Ventricular regions that experienced the greatest systolic stress were (severe AS vs. baseline) basal inferior , mid-anteroseptal , and apex . This data serves as a reference for future studies that will incorporate patient-specific ventricular geometries and material parameters, aiming to correlate LV biomechanics to AS severity.The severity of aortic stenosis (AS) has traditionally been graded by measuring hemodynamic parameters of transvalvular pressure gradient, ejection jet velocity, or estimating valve orifice area. Recent research has highlighted limitations of these criteria at effectively grading AS in presence of left ventricle (LV) dysfunction. We hypothesized that simulations coupling the aorta and LV could provide meaningful insight into myocardial biomechanical derangements that accompany AS. A realistic finite element model of the human heart with a coupled lumped-parameter circulatory system was used to simulate AS. Finite element analysis was performed with Abaqus FEA. An anisotropic hyperelastic model was assigned to LV passive properties, and a time-varying elastance function governed the LV active response. Global LV myofiber peak systolic stress (mean ± standard deviation) was 9.31 ± 10.33 kPa at baseline, 13.13 ± 10.29 kPa for moderate AS, and 16.18 ± 10.59 kPa for severe AS. Mean LV myofiber peak systolic strains were −22.40 ± 8.73%, −22.24 ± 8.91%, and −21.97 ± 9.18%, respectively. Stress was significantly elevated compared to baseline for moderate ( Aortic stenosis (AS) is the most prevalent valvular heart disease in the developed world . WithoutRather than being viewed as an isolated valve disease, AS warrants a renewed understanding of its complex pathophysiology wherein its detrimental effects on the cardiovascular system are considered as derangements of the LV, the aortic valve, and systemic vasculature together . AdvanceWe believe that simulations of LV-aortic coupling with AS will enable us to identify LV biomechanical parameters that are prognostically significant markers of AS. Accordingly, as a first step, we created isolated AS in an idealized human heart model with the goal of gaining meaningful insight into the biomechanics of the actual end-organ, the LV, which AS treatment seeks to preserve. This investigation is one of the first of its kind to explore this critical aspect of AS pathophysiology with the aid of powerful computational simulation techniques.An idealized human heart model was generated from methods described in The solid model of the human heart portrays realistic anatomy of four chambers, four valves, trabeculae in the ventricles, and great vessels including the ascending aorta, the aortic arch, pulmonary artery, and superior vena cava . The LV The ventricular material model passive response uses the anisotropic hyperelastic formulation developed by Holzapfel and Ogden, and has been widely published in many cardiac modeling studies , 2018a. f, bf, as, bs, afs, bfsa, b, a, and four strain invariants a = 3.354 kPa, b = 7.08, af = 2.501 kPa,fb = 5.34, while the remaining parameters were set to null. The strain invariants are derived from the isochoric right Cauchy-Green tensor:Eight material parameters F is the deformation gradient, J is the determinant of the deformation gradient, J = det (F) and f0 and s0 are orthogonal vectors in the fiber and sheet direction in the reference configuration. The volumetric response is governed by:Terms J is the third deformation gradient invariant, and D is the multiple of the bulk modulus where modeling .The active myocardial tissue response is represented as a time-varying elastance model :with functions defined as:Tmax is the maximum allowable active tension and is multiplied by terms regulating calcium concentration and the time course of the contraction. These two terms are dependent on the sarcomere length l. This law has been used extensively in prior published studies on ventricular mechanics  in the sheet direction of the fiber is represented by:s, is the sum of passive stress, σps, and a portion of fiber direction stress, n*σaf. The parameter n is a scalar value less than 1.0 and represents the interaction between adjacent muscle fibers; a value of n = 0.4 was used  with an LV ejection fraction (LVEF) of 60% for all three simulation conditions, with end-diastolic volume of 136–138 ml. The same LV geometry and mass were used across all three simulations for control and represented a non-remodeled heart. A complete cardiac cycle occurred over 0.7 s. The heart model is constrained in space by fixed node sets at the cut planes of the aortic arch, pulmonary trunk, and superior vena cava. An acceptable steady state was achieved after running three consecutive cardiac cycles, with further cycles producing <5% variation in the model’s chamber pressures. LV myofiber stress and strain values were obtained at end-diastole and peak systole (defined as the point at which greatest LV pressure was generated). Data are expressed as mean ± standard deviation. T-tests were used for statistical comparison of continuous variables.Cardiac cycles were simulated with Abaqus FEA (SimuliaLV pressure-volume loops and LV-aorta pressure-time curves for the different degrees of AS are shown in p < 0.01) as well as between severe AS and baseline (p < 0.001).Global mean LV myofiber stress and strain values at end-diastole and peak systole for each simulation condition are listed in Regional segmentation of the LV was performed in accordance to the American Heart Association standardized myocardial regions, creating 17 segments of LV myocardium based on anatomic location and coronary perfusion territories . The meaAortic stenosis has typically been graded by hemodynamic parameters that can be measured on echocardiography: mean pressure gradient across the valve, effective orifice area, and peak blood jet velocity. However, a big limitation is that all of these are derived from other specific parameters and are flow-dependent. To better understand disease pathophysiology and find new clinically useful markers, we created a realistic human LV model with coupled circulatory system parameters, and isolated moderate and severe AS. This is one of the first computational studies to specifically investigate the impact of AS on LV myofiber stress. In this model, moderate stenosis provided peak LV myofiber stress 1.4 times greater than baseline, and severe stenosis yielded peak stress of 1.7 times greater than baseline. With all other parameters being held nearly equal , it is an anticipated physiologic response that higher degrees of afterload will increase the amount of stress experienced by the LV.2 (LV systolic performance and ventricular stress have been investigated as prognostic markers for AS patients . After a2 . End-sysp < 0.001) at the time severe AS was diagnosed . After aortic valve replacement, LV end systolic stress decreased to 7.08 kPa. These results help link the clinical benefit of aortic valve replacement with LV biomechanics data, but are limited by the use of finite element models with fairly few elements and assigning a linearly elastic, isotropic material model to the myocardium. Aside from the difference in the aortic valve disease studied by Wollmuth et al., our LV model uses a highly refined mesh for the finite element model, and a sophisticated myocardial material model that accounts for microstructure, active, and passive properties. The complexities of the myocardium need to be accounted for in the material law in order to obtain the most accurate results.The ability to create patient-specific finite element models would offer more accurate wall- stress results, as well as more insight into the ventricular pathophysiology. One of the earliest published finite element studies exploring the link between aortic valve pathology and LV stress was done on aortic insufficiency patients . Patient2. Although differences in ventricular topography and dimensions between the two groups are not surprising, the study illustrates the added benefit of using regional segments to perform more in-depth analysis of the LV.Analysis of regional myocardial biomechanics is facilitated with a solid, finite-element model. Our data from each of the 17 standardized myocardial segments reveals a range of stress at peak systole, with anatomic regions each harboring regions of localized peak stress. Examining how this ventricular stress profile may vary among patients, or shift as disease progresses, can offer a unique biomechanical profile for each patient. For example, Jung et al. classified cardiac computed tomography images of normal control patients and those with severe AS by physical parameters associated with the 17 segments . Two andIn another study, the 17 segments were used to map the distribution of myocardial fibrosis in AS patients by analyzing gadolinium-contrast cardiac magnetic resonance imaging . PatientWith advances in computational modeling techniques, cardiovascular imaging, and a detailed understanding of myocardial material properties, patient-specific models of clinical utility are within reach and poised for more translational research roles. Non-invasive means of determining parameters to tune patient-specific models exist or are being perfected . EnhanceThe primary limitation of this study is that our model represents an idealized human heart geometry and simulates AS without any LV remodeling. The material properties were based on established, comprehensive models that describe normal myocardial physiology, whereas AS is often a progressive condition accompanied by a degree of LV remodeling and fibrosis. Future studies with computational modeling should incorporate patient-specific ventricular geometries, myocardial material properties, and should address all possible cardiovascular derangements in presence of AS.In this study, we used a realistic human heart model with coupled circulatory system to simulate AS and quantify the LV myofiber stresses. This preliminary investigation used computational methods to better assess the role of LV-aortic coupling in the pathophysiology of aortic stenosis. Our goal is to apply computationally derived data toward patient-specific assessment of AS to guide management and intervention before irreversible LV remodeling occurs.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.AW, YW, SP, and JG were involved in the conception, design of the study, and analysis of the results. YW ran simulations and provided data for analysis. JY and KS offered guidance on the conditions for the computational simulations and assisted in conducting the data analysis. AW, AH, TD, and JG were involved in data analysis, interpretation of results, and the clinical concepts of this study. All authors contributed to the article and approved the submitted version.YW was employed by the company Thornton Tomassetti Lifesciences Division. KS is currently employed by the company Medtronic Inc. JY was employed by the company Dassault Systemes Simulia Corp. 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. The reviewer HG declared a past co-authorship with one of the authors JY to the handling editor.


Despite advances in medical and mechanical therapy, outcomes for end-stage heart failure patients remain poor. For a carefully selected cohort, heart transplantation remains the treatment of choice. Given limited organ availability, heart transplantation warrants careful consideration of the risk and benefits of transplant as well as a patient’s individual capacity to benefit from the transplant relative to the other pool of candidates. Heart transplantation presents an area of ongoing research and innovation aimed at solving the key limitations to transplantation, namely organ shortage and post-transplant allograft rejection.. Since 2015, several centers have used hearts obtained following donation from select donors after circulatory-determined death (DCD). A study from the United Kingdom reported similar allograft performance and 90-day survival rates following DCD heart transplantation as compared to DBD transplantation. These promising results offer a potential solution to bridge the gap between organ demand and availability. Forecasts have predicted that DCD heart transplantation could increase transplantation rates by up-to 20%. Over the coming decade, we envisage more wide-spread use of DCD transplantation with greater standardization of procurement protocols to ensure organ quality and protection.Traditionally, cardiac allograft procurement occurs following donation after brainstem-determined death (DBD). Advantages of DBD include the ability to assess the organ prior to procurement allowing for optimal organ selection, controlled cardiac arrest and protection with cold cardioplegia, and avoidance of the detrimental effects of warm ischemiaEx-vivo perfusion of donor organs is another potential method for expanding donor heart availability. By preserving organs for longer time thus enabling transport to more distant locations, the use of such systems can allow for more even distribution of organs across regions. Moreover, donor hearts can be resuscitated, reducing the number of organs considered unsuitable for transplant. The PROCEED II trial showed ex-vivo perfusion using the Organ Care System®  to be equivalent to cold ischemic preservation and with no detrimental effect on short-term patient or graft outcomes. In addition, ex-vivo machine perfusion in the context of DCD donation could allow reanimation of the heart and restoration to near physiological state prior to transplantation. Starting this year, the Mayo Clinic Transplant Center is launching a lung restoration center with the goal of increasing the availability for transplantation using ex-vivo lung perfusion. Optimized lungs will then be available to several transplant centers across the United States of America. With the improvement in ex-vivo perfusion technology, we envisage that a similar concept for heart preservation could be developed.. Transplanting across a positive crossmatch is avoided due to the associated high risk of early graft rejection and poorer long-term outcomes from higher rates of coronary allograft vasculopathy. However, in select, sensitized patients, de-sensitization protocols have been trialed to increase the chances of a negative crossmatch and improve transplant outcomes. Plasmapheresis and intravenous immunoglobulins have historically been the mainstay for de-sensitization treatments, aimed at removing currently circulating antibodies. More recently, monoclonal antibodies targeting specific cells of the immune system have been paired with these. Rituximab, which targets CD20 on B cells, aims to reduce the production of new antibodies. Similarly, Bortezumab, targeting plasma cells, inhibits antibody production but is more targeted towards anti-HLA I antibodies. Finally, Eculizumab targets the complement pathway and is currently being investigated for the treatment of sensitized heart transplant candidates in the DUET Cardiac Trial.Allosensitization to human leukocyte antigens (HLA) remains one of the biggest impediments to safe heart transplantation. It reduces access to otherwise ABO-compatible donors thus increasing transplant waiting time.Multi-organ transplantation involving the liver offers a unique opportunity to address pre-transplant sensitization. Liver allografts alone can decrease donor-specific antibodies (DSA) levels. Mayo Clinic reported the first experience of performing combined heart-liver transplantation with implantation of the liver prior to the heart allograft. The aim was to protect the heart from antibody-mediated rejection (AMR). This technique successfully reduced DSA levels immediately after the liver transplant in all patients and resulted in normal cardiac allograft function, and only one case of AMR was recorded post-transplant. Alternatively, measurement of donor-specific cell-free deoxyribonucleic acid (DNA) has been proposed as a marker for cellular injury caused by rejection. With a growing body of evidence to support less invasive methods of monitoring cardiac graft rejection, we hope to see a reduction in the need for invasive endomyocardial biopsies in the future.A final area of ongoing research in the context of heart transplantation focuses on detection of graft rejection. Traditionally, repeated endomyocardial biopsies are assessed for histological evidence of rejection. However, these procedures are associated with considerable morbidity over the long term such as catheter-related complications, tricuspid regurgitation, and renal insufficiency. In a select recipient population at low risk of rejection, gene expression profiling has reduced biopsy numbers without increased risk of serious adverse outcomesWhile clearly conferring a survival benefit, heart transplantation is currently only available to a select cohort of patients. In addition, long-term morbidity and limited graft longevity renders transplantation a treatment rather than cure for heart failure. The ongoing developments promise to contribute to improve both organ availability and graft longevity over the coming decade.


Peak signal intensity as well as upslopes were strongly correlated (r2 > 0.9 for both) for all positions with a systematic overestimation in all positions relative to the AoS . Differences between the positions were more pronounced for patients with reduced ejection fraction. The error of averaged MBF quantification was 8%, 13% and 27% for the base, mid and apex. The location of the AIF significantly influences core parameters for perfusion quantification with a systematic and ejection fraction dependent error. Full quantification should be based on obtaining the AIF as close as possible to the myocardium to minimize these errors.The aim of this study is to provide a systematic assessment of the influence of the position on the arterial input function (AIF) for perfusion quantification. In 39 patients with a wide range of left ventricular function the AIF was determined using a diluted contrast bolus of a cardiac magnetic resonance imaging in three left ventricular levels  as well as aortic sinus (AoS). Time to peak signal intensities, baseline corrected peak signal intensity and upslopes were determined and compared to those obtained in the AoS. The error induced by sampling the AIF in a position different to the AoS was determined by Fermi deconvolution. The time to peak signal intensity was strongly correlated (r Perfusion cardiovascular magnetic resonance (perfusion-CMR) is an established test for assessing the presence and severity of myocardial ischemia with a class IA indication in the most recent European Guidelines . It has However, the influence of the location on the AIF has not been systematically assessed. We determined the presence and extent of error induced by positioning the AIF in the basal, mid and apical left ventricle, rather than the aortic sinus (AoS) which is closest to the coronary ostiae. For quantification we used Fermi deconvolution. It is currently the most widely used convolution method.In 39 patients with suspected nonischemic cardiomyopathy and a wide range of ejection fractions a weight adapted contrast agent bolus  was injected with a flow rate of 3 ml/sec followed by a 20 ml saline chaser  (adapted from ). DuringAll patients were scanned at 3 T  using 32-channel surface coils. All patients gave written informed consent and the study was approved by the local ethics committee .Using MEDIS Suite 3.2 , three regions of interest (ROI) were placed in each dynamic of the first pass perfusion 3-chamber images at a basal, mid and apical level. These levels are defined as 25%, 50% and 75% of the endsystolic distance from the mid of the mitral valve to the tip of the apical blood pool of the LV blood . These pDifferences versus the AIF in the AoS were then calculated in [ms] for time to peak and in [%] for peak signal intensity and the upslope.To calculate the error induced by the different locations, four average AIFs of all patients were calculated after baseline correction and normalization for time to peak and peak signal intensity using the data obtained from the four sampling positions. A single myocardial response function was created from a patient with an ejection fraction closest to the mean of all patients. The difference in MBF based on the AIF obtained in the three ventricular positions versus the AIF obtained in the AoS was calculated and provided in [%].Ejection fraction was measured using automatically generated contours with final user correction and approval . Patients were categorized into preserved ejection fraction (EF > 50%) or reduced ejection fraction (EF ≤ 50%).The following statistical tests were used as appropriate: rANCOVA for repeated measurements with F-test for significance, unpaired Welch two sample t-test for differences between group categories, linear regression with correlation coefficient for comparing two continuous variables . All tests were 2-tailed. Statistical significance was reached at p < 0.05. Statistical analysis was performed with R studio (Version 1.2.5001).Patient characteristics are shown in Table 2 between 0.91 and 0.99, p < 0.01 for all, Fig. Strong correlations were observed between the respective parameters  in the AoS, and the three left ventricular positions  for perfusion quantification has significant effects on its shape. Some of these differences are systematic, however, there is also large random error. Most importantly, the magnitude of the error correlates negatively with the ejection fraction resulting in larger errors in patients with reduced ejection fraction. This is of specific importance, as one of the core reasons to use the AIF is to control for differences of contrast agent delivery in patients with reduced ejection fraction.For two decades myocardial perfusion-CMR studies were mainly reported visually by assessing the speed and amount of signal increase within the myocardium during the first arrival of the contrast agent bolus during maximal vasodilation. Until recently, most attempts to quantify MBF based on perfusion-CMR were restricted to academic research sites and the effort for full quantification, in combination with a large amount of assumptions, was tedious. If quantification was performed, frequently a semiquantitative approach based on differences of specific parameters (e.g. upslope) between stress and rest was used. This has the advantage to cancel out some of the systematic effects. In addition, local normal ranges or a comparison between segments of a given patient were applied. These approaches, however, have the disadvantage to not allow provision of absolute ranges of normal with objective and transferable cut-off values, require stress and rest imaging and are limited in their ability to address changes, e.g. with therapy. More recently fully quantitative methods have become readily available and started to be used in clinical practice. However, when providing full quantification of MBF in ml/g/min myocardium any error will be misleading, as the claim for an absolute number inherently infers, that all assumptions in the processing chain of the parameter are sufficiently controlled.The role of the position of the input function has been analyzed previously, mainly for cardiac positron emission tomography where a similar dependency of MBF on the position of the input function was found , 14. ItsThe obtained data in the current report provide important knowledge:First, the position of the AIF creates a systematic error. This error could be addressed by either using normal values for the specific pathway used to obtain the results, by applying correction factors or potentially, by calculating a myocardial perfusion reserve based on the quantitative data, rather than providing MBF itself. The excellent correlation between the various locations for each parameter demonstrate, that the systematic effect is strong and can be accounted for. Importantly, the largest systematic difference was found for time to peak, which is least important for full quantification.Second, the position of the AIF creates a random error demonstrated by the large confidence intervals in the Bland–Altman analyses. While it may be possible to reduce these effects by better fitting algorithms and modern machine learning optimization, they highlight the need to minimize this error during data acquisition.Third, the error created by different positions of the AIF varies with important core parameters, such as ejection fraction. Again, it may be possible to partially account for this by taking the ejection fraction or other parameters of cardiac function into account, this observation is especially disturbing, as one of the core functions of the AIF was to correct for physiological differences, such as cardiac function. Given that normal values for myocardial perfusion are mainly based on healthy controls which also have normal ejection fraction, this error may cause a misunderstanding of underlying pathophysiologies, or systematic misclassification of the presence and extent of myocardial ischemia in specific subgroups of patients.In order to determine the best AIF, it is necessary to consider and analyze the systematic errors mentioned above and the limitations of the current study mentioned below. Diseases that affect the ejection fraction or cardiac blood flow may have a strong impact on the AIF.A first opportunity to generate further data in the future is to examine stress images (taking the aspects mentioned above into account) and compare them with the data obtained here. After that a first concept for the standardization of CMR perfusion studies can be proposed.Several limitations apply. The study was meant to highlight the need to standardize the location of the AIF and obtain it in the aortic sinus whenever possible. As such, we did not address other potential confounders, such as valvular heart disease, arrhythmia, or aortic pathologies. It can be speculated, that even larger errors may be observed in these patient groups. We did not perform stress, calculate diagnostic accuracy, or perform full quantification of MBF. Extending our observations to these topics would add data, but not broaden the core results underlined by strong significant differences. Furthermore, gender-specific differences, e.g. regarding the ejection fraction, were not investigated. Our study did not provide any clear signs for this. Investigating this would be relevant from our point of view, but would go beyond the scope of this scientific work. In addition, due to the small number of patients, an adequate statement to that topic could not be made here.The location of the AIF significantly influences core parameters for perfusion quantification such as peak signal intensity, time to peak SI and upslope. The placement of the AIF creates systematic errors, random errors and ejection fraction dependent errors. Full quantification should therefore be based on obtaining the AIF as close as possible to the myocardium to minimize these errors.


P < 0.001). Moreover, there was a graded increase in the rates of VTA with the LGE extent evaluated semi-quantitatively and quantitatively. The extent of LGE was identified as an independent predictor of VTA events and more extensive LGE (positive ≥ 4 segments) significantly raised the risk of VTA, irrespective of the presence of conventional risk factors for SCD including family history, unexplained syncope, LV wall thickness ≥30 mm. The extent of LGE, whether assessed by semi-quantitative or quantitative methods, was closely associated with an increased risk of spontaneous VTA events in HCM patients.Extent of late gadolinium enhancement (LGE) quantified by cardiac magnetic resonance was reportedly helpful for predicting the risk of ventricular tachyarrhythmias (VTA) in hypertrophic cardiomyopathy (HCM) patients. However, only a few data exist on the clinical implication of semi-quantitative assessment LGE extent in this patient population. The extent of left ventricular (LV) LGE was measured in 310 consecutive HCM patients using semi-quantitative  and quantitative  methods, respectively. LV LGE was detected in 255 (82%) patients, most frequently in the mid-LV septum . During the 49 ± 45 month follow-up, spontaneous VTA events were observed in 48 patients (16%) including aborted sudden cardiac death (SCD), appropriate defibrillator shock, and non-sustained VTA. The extent of LGE assessed by the two different methods showed a strong positive correlation (Spearman’s r = 0.63,  Patients with LGE compared to those without were found to be at greater risk of adverse outcomes including sudden cardiac death (SCD), ventricular tachyarrhythmias (VTA), appropriate shock of implantable cardioverter-defibrillator (ICD), and heart failure (HF)-related hospitalization4. In patients with hypertrophic cardiomyopathy (HCM) as well, the presence of LGE was identified as an independent risk factor for SCD or VTA6. Recently, extent of LGE was reported to be a better predictor of VTA/SCA for the HCM patients rather than the mere qualitative characterization (presence or absence) of LGE9. However, quantitative assessment of LGE is not always easy, frequently requiring a specialized software, longer time, and more cost for the measurement. Moreover, quantitative assessment of LGE has not been standardized. Semi-quantitative assessment of LGE, if well-correlated with quantitative one, would be easier to perform and very useful in usual clinical practices as was shown in patients with myocarditis10.Late gadolinium enhancement (LGE) detected by cardiac magnetic resonance (CMR) imaging has been established as a useful method for 11. However, limited data exist showing a good correlation between the extents of LGE measured by quantitative and semi-quantitative methods, particularly in terms of risk stratification for VTA/SCD. Therefore, in the present study, we evaluated the risks of VTA/SCD according to the extent of LGE assessed using two different methods; quantitative and semi-quantitative. We also investigated whether a more extensive LGE reflected a greater risk for ventricular arrhythmic events in this patient population.Previously, we reported that semi-quantitative measurement of LGE extent, counting the number of ventricular segments with LGE, was closely related to composite adverse events including atrial and ventricular arrhythmias, HF-related hospitalization, and stroke in the HCM patientsIn all patients undergoing LGE-CMR at our institute, various parameters are prospectively collected and entered into our database such as clinical, electrocardiographic, and echocardiographic variables. In particular, information regarding previous syncopal episodes, family history of SCD, and unexplained palpitation are carefully included into the clinical data.For the present study, we retrospectively selected a total of 310 consecutive patients who satisfied all the following criteria: (a) patients under regular follow-up in the cardiology clinic; (b) patients with an unexplained increase in end-diastolic left ventricular (LV) wall thickness (≥15 mm or ≥13 mm with positive family history of HCM) noted on echocardiographic examination ; and (c) patients who underwent LGE-CMR examination between June 2008 and December 2011.We excluded patients with (a) uncontrolled hypertension (systolic ≥160 or diastolic blood pressure ≥100 mm Hg despite use of antihypertensive drugs), (b) moderate or severe aortic valve stenoinsufficiency, (c) myocardial infiltrative or storage disease, (d) history of septal myectomy or alcohol ablation, (e) CMR study performed without LGE protocol, or (f) inadequate image quality for assessing the presence of LGE. The study protocol was approved by the institutional review board of our institute, and the requirement for written informed consent was waived.13. Briefly, all images were acquired using a 1.5-T scanner  with a SENSE cardiac coil. The LV endocardial and epicardial borders were planimetered using the short-axis images acquired at end-diastole and end-systole during the breath-holding period. The LV end-diastolic volume (EDV), end-systolic volume (ESV), myocardial volume, and ejection fraction (EF) were computed using Simpson’s algorithm. The LV myocardial mass was multiplied by the specific gravity of the myocardium (1.05 g/mol) to obtain the LV mass. The LV volume and mass data were then indexed for body surface area. Maximal LV wall thickness (LVWT) measured at end-diastole was also obtained across all short axis images.The detailed LGE-CMR protocol used in the present study has been described in previous studiesThe LGE was measured 10 minutes after administering intravenously 0.15 mmol/kg of gadolinium-diethylenetriamine pentaacetic acid  in 10−12 contiguous slices. Slice thickness and interslice gap were 6 and 4 mm, respectively. A multi-shot turbo field echo breath-hold sequence with a non-selective inversion was used; typical repetition time of 4.6msec, time to echo of 1.4msec. We used a Look-Locker sequence to find an inversion time when healthy myocardium was optimally nulled, which was between 200 to 300 msec. The field-of-view and image matrix were 35 cm × 35 cm and 256 × 256, respectively.14. Therefore, the semi-quantitative spatial extent of LV LGE scored from 0 to 13. Second, after finishing the semi-quantitative visual assessment, the LV LGE volume was quantified as a percentage of the total LV myocardial volume (vol%), which was computed by summing the percent area of LGE in each short-axis scan image, multiplied by the slice thickness along the entire LV. LGE was defined by the presence of enhanced areas with signal intensity greater than 6 standard deviation (SD) above that of remote normal myocardium15, which was automatically measured using commercialized software  and manually corrected as needed. Low intra- and interobserver variability in assessing LGE in our laboratory has been demonstrated in previous reports15.CMR image analysis was performed at our CMR core laboratory by two experienced investigators (training level III) blinded to clinical information. The degree of the LV LGE was assessed using two different methods. First, the extent of LGE in spatial distribution was calculated semi-quantitatively by counting the number of LV segments showing visually-determined LGE. For this, the LV wall was divided into 13 segments using a similar method previously described: anterior, septal, inferior, and lateral walls in each basal, mid, and apical LV segments with the final apical-cap segment includedNSVT; ≥3 consecutive ventricular beats at a rate of ≥100 beats/min for <30 seconds). Only spontaneous VTA episodes with ECG documentation were included. However, induced VTA during electrophysiologic studies were excluded from the present analysis.The occurrence of VTA was investigated following the LGE-CMR examinations. Regular clinical and electrocardiographic follow-up  was performed in all patients. If patients reported symptoms suggesting arrhythmia at any time, 12-lead ECG and 24-hour Holter monitoring were repeated. VTA events included SCD, ventricular fibrillation (VF), sustained ventricular tachycardia (≥100 beats/min for ≥30 seconds), hemodynamically unstable VTA requiring immediate cardioversion, VTA events terminated by appropriate ICD shock or anti-tachycardia pacing therapy, and non-sustained ventricular tachycardia (Continuous variables were represented as medians with interquartile range (IQR) and categorical variables as number with percentages. Continuous variables were compared using the Mann-Whitney test whereas categorical variables using the Fisher’s exact test. Correlations (1) between the extents of LGE measured by two different methods, and (2) between the (quantitative and semi-quantitative) LGE extent and the rate of VTA were assessed by the Spearman rank correlation coefficient, respectively. VTA-free survival rates according to the degree of LV LGE were analyzed using Kaplan-Meier curves and differences between survival rates were assessed using the log-rank test. Cox regression analyses were performed to determine independent predictors for the development of VTA, after testing the proportional hazards assumption based on Schoenfeld residuals. Statistically significant variables identified from univariate analysis along with other clinical variables considered important irrespective of their univariate P-values, were included in multivariate analysis model. All P-values were two-sided, and a P-value < 0.05 was considered statistically significant. All data were analyzed using PASW Statistics 23 software for Microsoft .The Institutional Review Board (IRB) or our institute approved the study protocol (IRB No. 2019-09-133). The present study was carried out in accordance with the ethical principles of the Declaration of Helsinki and the requirement for written informed consent was waived.The median (IQR) age of the 310 patients was 55 (49–65) years and the proportion of male patients was 78% (241/310). A family history of SCD was noted in 12% of patients (37/310). Twenty-eight patients (9%) had unexplained syncopal episodes. The median (IQR) values of LV EF and the maximal LVWT were 66 (61–71) % and 18 (15–23) mm. Patients with apical, asymmetric septal, and mixed/diffuse HCM subtypes accounted for 35, 41, and 24% of the total population, respectively.Ventricular LGE was detected in 82% of patients (255/310) who underwent the LGE-CMR study. In the LV short-axis images, LGE was detected primarily in the septal wall, followed by the anterior, inferior, and lateral walls in descending order of frequency. Along the LV long-axis, the mid-LV segments displayed LGE more frequently than the apical or basal ones. Therefore, the mid-LV septal segment was the most common site of gadolinium enhancement. A detailed spatial distribution of the LV LGE is presented in Fig. P < 0.001, Fig. The extent of LGE was quite variable among individuals in semi-quantitative spatial distribution , as well as in quantitative volume percentage . However, the extents of LGE, calculated by the two different methods, demonstrated a good positive correlation . There was a trend toward a higher frequency in the family history of SCD, HF, previous stroke, and beta-blocker use in the LGE group compared to the no LGE group (Table Patients with LGE were younger than those without (up Table .Table 1CP < 0.001) and maximal LVWT (P = 0.001) than those without LGE. Additionally, the QRS duration (P = 0.006) and corrected QT interval (P = 0.037) were significantly longer in patients with LGE.LV EDVi and ESVi did not differ significantly between the two groups Table . HoweverIn the mean follow-up duration of 49 ± 45 months, 48 (16%) patients developed VTA events: aborted SCD due to VF (n = 2), appropriate ICD shock for VF (n = 4), and NSVT (n = 42). There were only two cases of cancer-related deaths, and no cardiac deaths during the follow-up period.To assess the rate of VTA depending on the semi-quantitative extent of LV LGE, we divided the 310 patients into three groups; Supramedian-LGE , Intermediate (Inframedian)-LGE , and No-LGE  groups. With increase in the semi-quantitative  extent of LGE, there was a graded elevation in the rates of NSVT (P = 0.049) and total VTA  and quantitative (LV LGE vol%) ones. In addition, there was a graded increase in the rate of VTA events with the extent of LV LGE, if assessed semi-quantitatively or quantitatively, in patients with HCM. Moreover, the presence of more extensive LV LGE (≥4 segments) made the risk of VTA more striking irrespective of conventional risk factors for SCD such as family history of SCD, unexplained syncopal episode, or maximal LVWT ≥30 mm  or quantitatively (LGE vol% ≥17.6%). Interestingly, our findings are in good agreement with those from previous studies in which the arrhythmic risk was significantly elevated in patients with LGE volume ≥15%2. As the myocardium is replaced by scar tissue following any disease process, ventricular activation and myocardial conduction becomes more heterogeneous and delayed around the scar or peri-infarcted zone, thereby increasing the risk of reentrant fatal ventricular arrhythmia. In a cross-sectional study that included 108 patients with HCM, the QRS complex on 12-lead ECG became more fragmented along with the extent of LV LGE18. In our data, the extent of LGE (as a continuous variable) revealed significant positive correlations with QRS duration . These findings might suggest a greater conduction delay in HCM patients with a higher LGE burden. Additionally, more frequent NSVT events detected in patient with more extensive LGE, could serve as triggers for sustained VTA events.It has been well-demonstrated that the presence of LGE on CMR imaging reflects the presence of myocardial scarring or peri-infarct zones in patients with ischemic or non-ischemic cardiomyopathy20. However, the annual rates of appropriate ICD therapy have been reportedly low (1 to 10%), even if patients were selected for ICD treatment based on conventional risk factors20. In a multicenter ICD registry study that included 506 HCM patients, the likelihood of appropriate discharge was not increased by number of conventional risk factors, suggesting an ongoing need for another marker that can improve current risk-stratification strategies19. Scar extent quantified by LGE-CMR is being actively investigated as a promising biomarker in this respect. Several studies have already proved that quantitative assessment of LGE was better than qualitative one for risk stratification of VTA events in HCM patients9. However, a specialized software for LGE quantification is not always available in every imaging center. If available, scar quantification often depends on manual tracing of the hyperenhanced areas on LGE-CMR images, which is laborious, time-consuming, and operator-dependent, limiting the reproducibility of the method16.ICD implantation is the most effective treatment for the prevention of SCD, which is the leading cause of death in HCM patientsOur data showed a semi-quantitative method  was comparable to quantitative one  in estimating the extent of LV LGE and the risk of VTA events during the follow-up. There was a similar stepwise increase in the rate of VTA according to the extents of LV LGE determined by the two methods. Additionally, the risk of VTA events was significantly elevated in patients with more extensive LGE, regardless of the presence or absence of conventional risk factors Fig. . TherefoThe present study has the limitations inherent to a retrospective observation study, in which unmeasured confounders may preclude definite conclusions. Therefore, prospective studies on a larger scale are needed to validate our results in this patient population.Second, ventricular arrhythmia was used as a surrogate marker for SCD in the present study, with NSVT comprising the majority of VTA events. However, most SCD cases are thought to be caused by VTA. In our study, aborted SCD or appropriated ICD therapy occurred only in the patient group with the greatest LGE burden. The risk of NSVT also gradually increased with the extent of LGE.Next, we did not compare the performance of the two different methods to predict risk of other clinical outcomes such as new-onset HF or atrial fibrillation, stroke, or cardiac death. Therefore, further study might be worthwhile to ascertain whether visual grading is not inferior to quantitative method in various risk assessment.21. However, there was a recent study which showed that CMR-LGE measured at 4−5SD yielded the closest approximation to the extent of total fibrosis measured by the histopathological standard of reference in HCM patients who underwent CMR imaging study and septal myectomy2. We also believe that a close correlation must have been maintained between our visual grading and quantitative assessment even if we had used a low-SD method.Finally, quantitative assessment of CMR-LGE was not performed using different methods in the present study; it was reported that a relatively low-SD threshold  can be better for capturing entire arrhythmic substrate in HCM patients with more diffuse fibrosis, compared to a higher SD . In addition, the full width at half maximum (FWHM) technique is known to have the best intra- and inter reader variability, particularly in cardiac conditions with diffuse fibrosisA strong positive correlation was observed in the extents of LV LGE assessed by quantitative and semi-quantitative methods for patients with HCM. Additionally, the degree of LV LGE, whether measured in terms of semi-quantitative spatial extent or quantitative volume percentage, was closely associated with an increased risk of spontaneous VTA events in this patient population.Supplementary Data.


While the ischemic (51 patients) and infarcted (58 patients) groups showed elevated resting T1 compared to controls , neither of which presented significant T1 reactivity . We concluded that adenosine stress and rest T1 mapping may be useful to differentiate normal, ischemic and infarcted myocardium.Cardiac magnetic resonance (CMR) spin-lattice relaxation time (T1) may be influenced by pathologic conditions due to changes in myocardial water content. We aimed to validate the principle and investigate T1 mapping at rest and adenosine stress to differentiate ischemic and infarcted myocardium from controls. Patients with suspected coronary artery disease who underwent CMR were prospectively recruited. Native rest and adenosine stress T1 maps were obtained using standard modified Look-Locker Inversion-Recovery technique. Among 181 patients included, T1 values were measured from three groups. In the control group, 72 patients showed myocardium with a T1 profile of 1,039 ± 75 ms at rest and a significant increase during stress (4.79 ± 3.14%,  Although invasive coronary angiogram is the gold standard for diagnosis of CAD, report from national registry of United States of America revealed only 41% obstructive coronary lesion of patients who were scheduled for invasive coronary angiogram with clinical indication2. To avoid unnecessary invasive procedures, many non-invasive investigations have been proposed as a gatekeeper before coronary angiogram4. Practice guidelines recommend, in order to benefit from revascularization, patients should have at least 10% of ischemic myocardium5.Coronary artery disease (CAD) is one of the major health problems that creates economic burden in both developed and developing countries6. Conventional CMR performed during stress perfusion and late gadolinium enhancement (LGE) differentiates myocardial area of ischemia and scar based on perfusion defect revealed by contrast bolus during adenosine stress and hyperintensity at the late stage after contrast injection, respectively7. Recently, CMR T1 mapping and extracellular volume (ECV) assessment has shown an emerging role for myocardial tissue characterization that offers possibility in diagnosis of various types of cardiomyopathy and infiltrative disease9. For example, in chronic infarction an increase in the native T1 value has been revealed due to expansion of ECV caused by replacement fibrosis within both infarct territory and remote myocardium13. As the measured quantitative values directly reflect intrinsic myocardial properties in both healthy and diseased conditions14, the technique may potentially help to indirectly estimate myocardial blood volume (MBV) and myocardial water content15. Recently a proof-of-principle exploiting rest and stress T1 mapping for detection of increased MBV induced by coronary vasodilatation in patients with severe aortic stenosis and CAD was successfully demonstrated in a small number of patients17.Cardiac magnetic resonance (CMR) is one of the standard imaging techniques for assessment of myocardial ischemia and scarThe aim of this study was to validate and assess this principle of native CMR T1 mapping at rest and during adenosine stress ; (2) ischemic group was defined as the presence of perfusion defect without LGE (51 cases); and, (3) infarcted group was defined as the presence of LGE (58 cases). Characteristics of these 3 groups, including CMR findings, are shown in Table One hundred eight-one suspected CAD patients were enrolled, and 95 52.5%) were male. Patients were divided into 3 groups according to the results of first-pass perfusion and LGE imaging. Details were as follows: (1) .5% were p < 0.001), with an absolute difference ΔT1 of 52 ± 35 ms and percentage change (%) of 4.79 ± 3.14, both of which were significantly greater than those observed in the ischemia and infarct groups . During adenosine stress the mean T1 significantly increased to 1,092 ± 50 ms (p < 0.001). At the same time, minimal T1 change was observed in the ischemia (ΔT1 = 15 ± 34 ms or 1.38 ± 3.02%) and infarct (ΔT1 = 23 ± 86 ms or 1.55 ± 5.25%) groups. The remote myocardium in both infarct and ischemia groups did not show significant difference compared to the control for both T1 rest and ΔT1 (in ms or %). Only remote infarcted myocardium revealed significantly lower T1 than the control during adenosine stress . Similar findings were also observed from the per-segment analysis comparing native T1 values at rest and during stress in different groups. Details are presented in Table The resting T1 in the infarcted myocardium/group was 1,239 ± 121 ms, which was significantly higher than that found in the control myocardium/group and ischemic myocardium/group  sequence, which is based on the same Look-Locker inversion recovery technique and has been widely investigated with well proven precision and repeatability21. The obtained result from the group analysis could serve as reference value for future studies applying the same approach. While an overall availability of the MOLLI sequence on clinical systems may facilitate clinical adoption, clinical trials with more strictly defined cohorts or in different populations are needed to and further verify the clinical findings.This study has validated the principle of using native T1 at rest and during adenosine stress to differentiate area of myocardial ischemia and infarction from control in suspected coronary artery disease (CAD). The concept has been previously proposed by Liu et al. using the ShMOLLI sequence3. In chronic myocardial infarction, replacement of myocardial cells by scar or fibrosis has been observed with increased extracellular collagen, while area of acute and recent myocardial infarction contains increased intracellular water due to myocardial cell injury and swelling12. Both conditions lead to an increase of MBV and native T1. This has been reported in numerous studies in diagnosis and disease monitoring15. In the current study a native T1 at rest > 1,113 ms can well identify infarcted myocardium, which confirms its potential diagnostic role from the previously published studies, although the value varies among studies. For example, Liu, et al.16 reported a higher value , which might be explained by the partial volume effect due to possible contamination of the blood pool for subendocardial scar12. In addition, different ages of infarction also contribute to such a variation, as scar tends to become denser over time15.Identification of myocardial ischemia and infarction is crucial in patients with suspected or known coronary artery disease in term of both diagnosis of ischemic heart disease, determination of myocardial area at risk and assessment of myocardial viability22. This is different to the non-ischemic pathology, where normal-appearing myocardium may have an increased T1 due to subtle pathology23. The patients in this study, including both acute and chronic CAD, may or may not develop significant microfibrosis. We postulate that this explains the finding of the insignificant difference. Nevertheless, the lower T1 reactivity in the remote area may be associated with the blunt response to stress and indicate the subtle pathology that cannot be visualized in perfusion and LGE images. Moreover, in the control group a ROI was drawn in the septum, while in the infarct and ischemia groups it was placed in different segments to avoid perfusion defect or LGE. The inter-segment variation, as shown in multiple reports previously25 may also contribute to this finding here.For T1 in the remote area in two CAD groups, although it revealed a lower adenosine reactivity compared to the control group, the result did not reach statistically significant difference, which has also been shown in the previous study12. It is, therefore, difficult to be differentiated by native T1 at rest, as shown in Table 26. This difference of the resulting T1 reactivity could be used to distinguish these two groups, as demonstrated previously in the proof-of-concept study22 as well as the current validation study. While the blood flow increase distal to the stenotic coronary artery during adenosine stress is limited due to already compensated resistance vessels at rest, adenosine-induced vasodilation results in an increased MBV and thus a greater change of T1 increase in the normal coronary arteries (Table 22.Ischemic myocardium at rest do not differ much from the controls in both structure and extracellular volume (ECV)es Table . A simil22. Notably, for T1 mapping during adenosine stress in the present study, image acquisition using the MOLLI technique was performed 2 min after intravenous adenosine infusion, in contrast to 3 to 6 min in the previous reports15. As shown in the early study in human, adenosine is very short-acting medication and its intravenous infusion reached a maximal response on coronary blood flow at about 84 s after infusion start, with an appropriate dose of 140 µg/kg/min26. The shorter adenosine infusion time may be one of the reasons for the difference in T1 reactivity between our study and the previous one16. Another potential reason may be the study population difference. Liu et al. used healthy volunteers, whereas we had suspected CAD patients with negative adenosine stress CMR as controls. In addition, it could be partly explained by the sequence-specific phenomenon, as the applied MOLLI sequence in this work had a slightly different scheme comparing to ShMOLLI that was used previously16. In a different study a similar extent of T1 reactivity as ours was observed using the same MOLLI scheme27. Though this difference  may be deemed as clinically insignificant, whether or not these factors might cause discrepancy in the exact T1 reactivity remains to be further investigated.Despite T1 during adenosine stress appeared significantly lower in the remote infarcted area than that in the control group, its changes or T1 reactivity was not significantly different among the three groups. The main reason could be explained by the fact that T1 at rest was slightly lower in both ischemic and infarct groups as well, as described above. This is also in consistency to the previous reportThe per-segment analysis mitigating the T1 measurement bias has demonstrated a similar finding to the ROI analysis. Native T1 of the infarct segment was higher compared to that of the ischemia segment and of the control segment. It was, however, lower than that of the infarct group from the ROI analysis Fig. . This co28. On the other hand, injection of contrast agent in healthy volunteers is not practical in a clinical setup and it is difficult to obtain the ethical approval. Although they did not show ischemia or scar, obstructive CAD might still be present due to false negative results from adenosine stress test. Even if obstructive CAD could be excluded patients might still have subtle myocardial pathologies with symptoms mimicking CAD. We did not have coronary angiography data to confirm CAD in those with ischemia or infarct. However, the T1 reactivity to adenosine stress in our study is consistent to the previous reports that had angiographic confirmation22. Secondly, T1 maps were obtained only from one short-axis slice at the mid LV level and no segment analysis was peformed in this study. However, this should not affect our result since we aimed to assess ischemic or infarcted myocardium compared to myocardium in the control group with T1 mapping. In addition, a second-based and heart rate-independent MOLLI scheme21 was not available on our system at the time of this study. We used the classic beat-based scheme and adapted it for different heartrate ranges to allow for sufficient longitudinal magnetization recovery between singal acquisitions that helped to compensate for heartrate variability. Despite this, a commercial implementation may be expected to fully address the issue in the future studies29 Further, T1 is well known to be technique and sequence dependent. This might limit the generalizability of the methodology. However, comparisons among different T1 mapping acquisition techniques demonstrated that myocardial T1 mapping is a powerful clinical tool for soft tissue characteristic classification in spite of the absence of established reference values30. Thirdly, we did not perform post-contrast T1 mapping and thus did not calculate extracellular volume (ECV). One potential source of improvement in the future studies would be estimation of ECV that reflects extracellular matrix comprising the interstitial space and intravascular space33. A direct estimation from the same MR scan may provide additional information helping to differentiate myocardium with subtle pathology from the normal one. However, as MBV indicates the blood volume within the microvascular circulation and thus a more comprehensive global marker for ischemia, it directly reflects the functional capacity of the coronary artery and provides the information of coronary stenosis severity34. Finally, yet importantly, the proposed algorithm was not tested in a foreign cohort for verification since it is beyond the scope of the study. This needs to be further investigated in the future prospective study.There are some limitations of this study. Firstly, no healthy volunteer data were included. Patients having clinical indication for CMR as suspected CAD without evidence of ischemia or infarction was used as the control group. It has been shown that patients with suspected CAD and negative adenosine CMR had an excellent prognosis with no major adverse cardiac event during follow upIn summary, the approach of using adenosine stress and rest CMR T1 mapping to differentiate myocardial tissue characteristics in CAD was validated. The obtained stress and rest T1 values in ischemic, infarcted and remote myocardium as well as controls can be used as reference, where future investigation is warranted to further derive the diagnostic algorithm and verify its clinical performance.Patients aged years and above presenting symptoms suggestive of coronary artery disease (CAD) who underwent CMR during the study period from March to June 2017 were included. Patients having one or more of the following conditions were excluded: (1) clinical congestive heart failure; (2) pregnancy; (3) history of gadolinium allergy; (4) poor CMR image quality; (5) history of claustrophobia; and/or (6) inability to perform CMR due to implantation of a permanent pacemaker, internal defibrillator, or other metallic device. In addition, patients who had mixed infarction and ischemia were also excluded. Written informed consent was obtained from all patients before the MRI examination. The study protocol was approved by the Institutional Review Board of Siriraj Hospital and complied with the principles set forth in the Declaration of Helsinki (1964) and all of its subsequent amendments.CMR imaging was performed on a clinical 1.5 T (T) system  and included conventional cardiac function, myocardial perfusion, and late gadolinium enhancement (LGE) following the multi-slice multi-direction survey images. Functional cine was acquired by using a balanced turbo field-echo (bTFE) or steady-state free precession (bSSFP) sequence at two-, three-, and four-chamber views, as well as short-axis slices parallel to the mitral valve ring covering the left ventricle (LV) from base to apex.35. This scheme was extended to compensate for heartrate dependence, particularly to ensure sufficient recovery of longitudinal magnetization during the pause interval between two acquisition blocks21.T1 maps were acquired at rest and during peak adenosine stress  on the matching short-axis slice to cine at the midventricular level before gadolinium administration and first-pass perfusion. A standard ECG-triggered modified Look-Locker (MOLLI) sequence was applied within one single breathhold. The “native” 5-(3)-3 scheme was used including 5 acquisitions after the first inversion preparation, followed by a 3-heartbeat pause, and a second inversion pulse for the last 3 acquisitionsFirst-pass perfusion imaging was performed, on three matching short-axis slices to cine at the apical, mid, and basal left ventricular levels, during peak adenosine stress with an intravenous bolus of gadolinium . LGE imaging was performed approximately 10 min after an additional bolus of gadolinium (0.1 mmol/kg), on matching short- and long-axis views to cine. The examination paradigm as well as scan protocol is presented in Fig. 7. Our earlier work demonstrated that using stress perfusion and viability images are accurate for the detection of significant coronary stenosis from conventional angiogram36. Presence of LGE was interpreted based on the increased signal intensity compared to area of normal myocardium. The visual assessment of LGE has been validated in our earlier study37.T1 maps were automatically generated on the MR system from the MOLLI series. All images including LV function, first-pass myocardial perfusion and LGE were analyzed offline using CVI42 software . LV volume and mass was calculated and indexed according to the body surface area. Left ventricular ejection fraction (LVEF) was assessed using end-systolic and end-diastolic volume calculated from multiple-slice short-axis images. Myocardial perfusion defect and LGE images were analyzed by visual assessment. Myocardial ischemia was defined as the presence of perfusion delay at least 5 consecutive phases in 1 myocardial segment or more during first-pass perfusionAs controls in this study, myocardial T1 was measured by placing a region of interest (ROI) in the septum without evidence of first-pass perfusion defects or LGE. Ischemic myocardial T1 was measured by contouring a ROI on T1 maps in an area corresponding to the reversible perfusion defect on first-pass stress CMR but without LGE. Infarcted myocardial T1 was measured by a ROI in the core of the infarcts on LGE images. Remote myocardial ROI were placed contralateral to the ischemic myocardium in areas without evidence of perfusion defects, regional wall motion abnormalities, or LGE. To avoid partial volume contamination from the blood pool, all ROIs were positioned away from the endo- and epicardial borders. The difference in T1 between rest and adenosine stress, i.e. T1 reactivity, was reported in absolute numbers ΔT1 (ms) = T1 adenosine − T1 rest, and as percentages (%) = ΔT1 ÷ T1 rest × 100.Additional per-segment analysis was performed in the subendocardial region as a blinding process. Each segment was classified as infarct for segment with positive LGE, ischemia for segment with stress induced perfusion defect without infarct, remote ischemia for segment without ischemia in patients with positive ischemia, remote infarct for segment without infarct in patients with positive LGE, and control for segment in patients without ischemia or infarct.p value < 0.05 was considered statistically significant.All statistical analyses were performed using SPSS Statistics version 20.0 . Continuous data were presented as mean ± standard deviation and compared by the student t-test for unpaired data. Categorical variables are described as number and percentage, and were compared using Fisher’s exact test or chi-square test. ANOVA test with Bonferroni post hoc test was used to compare continuous data from more than 2 groups. Receiver operating characteristic (ROC) curve was used to calculate area under the curve (AUC). A 2-tailed


Common types of congenital heart disease exhibit a variety of structural and functional variations which may be accompanied by changes in the myocardial microstructure. We aimed to compare myocardial architecture from magnetic resonance diffusion tensor imaging (DTI) in preserved pathology specimens.n = 24) formalin-fixed for 40.8 ± 7.9 years comprised tetralogy of Fallot , dextro-transposition of great arteries  five with ventricular septal defect (VSD), systemic right ventricle (n = 4), situs inversus totalis  and levo-TGA . Specimens were imaged using a custom spin-echo sequence and segmented automatically according to tissue volume fraction. In each specimen T1, T2, fractional anisotropy, mean diffusivity, helix angle (HA) and sheet angle (E2A) were quantified. Pathologies were compared according to their HA gradient, HA asymmetry and E2A mean value in each myocardial segment .Pathology specimens (TOF and D-TGA with VSD had decreased helix angle gradient by − 0.34°/% and remained symmetric in the septum in comparison to D-TGA without VSD. Helix angle range was decreased by 45°. It was associated with a decreased HA gradient in the right ventricular (RV) wall, i.e. predominant circumferential myocytes. The sheet angle in the septum of TOF was opposing those of the left ventricular (LV) free wall. Univentricular systemic RV had the lowest HA gradient (− 0.43°/%) and the highest HA asymmetry (75%). HA in SIT was linear, asymmetric, and reversed with a sign change at about 70% of the depth at mid-ventricle. In L-TGA with VSD, HA was asymmetric (90%) and its gradients were decreased in the septum, anterior and lateral wall.The organization of the myocytes as determined by DTI differs between TOF, D-TGA, L-TGA, systemic RV and SIT specimens. These differences in cardiac structure may further enlighten our understanding of cardiac function in these diverse congenital heart diseases. Congenital heart disease (CHD) is the most common malformation arising during fetal development, with increasing prevalence worldwide . AdvanceDiffusion tensor imaging (DTI) provides quantitative information on tissue microstructure. In myocardium, myocytes are packed with collagen into sheetlets to facilitate wall thickening , 9. DTI Although ex vivo and in vivo myocardial structure have been investigated with DTI in a number of studies –19, littn = 10), dextro transposition of the great arteries , systemic right ventricle (RV) pathologies , situs inversus totalis  and levo-TGA . Of these 24 specimens, 17 had a ventricular septal defect (VSD), including all the TOF, 5 D-TGA specimens and 2 systemic RVs.DTI was performed on 24 historical formalin-fixed heart specimens, consisting of tetralogy of Fallot  due to the reduction of T2 from the formalin fixation process. In this study, the specimens were scanned on a 3 T  with a custom spin-echo sequence with monopolar diffusion encoding gradients, eliminating susceptibility artefacts and minimising echo time (TE) as compared to typical echo planar imaging (EPI)-based techniques. To preserve the integrity of the tissues, all specimens were scanned in the same formalin-filled container that was used for their long-term storage. To comply with our ethical permissions, each specimen was scanned in its jar filled with formalin and placed at isocentre and scanned with a body 18 3 T Tim coil and the 32 channel spine coil. The diffusion sequence design, the data analysis for each specimen and congenital heart diseases groups are provided on δ = 20.67ms, ∆ = 27.09 ms, slices = 3 , slice thickness = 4 mm, matrix = 100 × 100, field of view = 200 × 200 mm2, acquisition voxel = 2x2x4 mm3, 2 averages; 32 diffusion-encoding gradients on a single half-shell calculated by dmritool [s/mm2) were acquired every ten directions to minimize the load on the gradients. The optimal highest b value required without loss of diffusion information was calculated by b = 1.11/ADC [−3mm2/s, a b-value of b = 853 s/mm2 was used.Imaging parameters used were: TR = 1 s, TE = 56.88 ms, 1.11/ADC , 22. SinThe radial vector was defined as the direction of minimal distance between epicardium and endocardium in both the left ventricle (LV) and RV. The location of each pixel between the endocardium and epicardium along the radial vector was calculated and expressed as a percentage, with 0% corresponding to the endocardium and 100% to the epicardium. The circumferential direction was defined to be perpendicular to the radial direction and the longitudinal direction was defined in the apex-base direction (out of the plane of the image).Figure mentclasspt{minimamyocytes , 24. TheThe myocardium was manually segmented with papillary muscles removed from the myocardial mass. The myocardium was divided into five regions with four LV regions  and one region representing the RV free wall Fig. .Fig. 2AHA values were averaged for each percentage of depth over the three acquired short axis slices acquired beneath the VSD or at the base of the ventricles in the absence of VSD. We observed that most segments showed a linear variation of HA with transmural depth over the range 20–80% depth Fig. . An averHA0 will be less than 50% depth for predominantly left handed myocytes  and HA0 will be greater than 50% depth for predominantly right handed myocytes .Oblique myocytes may not necessarily realign circumferentially at mid-wall, influencing the amount of right or left handed myocytes across the wall. Asymmetry can be computed from the intersection of the fitted line and the x axis, i.e. the depth at which the HA is zero. Hence, The median age at death was 8 years  .Figure p < 0.001) but not from D-TGA without VSD .The HA asymmetry in the septum of the TOF group was different from that in a bi-ventricular D-TGA with VSD  although not in biventricular D-TGA without VSD . D-TGA with and without VSD had opposing E2A in the LV. RV E2A were not different across CHD pathologies.The mean E2A were opposing in the septum of TOF and biventricular D-TGA with VSD  (Table p < 0.001). The sheet angle in biventricular systemic RV was positive, which was significantly different from the negative sheet angle seen in the TOF group (p < 0.001).The group with biventricular systemic RV, in comparison to the univentricular group, had similar sheet angles with the exception of the lateral wall (8) Table . Within The HA gradient in the single SIT specimen was reversed Table  relativep < 0.001). E2A in the septum were more similar to D-TGA with VSD than D-TGA without VSD.L-TGA with VSD had a more decreased HA gradient in the septum and anterior walls compared with the D-TGA with VSD group Fig. , Table 6Common CHD subtypes such as TOF and TGA are associated with increased prevalence of heart failure and adverse events –29, whicSanchez-Quintana  observedIn healthy hearts, the HA of the septum is sigmoidal (high HA gradient) because of the dominant longitudinal myocytes across the wall . From biThe presence of a VSD was also associated with a decreased HA gradient in the RV wall of TOF and D-TGA with VSD. The decreased HA gradient in the RV was not observed in the D-TGA specimens without VSD. The abnormal predominance of circumferential myocytes at mid ventricle in the RV may facilitate apical and basal dilatation, acting like a girdle on a balloon .In the healthy heart, the epicardial myocytes of the RV wall wrap the LV free wall at the epicardium , 34 to ip < 0.001). The septum bulges toward the LV [The E2A is independent from the HA organisation . In the d the LV , 38 whicd the LV . The difThe RV, regardless of the CHD pathology, had HA asymmetry beyond 70%  with dominant circumferential myocytes across the myocardial wall (low helix angle gradient). The dominant endocardial orientation facilitates the contraction of the inner chamber at the expense of reduced epicardial lever arm force made of oblique and longitudinal orientations. Ventricular twist in RV is thus decreased and the torsion is lower, possibly increasing endocardial stress . ConsequIn this study, univentricular systemic RV had the lowest HA gradient among all the investigated CHD groups, which is consistent with previous observations . The bivThe changes observed in myocardial microstructure of these CHD specimens may affect their electrophysiology. Electrical activation encounters less resistivity along the myocytes’ length –45. TherThe reversed HA in this case of SIT was in agreement with previous findings –53 and sAlthough a L-TGA has ventricles and valves reversed, the myocytes orientations are not reversed . As prevThe analysis of single case specimen either in L-TGA or SIT limits the conclusion about the myocytes’ orientation for this CHD group. The difference may either be due to specific defects from the individual rather than from the group or by way of noise from a single data point measurement.The specimens investigated in this study comprised numerous specific and individualised diagnoses Table S. FurtherThe collection of pathological specimens spans many decades of changing treatment and outcome. In many of cases, the conditions were untreated, or the treatment was unsuccessful. For example, some of the cases with transposition had an atrial switch procedure but did not survive long after operation. Others succumbed from RV failure and others from pulmonary vascular obstructive disease and left heart failure. Thus, we might observe a combination of abnormal fiber orientations associated with the congenital condition and then modified by the evolving pathology. Further, the results of congenital cardiac surgery have improved significantly improved during the last five decades , 59. As Owing to the nature of the tissue collection used in this study, preserving the integrity of the specimens was paramount. Other studies assessing myocardial fiber structure have used specific tissue preparation techniques prior to scanning, or performed histological analysis on tissues . In our The phase of the cardiac cycle affects the HA and E2A . Given tThe CHD collection investigated in our study has a disparate heart maturation which can affect the interpretation of fibre orientations as a consequence of aging rather than the pathology itself. Sanchez-Quintana  found thMyocardial architecture can be non-destructively examined in specimens of CHD with CMR DTI. The differences in microstructure observed between different types of CHD specimens may explain some of the anatomical and functional observations made in CHD patients.Additional file 1: Table S1. Complete diagnosis of the specimens according to their corresponding scan order (CHD number #).


Imaging derived parameters can provide data on tumor phenotype as well as cancer microenvironment. Radiomics has recently shown potential in realizing personalized medicine. The aim of the manuscript is to detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters. We demonstrated that DKI derived parameters allows to detect RAS mutation in liver metastasis.−3, and by the mean value of MD with AUC of 0.80  using a cut-off of 1694.30 mm2/s × 10−6. Considering all extracted features or the predictors obtained by the features selection method , the tested pattern recognition approaches did not determine an increase in diagnostic accuracy to detect RAS mutation . Conclusions: Diffusion-Weighted imaging and Diffusion Kurtosis imaging could be used to detect the RAS mutation in liver metastasis. The standard deviation value of MK and the mean value of MD were the more accurate parameters in the RAS mutation detection, with an AUC of 0.80.Objectives: To detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters. Methods: In total, 106 liver metastasis (60 metastases with RAS mutation) in 52 patients were included in this retrospective study. Diffusion and perfusion parameters were derived by DWI (apparent diffusion coefficient (ADC), basal signal (S0), pseudo-diffusion coefficient (DP), perfusion fraction (FP) and tissue diffusivity (DT)) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis (MK)). Wilcoxon–Mann–Whitney U tests for non-parametric variables and receiver operating characteristic (ROC) analyses were calculated with area under ROC curve (AUC). Moreover, pattern recognition approaches , with features selection methods and a leave-one-out cross validation approach, were considered. Results: A significant discrimination between the group with RAS mutation and the group without RAS mutation was obtained by the standard deviation value of MK (MK STD), by the mean value of MD, and by that of FP. The best results were reached by MK STD with an AUC of 0.80  using a cut-off of 203.90 × 10 Imaging derived parameters, when linked to other clinical data and correlated with outcome, can produce robust and accurate clinical decision support systems ,2,3. ImaThe traditional DWI data analysis approach is founded on the hypothesis that voxel water diffusion has a single component and follows a normal Gaussian distribution, and that water molecules diffuse without any constraint . HoweverThe aim of this study is to determine the potential of DWI- and DKI-derived parameters in the detection of RAS mutation in colorectal liver metastasis.The National Cancer Institute of Naples institutional review board approved this retrospective study. The study was made in accordance with local relevant guidelines and regulations ,22,25. IIn total, 66 patients with 126 liver metastases confirmed at pathology fulfilled the inclusion criteria during the study period. Among them, 14 patients were excluded for the following reasons: (a) 8 patients had no availability of diagnostic quality pictures of MR study; (b) 6 patients had no availability of contrast studies. Finally, 52 patients  with 106 liver metastases comprised our study population. All liver metastases were analyzed. Among them 60 metastases with histologically confirmed RAS mutation were found. The characteristics of the patients and of metastases are summarized in 2). Details of sequence parameters were reported in A 1.5 T Magnetic Resonance scanner  with an 8-element body and phased array coils was used for MRI acquisition. MRI included basal images before intravenous (IV) injection of a hepato-specific contrast medium (CM) and then dynamic sequences obtained after IV injection of CM; the last series of images was acquired with a delay of 20 min during the CM hepatobiliary excretion. The MRI sequences fit the following criteria: coronal Trufisp T2-weighted free breathing; axial Half-Fourier Acquisition Single-Shot Turbo Spin-Echo (HASTE) T2-weighted, with controlled respiration, without and with fat-suppressed (FS) gradient-echo pulse; coronal HASTE T2-weighted, without FS; axial flash in-out phase T1-weighted, with controlled respiration; Volumetric Interpolated Breath-hold Examination (VIBE) T1-weighted Spectral adiabatic inversion recovery (SPAIR) with controlled respiration; DWI with echo-pulse planar sequence (EPI) at several b values , at an infusion rate of 2 mL/s.As liver-specific CM, the Gd-EOB-BPTA , was employed—0.1 mL/kg of Gd-EOB-BPTA administrated using a power injector  for each lesion [Manual segmentation was performed by two expert radiologists, with at least 15 years of experience in MR liver imaging, drawing manually (and simultaneously avoiding encircling any distortion artefacts) the region of interests (ROIs) on diffusion-weighted imaging with the highest h lesion . DWI anab values, the Diffusion Kurtosis imaging model with one low b-value 50 s/mm2 and multiple high b-values >200 s/mm2  and the Intra-Voxel Incoherent Motion using a conventional bi-exponential fitting method based on the Levenberg Marquardt algorithm with multiple low b-values < 200 s/mm2  and higher b-values  [Voxel by voxel, by DWI 14 features were extracted using the mono-exponential model with all 0 s/mm2) ,22,25.ADC) was obtained using the mono-exponential model [bS is the MRI signal intensity with diffusion weighting band, and S0 is the non-diffusion-weighted signal intensity.The apparent diffusion coefficient ADpD indicated also with D *), the perfusion fraction , the tissue diffusivity (tD) and the basal signal S0:In addition to the mono-exponential model, a conventional biexponential model using the Levenberg Marquardt fitting method was used to estimate the following IVIM-derived parameters: the pseudo-diffusivity  and mean of Diffusional Kurtosis (MK) using Equation (3) by a two-variable linear least-squares algorithm :(3)SbSo=D is a corrected diffusion coefficient, and K is the excess Diffusion Kurtosis coefficient. K describes the degree that molecular motion deviates from the perfect Gaussian distribution. When K is equal to 0, Equation (3) evolves into a conventional mono-exponential Equation (1).In this equation, The parameters of conventional DWI (ADC), IVIM  and DKI (MK and MD) were obtained per voxel by the prototype post-processing software Body Diffusion Toolbox .Then, the mean and standard deviation values (STD) were calculated as representative values of each descriptor on ROI.Statistical analysis includes both univariate and multivariate approaches performed considering per-patient analysis.The mean, median and standard deviation value (STD) and interquartile range were calculated as representative values of each descriptor among the group of patients with RAS mutation and without RAS mutation. Receiver operating characteristic (ROC) analyses were performed and the Youden index was used to individuate the optimal cut-off value for each feature. Considering the optimal cut-off values, area under ROC curve (AUC), sensitivity (SEN), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were calculated. The non-parametric Wilcoxon–Mann–Whitney U test for continuous variables was used for two-groups comparisons.p value < 0.05 was considered as significant. However, False Discovery Rate (FDR) adjustment according to Benjamini and Hochberg [A Hochberg  for multThe Statistics Toolbox of Matlab R2007a  was used for statistical calculations.Linear classifier, support vector machine, k-nearest neighbours (KNN) and decision tree were considered to assess the diagnostic accuracy using all DWI- and DKI-derived parameters . A non-pCross-validated using the leave-one-out validation approach and median values of AUC, accuracy, sensitivity and specificity were considered.The Statistics and Machine Learning Toolbox of Matlab R2007a  was used.−3, and by the mean value of MD with AUC of 0.80  using a cut-off of 1694.30 mm2/s × 10−6  and the wild-type group . The lower ADC and ADC mean values were found to be statistically significantly lower in the KRAS (+) group compared to the wild-type group. ROC curve analysis revealed a statistically significant difference in terms of lower ADC and ADC mean with area under the curve (AUC) values of 0.680 and 0.760, respectively. Therefore, they concluded that the lower ADC and ADC mean values of colorectal liver metastasis are associated with the presence of KRAS mutation.In this study we assessed Diffusion-Weighted MRI- and Diffusion Kurtosis imaging-derived parameters to detect RAS mutation in liver metastasis, showing a significant discrimination between the group with RAS mutation and the group without RAS mutation by the standard deviation value of MK (MK STD), by the mean value of MD and by the mean value of FP. The diagnostic performance for ADC and for the extracted DWI (IVIM) and DKI features in detecting RAS mutation using monovariate and multivariate analysis was evaluated. In the monovariate analysis, the best results were reached by MK STD and by the mean value of MD with an AUC of 0.80 (accuracy of 79% and 77% respectively). Instead, considering all extracted features or the predictors derived by the feature selection procedure , the tested pattern recognition approaches did not determine an increase in diagnostic accuracy to detect RAS mutation with respect to the single parameter. To the best of our knowledge, this is the first paper that evaluates the correlation between IVIM and DKI parameters and RAS mutation in liver metastasis. Gültekin et al.  investigMRI quantitative analysis has recently shown potential in realizing personalized medicine for selecting the more appropriate therapy correlated with the different subtypes of tumor ,32,33,34Considering that a large rate of tumors were determined by mutations located in the MAPK pathway, KRAS and KRAS status should routinely be examined in combination with other clinic-pathological predictors in order to adapt optimal therapies and to stratify patient prognosis. In fact, the most significant finding related to KRAS-mutated tumors has been its ability to determine the anti-EGFR-based therapy’s eligibility. Moreover, KRAS mutations have been investigated for their potential to prognosticate recurrence and survival . Data frThis study had some limitations, as follows: data were derived from only one cancer center, with a single MR scanner; a small number of patients was considered, and this could influence the generalization of the conclusions; small sample size and use of Benjamini and Hochberg correction may lead to higher chances of type 2 error, and real effects might be overlooked; the retrospective nature of the study, and the absence of inter- and intra-reader variability and of a validation data set could affect the robustness of the results. Therefore, multicenter-perspective analyses, including more patients, inter- and intra-reader variability analysis and a validation data set are needed.Diffusion-Weighted imaging and Diffusion Kurtosis imaging could be used to detect the RAS mutation in liver metastasis. The standard deviation value of MK and the mean value of MD were the more accurate parameters in the RAS mutation detection, with an AUC of 0.80; therefore, linked to other clinical data and correlated with outcome, if confirmed on a larger and different data set, these result could produce robust and accurate clinical decision support systems and tools for personalized medicine.