The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.

In patients with bicuspid valve (BAV), ascending aorta (AAo) dilatation may be caused by altered flow patterns and wall shear stress (WSS). These differences may explain different aortic dilatation morphotypes. Using 4D-flow cardiovascular magnetic resonance (CMR), we aimed to analyze differences in flow patterns and regional axial and circumferential WSS maps between BAV phenotypes and their correlation with ascending aorta dilatation morphotype. One hundred and one BAV patients (aortic diameter ≤ 45 mm, no severe valvular disease) and 20 healthy subjects were studied by 4D-flow CMR. Peak velocity, flow jet angle, flow displacement, in-plane rotational flow (IRF) and systolic flow reversal ratio (SFRR) were assessed at different levels of the AAo. Peak-systolic axial and circumferential regional WSS maps were also estimated. Unadjusted and multivariable adjusted linear regression analyses were used to identify independent correlates of aortic root or ascending dilatation. Age, sex, valve morphotype, body surface area, flow derived variables and WSS components were included in the multivariable models. The AAo was non-dilated in 24 BAV patients and dilated in 77 (root morphotype in 11 and ascending in 66). BAV phenotype was right-left (RL-) in 78 patients and right-non-coronary (RN-) in 23. Both BAV phenotypes presented different outflow jet direction and velocity profiles that matched the location of maximum systolic axial WSS. RL-BAV velocity profiles and maximum axial WSS were homogeneously distributed right-anteriorly, however, RN-BAV showed higher variable profiles with a main proximal-posterior distribution shifting anteriorly at mid-distal AAo. Compared to controls, BAV patients presented similar WSS magnitude at proximal, mid and distal AAo (p = 0.764, 0.516 and 0.053, respectively) but lower axial and higher circumferential WSS components (p < 0.001 for both, at all aortic levels). Among BAV patients, RN-BAV presented higher IRF at all levels (p = 0.024 proximal, 0.046 mid and 0.002 distal AAo) and higher circumferential WSS at mid and distal AAo (p = 0.038 and 0.046, respectively) than RL-BAV. However, axial WSS was higher in RL-BAV compared to RN-BAV at proximal and mid AAo (p = 0.046, 0.019, respectively). Displacement and axial WSS were independently associated with the root-morphotype, and circumferential WSS and SFRR with the ascending-morphotype. Different BAV-phenotypes present different flow patterns with an anterior distribution in RL-BAV, whereas, RN-BAV patients present a predominant posterior outflow jet at the sinotubular junction that shifts to anterior or right anterior in mid and distal AAo. Thus, RL-BAV patients present a higher axial WSS at the aortic root while RN-BAV present a higher circumferential WSS in mid and distal AAo. These results may explain different AAo dilatation morphotypes in the BAV population.

BACKGROUND: Arterial stiffness, as measured by arterial pulse wave velocity (PWV), is an established biomarker for cardiovascular risk and target-organ damage in individuals with hypertension. With the emergence of new devices for assessing PWV, it has become evident that some of these devices yield results that display significant discrepancies compared with previous devices. This discrepancy underscores the importance of comprehensive validation procedures and the need for international recommendations. METHODS: A stepwise approach utilizing the modified Delphi technique, with the involvement of key scientific societies dedicated to arterial stiffness research worldwide, was adopted to formulate, through a multidisciplinary vision, a shared approach to the validation of noninvasive arterial PWV measurement devices. RESULTS: A set of recommendations has been developed, which aim to provide guidance to clinicians, researchers, and device manufacturers regarding the validation of new PWV measurement devices. The intention behind these recommendations is to ensure that the validation process can be conducted in a rigorous and consistent manner and to promote standardization and harmonization among PWV devices, thereby facilitating their widespread adoption in clinical practice. CONCLUSIONS: It is hoped that these recommendations will encourage both users and developers of PWV measurement devices to critically evaluate and validate their technologies, ultimately leading to improved consistency and comparability of results. This, in turn, will enhance the clinical utility of PWV as a valuable tool for assessing arterial stiffness and informing cardiovascular risk stratification and management in individuals with hypertension.

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12 ^th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.

Arterial pulse waves (PWs) such as blood pressure and photoplethysmogram (PPG) signals contain a wealth of information on the cardiovascular (CV) system that can be exploited to assess vascular age and identify individuals at elevated CV risk. We review the possibilities, limitations, complementarity, and differences of reduced-order, biophysical models of arterial PW propagation, as well as theoretical and empirical methods for analyzing PW signals and extracting clinically relevant information for vascular age assessment. We provide detailed mathematical derivations of these models and theoretical methods, showing how they are related to each other. Finally, we outline directions for future research to realize the potential of modeling and analysis of PW signals for accurate assessment of vascular age in both the clinic and in daily life.