require either complex measurements that would add time to the imaging study that could disturb the clinical flow of any echo lab as well as not requiring any additional off-line measurement or complex calculations.