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Postmortem imaging is increasingly used in forensic practice as good complementary tool to conventional autopsy investigations. Over the last decade, postmortem cardiac magnetic resonance (PMCMR) imaging was introduced in forensic investigations of natural deaths related to cardiovascular diseases, which represent the most common causes of death in developed countries. Postmortem CMR application has yielded interesting results in ischemic myocardium injury investigations and in visualizing other pathological findings in the heart. This review presents the actual state of postmortem imaging for cardiovascular pathologies in cases of sudden cardiac death (SCD), taking into consideration both the advantages and limitations of PMCMR application.

The evaluation of the credibility of results from a meta-analysis has become an important part of the evidence synthesis process. We present a methodological framework to evaluate confidence in the results from network meta-analyses, Confidence in Network Meta-Analysis (CINeMA), when multiple interventions are compared. CINeMA considers 6 domains: (i) within-study bias, (ii) reporting bias, (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. Key to judgments about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The contribution matrix can easily be computed using a freely available web application. In evaluating imprecision, heterogeneity, and incoherence, we consider the impact of these components of variability in forming clinical decisions. Via 3 examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgments, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks.

Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular (LV) coverage in a single breath-hold. However, 3D imaging remains limited to anisotropic resolution and long reconstruction times. Recently deep learning has shown promising results for computationally efficient reconstructions of highly accelerated 2D CINE imaging. In this work, we propose a novel 4D (3D + time) deep learning-based reconstruction network, termed 4D CINENet, for prospectively undersampled 3D Cartesian CINE imaging. CINENet is based on (3 + 1)D complex-valued spatio-temporal convolutions and multi-coil data processing. We trained and evaluated the proposed CINENet on in-house acquired 3D CINE data of 20 healthy subjects and 15 patients with suspected cardiovascular disease. The proposed CINENet network outperforms iterative reconstructions in visual image quality and contrast (+ 67% improvement). We found good agreement in LV function (bias ± 95% confidence) in terms of end-systolic volume (0 ± 3.3 ml), end-diastolic volume (− 0.4 ± 2.0 ml) and ejection fraction (0.1 ± 3.2%) compared to clinical gold-standard 2D CINE, enabling single breath-hold isotropic 3D CINE in less than 10 s scan and ~ 5 s reconstruction time.

Purpose This study aimed to quantify the respiration‐induced motion in each pancreatic region during motion mitigation strategies and to characterize the correlations between this motion and that of the surrogate signals in cine‐magnetic resonance imaging (MRI). We also aimed to evaluate the effects of these motion mitigation strategies in each pancreatic region. Methods Sagittal and coronal two‐dimensional cine‐MR images were obtained in 11 healthy volunteers, eight of whom also underwent imaging with abdominal compression (AC). For each pancreatic region, the magnitude of pancreatic motion with and without motion mitigation and the positional error between the actual and predicted pancreas motion based on surrogate signals were evaluated. Results The magnitude of pancreatic motion with and without AC in the left–right (LR) and superior–inferior (SI) directions varied depending on the pancreatic region. In respiratory gating (RG) assessments based on a surrogate signal, although the correlation was reasonable, the positional error was large in the pancreatic tail region. Furthermore, motion mitigation in the anterior‐posterior and SI directions with RG was more effective than was that with AC in the head region. Conclusions This study revealed pancreatic region‐dependent variations in respiration‐induced motion and their effects on motion mitigation outcomes during AC or RG. The magnitude of pancreatic motion with or without AC and the magnitude of the positional error with RG varied depending on the pancreatic region. Therefore, during radiation therapy for pancreatic cancer, it is important to consider that the effects of motion mitigation during AC or RG may differ depending on the pancreatic region.

Objectives Assessment of myocardial strain by feature tracking magnetic resonance imaging (FT-MRI) in human fetuses with and without congenital heart disease (CHD) using cardiac Doppler ultrasound (DUS) gating. Methods A total of 43 human fetuses (gestational age 28–41 weeks) underwent dynamic cardiac MRI at 3 T. Cine balanced steady-state free-precession imaging was performed using fetal cardiac DUS gating. FT-MRI was analyzed using dedicated post-processing software. Endo- and epicardial contours were manually delineated from fetal cardiac 4-chamber views, followed by automated propagation to calculate global longitudinal strain (GLS) of the left (LV) and right ventricle (RV), LV radial strain, and LV strain rate. Results Strain assessment was successful in 38/43 fetuses (88%); 23 of them had postnatally confirmed diagnosis of CHD (e.g., coarctation, transposition of great arteries) and 15 were heart healthy. Five fetuses were excluded due to reduced image quality. In fetuses with CHD compared to healthy controls, median LV GLS (− 13.2% vs. − 18.9%; p  < 0.007), RV GLS (− 7.9% vs. − 16.2%; p  < 0.006), and LV strain rate (1.4 s −1 vs. 1.6 s −1 ; p  < 0.003) were significantly higher (i.e., less negative). LV radial strain was without a statistically significant difference (20.7% vs. 22.6%; p  = 0.1). Bivariate discriminant analysis for LV GLS and RV GLS revealed a sensitivity of 67% and specificity of 93% to differentiate between fetuses with CHD and healthy fetuses. Conclusion Myocardial strain was successfully assessed in the human fetus, performing dynamic fetal cardiac MRI with DUS gating. Our study indicates that strain parameters may allow for differentiation between fetuses with and without CHD. Clinical relevance statement Myocardial strain analysis by cardiac MRI with Doppler ultrasound gating and feature tracking may provide a new diagnostic approach for evaluation of fetal cardiac function in congenital heart disease. Key Points • MRI myocardial strain analysis has not been performed in human fetuses so far. • Myocardial strain was assessed in human fetuses using cardiac MRI with Doppler ultrasound gating. • MRI myocardial strain may provide a new diagnostic approach to evaluate fetal cardiac function.

Taking into account the complexity and limitations of clinical assessment in hypertrophic cardiomyopathy (HCM), imaging techniques play an essential role in the evaluation of patients with this disease. Thus, in HCM patients, imaging provides solutions for most clinical needs, from diagnosis to prognosis and risk stratification, from anatomical and functional assessment to ischaemia detection, from metabolic evaluation to monitoring of treatment modalities, from staging and clinical profiles to follow-up, and from family screening and preclinical diagnosis to differential diagnosis. Accordingly, a multimodality imaging (MMI) approach (including echocardiography, cardiac magnetic resonance, cardiac computed tomography, and cardiac nuclear imaging) is encouraged in the assessment of these patients. The choice of which technique to use should be based on a broad perspective and expert knowledge of what each technique has to offer, including its specific advantages and disadvantages. Experts in different imaging techniques should collaborate and the different methods should be seen as complementary, not as competitors. Each test must be selected in an integrated and rational way in order to provide clear answers to specific clinical questions and problems, trying to avoid redundant and duplicated information, taking into account its availability, benefits, risks, and cost.

Objective This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR). Methods This retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM. Results A total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47–1.00]) and lowest Brier score (0.19, 95% CI [0.13–0.27]), showing competitive diagnostic accuracy and calibration. At the Youden’s index, sensitivity was 0.72 (95% CI [0.68–0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients. Conclusion The current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy. Clinical relevance statement A machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition. Key Points • The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols. • Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies. • Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies. Graphical Abstract

ObjectivesIndications for pulmonary valve replacement (PVR) in patients with pulmonary regurgitation (PR) after repaired tetralogy of Fallot (rToF) are debated. We aimed to compare right (RV) and left ventricular (LV) kinetic energy (KE) measured by 4D-flow magnetic resonance imaging (MRI) in patients to controls, to further understand the pathophysiological effects of PR.MethodsFifteen patients with rToF with PR > 20% and 14 controls underwent MRI. Ventricular volumes and KE were quantified from cine MRI and 4D-flow, respectively. Lagrangian coherent structures were used to discriminate KE in the PR. Restrictive RV physiology was defined as end-diastolic forward flow.ResultsLV systolic peak KE was lower in rToF, 2.8 ± 1.1 mJ, compared to healthy volunteers, 4.8 ± 1.1 mJ, p < 0.0001. RV diastolic peak KE was higher in rToF (7.7 ± 4.3 mJ vs 3.1 ± 1.3 mJ, p = 0.0001) and the difference most pronounced in patients with non-restrictive RV physiology. KE was primarily located in the PR volume at the time of diastolic peak KE, 64 ± 17%.ConclusionThis is the first study showing disturbed KE in patients with rToF and PR, in both the RV and LV. The role of KE as a potential early marker of ventricular dysfunction to guide intervention needs to be addressed in future studies.Key Points• Kinetic energy (KE) reflects ventricular performance• KE is a potential marker of ventricular dysfunction in Fallot patients• KE is disturbed in both ventricles in patients with tetralogy of Fallot• KE contributes to the understanding of the pathophysiology of pulmonary regurgitation• Lagrangian coherent structures enable differentiation of ventricular inflows

ObjectivesWe assessed whether an association exists between myocardial oxygenation and myocardial fibrosis in patients with hypertrophic cardiomyopathy (HCM), using blood-oxygen-level-dependent (BOLD) T2* cardiac magnetic resonance imaging (T2*-CMR) and T1 mapping.MethodsT1 mapping and T2*-CMR data were collected from 55 HCM patients using a 3-T MR and were prospectively analyzed. T2*-CMR was conducted using the black blood, breath-hold, multi-echo, and gradient echo sequence. Over 10 min, inhalation of oxygen at the flow rate of 10 L/min, T2* for mid-septum was measured following room-air and oxygen inhalation, and ΔT2* ratio (T2*oxy-T2*air/T2*air, %) was calculated. During pre- and post-gadolinium enhancement, native T1 (ms) and extracellular volume fractions (ECV, %) were calculated at sites same as the T2* measurement. Hypoxia was defined as the segment with an absolute value of the ΔT2* ratio ≥ 10%.ResultsΔT2* ratio was significantly higher for segments with native T1 ≥ 1290 ms than those with native T1 < 1290 ms (21 ± 32% vs. 8 ± 6%, p = 0.005). ΔT2* ratio was also significantly higher for segments with ECV ≥ 28% than those with ECV < 28% (21 ± 32% vs. 8 ± 8%, p = 0.0003). ROC curve analysis revealed that ΔT2* ratio could detect segments with native T1 ≥ 1290 ms and ECV ≥ 28% and c-statistics of 0.72 and 0.79. According to the multivariate logistic regression analysis results, ECV is an independent factor in hypoxia (odds ratio, 1.47; 95% confidence interval, 1.02–2.13; p < 0.05).ConclusionsAnalysis of BOLD T2*-CMR and T1 mapping revealed that ECV is strongly associated with ΔT2* ratio, suggesting that the onset of myocardial fibrosis is related to hypoxia in HCM patients.Trial registrationOur study was approved by the ethics committee of our institute (#4036, registered on 21 July 2016)Key Points• Analysis of ΔT2* ratio and ECV with BOLD-T2* and T1 mapping revealed a strong association between myocardial fibrosis and hypoxia in HCM patients.