Though these modalities can detect the pathological feature of the disease, combined assessment has not been fully examined. Multimodality image fusion is known to be useful for further comprehension, while most image interpretation is performed with a side by side comparison in clinical routine. We investigated the similarity and discrepancy of active inflammation, regional fibrosis, and wall function by image fusion of CMR and FDG.Diagnosis and evaluation of cardiac sarcoidosis (CS) are mainly based on the combined use of cardiac magnetic resonance imaging (CMR) and Patients with CS who underwent both CMR and FDG were retrospectively enrolled. The extent of myocardial late gadolinium enhancement (LGE) in left ventricle (LGE volume), cardiac function, and volume  was measured from CMR. The FDG uptake in whole myocardium (whole SUVmax), cardiac metabolic volume (CMV), and cardiac metabolic activity (CMA) was calculated from FDG. CMR and FDG were fused and divided into AHA 17 model for segmental analysis. Wall motion, the magnitude of LGE in myocardial wall , and corresponding FDG uptake  were analyzed.r = − 0.56, p < 0.0001, and r = 0.08, p = 0.048, respectively). Discrepancy between LGE volume and CMV showed a significant positive correlation to whole SUVmax and CMA . In image fusion analysis, segmental SUVmax showed a significant inverse correlation to LGE%wall . LGE%wall also showed significant inverse correlation to wall motion .Forty-one patients were retrospectively enrolled. In patients with FDG uptake, LVEF inversely correlated to LGE volume and positively correlated to SUVmax (Combined and fusion analysis with CMR and FDG demonstrated the discrepancy of myocardial inflammation and extensive fibrosis. Active inflammation was present in the earlier stage of myocardial fibrosis and was found to be less in the wall with advanced fibrosis and remodeling. Combined analysis of CMR and FDG can incrementally reclassify the pathological stage of CS. However, FDG findings may vary according to the presence and the activity of myocardial inflammation and disease progression among each individual, and late gadolinium enhancement (LGE) findings are not capable of identifying the appropriate candidate for anti-inflammatory therapy  by image fusion analysis of CMR and FDG by patient-based and quantitative methods.We retrospectively investigated consecutive patients with CS who underwent both CMR and FDG between October 2013 and May 2017. Patients were retrospectively diagnosed based on the latest guidelines by the Japanese Circulation Society in 2017 and the expert consensus recommendation on criteria by the Heart Rhythm Society in 2014  Patients who have received anti-inflammatory therapy including corticosteroid in any case. (2) Patients with past history of other cardiac disease . (3) Failure in high-fat and low-carbohydrate diet at FDG scans. (4) Poor image quality, unable for image fusion, or contraindication for CMR. (5) According to the reports by Osborne and Ahmadian, patients without significant FDG uptake  were excluded -gated CMR was performed in breath-hold using 1.5T Ingenia or 3.0T Intera  with a four-element phased-array coil in a supine position with breath-holds during expiration and ECG gating as suggested by the Society for Cardiovascular Magnetic Resonance/ European Cardiovascular Magnetic Resonance recommendations . Ninety minutes after FDG injection, cardiac scan was performed. Acquisition was performed from the craniocaudal direction with a 2-min emission per bed position from the mid-thigh to the skull base with 120 kVp and 50 mAs. CT images acquired with respiratory gating or shallow-expiration breath-hold were used as a transmission map for attenuation correction. Two experienced nuclear medicine physicians (UI and MM) interpreted FDG images. FDG uptake was measured by MN oncology in Syngo.via® . Volume of interest was drawn around the heart not to include the liver, stomach, and mediastinum. Whole myocardial FDG uptake (whole SUVmax) was measured. The volume of standardized uptake value (SUV) over 2.7 was measured as cardiac metabolic volume (CMV mL), and cardiac metabolic activity (CMA) was calculated by multiplying SUV volume and mean SUV according to previous reports , and the percentage in LV was measured as LGE%LV, and segmental extent of LGE in myocardial wall was converted to AHA 17-segment map by trisecting LV for output as LGE%wall as well as cine-MRI  Fig. . In cineMRI Fig. .Fig. 1ScThe process of CMR and FDG image fusion is shown in the bottom part of Fig. p < 0.05 was considered significant.All data was expressed as mean ± SD. Linear regression and Spearman’s rank correlation were enrolled in correlation analysis in graphs. Statistical analysis was done by PRISM® ver.7 . The value of two-tailed Patient selection flow chart is shown in Fig. r = 0.62, p < 0.0001)   Fig. a. CMA al01) Fig. b.Fig. 3Pr = − 0.13, p = 0.0011, Fig. r = 0.201, p < 0.0001, Fig. Segmental analysis for CMR, wall motion, and LGE%wall are shown in Fig. Typical representative cases of CS in Fig. In this study, we investigated the relationship of the severity of myocardial fibrosis and the active inflammation in CS per patient and segment basis by quantitative methods. As is well known, definitive diagnosis of CS is quite challenging. Thus, we enrolled the patients with a focus on positive findings for both CMR and FDG as the criteria of clinical diagnosis  according to previous reports  were excluded. Vita et al. investigated 107 patients with CS and found 66% showed abnormal FDG uptake among those with positive LGE (Vita et al. Segmental SUVmax also showed regional variation in the wall analysis. The value ranged up to around 20 as the maximum in the myocardium with minimum to mild fibrosis, and the range converged down to around 10 with advanced fibrosis. And in the analysis of wall thickness, segmental FDG uptake correlated to wall thickness and significantly reduced in myocardium where remodeled thinned wall were present. In general, FDG shows significantly high uptake in granulomatous inflammation (Sebro et al. As shown in Fig. The former Japanese guideline in 1993 described thinned, aneurysmal, or/and thickened myocardium determined by echocardiography as one of the minor criteria. This conflicting criterion can be explained by the results of our study. The results showed significant thickened myocardial wall that maybe swollen due to the active inflammation, and the advanced fibrosis with less active inflammation showed thinned and aneurysmal wall. It is presumed that the former guideline has fragmentally captured the wall characteristic in the disease progression. The results of our study may contribute to the additional insights for re-classifying the pathological stage of CS to identify an appropriate candidate for anti-inflammatory therapy. Combined assessment by these imaging approaches is considered a useful framework for evaluation of CS, and fusion analysis enables us to comprehend the pathological status of the disease.Our study has several limitations. First, we defined significant myocardial FDG uptake as SUV 2.7 which may include physiological uptake, and it is difficult to affirm that those uptakes were completely excluded (Osborne et al. Comparative analysis of myocardial fibrosis and inflammation using CMR and FDG is a useful approach to access the pathophysiological stage in CS.


Background: The right ventricular (RV) function is an important prognostic marker of asymptomatic aortic stenosis (AS). However, previous publications have not addressed the additive value of conventional RV parameters over left heart parameters. Whether three-dimensional echocardiography (3DE)-derived RV ejection fraction (RVEF) has prognostic utility independent of 3DE derived left heart parameters is also unknown. We investigated the prognostic utility of 3DE RVEF in patients with asymptomatic AS.Methods: We retrospectively selected 392 asymptomatic AS patients. RVEF, left ventricular ejection fraction (LVEF) and left atrial volumes (LAVs) were measured using 3DE datasets. We determined the association of those parameters, as well as of aortic valve replacement (AVR), and Charlson's comorbidity index with cardiac events. We also analyzed whether RVEF has incremental value over two-dimensional echocardiography (2DE) RV parameters.Results: During a median follow-up of 27 months, 57 patients developed cardiac events, and 68 patients received AVR. Univariate Cox proportional hazard analysis revealed that RVEF was associated with cardiac events (p < 0.001). Multivariate analysis revealed that RVEF was significantly associated with cardiac events (p < 0.001) even after adjusting for AVR, Charlson's comorbidity index, LVEF, LAV, E/e', and indexed aortic valve area (iAVA). An incremental value of RVEF over left heart parameters was also demonstrated using a nested regression model. Classification and regression-tree analysis selected RVEF first with a cut-off value of 41%. RVEF had incremental value over iAVA, LVEF, and 2DE conventional RV parameters for its association with future outcomes.Conclusions: 3DE RVEF had significant prognostic value even after adjusting for comorbidities, left heart parameters, and conventional 2DE RV parameters in asymptomatic aortic stenosis. The number of patients with aortic stenosis (AS) has been increasing rapidly due to the aging of society, especially in developed countries . This chThis was a single-center observational study. Using a 3DE database, we retrospectively selected 3DE datasets of Japanese patients with AS who had undergone transthoracic echocardiography in the University of Occupational and Environmental Health hospital from April 2008 to December 2018. Individuals who had no AS-related symptoms were selected from the database. Patients with fewer than 30 days of follow-up after echocardiography were excluded. We also collected several clinical parameters to calculate Charlson's comorbidity index . The stu3DE was performed immediately after standard transthoracic 2D echocardiography (2DE) and Doppler echocardiography. 3DE images were acquired using an apical approach and commercially available ultrasound machines  equipped with a 3DE transducer . Trans-mitral flow velocity was recorded at the coaptation point of both leaflets. Mitral annular velocities were recorded at septal and lateral sides of the mitral annulus, and average e' was calculated. Peak aortic flow velocity was recorded in multiple transducer positions, and the highest value was used for the measurements of the mean pressure gradient (PG) and velocity-time integral (VTI). RV fractional area change (RVFAC) was calculated by standard formula. 2DE RV speckle tracking analysis was performed using commercially available, vendor-independent, fully automated strain analysis software . The software automatically determined the RV endocardial border and performed speckle tracking analysis during a single cardiac cycle. The endocardial border was manually corrected as required. RV free-wall longitudinal strain (RVfwLS) and global longitudinal strain (RVGLS) were calculated.LV and RV 3DE speckle tracking analyses were performed using commercially available, vendor-independent, 3DE quantification software . These methods have been described in detail previously, and their accuracy and reliability are recognized . For LV 3D maximal and minimal left atrial volumes (LAVs) were calculated using vendor-specific software . Detailed methods have been described previously . Briefly2 or iAVA <0.6 cm2/m2. The patients were classified as high-gradient (peak velocity ≥4.0 m/s or mean PG ≥40 mmHg) AS (HG-AS), and low-gradient (peak velocity <4.0 m/s and mean PG <40 mmHg) AS (LG-AS) according to the current ESC guideline (2) low-gradient AS (LFLG-AS) with preserved LVEF, LFLG-AS with reduced LVEF, and normal-flow low-gradient AS (NFLG-AS).Stroke volume (SV) was calculated as the difference of LVEDV and LVESV measured by 3DE. Stroke volume index (SVi) was calculated as SV divided by BSA. Indexed aortic valve area (iAVA) was calculated as SV divided by AV VTI times BSA. We defined more-than-moderate-to-severe AS as AVA <1.0 cmuideline . LG-AS wPatients were followed up in an outpatient clinic. Prognostic information was obtained by interviewing attending physicians or by searching digital medical records. If patients were followed up in other hospitals, we also contacted corresponding physicians to obtain prognostic information. The primary endpoint was defined as a composite adverse cardiac event, including cardiac death, hospitalization due to heart failure, ventricular tachycardia/fibrillation, or non-fatal myocardial infarction.t-test was used to compare continuous variables between two groups when data were normally distributed. Wilcoxon sum rank test was used when data were not normally distributed. Fisher's exact test or a chi-square test were used to compare categorical variables. Cox proportional hazards analysis was used to calculate hazard ratios and 95% confidence intervals. Univariate and multivariate Cox proportional hazard analyses were used to assess the prognostic utility of echocardiographic parameters. AVR was treated as a time-dependent covariate. Survival analysis was performed using the Kaplan-Meier method, and differences in survival rates between groups were analyzed by the log-rank test. Net-reclassification improvement (NRI) analysis and DeLong's test were used to compare risk prediction utility between the two models. Classification and regression-tree (CART) analyses were conducted to predict cardiac events. A two-sided p-value < 0.05 was considered statistically significant. All statistical analyses were performed using commercial software .Continuous data were expressed as means ± SDs or as medians and interquartile ranges . Categorical variables were presented as absolute numbers or percentages. Student's Using a 3DE database, we found 392 asymptomatic AS patients. Of those patients, 25 were excluded because their follow-up duration after baseline echocardiography was <30 days. Finally, first-time echocardiographic datasets from 367 patients were selected for the analysis. We could not analyze 3DE datasets of twelve patients due to a low volume rate or extremely poor image quality. We performed 3DE LV and RV analysis in the other 355 patients, resulting in feasibility of 97% for both LV and RV analyses. Image quality of LV was good in 35%, fair in 53%, and poor in 12%. Corresponding RV values were good in 13%, fair in 57%, and poor in 30%. It was impossible to analyze 3D LAVs due to the fact that datasets failed to encompass all of the LA wall in 10 patients, and the feasibility of 3D LA analysis was 94%. LA image quality was good in 35%, fair in 53%, and poor in 11%. Clinical characteristics are summarized in Severe AS was observed in 43% (152/354) of the study population. Median values of LVEF and RVEF by 3DE analysis were 52 and 48%, respectively. 30% (107/355) of patients had reduced LVEF (LVEF <50%), and 32% (112/355) had reduced RVEF (RVEF <45%). 53% (189/355) of patients had preserved both ventricular EFs, and 15% (53/355) of patients had reduced bilateral ventricular EFs. During a median follow-up of 26.7 months , 57 patients reached a primary endpoint, including 19 cardiac deaths, 32 HFs requiring hospital admission, four myocardial infarctions, and two ventricular tachyarrhythmias. Notably, four cardiac deaths and three HFs occurred after AVR. Sixty-eight patients received AVR during the follow-up. The median interval from baseline echocardiography to AVR was 20.5 months. Univariate Cox proportional hazard analysis revealed that mean PG, iAVA, E/e', SVi, Charlson's comorbidity index, LVEF, RVEF, and LAVIs were associated with cardiac events .p < 0.001).For survival CART analysis, we entered 18 variables including age, sex, Charlson's comorbidity index, LVEDVI, LVESVI, LVEF, LVMI, SVi, LAVIx, LAVIn, peak velocity, mean PG, iAVA, E wave, E/e', RVEDVI, RVESVI, and RVEF. CART first selected RVEF (with a cut-off of 41%), followed by LVEF (with a cut-off of 39%), Charlson's cormorbidity index, LAVIn, and mean PG . Generatp < 0.001).For the Kaplan-Meier analysis, we divided patients into four groups according to cut-off values of LVEF (50%) and RVEF (45%) . There wp < 0.001), whereas adding it to the model containing iAVA, LVEF and RVGLS increased it from 61.5 to 72.7 (p < 0.001), respectively . For sensitivity analysis, we performed subgroup analyses in a subset of patients who had ≥50% of LVEF. Among 248 patients with LVEF ≥50%, 23 patients developed cardiac events during a median of 32 months follow-up. Multivariate Cox analysis, revealed that RVEF was a significant marker even after adjusting for LVEF , chronic kidney disease (CKD), and atrial fibrillation (AF) were significantly associated with reduced RVEF. Multivariate logistic regression analyses revealed that iAVA, SVi, LVEF, and LAVIn were significantly associated with reduced RVEF, even after adjusting for CAD, CKD, and AF .To the best of our knowledge, this is the first study to demonstrate the prognostic utility of 3DE determined RVEF in patients with asymptomatic AS.Indications and best timing of AVR for patients with asymptomatic AS are not clearly defined. Some studies suggest that early elective AVR improves the prognosis of patients with asymptomatic severe AS . GenereuSome reports have explained the prognostic utility of RV functional parameters in AS. Galli et al. reported the prognostic importance of RV function among 200 patients with severe AS . The subWe found that 3D RVEF had significant utility in predicting future cardiac events, even after adjusting for LVEF, AS severity, LV diastolic function, AVR, and comorbidities. Statistical analysis revealed that RVEF had incremental value over comorbidities, left heart parameters, and AS severity to predict cardiac events. Kaplan-Meier analysis indicated that both LVEF and RVEF were useful for risk stratification of patients with asymptomatic AS.In our cohort, 73% of asymptomatic AS patients had some non-cardiac comorbidities. Not only AS severity and left heart function, but also other comorbidities that develop RV volume or pressure overload, such as pulmonary disease and CKD can affect RV function. Thus, RV function may represent status of the whole heart. In fact, reduced RVEF was significantly associated with echocardiographic parameters, CKD, CAD, and atrial fibrillation. We cannot determine the causal relationship because of the cross sectional nature of the study; however, optimal medical therapy or intervention targeting HF, ischemia, and AF have potential to improve management of AS patients with reduced RVEF.Our results are consistent with previous publications. Previously, we determined the prognostic utility of 3D RVEF in 446 patients with diverse backgrounds . 3D RVEFIn a subset of patients with “more-than-moderate-to-severe” AS, CART analysis selected LVEF first. The current AHA/ACC guideline  recommenAssessment of RVEF using 3DE provides robust information for predicting future outcomes in patients with asymptomatic AS. Overall, 3D RVEF was significantly associated with cardiac events regardless of AS severity or left chamber function. 3DE determined that RVEF is a much more useful parameter than 2DE-derived RV parameters for predicting future outcome. Thus, combined 3DE assessment of left and right chamber function might be the best way for appropriate management of patients with asymptomatic AS.This study has several limitations. First, it includes not only severe AS patients, but also mild to moderate AS patients. However, because AS is a progressive disease, predicting future outcomes of patients with mild to moderate AS using baseline echocardiography is essential for appropriate management. In fact, RV systolic functions were significant prognostic parameters, independent of AS severity. Second, 3D LAV analysis was based on manual tracing, even though 3D LV and RV analyses were performed using semi-automated 3D speckle tracking software. This difference could underrepresent the prognostic value of LAVs in this study, and further studies using 3D LA speckle tracking analysis are required. Third, we did not include 3D global strain values. It is crucial to determine whether 3D global strain values are superior to EFs and volumetric parameters. However, there are few commercial software packages that can incorporate 3D RV and LA strains. Fourth, although we demonstrated that 3D RVEF is better than 2D RV parameters, its accuracy and reliability depend on the expertise of the examiner. 3DE data acquisition was not always possible in every AS patient. In such situations, 2D RVFAC and RVGLS are a potential alternative. Fifth, although significant tricuspid regurgitation (TR) and mitral regurgitation (MR) have potential to affect the prognosis of patients with AS, there were not significant association with cardiac events in this study. This may be due to the small sample size in more than moderate TR and MR. Finally, this was a single-center retrospective study. To validate our concepts, a multi-center prospective study is required.3D RVEF had significant prognostic value, even after adjusting 3D left heart parameters and comorbidities in patients with asymptomatic AS. A cut-off value of RVEF ≤ 40% should be considered for better management in asymptomatic AS patients.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee at the University of Occupational and Environmental Health. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.YN: data curation, data analysis, and writing—draft. TK: data curation. MT: conceptualization, methodology, supervision, writing—review, and editing. All authors contributed to the article and approved the submitted version.The 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.


One of the most common acute and chronic cardiovascular disease conditions is aortic stenosis, a disease in which the aortic valve is damaged and can no longer function properly. Moreover, aortic stenosis commonly exists in combination with other conditions causing so many patients suffer from the most general and fundamentally challenging condition: complex valvular, ventricular and vascular disease (C3VD). Transcatheter aortic valve replacement (TAVR) is a new less invasive intervention and is a growing alternative for patients with aortic stenosis. Although blood flow quantification is critical for accurate and early diagnosis of C3VD in both pre and post-TAVR, proper diagnostic methods are still lacking because the fluid-dynamics methods that can be used as engines of new diagnostic tools are not well developed yet. Despite remarkable advances in medical imaging, imaging on its own is not enough to quantify the blood flow effectively. Moreover, understanding of C3VD in both pre and post-TAVR and its progression has been hindered by the absence of a proper non-invasive tool for the assessment of the cardiovascular function. To enable the development of new non-invasive diagnostic methods, we developed an innovative image-based patient-specific computational fluid dynamics framework for patients with C3VD who undergo TAVR to quantify metrics of: (1) global circulatory function; (2) global cardiac function as well as (3) local cardiac fluid dynamics. This framework is based on an innovative non-invasive Doppler-based patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid-solid interaction. We validated the framework against clinical cardiac catheterization and Doppler echocardiographic measurements and demonstrated its diagnostic utility by providing novel analyses and interpretations of clinical data in eleven C3VD patients in pre and post-TAVR status. Our findings position this framework as a promising new non-invasive diagnostic tool that can provide blood flow metrics while posing no risk to the patient. The diagnostic information, that the framework can provide, is vitally needed to improve clinical outcomes, to assess patient risk and to plan treatment. For aortic stenosis patients, heart failure is the primary cause of death, and half of them will die within two years of symptom onset if aortic valve disease is left untreated2. Prior hospitalization due to heart failure is associated with poor outcomes following aortic stenosis intervention; some research suggests that by performing an earlier treatment, before patients experience hospitalization for heart failure, outcomes may be improved3. It is important to note that aortic valve disease commonly exists in combination with other conditions, so many patients suffer from the most general and fundamentally challenging condition: complex valvular, ventricular and vascular diseases (C3VD). In C3VD, mechanical interactions occur between multiple valvular, ventricular and vascular pathologies wherein the physical phenomena associated with each pathology exhibit magnified effects on the cardiovascular system due to the presence of the other cardiovascular conditions10.One of the most common acute and chronic cardiovascular disease conditions is aortic stenosis, a disease in which the aortic valve is damaged and can no longer function properly. This condition can progress to heart failure through the rapid deterioration of the pumping action of the heart. Heart failure is a disease associated with high mortality and morbidity rates that is increasing in prevalence, affecting at least 26 million people worldwide. It is responsible for about $108 billion per year, or 1–3%, of global health expendituresacross a broad risk spectrum. Prior to the recent introduction of TAVR, the only possible choice for high-risk patients with aortic stenosis was surgical replacement of the aortic valve (SAVR). TAVR is a growing alternative to surgical intervention that has provided positive outcomes and has reduced the mortality rate, with many patients experiencing a significant improvement following intervention. TAVR is also increasingly being used in lower-risk patients who may be younger and/or have moderate valvular disease. However, there are risks associated with TAVR, because in some cases, the situation worsens or the pre-existing cardiovascular disease changes to another form of cardiovascular disease8. The following series of questions must be answered before and after TAVR to ensure the procedure is completed safely and effectively: What impacts will the procedure have on the heart mechanics and function? When is the best time to perform the intervention? Is there a means to assess which patients will have a better or worse outcome? If performed, what impacts will there be on the cardiac function, circulatory mechanics and valve function? A tool that can answer these questions for each patient while considering their specific conditions is highly needed.Transcatheter aortic valve replacement (TAVR) is an emerging minimally invasive intervention for patients with aortic stenosis "Cardiology is flow”11, and therefore, the essential sources of cardiovascular mortality and morbidity can be explained on the basis of adverse hemodynamics: abnormal biomechanical forces and flow patterns, leading to the development and progression of cardiovascular disease12. Despite its importance, there exists no diagnostic tool that can quantify fluid dynamics for many cardiovascular diseases, including C3VD and TAVR, in a patient-specific manner, because the fluid-dynamics methods that can be used as engines of new diagnostic tools are not yet well developed. Moreover, there are varying prognostic implications, so careful diagnosis is vital13. In this research, we contributed to advancing computational mechanics as a powerful means to enhance clinical measurements and medical imaging to make novel diagnostic methods for patients with C3VD and TAVR that pose no risk to the patient.global hemodynamics: (1)Metrics of circulatory function, e.g., detailed information of the dynamics of the circulatory system, and (2) Metrics of cardiac function, e.g., heart workload and the breakdown of workload contributions from each cardiovascular disease component, and of the local hemodynamics: (3) Cardiac fluid dynamics, e.g., details of the instantaneous 3-D flow, vortex formation, growth, eventual shedding, and their effects on fluid transport and stirring inside the heart. Despite its importance and advances in medical imaging, as described in the following, the current clinical diagnostic tools cannot sufficiently quantify flow conditions in patients with many cardiovascular diseases, including in patient with C3VD who undergo TAVR. Cardiac catheterization, a clinical gold standard, evaluates heart function metrics. However, it is invasive and carries high risk14, and is not practical for diagnosis. Phase-contrast magnetic resonance imaging (MRI) can provide velocity field. but it has a lower temporal resolution than Doppler echocardiography (DE) resolution16. It is important to note that MRI cannot be used for patients with most implanted medical devices except safely for MRI-conditional devices. 2-D Doppler echocardiography is risk-free and the most practical tool for hemodynamics and has high temporal resolution . Studies with 4-D phase-contrast magnetic resonance unveiled that the intraventricular flow is mainly parallel to the apical long-axis plane, and measurement of 2-D flow on this plane can provide a very good estimate of the 3-D flow17. This makes the apical long-axis plane, passing through the left ventricle (LV) apex and the centers of the mitral valve, aortic valve, left atrium and proximal ascending aorta, the optimal 2-D representation of the 3-D LV flow. Recent advances in DE to measure flow velocity are: (1) Echo-PIV, is a promising adaptation of Particle Image Velocimetry (PIV)20, but it may underestimate high velocities21 so it may hinder appropriate diagnosis22; (2) Colour-Doppler vector flow mapping (VFM) can calculate the flow velocity using color DE images23; color DE cannot measure flow velocity in the direction vertical to the beam. Despite all the potential that DE has and the progress made with VFM and Echo-PIV, there is no DE method that can comprehensively evaluate local hemodynamics in the LV, valves, ascending aorta and left atrium in terms of vortical structures, their temporal evolutions, fluid transport and mixing. There is also no DE method to evaluate global hemodynamics and to break down the contributions of each cardiovascular system component.The heart resides in a sophisticated vascular network whose loads impose boundary conditions on the heart function. Precise and effective diagnosis hinges on the quantification of the following three Requirements: purposefully using limited and reliable non-invasive input parameters acquired with Doppler echocardiography and sphygmomanometers to continuously calculate patient-specific global hemodynamics quantities (Requirements #1 and #2). We used the clinical data of eleven patients with C3VD in both pre and post TAVR conditions (twenty-two cases) not only to validate the proposed framework but also to demonstrate its diagnostic abilities by providing novel analyses and interpretations of clinical data. The validation was done against clinical cardiac catheterization data24 and clinical Doppler echocardiographic measurements.In this study, we developed a highly innovative computational-mechanics framework that can eventually, upon further development and validation, function as a diagnostic tool for the most general and fundamentally challenging condition, C3VD, in both pre and post TAVR states. Such a diagnostic tool should dynamically couple the local hemodynamics with the global circulatory cardiovascular system to provide a framework to evaluate the effects of the global (Requirements #1 and #2) and local hemodynamics (Requirement #3) in a patient-specific manner. For this purpose, we developed a framework based on an innovative Doppler-based patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid–solid interaction. It satisfies all three requirements for developing a clinically-effective computational diagnostic framework that can quantify local and global hemodynamics in patients who have C3VD in both pre and post intervention states. Our lumped-parameter algorithm allows for the analysis of any combination of complex valvular, vascular and ventricular diseases in C3VD patients in both pre and post intervention states by 24, and a 3-D strongly-coupled fluid–solid interaction (FSI)  metrics of circulatory function ; 2) metrics of cardiac function  as well as 3) cardiac fluid dynamics  in patients with C3VD in both pre and post TAVR states. This framework is based on an innovative non-invasive Doppler-based patient-specific lumped-parameter algorithm that allows for the analysis of any combination of complex valvular, vascular and ventricular diseases metrics  metrics  cardiac We retrospectively selected 11 C3VD patients with severe aortic valve stenosis who underwent TAVR between 2013 and 2017 at St. Paul’s Hospital . Informed consents were obtained from all human participants and the protocols were reviewed and approved by the Institutional Review Boards of the institution . The selections were done by operators blinded to the objectives and contents of this study. All methods and measurements were performed in accordance with relevant guidelines and regulations including guidelines of the American College of Cardiology and American Heart Association. Demographic and procedural data were collected from the patient medical records. The clinical outcome was evaluated using medical records and the New York Heart Association (NYHA) functional class, assessed at baseline and at 90-days post-TAVR. The protocol was reviewed and approved by the Ethics Committee of the institutions. Data were acquired at two time points: pre-procedure and 90-days post-procedure. Valve type and size were decided prior to the procedure by the local clinical team based on pre-procedural echocardiographic, computed tomographic, and angiographic imaging and data.25. The following metrics were measured:Doppler echocardiography (DE) data included raw images and documented reports that were collected at baseline and at 90-days post-procedure. Echocardiograms and reports were reviewed and analyzed in a blinded fashion by three senior cardiologists using OsiriX imaging software  as recommended by the American Society of Echocardiography (ASE)25  employing ITK-SNAP (version 3.8.0-BETA)age Fig. . These 3Complex valvular-vascular-ventricular diseases (C3VD). For simplicity, this C3VD computational mechanics framework is called C3VD-CMF, described in details elsewhere24. C3VD-CMF enables the quantification of (1) details of the physiological pulsatile flow and pressures throughout the heart and circulatory system; (2) heart function metrics, e.g., left ventricle workload and instantaneous left ventricular pressure, etc. C3VD-CMF also provides the breakdown of effects that each disease constituent imposes on the global function of the cardiovascular system. Currently, none of the above metrics can be obtained noninvasively in patients, and when invasive procedures are undertaken, the collected metrics cannot be as complete as the results that C3VD-CMF provides by any means24.We developed an innovative non-invasive image-based patient-specific diagnostic, monitoring and predictive computational-mechanics framework for 24. Examples of C3VD components include: valvular disease , ventricular disease , vascular disease , paravalvular leaks and LV outflow tract obstruction in patients with implanted cardiovascular devices such as transcatheter valve replacement (TVR), changes due to surgical procedures for C3VD , etc.27. Some sub-models have already been used32 and validated against in vivo cardiac catheterization (N = 34)33 and in vivo MRI data (N = 57)34.The developed algorithm (C3VD-CMF) consists of a parameter estimation algorithm and a lumped-parameter model that includes several sub-models allowing analysis of any combination of complex valvular, vascular and ventricular diseases in both pre and post intervention conditions: 1) left atrium, (2) left ventricle, (3) aortic valve, (4) mitral valve, (5) systemic circulation and 6) pulmonary circulation  as follows:1.mentclass2pt{minimmentclass2pt{minim24; parameter values used can be found in Table Coupling between LA pressure and volume was done through a time varying elastance E(t), a measure of cardiac muscle stiffness, using the same procedure as outlined above for the LV. The elastance function used for the LA is defined in Eqs.  and 3)223)24; paThe aortic valve was modeled using the net pressure gradient formulation andAortic regurgitation (AR) was modeled using the same analytical formulation as aortic stenosis as follows. The AR pressure gradient is the difference between aortic pressure and LV pressure during diastole.andThe mitral valve (MV) was modeled using the analytical formulation for the net pressure gradient Mitral regurgitation (MR) was modeled using Eq. . The MR MPV as follows.MPV, tee and T are the mean flow rate of the pulmonary valve, end-ejection time and cardiac cycle time period, respectively. In this study, it is very important to note that forward left ventricular outflow tract stroke volume (Forward LVOT-SV) is the only input flow condition that can be reliably measured using DE. QMPV, the mean flow rate of the pulmonary valve, was optimized so that the lumped-parameter model could reproduce the desirable DE-measured Forward LVOT-SV.The pulmonary valve flow waveform was simulated by a rectified sine curve with duration The total systemic resistance was computed as the quotient of the average brachial pressure and the cardiac output  compared to aortic pressure (mean ~ 100 mmHg)). This total systemic resistance represents the equivalent electrical resistance for all resistances in the current model. Because the LV faces the total systemic resistance as opposed to the individual resistances, we considered the aortic resistance, ao) and systemic (CSAC)). Therefore, for each degree of arterial hypertension, the compliance was evaluated using an optimization scheme outlined in the patient-specific parameter estimation section 24.For each degree of hypertension, we fit the predicted pulse pressure to the actual pulse pressure (measured by arm cuff sphygmomanometer) obtained from clinical study by adjusting the compliances (aorta (CForward LVOT-SV), cardiac cycle time (T), ejection time (TEJ), EOAAV, EOAMV, AAO, ALVOT, EOAAR, EOAMR and brachial systolic and diastolic pressures measured by a sphygmomanometer. The following procedure was used to set up the patient-specific lumped-parameter model:Flow inputs The lumped-parameter model used only one reliably measured flow parameter as an input: Forward LVOT-SV (Eq. Forward LVOT-SV is defined as the volume of blood that passes through the LVOT every time the heart beats.T-SV Eq. . ForwardTime inputs Cardiac cycle time (T) and ejection time (TEJ) were measured using Doppler echocardiography.Aortic valve inputsing Eqs.  and 12)Aortic vainto Eq. . SubsequAortic regurgitation inputs To model blood flow in the reverse direction , Mitral valve inputs To model the blood flow in the forward direction, the mitral valve area was substituted into Eq. , EOAMR was substituted into Eq. End systolic volume and end diastolic volume The end systolic volume (ESV) or end diastolic volume (EDV) measured using Doppler echocardiography were fed to the lumped-parameter model to adjust the starting and ending volumes in the P–V loop diagram.Left ventricle inputs The cardiac cycle time (T) was substituted into mentclass2pt{minimLeft atrium inputs The cardiac cycle time (T) was substituted into mentclass2pt{minim(10)Parameter estimation for systemic circulation Parameters RSA, CSAC and Cao were optimized so that the aortic pressure calculated using the model matched the patient’s systolic and diastolic brachial pressures measured using a sphygmomanometer . The initial values of these parameters are given in Table (11)Simulation execution Please see the computational algorithm section.The lumped-parameter model took the following patient-specific parameters as its inputs: forward left ventricular outflow tract stroke volume  to reach steady state before starting the response optimization process described below.The lumped-parameter model was analyzed numerically by forming and solving a system of ordinary differential equations in Matlab Simscape , augmented by including additional functions written in Matlab and Simscape. Matlab’s ode23t trapezoidal rule variable-step solver was used to solve the system of differential equations with an initial time step of 0.1 ms. The convergence residual criterion was set to 1036 was used to generate a signal to model LV elastance. It was shown that this elastance formulation can correctly represent the LV function independent of its healthy and/or pathological condition. Simulations began at the onset of isovolumic contraction. The instantaneous LV volume, V(t), was computed using the LV pressure, PLV, and the time-varying elastance . Lastly, for each patient, RSA, CSAC and Cao were optimized to fit the aortic pressure from the model to the patient systolic and diastolic pressures measured using a sphygmomanometer.A double Hill function representation of a normalized elastance curve for human adultsance Eq. . The LV ured Eq.  by optim–6. In the first step, QMPV, the mean flow rate of the pulmonary valve, was optimized to minimize the error between the Forward LVOT-SV calculated by the lumped-parameter model and the one measured in each patient. In the second step, RSA, CSAC and Cao were optimized so that the maximum and minimum values of the aortic pressure were respectively equal to the systolic and diastolic pressures measured using a sphygmomanometer in each patient.The Simulink Design Optimization toolbox was used to optimize the response of the lumped-parameter model using the trust region reflective algorithm implemented in the Matlab fmincon function. The response optimization was performed in two sequential steps with tolerances of 1037 in which the system of equations governing the FSI problem is formulated using the finite volume method.In this study, blood flow simulations rely on 3-D fluid–solid interaction (FSI) computational fluid dynamics using FOAM-Extend39 and was assumed to be a Newtonian and incompressible fluid with a dynamic viscosity of 0.004 Pa·s and a density of 1060 kg/m340. The following continuity and momentum equations were used41:n is the normal vector to the surface S,V is the fluid velocity, µ is the fluid dynamic viscosity, P is the blood pressure and ρ is the fluid density. Due to the deformation of the fluid–solid interface, momentum Eq. 53. However, in those studies, the models and the parameters were developed for non-pulsatile blood flow of healthy LVs. Additionally, the direction of myocardial fibers is required to optimize such models, thus requiring additional imaging data such as tensor diffusive MRI58. Other studies have focused on pulsatile blood flow, but they did not optimize tissue parameters to be patient-specific66. All of these studies relied on high-resolution MRI data to simulate the moving boundary of the LV wall and usually excluded the thickness of the LV in their modeling. In addition, several recent studies have combined LPM with MRI data to obtain anisotropic material properties of the LV for electro-mechanical models68. However, MRI is costly, lengthy and not possible for many patients with implanted devices like TAVR16.Because the LV is passive during diastole, its deformation depends on the tissue structure and the blood pressure inside the LV24. The algorithm decisively uses reliable non-invasive input parameters collected using DE. As opposed to MRI, DE is potentially the most versatile tool for hemodynamics and is low-cost and risk-free for all patients. LV tissue was assumed to be isotropic by the Saint Venant–Kirchhoff solid model74. We adjusted the ventricular non-linear material properties during diastole using the results of our LPM algorithm as follows. The LPM algorithm provided the LV diastolic pressure as well as the LV pressure–volume (P–V) diagram. We applied the diastolic pressure as the boundary condition at the inner wall of the LV, and by assuming different values for material properties, we obtained a series of LV P–V diagrams. Material properties were then interpolated to find the best value that could match the LV P–V results obtained using solid modeling to those acquired using the LPM. Young’s modulus was then interpolated to match the LV P–V results to those obtained using our LPM algorithm.In this study, we developed a method to adjust patient-specific passive material properties of the LV for patients who undergo TAVR, based on our patient-specific Doppler-based LPM algorithm76:F = I + (∇u)T; I is the second order identity tensor.According to the total Lagrangian form of the law of conservation of linear momentum, the deformation of elastic and compressible solid were considered as followsAlso, in Eq. , Ʃ is thUsing the St. Venant–Kirchhoff constitutive material model, Ʃ is explained through isotropic Hooke’s law:76:By substituting Eqs.  and 20)20) into 77. The fluid and solid solvers were coupled by the kinematic and dynamic conditions for the LV. To satisfy the kinematic condition, the velocity and displacement must be continuous across the interface76.i, s and f indicate the interface, solid and fluid regions, respectively. To satisfy the dynamic condition, the forces at the interface must be in equilibrium:The fluid and solid solvers were strongly coupled together to simulate the LV under pathophysiological flow and pressure conditions. We used the partitioned approach to separately solve the system of equations of the fluid and solid domains while the data were transferred at the interface. Both the solid and fluid were modeled using a finite-volume approach to reduce the cost of transferring information between the domains76.The Dirichlet–Neumann procedure at the interface indicates that the fluid domain is solved for a given velocity/displacement while the solid domain is solved for a given traction78. Spatial mesh resolution had been examined to optimize the number of elements for FSI simulation. Mesh definition  for both fluid and solid domains was considered acceptable when no significant difference (less than 2%) between successive meshes was noticed in velocity profiles. To maintain the initial quality of the cells, the fluid dynamic mesh was governed by Laplace mesh motion, which was controlled by variable diffusivity79. Mesh at the interface of the fluid and solid domains was not conformal, and consequently, interpolation was performed between the fluid and solid boundaries. The interpolation was performed based on the face-interpolation and vertex-interpolation procedures41. Moreover, time step independency had been studied for all models. The solution marched in time with a time step of 0.0001 s, yielding a maximum Courant number of 0.2. To improve the accuracy of the numerical simulation and to reduce numerical dispersion, the Courant number was lower than 0.25 for all simulations investigated in this study. Convergence was obtained when all residuals reached a value lower than 10–6. Temporal discretization was performed with a second-order Euler backward scheme, and a second-order accurate scheme was used for the spatial discretization.Mesh for all models was generated using SALOME, an open-source mesh generation software24: (1) to provide the time-dependent trans-mitral blood flow rate with the physiologic E and A waveforms; (2) to calculate material properties; (3) to provide the reference pressure, set inside the LV. All geometries were reconstructed based on images obtained at the beginning of diastole and, because LV diastolic dysfunction occurs in the left ventricular filling phase, all simulations were performed during diastole. Therefore, the TAVR was modeled to be rigidly closed and the mitral valve was modeled fully open during the diastolic phase. The effect of the chordae tendineae was not considered as the chordae tendineae do not play a significant role during mitral valve opening and do not influence the diastolic fluid dynamics66. A moving wall boundary condition was applied at the boundary surfaces between the fluid and solid inside the LV66. During diastole, there is an inflow from the atrium to the LV, but there is no outflow from the LV due to the closed aortic valve. Since the blood is incompressible39, interactions between the solid and fluid domains should be considered to conserve mass by allowing the blood to expand and contract the LV wall. The no-slip boundary condition was applied to the fluid–solid interface. In order to solve the FSI problem inside the nonlinearly deforming LV, we used the Robin boundary condition for pressure based on the approach proposed by Tukovic et al.80. The boundary condition for pressure was obtained from the following momentum equation 80:Imposing the correct boundary conditions to the flow model is critical because the local flow dynamics are influenced by downstream and upstream conditions. Boundary conditions were obtained from our patient-specific image-based lumped-parameter algorithm Fig. 24: (1) t80:Vn is the normal component of LV wall and fluid velocity.At the non-permeable moving LV wall, the following equation holdsρs is the density of the LV structure and hs is the LV thickness calculated as81:λ and µ are Lame constants of the LV and Δt is the time step size. Finally, combining Eqs. (80:Fluid pressure at the interface was estimated by solid inertia as follows:ing Eqs.  and 27)27\documeTherefore, the coupled FSI problem employed the Robin-Neumann approach in which the fluid component used the Robin boundary condition for pressure, and subsequently, velocity was calculated based on that pressure.37 in which the system of equations governing the FSI problem were formulated using the finite volume method  of the LV was controlled using a dynamic mesh methodology which was updated with the movement of the solid boundary. This method, based on the Laplace equation, was used for updating the computational and geometric nodes of the fluid mesh and was discretized by the cell-centered finite-volume method86 algorithm to couple the discretized governing equations of the fluid and solid domains. The IQN-ILS method has been compared with the monolithic method and other partitioned methods such as Aitken’s dynamic relaxation and has been proven to have better performance and stability87. In this partitioned approach, traction was calculated at the fluid side of the interface and was used as a traction boundary condition at the solid side of the interface.We used the interface Quasi-Newton-Implicit Jacobian Least-Squares (IQN-ILS)The calculations of the 3-D flow fields were done on a discrete mesh and the data obtained from our calculations were discrete. Continuous contours for the 3-D flow fields were created in Paraview  using linear interpolation.Figures Depending on the direction of the flow, there are positive and negative values for DE velocity: red and blue colors represent blood flow towards and away from the transducer, respectively. As shown in Figs. Figures 88 . Clinical assessment of AS for intervention decision-making was performed based on the symptoms of aortic valve hemodynamic metrics. As expected, aortic valvular metrics improved significantly after TAVR by removing the aortic valve obstruction that was causing an excessive pressure gradient and LV afterload. Our DE data showed that for all patients, TAVR significantly reduced the maximum pressure gradient across the aortic valve to a normal range88. Indeed, the maximum velocity measured less than 2.5 m/s and the maximum pressure gradient measured less than 25 mmHg for all patients after TAVR. The aortic valve maximum pressure gradient reductions ranged between 42% (patient #1) and 67% (patient #6)  to 90 days after TAVR are presented in Fig. 89. Although the EF slightly increased for some patients after TAVR, with six patients demonstrating a 5–7% increase in EF, the EF still remained less than 41% for all patients from baseline to 90 days after intervention /EDV; EDV and ESV are end diastolic volume and end systolic volume, respectively) is considered to be abnormal once it is less than 41%As an indicator of heart failure symptoms, the New York Heart Association (NYHA) functional class was determined for each patient based on the level of limitation in their ordinary activities, from 1 (no limitation in daily activity) to 4 (severe limitations in daily activity). All the patients had symptoms of heart failure, defined as a NYHA functional class greater than 2, both at baseline and at 90 days after TAVR. Post-TAVR, five patients’ symptoms slightly improved (from NYHA 4 to NYHA 3), five patients remained unchanged and one patient’s symptoms worsened (from NYHA 3 to NYHA 4) Fig. C.25. The diastolic dysfunction grade was obtained according to the American Society of Echocardiography recommendations based on the mitral valve velocity ratio of early diastolic velocity (E) to atrial contraction velocity (A), defined as the E/A ratio, as well as the annulus velocity (e’) as an index of LV diastolic filling efficiency25  had an E/A > 2 both at baseline and at 90 days after TAVR; only one patient had E/A = 1.1 for both pre and post-TAVR. While the unchanged diastolic dysfunction gradient correlated with the E/A gradient before and after TAVR, these results did not correlate with the maximum mitral valve pressure gradient  and 14% (patient # 6)), the maximum LV thickness remained greater than 15 mm for all patients even after TAVR 2/mmHg) is associated with an increased risk of morbidity for patients with AS94. As shown in Fig. 95). The increased LA pressure was correlated with high velocity during the E wave and a high E/A ratio during diastole  in all patients: TAVR reduced the LV workload in 70% of patients, but increased the workload in 30% of patients   is defined to be greater than 2, and a lower SI is associated with a higher risk of apex thrombosis or myocardial infarction98. For all patients in our study, the vortex sphericity index was lower than 2 both at baseline and 90 days after TAVR (maximum SI = 1.7), making the vortex more spherical and thus increasing the risk of thrombosis at the apex . This is an established complication in many cardiac conditions, with the highest rate detected in myocardial infarction and congestive heart failure.In order to explain the vortex morphology more precisely, the vortex sphericity index was calculated in the long axis view (2-D plane) by dividing the vortex length (D2) by the vortex width (D1) Figs. B and 10BAs previously mentioned, our results showed that pressure reduced after TAVR both for the left ventricle and the left atrium Fig. B. We als99. In general, very high wall shear stress could damage the tissue and very low wall shear stress could lead to thrombus formation99. It has been reported that wall shear stress for a normal LV lies between 0.2 and 1.2 Pa100 and increases slightly as a result of hypertrophy100. The total shear stress exerted on the wall throughout the cardiac cycle was evaluated using the time-averaged wall shear stress (TAWSS) which is obtained as Wall shear stress, as a force induced by blood flow, has a major impact on regulating endothelial functionQuantification of the complex flow plays an essential role in accurate and early diagnosis of patients with C3VD102. It can be used to effectively plan interventions and make critical clinical decisions with life-threatening risks. Once a C3VD patient develops symptoms, intervention becomes a class I recommendation104. However, at the time of diagnosis, symptoms are not reported by almost 40% of these patients105. Furthermore, there are often discrepancies found during clinical evaluation in one third of C3VD patients106. It is therefore essential to accurately diagnose individuals through careful hemodynamic evaluation to identify who may benefit from intervention  and experience improved outcomes108. Overall, upon diagnosis, the results of anatomic and hemodynamic measures should agree107. Thus, accurate quantification of hemodynamic parameters is critical to resolve any inconsistencies and to identify the optimal course of treatment for each individual patient107. With an increasing appreciation for the hypothesis that valvular disease is complex and is also influenced by the principals of the LV and arterial system, quantitative investigations of hemodynamics that consider the interactive coupling of the valve, ventricle and arterial system have become extremely desirable112. The following three requirements should be quantified by a clinically useful computational diagnostic framework that evaluates both local and global hemodynamics for patients with C3VD and TAVR:Metrics of circulatory function . The heart resides in a sophisticated vascular network whose loads impose boundary conditions on the heart function113. Furthermore, it is critical to replicate the correct flow and pressure conditions when developing a patient-specific cardiovascular simulator because the local flow dynamics are influenced by both downstream and upstream conditions. This ensures that patient-specific flow and pressure conditions are provided to the local flow while also enabling analysis of the effects of local hemodynamics on the global circulatory physiology. Complex valvular, ventricular and vascular diseases (C3VD) is the most fundamentally challenging cardiovascular pathology, in which several pathologies have mechanical interactions with one another wherein adverse physical phenomena associated with each pathology amplify the effects of others on the cardiovascular system27. TAVR often coexists with C3VD, thus making the investigation of flow and pressure details in the presence of TAVR very challenging. Although cardiac catheterization is currently the clinical gold standard for evaluating the global function of the heart and circulatory system using pressure and flow measurements, it is not practical for diagnosis in routine daily clinical practice or serial follow-up examinations, as it is invasive, expensive and high risk14. Furthermore, it is important to note that cardiac catheterization does not provide details of the physiological pulsatile flow and pressures throughout the heart and circulatory system, but instead, only enables access to blood pressure in very limited regions.(1) 120. Indeed, due to these interactions, several regions throughout the heart and surrounding system are often affected 120, and certain conditions of the circulatory system may prevent the accurate assessment of C3VD or affect the outcomes of TAVR106. For example, from a large registry of C3VD patients undergoing endovascular TAVR, almost one quarter suffered from coexisting peripheral artery disease, which was found to be associated with higher odds of vascular complications and major bleeding121. Moreover, regardless of the flow conditions, the presence of hypertension or reduced arterial compliance in patients with C3VD may reduce the transvalvular gradient and peak transvalvular velocity, thus causing an underestimation of aortic stenosis severity106. Hypertension is also a risk factor for cardiovascular events and may be associated with worse outcomes and faster progression of C3VD116. Overall, precise knowledge of these interactions and careful assessment of hemodynamics in a patient-specific manner helps optimize the diagnosis process to provide the best possible outcomes for patients120, to decide upon the required course of treatment and determine if more than one intervention is required for the C3VD patients117.Effective diagnosis is critically dependent on quantifying details of the physiological pulsatile flow and pressures throughout the heart and circulatory system as well as the interactions within C3VD and how individual disease progressions may affect one anotherMetrics of cardiac function . In the presence of TAVR and/or C3VD, the heart is overloaded since the healthy instantaneous left-ventricle pressure and/or left-ventricle flow are altered27. In clinics, cardiac catheterization is the gold standard for evaluating heart function in terms of the heart workload obtained from the instantaneous left-ventricle pressure and/or left-ventricle flow. However, there is no method to invasively or non-invasively quantify the heart workload  that can provide the contribution breakdown of each component of the cardiovascular system. This is especially crucial in C3VD and TAVR because quantification of the left-ventricle workload and its breakdown are vital to guide the prioritization of interventions and to sufficiently validate devices in regulatory testing machines. Moreover, there is no non-invasive method for determining left-ventricular end-diastolic pressure, instantaneous left-ventricular pressure, and contractility—all of which provide valuable information about the patient’s state of cardiac deterioration and heart recovery.(2) 123. Following TAVR, in many cases, there is improvement in the structure and function of the left ventricle, with regression of the myocardial cellular hypertrophy and diffuse fibrosis122. However, the development of focal fibrosis, which provides evidence of cardiomyocyte necrosis, is irreversible122. To optimize the intervention outcome before the ventricle is permanently damaged124, to choose the optimal time of intervention125, and to drastically reduce the risk of mortality115, knowledge of the heart workload (cardiac function) and the contribution breakdown of each component in C3VD should be precisely quantified and evaluated at the time of diagnosis.In patients with C3VD, the valves and left ventricle are diseased, thus altering the overall cardiac functionCardiac fluid dynamics . The complex pulsatile flow in the left ventricle and its valves becomes even more complicated in C3VD. Chirality of the human heart causes this flow to be strongly three dimensional96. Moreover, as a result of TAVR, new interactions occur between the artificial implant and the native valve geometry, thus altering the fluid dynamics126. During filling of the normal heart, the blood entering the left ventricle through the mitral valve forms a vortex that minimizes energy dissipation, prevents blood stagnation and optimizes pumping efficiency96. C3VD and TAVR alter this optimized flow7: the vortex dynamics become less synchronized with the heart contraction, and vortices other than the healthy vortex ring may emerge and interact with one another. To predict the success of TAVR and plan the best deployment possible in each patient, it is crucial to know details of the instantaneous 3-D flow, vortex formation, growth, eventual shedding, and their effects on fluid transport and stirring inside the left ventricle and in the vicinity of the valves after deployment127. It is essential for a diagnostic tool to carefully quantify and predict cardiac fluid dynamics in a patient-specific manner because there can be high inter-patient variability in the success of any given intervention128. Altered hemodynamics should be optimized with treatment, as they can lead to adverse outcomes such as an increased risk of thrombus formation126. One meta-analysis identified the risk of stroke being four times greater in TAVR patients with leaflet thrombosis129.(3) 130. Therefore, although clinical assessments have shown improvements , geometrical alterations at the left ventricular outflow tract after TAVR could increase the risk of thrombus formation. Indeed, a recent study showed that left ventricular outflow tract calcification increases the risk of annular rupture and residual aortic regurgitation131. Therefore, despite the improvements of clinical parameters, our results depicting the details of local hemodynamics in this patient might partially explain how TAVR could adversely increase the risk of LVOT calcification and subsequent long-term complications.A clinically-useful computational diagnostic framework should quantify both local and global hemodynamics. As examples: (1) Patient #1 Fig. ; global 136. In addition, several researchers have recently used FSI as a promising tool for computational cardiology because it allows for the complete coupling of the heart wall and blood flow mechanics, thus demonstrating its worth as the most comprehensive tool for numerical modeling of the LV154. However, since: (1) patient-specific boundary conditions were not used; (2) normal valves and ventricles were modeled instead of those with C3VD; and (3) patient-specific geometries were not used, the models developed in these studies didn’t satisfy the three requirements outlined in the Introduction154. While some models were partially validated using DE152 or MRI60, many were not validated. Five of the studies147 did impose boundary conditions on the calculations by coupling fluid–structure modeling calculations with lumped-parameter modeling, but the lumped-parameter models were either not patient-specific and/or they required information from MRI. MRI is not feasible in patients with implanted devices, and it is not available in all clinics, therefore restricting the collection of the necessary blood-flow and geometrical measurements. Additionally, idealized geometries were used in these studies, which could significantly affect the flow and vortex structure.A clinically-useful computational diagnostic framework that can quantify both local and global hemodynamics for patients with C3VD and TAVR should quantify the three requirements mentioned in the Introduction and Discussions. Several studies have used computational fluid dynamics (CFD) based on the discretization of the Navier–Stokes equations  with a moving boundary in an attempt to quantify blood flow  inside the LV, but none of these studies considered LV tissue thickness or other tissue characteristicsIn this study, we developed an innovative computational diagnostic framework for complex diseases like C3VD and TAVR that dynamically couples the local hemodynamics  with the global circulatory cardiovascular system  and satisfies the three requirements. This computational diagnostic framework is promising for future clinical adoption and can quantify: (1) metrics of circulatory function ; (2) metrics of cardiac function  as well as (3) cardiac fluid dynamics  in patients with C3VD in both pre and post intervention states. Such information is vitally needed for effectively using advanced therapies to improve clinical outcomes and guide interventions in C3VD patients.24. In the present study, we used the validated lumped-parameter model24 to obtain time varying pressure and volume of the left ventricle as the inputs to the solid model of the LV. We modeled the LV as an isotropic Saint Venant–Kirchhoff solid and found material parameters that could best reproduce the LV volume changes (obtained from the lumped-parameter model24 while applying LV pressure (obtained from the lumped-parameter model) to the LV wall. Using this approach, in all patients that we have investigated in this study, we could always find material parameters that produce consistent results with the lumped-parameter model. Moreover, we performed a comprehensive parameter sensitivity analysis on the outputs of the lumped-parameter model that are used in the present study to find cardiac parameters. We found that the outputs from the lumped-parameter model were most sensitive to the forward left ventricular outflow tract stroke volume : LV pressure: 27%, LV Volume 19% by a ± 20% change in the Forward LVOT-SV. The other input parameters affected the output to a much lower degree. We should point out that Forward LVOT-SV is measured reliably using Doppler echocardiography with high accuracy and sensitivity of the model to this parameter does not jeopardize the results obtained from the model. In addition, sensitivity analysis revealed negligible effects of changes (± 20%) in the free parameters on the model output variables. Indeed, as shown in Figs. Due to the complex multiphysics nature of the left ventricle and heart valves, the overall estimation of cardiac parameters is very dependent on the outputs of the lumped-parameter model that are in-turn depend on the parameters used in the lumped-parameter model. Our patient-specific Doppler-based lumped-parameter algorithm, which provided boundary conditions, was validated against clinical catheterization data in forty-nine C3VD patients with a substantial inter- and intra-patient variability with a wide range of disease24. These observations made us more confident that the limitation in the number of patients in this study does not affect our conclusions.This study was performed and validated on 11 patients with C3VD and TAVR using a 3-D strongly-coupled fluid–solid interaction and lumped-parameter modeling framework in both pre and post intervention states (22 cases). Future studies must consider further validation of the computational framework in a larger population of C3VD patients. However, our results in this study demonstrate the ability of the framework to track changes in both cardiac and vascular states. Our LPM algorithm allows analysis of any combination of complex valvular, vascular and ventricular diseases in both pre and post intervention conditions. It is important to note that this algorithm was validated against clinical catheterization data in forty-nine C3VD patients with a substantial inter- and intra-patient variability with a wide range of disease


Left ventricular (LV) and RV volumetric, functional, CMi, and TMi values were measured with a threshold-based post-processing CMR method. The volumetric parameters, RV-CMi, and RV-TMi values were larger, and the ejection fraction (EF) was lower in males. The RV-CMi did not correlate with age in either of the sexes, while the RV-TMi decreased with age in females but remained stable in males. The RV-TMi and RV-CMi correlated positively with RV volumetric parameters, the LV-CMi, the LV-TMi, and each other in both sexes. LV-TMi, LV-CMi, RV end-systolic volume, and sex were independent predictors of RV-TMi. Understanding the characteristics of RV-trabeculated and RV-compacted myocardium might have additive value in diagnosing different conditions with RV hypertrophy or hypertrabeculation.The age and sex-specific characteristics of right ventricular compacted (RV-CMi) and RV-trabeculated myocardial mass (RV-TMi) and the determinants of RV myocardium are less well-studied; however, in different conditions, these might provide additional diagnostic information. We aimed to describe the age- and sex-specific characteristics of RV-CMi, RV-TMi, and RV volumetric and functional parameters and investigate the determinants of RV myocardial mass with cardiac magnetic resonance (CMR). Two hundred healthy Caucasian volunteers free of known cardiovascular or systemic diseases were prospectively enrolled in this study. Four different age groups were established with equal numbers of males and females: Group A ( The age- and sex-related characteristics of right ventricular (RV) volumetric and functional parameters have already been described in healthy populations , 2. In cOne hundred male and one hundred female Caucasian volunteers free of known diseases were prospectively enrolled between January 2019 and September 2020. Each participant completed a questionnaire including demographic data, cardiovascular symptoms, medical history, medication use, and sport activity. The inclusion criteria were as follows: the absence of cardiac diseases , lack of sudden cardiac death in the family history, absence of other systemic disorders , and sport activity <6 h per week . CMR exan = 50, 20-29 years, mean age: 24.3 ± 3.2 years), Group B , Group C , and Group D . The male and female participants' baseline demographic, morphometric, and left and right ventricular parameters are shown in Tables 2, 3. The sports activities of the studied groups are presented in The study population was divided into four different age groups, which were set up with equal numbers of males and females: Group A . Functional imaging was performed with balanced steady-state free precession cine sequences in 2-, 3-, and 4-chamber long-axis views and breath-hold short-axis views from base to apex with complete coverage of the LV and RV according to current recommendations , 12. TheMedis Suite version 3.0 was used for the segmentation . Automatic tracing with manual correction of the endo- and epicardial contours of the LV and RV was performed by two observers (ASz with 10 years of experience and ARK with 5 years of experience). Inter-and-intraobserver agreement values are presented in t-test or the Mann-Whitney U-test was used to compare male and female groups appropriately. The Pearson or Spearman test was used for correlation analysis as appropriate. Multiple linear regression analysis was applied to determine independent predictors for RV-compacted and RV-trabeculated myocardial mass. A p-value < 0.05 was considered statistically significant. IBM SPSS Statistics  was used for calculations.The intra-and interobserver agreement of the two observers was tested using the intraclass correlation coefficient (ICC). An ICC <0.4, between 0.4 and 0.75, and >0.75 indicates poor, fair to good, and excellent interobserver agreement. The Shapiro-Wilk test was used to assess the normality of each distribution. Normally distributed continuous variables are presented as the mean and standard deviation (SD), while median and interquartile range (IQR) is shown in the case of non-normal distribution. An unpaired Student's The inter- and intraobserver agreement of the two observers was tested on 25 randomly selected participants. All of the measured parameters showed excellent inter-and intraobserver agreement.Studying the LV and RV parameters in the total population, we found significant differences between males and females: both LV and RV EDVi, ESVi, CMi, and TMi values were significantly larger, while the EF was significantly lower in males , 3.We further described the sex-related differences in each age group . The volRegarding sex-related differences of the studied ratios, the RV-TMi/RV-CMi ratio was comparable in the youngest age group between males and females, but it was higher in males than in females in Group B, Group C, and D and was significantly different between sexes in Group C. The RV-CMi/RV-EDVi and RV-TMi/RV-EDVi ratios were larger in males. The former was significantly different in Group C, and the latter was significant in Group C and Group .Age-related changes in the studied parameters were as follows , 3. The The RV-TMi/RV-CMi did not correlate with age in either males or females, while the RV-CMi/RV-EDVi ratio positively correlated with age in both sexes, and the RV-TMi/RV-EDVi ratio showed a positive correlation only in males.Linear relationships were studied between RV-TMi, RV-CMi, RV volumetric and functional parameters, and LV myocardial mass values . RV-TMi R-value of 0.701 (p < 0.0001). Independent predictors of RV-CMi were age, sex, RV-EDVi, and LV-CMi, with a cumulative R-value of 0.728 (p < 0.0001).Multiple regression analysis was performed on the total population to find independent predictors of RV-TMi and RV-CMi . RV-TMi In this study, we assessed the characteristics of RV in different age groups stratified by sex. We described smaller RV volumes and higher RV-EF, while the RV compact myocardial mass values were similar to those published , 13. DurAssessing the sex-related differences in the studied RV parameters, we found higher compacted and trabeculated myocardial mass values, higher volumetric parameters, and smaller RV-EF in males. These were consistent with the literature and could be explained by biometric and hormonal differences between sexes , 15, 17.Regarding the trabeculated myocardial mass, we found a slight decrease with age in females, and this negative age dependency is in line with Andre's results, although the mechanism is unknown , 21. In The RV-EF was positively correlated with age in females but not in males. The data are controversial about the age-related changes in the RV-EF: Petersen et al. found an age-related increase in females only, which is similar to our results, while Maceira et al. described an increase in the RV-EF in males as well. However, the RV-EF did not change significantly over time in either males or females in Sandstede's study , 18, 19.The RV trabeculated myocardial mass-to-compact myocardial mass ratio did not show sex-related differences and was not correlated with age. To the best of our knowledge, there is no information about the RV trabeculated myocardial mass-to-compact myocardial mass ratio in healthy male and female populations. However, previous studies found no significant association between sex and non-compacted-to-compacted myocardial thickness of the LV , 28. TheDifferent myocardial mass-to-volume ratios have been previously studied in the left ventricle but not in the right ventricle. Czimbalmos et al. described the high diagnostic accuracy of LV muscle-to-volume ratios in the differential diagnosis of hypertrophic cardiomyopathy and athletes' hearts . The RV-In studying the relationship between RV and LV myocardial mass values and RV volumetric parameters, we found that compacted RV myocardial mass correlated with trabeculated RV myocardial mass and vice versa. Furthermore, both compacted and trabeculated RV masses correlated with LV compacted and trabeculated myocardial masses, and LV trabeculation was an independent predictor of RV trabeculation. We did not find previous studies about the connection between either RV or LV compacted and trabeculated myocardial masses. Some researchers suggest that left and right ventricular myocardial mass is genetically determined; however, the degree of trabeculation might vary between the ventricles due to the different physiological mechanisms and stimulating factors . RV voluTo summarize, the RV-compacted myocardial mass remained stable over time in both sexes, but the RV trabeculated myocardial mass decreased with age in females. RV trabeculated myocardial mass correlated significantly with RV-compacted myocardial mass and vice versa. Furthermore, LV compacted, and trabeculated myocardial mass values and RV volumetric parameters were independent predictors of RV-compacted and trabeculated myocardial mass. The age- and sex-related characteristics of RV compacted and trabeculated myocardial masses described in a healthy population have not been previously studied; however, this additional information might help differentiate conditions with RV hypertrophy or hypertrabeculation.We need to mention the limitations of this study. It was conducted on a large cohort; however, the subgroups were relatively small after dividing by age. This study did not cover elderly populations as we needed to exclude these participants due to cardiovascular or other systemic diseases (mainly hypertension). The CMR cine images were acquired using two different CMR scanners, and although the scan parameters were similar, this might be considered a limitation. We need to mention a few limitations regarding the threshold-based software as well. The current ejection fraction and volume quantification use a stack of thick short-axis slices, and 8 mm is common for Z-direction spatial resolution. In the meantime, trabeculae and papillary muscles do not cross the slice in an exactly perpendicular fashion, which creates partial volume effects. Depending on the actual path of the trabeculae, this will influence the threshold-based quantification , 16. TheThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.AK: methodology, formal analysis, investigation, resources, data curation, writing—original draft, and visualization. ZG: data curation. ÁF: data curation. LS: writing—review and editing. ZD: writing—review and editing. BM: supervision. HV: writing—review and editing and supervision. AS: conceptualization, methodology, investigation, resources, writing—review and editing, supervision, and project administration. All authors contributed to the article and approved the submitted version.This research was financed by the Thematic Excellence Programme  of the Ministry for Innovation and Technology in Hungary within the framework of the Therapeutic Development and Bioimaging Programs of Semmelweis University; by the Development of Scientific Workshops of Medical, Health Sciences and Pharmaceutical Education (Project identification number: EFOP-3.6.3-VEKOP-16-2017-00009); and by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program. Project no. NVKP_16-1–2016-0017  has been implemented with the support provided by the National Research, Development, and Innovation Fund of Hungary, financed under the NVKP_16 funding scheme.The 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.


Objectives: To analyze the association between global myocardial work indices evaluated by non-invasive left ventricular (LV) pressure-strain loop (PSL) and LV myocardial fibrosis in patients with dilated cardiomyopathy (DCM).Methods: A total of 57 patients with DCM were included in this prospective study. Global work index (GWI), global constructive work (GCW), global wasted work (GWW), global work efficiency (GWE) and global longitudinal strain (GLS) were measured using LVPSL. LV volumes and LV ejection fraction (LVEF) were evaluated using cardiac magnetic resonance imaging (CMRI), LV myocardial fibrosis was estimated at CMRI by qualitative assessment of late gadolinium enhancement (LGE). According to the CMRI, the studied population was divided into two groups, namely: patients without LGE (LGE-) and patients with LGE (LGE+).Results: The LGE+ group presented with increased age, LV end systolic volume (LVESV) index and reduced GWI, GCW, GWE, GLS, CMRI-derived LVEF (LVEFCMRI), the differences between the two groups were statistically significant (P < 0.05). After correcting for age and LVESV index, LVEFCMRI, GLS, GWI, GCW, and GWE retained independent associations with LV myocardial fibrosis. According to receiver operating characteristics (ROC) analysis, LVEFCMRI, and GCW showed larger AUC and higher accuracy, sensitivity, and specificity than GLS, the accuracy of predicting LV myocardial fibrosis ranged from high to low as: LVEFCMRI, GCW, GWE, GWI, and GLS.Conclusions: LVEFCMRI, GWI, GCW, GWE, and GLS remained significant predictors of LV myocardial fibrosis. LVEFCMRI, and GCW appeared to better predict LV myocardial fibrosis compared with GLS. Dilated cardiomyopathy (DCM) is defined as the dilatation and dysfunction of one or both ventricles in the absence of abnormal loading or coronary heart disease (CHD) , 2. ThisCardiac magnetic resonance imaging (CMRI) is the gold standard for the measurement of cardiac function parameters , 7. In aThe left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) measured using echocardiography are often recommended for the evaluation of left ventricular systolic function (LVSF) . HoweverPrevious studies by Chan et al. and the author confirmed the feasibility of the myocardial work index to evaluate LVSF in patients with DCM , 13. TheWe conducted a prospective study on 101 consecutive patients with DCM in the heart failure department of Fuwai Central China Cardiovascular Hospital from January 2019 to February 2021. The diagnosis of DCM was established according to current guidelines . The incWe recorded medical history, including New York Heart Association (NYHA) functional class; biomarkers; cardiovascular risk factors; current medications and 12-lead electrocardiography (ECG). ECG, Transthoracic echocardiography and CMRI were performed within 24 h.All echocardiographic examinations were performed on a Vivid E95 ultrasound system  equipped with an M5Sc-D 1.4–4.6 MHz transducer. All the study subjects were placed in a left-side position and synchronously connected to the electrocardiogram. The average frame rate of the 2D image was 59 ± 7 frames/sec. The LVEF of each patient was measured using the biplane Simpson method. The peak velocity of the mitral valve in early diastolic period (E peak), and the average velocity of the mitral annulus (e′) were measured, and E/e′ was calculated. Apical four-, three-, and two-chamber images were continuously acquired for at least three cardiac cycles, and the Doppler blood flow spectrum of the aortic and mitral valves was obtained. The original data images were saved to a hard disk for analysis.Echopac version 203  was used for image analysis. According to the Doppler flow spectrum of the aortic valve and mitral valve, valvular event time was determined. Tracking was automatic, but if the tracking was not satisfactory, tracking points were adjusted manually to determine segmental and global longitudinal strain. After entering the cuff blood pressure value, the software automatically generated the LVPSL curve, GLS, and myocardial work parameters. The absolute value of GLS was recorded and the global myocardial work parameters included global work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE). GWI is equivalent to the area of the PSL, GCW is a sign of active contraction of the left ventricular myocardium, GWW is a sign of energy loss, and GWE is the percentage of GCW in the sum of GCW and GWW .A Siemens MAGNETOM Skyra 3.0 MR scanner , an 18-channel phased array coil for heart, and chest lead ECG gating technology were used for CMRI. The short- and long-axis images of the left ventricle were collected using a balance steady-state free procession sequence with the following parameters: TR, 3.3 ms; TE, 1.43 ms; FOV, 340 × 340 mm; matrix, 208 × 166; layer thickness, 8 mm; layer spacing, 2 mm; reversal angle, 80°; and dynamic breath-hold scanning, one cardiac cycle. A total of 25 images were collected.For LGE image acquisition, a high-pressure syringe was used for elbow vein bolus injection of gadolinium . Segmented inversion was adopted to restore the gradient echo sequence. The collected short-axis images of first pass perfusion and delay period had the following parameters: layer thickness, 8 mm; TR, 6.1 ms; TE, 2.9 ms; and reversal angle, 25°. A proper reversal time was chosen to suppress normal myocardial signals.All images were reevaluated by two experienced observers, and all clinical data were blinded for analysis. By using Siemens Argus post-processing software , the left ventricular endocardium and epicardium were automatically tracked and contoured on the image . The lefLVEDV, LVESV, and LVM were corrected as indices using BSA.In accordance with the recommendation of the American Heart Association in 2002 , the lefBlood samples of patients were drawn to measure the level of N-terminal pro-brain natriuretic peptide (NT-proBNP). Analysis was conducted in the clinical laboratory.t-test or Wilcoxon signed rank test (continuous variables) as appropriate. To investigate the associations between variables and LV myocardial fibrosis, univariate and multivariate logistic regression analyses were performed. Correlations between independent variables were examined by Pearson correlation coefficients. For collinearity reasons (Pearson's coefficient > 0.6), several multivariate logistic regression models were built. A value of P < 0.05 was considered statistically significant.Continuous variables were expressed by mean ± standard deviation when the variables obeyed normal distribution; otherwise, the median (quartile) was used. Categorical variables are presented as number (%). Baseline characteristics among patients with and without LGE were compared by chi-square or Fisher's exact test , and independent sample Receiver operating characteristics (ROC) curve analyses were used to determine optimal cutoff points for variables in predicting LV myocardial fibrosis, and to calculate the area under the curve (AUC), sensitivity, specificity and accuracy. Intra-observer variability and inter-observer variability of myocardial work indices were assessed in 20 patients and tested using intraclass correlation coefficients (ICCs).All statistical analyses were performed using a standard statistical software program .n = 32/57, 56.1%) and LGE - group .As depicted in n = 57), mean age was 43.9 ± 12.7 years, 45 (78.95%) patients were males. QRS duration 108  ms, 19 (33.33%) patients had wide QRS (duration ≥120 ms). Eight (14.04%) patients had left bundle branch block, The LGE+ group presented with increased age and the difference between the two groups was statistically significant (P < 0.001). There were no statistically significant differences in sex, BSA, heart rate, blood pressure, NYHA functional class, NT-proBNP, concomitant diseases, medications, or electrocardiographic parameters between the two groups (P > 0.05).Baseline characteristics are presented in P < 0.05) and reduced LVEFCMRI, GLS, GWI, GCW, and GWE. The differences between the two groups were statistically significant (P < 0.001). There were no statistically significant differences in LAV index, LVEFSimpson, E/e′, or LVM index between the two groups (P> 0.05).As shown in CMRI, GLS, GWI, GCW, and GWE. There was no significant correlation between age, LVESV index and parameters of LV function, including LVEFCMRI, GLS, GWI, GCW, and GWE. Considering the significant correlations among parameters of LV function, several multivariate logistic regression models with one parameter of LV function and age, and LVESV index were built .CMRI. These findings confirmed the association between myocardial dysfunction and fibrosis.Myocardial fibrosis leads to the decrease in ability of myocardial movement and deformation, manifested as impairment of myocardial global systolic function , 5, 20. CMRI. LVEFCMRI, and GCW showed larger AUC and higher accuracy, sensitivity, and specificity than GLS, the accuracy of predicting LV myocardial fibrosis ranged from high to low as: LVEFCMRI, GCW, GWE, GWI, and GLS. Therefore, apart from CMRI, LV myocardial work indices may provide further insights into LV myocardial fibrosis in patients with DCM. The optimal cutoff points of LVEFCMRI, GWI, GCW, GWE, and GLS might provide valuable information. Galli et al.  on the ECG represents intraventricular conduction delay. In the study, there was no statistically significant difference in QRS duration between LGE- and LGE+ patients, indicating no significant association between QRS duration and LV myocardial fibrosis. A previous study  found thThis study was conducted in a single center with a small number of samples and needs to be expanded for further study. A previous study  found thCMRI, GWI, GCW, GWE, and GLS remained significant predictors of LV myocardial fibrosis. LVEFCMRI, and GCW appeared to better predict LV myocardial fibrosis compared with GLS. LV myocardial work indices may be potential surrogate markers for the detection of fibrosis in addition to LVEFCMRI.LVEFThe original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the ethics committee of Fuwai Central China Cardiovascular Hospital. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.CC and LL designed the study, analyzed data, wrote the manuscript, and reviewed and edited the manuscript. YLi, YLiu, DH, YH, YW, and LM performed this study. All authors read and approved the manuscript.The 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.