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Abstract Blunt chest traumas are a clinical challenge, both for diagnosis and treatment. The use of Cardiovascular Magnetic Resonance can play a major role in this setting. We present two cases: a 12-year-old boy and 45-year-old man. Late gadolinium enhancement imaging enabled visualization of myocardial damage resulting from the trauma.

Background: Light chain (AL) and transthyretin (ATTR) amyloid fibrils are deposited in the extracellular space of the myocardium, resulting in heart failure and premature mortality. Extracellular expansion can be quantified by computed tomography, offering a rapid, cheaper, and more practical alternative to cardiac magnetic resonance, especially among patients with cardiac devices or on renal dialysis., Objectives: This study sought to investigate the association of extracellular volume fraction by computed tomography (ECV CT ), myocardial remodeling, and mortality in patients with systemic amyloidosis., Methods: Patients with confirmed systemic amyloidosis and varying degrees of cardiac involvement underwent electrocardiography-gated cardiac computed tomography. Whole heart and septal ECV CT was analyzed. All patients also underwent clinical assessment, electrocardiography, echocardiography, serum amyloid protein component, and/or technetium-99m ( 99m Tc) 3,3-diphosphono-1,2-propanodicarboxylic acid scintigraphy. ECV CT was compared across different extents of cardiac infiltration (ATTR Perugini grade/AL Mayo stage) and evaluated for its association with myocardial remodeling and all-cause mortality., Results: A total of 72 patients were studied (AL: n = 35, ATTR: n = 37; median age: 67 [IQR: 59-76] years, 70.8% male). Mean septal ECV CT was 42.7% ± 13.1% and 55.8% ± 10.9% in AL and ATTR amyloidosis, respectively, and correlated with indexed left ventricular mass (r = 0.426; P < 0.001), left ventricular ejection fraction (r = 0.460; P < 0.001), N-terminal pro-B-type natriuretic peptide (r = 0.563; P < 0.001), and high-sensitivity troponin T (r = 0.546; P < 0.001). ECV CT increased with cardiac amyloid involvement in both AL and ATTR amyloid. Over a mean follow-up of 5.3 ± 2.4 years, 40 deaths occurred (AL: n = 14 [35.0%]; ATTR: n = 26 [65.0%]). Septal ECV CT was independently associated with all-cause mortality in ATTR (not AL) amyloid after adjustment for age and septal wall thickness (HR: 1.046; 95% CI: 1.003-1.090; P = 0.037)., Conclusions: Cardiac amyloid burden quantified by ECV CT is associated with adverse cardiac remodeling as well as all-cause mortality among ATTR amyloid patients. ECV CT may address the need for better identification and risk stratification of amyloid patients, using a widely accessible imaging modality., Competing Interests: Funding Support and Author Disclosures Drs Moon and Treibel are directly and indirectly supported by the University College London Hospitals National Institute for Health Research Biomedical Research Centre and Biomedical Research Unit at Barts Hospital, respectively. This work was undertaken at University College London Hospital, which received a proportion of funding from the UK Department of Health National Institute for Health Research Biomedical Research Centre’s funding scheme. Dr Patel is funded by the British Heart Foundation Clinical Research Training Fellowship. Dr Gama is supported by a nonrestricted educational grant by Pfizer. Drs Treibel and Fontana are funded by British Heart Foundation intermediate fellowships (FS/19/35/34374 and FS/18/21/33447). Dr Thornton is supported by a British Heart Foundation Clinical Research Training Fellowship (FS/CRTF/21/24128). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training., (© 2022. The Author(s).)

Aims: Left atrial (LA) remodelling is a common feature of many cardiovascular pathologies and is a sensitive marker of adverse cardiovascular outcomes. The aim of this study was to establish normal ranges for LA parameters derived from coronary computed tomographic angiography (CCTA) imaging using a standardised image processing pipeline to establish normal ranges in a previously described cohort., Methods: CCTA imaging from 193 subjects recruited to the Budapest GLOBAL twin study was analysed. Indexed LA cavity volume (LACV i ), LA surface area (LASA i ), wall thickness and LA tissue volume (LATV i ) were calculated. Wall thickness maps were combined into an atlas. Indexed LA parameters were compared with clinical variables to identify early markers of pathological remodelling., Results: LACV i is similar between sexes (31 ml/m 2 v 30 ml/m 2 ) and increased in hypertension (33 ml/m 2 v 29 ml/m 2 , p = 0.009). LASA i is greater in females than males (47.8 ml/m 2 v 45.8 ml/m 2 male, p = 0.031). Median LAWT was 1.45 mm. LAWT was lowest at the inferior portion of the posterior LA wall (1.14 mm) and greatest in the septum (median = 2.0 mm) (p < 0.001). Conditions known to predispose to the development of AF were not associated with differences in tissue thickness., Conclusions: The reported LACV i , LASA i , LATV i and tissue thickness derived from CCTA may serve as reference values for this age group and clinical characteristics for future studies. Increased LASA i in females in the absence of differences in LACV i or LATV i may indicate differential LA shape changes between the sexes. AF predisposing conditions, other than sex, were not associated with detectable changes in LAWT. Clinical trial registration: http://www.ClinicalTrials.gov/NCT01738828., Competing Interests: The authors report no relationships that could be construed as a conflict of interest., (© 2020 Published by Elsevier B.V.)

Many of the structures and parameters that are detected, measured and reported in cardiovascular magnetic resonance (CMR) have at least some properties that are fractal, meaning complex and self-similar at different scales. To date however, there has been little use of fractal geometry in CMR; by comparison, many more applications of fractal analysis have been published in MR imaging of the brain.This review explains the fundamental principles of fractal geometry, places the fractal dimension into a meaningful context within the realms of Euclidean and topological space, and defines its role in digital image processing. It summarises the basic mathematics, highlights strengths and potential limitations of its application to biomedical imaging, shows key current examples and suggests a simple route for its successful clinical implementation by the CMR community.By simplifying some of the more abstract concepts of deterministic fractals, this review invites CMR scientists (clinicians, technologists, physicists) to experiment with fractal analysis as a means of developing the next generation of intelligent quantitative cardiac imaging tools.

Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM'13 workshop, in conjunction with MICCAI'13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.

In this paper, we present a novel technique based on nonrigid image registration for myocardial motion estimation using both untagged and 3-D tagged MR images. The novel aspect of our technique is its simultaneous usage of complementary information from both untagged and 3-D tagged MR images. To estimate the motion within the myocardium, we register a sequence of tagged and untagged MR images during the cardiac cycle to a set of reference tagged and untagged MR images at end-diastole. The similarity measure is spatially weighted to maximize the utility of information from both images. In addition, the proposed approach integrates a valve plane tracker and adaptive incompressibility into the framework. We have evaluated the proposed approach on 12 subjects. Our results show a clear improvement in terms of accuracy compared to approaches that use either 3-D tagged or untagged MR image information alone. The relative error compared to manually tracked landmarks is less than 15% throughout the cardiac cycle. Finally, we demonstrate the automatic analysis of cardiac function from the myocardial deformation fields.

Introduction and Objectives: In hypertrophic cardiomyopathy (HCM), it has been suggested that regional fiber disarray produces segments that exhibit no or severely reduced deformation, and that these segments are distributed nonuniformly within the left ventricle (LV). This contrasts with observations in other types of hypertrophy, such as in athlete's heart or hypertensive left ventricular hypertrophy (HLVH), in which abnormal cardiac deformation may exist but the reduction is not so severe that some segments exhibit no deformation. Our aim was to use the strain distribution to study deformation in HCM., Methods: We used tagged magnetic resonance imaging to reconstruct LV systolic deformation in 12 controls, 10 athletes, 12 patients with HCM, and 10 patients with HLVH. Deformation was quantified using a fast nonrigid registration algorithm and peak radial and circumferential systolic strain values were determined in 16 LV segments., Results: Patients with HCM had significantly lower average strain values than individuals in other groups. However, while the deformation observed in healthy subjects and HLVH patients clustered around the mean, in HCM patients, segments with normal contraction coexisted with segments exhibiting no or significantly reduced deformation, which resulted in a greater heterogeneity of strain values. Moreover, some nondeforming segments were observed even when fibrosis and hypertrophy were absent., Conclusions: The strain distribution characterized specific patterns of myocardial deformation in patients with LVH due to different etiologies. Patients with HCM had significantly lower mean strain values and a greater heterogeneity in strain values than controls, athletes and HLVH patients. In addition, they had nondeforming regions.

Blunt chest traumas are a clinical challenge, both for diagnosis and treatment. The use of cardiovascular magnetic resonance can play a major role in this setting. We present two cases: a 12-year-old boy and 45-year-old man. Late gadolinium enhancement imaging enabled visualization of myocardial damage resulting from the trauma.

Active shape models bear a great promise for model-based medical image analysis. Their practical use, though, is undermined due to the need to train such models on large image databases. Automatic building of point distribution models (PDMs) has been successfully addressed and a number of autolandmarking techniques are currently available. However, the need for strategies to automatically build intensity models around each landmark has been largely overlooked in the literature. This work demonstrates the potential of creating intensity models automatically by simulating image generation. We show that it is possible to reuse a 3D PDM built from computed tomography (CT) to segment gated single photon emission computed tomography (gSPECT) studies. Training is performed on a realistic virtual population where image acquisition and formation have been modeled using the SIMIND Monte Carlo simulator and ASPIRE image reconstruction software, respectively. The dataset comprised 208 digital phantoms (4D-NCAT) and 20 clinical studies. The evaluation is accomplished by comparing point-to-surface and volume errors against a proper gold standard. Results show that gSPECT studies can be successfully segmented by models trained under this scheme with subvoxel accuracy. The accuracy in estimated LV function parameters, such as end diastolic volume, end systolic volume, and ejection fraction, ranged from 90.0% to 94.5% for the virtual population and from 87.0% to 89.5% for the clinical population.