Complete motion-analysis workflow for the left atrium (LA) using 3D Cine MRI, combining an online-learning segmentation network with an image-registration network to compute LA displacement vector fields (DVF) and principal strains across the cardiac cycle. Validated on 10 healthy volunteers and 8 cardiovascular disease patients, Aladdin accurately tracks LA wall motion and can identify regional deformation abnormalities that may indicate focal pathology, agreeing well with 2D Cine MRI global function estimates.

Cross-modality cardiac image segmentation model that addresses spatial-temporal confounding -- where the anatomy and imaging-modality elements of cardiac images are intertwined across space and time. DCL performs multi-dimensional causal intervention, modeling causal relationships between images and labels as well as causality along the time and space dimensions, integrating historical optimal interventions to transfer knowledge across temporal contexts. A diffusion mechanism further keeps extracted anatomical elements causally invariant across modalities. On cross-modality cardiac images (MR, CT, and ultrasound), DCL achieved a mean Dice of 0.951, outperforming other advanced segmentation methods.

Disentangled representation learning model for cardiac image analysis that factorises 2D medical images (MRI, CT) into a spatial 'anatomy factor' (a semantically meaningful multi-channel map, produced by a U-Net-style anatomy encoder) and a non-spatial 'modality factor' (a latent vector capturing imaging-specific characteristics). This disentangled representation supports semi-supervised segmentation using only a fraction of labeled images (matching fully supervised performance), multi-task learning (e.g. jointly regressing cardiac indices), multimodal pooling of MRI and CT data, and image-to-image synthesis between modalities via latent-space arithmetic (swapping modality factors). SDNet also demonstrates that its modality factor alone can predict the input imaging modality with high accuracy.

Foundation vision-language model for cardiac MRI that learns pathophysiological visual representations directly from the natural-language radiology reports accompanying each scan, rather than from hand-labeled targets. A Multi-scale Vision Transformer (MViT, Kinetics-400-initialized) video encoder for cine CMR sequences is contrastively pretrained (InfoNCE) against a PubMed-pretrained BERT text encoder over 19,041 multi-institutional CMR studies. The frozen vision encoder transfers with strong performance to left-ventricular ejection-fraction regression (MAE 3.34% on a UK Biobank hold-out of ~4,259-45,623 participants) and detecting HFrEF (LVEF<40%, AUC 0.880), and the paper reports emergent zero-/few-shot performance across 39 cardiac and non-cardiac conditions including cardiac amyloidosis and hypertrophic cardiomyopathy. Code and pretrained MViT encoder weights are both released (Hugging Face, CC BY-NC 4.0).

Generalist reconstruction foundation model for accelerating cardiac MRI (CMR) acquisition, designed to recover diagnostic-quality images from highly undersampled (8x-24x) multi-coil k-space data across heterogeneous scanners, field strengths, and cardiovascular diseases. Combines a CLIP-ViT-based module for semantic/contextual understanding of the anatomy being imaged with a physics-informed data-consistency reconstruction network, trained on MMCMR-427K -- the largest public multimodal CMR k-space database to date. Intended as an upstream substrate that feeds downstream segmentation, phenotyping, and diagnosis models (e.g. automated cardiac-phenotype extraction via nnU-Net) rather than replacing them. Released by the CMRxRecon-challenge consortium; code and the underlying database are public for academic, non-commercial use, but no separately downloadable pretrained checkpoint is provided.

Multimodal cardiac MRI foundation model that fuses 3D+T cine CMR (short-axis and long-axis views) with tabular patient health records (demographics, metabolic, and lifestyle factors) from 42,000 UK Biobank participants. Two-stage self-supervised pretraining -- masked-image reconstruction, then imaging-tabular contrastive alignment -- produces representations that transfer to whole-heart segmentation, cardiac phenotype/physiological-feature regression, and cardiac/metabolic disease classification within one unified framework.

Fully automated deep learning workflow for characterizing cardiac mechanics from balanced steady-state free-precession (bSSFP) cine cardiac MRI. It decouples two convolutional networks—a segmentation net (CarSON) and a 3D motion-estimation net (CarMEN)—to derive left- and right-ventricular volumes plus global and regional myocardial strain and strain rate without manual tracing. Trained and validated on healthy and cardiovascular-disease subjects and shown to be robust across MRI vendors, with excellent intra-scanner repeatability for strain. Developed at Massachusetts General Hospital and the Harvard-MIT Division of Health Sciences and Technology.

Vision-language model that jointly embeds a cardiac MRI study, treated as video, with the impression section of its clinical report. Combines a video encoder over cine/LGE frame sequences with a Bio+ClinicalBERT text encoder using CLIP-style contrastive training. Supports zero-shot and few-shot classification of cardiomyopathies, amyloidosis, and LV dysfunction, plus image/report retrieval and structured report drafting. Trained on a private, single-institution corpus of roughly 11,000-14,000 CMR study-report pairs from Cleveland Clinic and Case Western.

Ensemble of ten self-configuring nnU-Net models (five 2D, five 3D) that segments the left ventricle, right ventricle, and myocardium from short-axis cardiac cine MRI. Won the 2020 M&Ms challenge, a multi-centre, multi-vendor, multi-disease benchmark spanning scanners from four vendors and three countries, demonstrating strong generalization across acquisition protocols. Developed by DKFZ, the group behind the widely used nnU-Net framework.