Cardiac MRI

MRI: Magnetic Resonance Imaging

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7 papers in the last four weeks, up 133% on the four weeks before. 0.1% of all new papers.

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Latest papers 45

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Motion-Guided Causal Disentanglement for Robust Multi-View Cine Cardiac MRI Diagnosis
Jun 3, 2026Chuankai Xu, Cristiane De Carvalho Singulane, Mohammad Abuannadi +10Disentangled Representation Learning Causal Representation Learning
University of Virginia, Charlottesville, VA, USA · University of Chicago, Chicago, IL, USA · Ohio State University, Columbus, OH, USA +5
Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins
Jun 1, 2026Yilin Lyu, Mark YY Chan, Ching-Hui Sia +13D Reconstruction 3D Medical Imaging
Department of Biomedical Engineering, National University of Singapore, Singapore · Department of Medicine, National University of Singapore, Singapore · Department of Cardiology, National University Heart Centre Singapore, Singapore
CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations
May 28, 2026Zixian Su, Hongkai Zhang, Fan Gao +12Magnetic Resonance Imaging Medical VLMs
Beijing Academy of Artificial Intelligence · Beijing Anzhen Hospital · Beihang University +1
Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction
May 25, 2026Himanshu SinghHealthcare Medical Image Analysis
Computing & Artificial Intelligence Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Self-Supervised Contrastive Learning for Cardiac MR Sequence Classification
May 24, 2026Yuli Wang, Hyewon Jung, Dongshen Peng +10Vision Transformer Vision Foundation Model Adaptation
Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA · Department of Electrical and Computer Engineering, Johns Hopkins University,2026 Baltimore, MD, 21218, USA · Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 +4
Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI
May 20, 2026Mahmut Yurt, Kanghyun Ryu, Zhitao Li +8Accelerated MRI Reconstruction Magnetic Resonance Imaging
Stanford University · KIST · GE Healthcare
Rapid online deep artifact suppression for real-time spiral bSSFP CMR with blipped-CAIPI simultaneous multi-slice imaging at 1.5 T
May 18, 2026Julius Åkesson, Iulius Dragonu, Einar Heiberg +6Accelerated MRI Reconstruction Magnetic Resonance Imaging
1: Centre for Translational Cardiovascular Imaging, Institute of Cardiovascular Science, University College London, London, United Kingdom. · 2: Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden. · 3: Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden. +1
CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI
May 13, 2026Xiaoyue Liu, Xiaohan Yuan, Mark Y Chan +23D Mesh Generation 4D Reconstruction
Department of Biomedical Engineering, National University of Singapore, Singapore · School of Automation, Southeast University, Nanjing, China · Department of Medicine, National University of Singapore, Singapore +1
Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI
May 11, 2026Yi Zhang, Yidong Zhao, Tijmen Toxopeus +3Medical Image Registration Magnetic Resonance Imaging
Department of Imaging Physics, Delft University of Technology, The Netherlands
Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs
May 8, 2026Yi Yu, Parker Martin, Zhenyu Bu +5Clinical Information Extraction LLM Information Extraction
†The Ohio State University · ‡NetMind.AI
Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation
May 7, 2026Farhana Yasmin, Mahade Hasan, Haipeng Liu +3Medical Image Analysis Medical Image Segmentation
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China. · 3Eastern University, Ashulia Model Town, Dhaka, 1345, Bangladesh. · School of Software, Nanjing University of Information Science and Technology, Nanjing, China.
Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models
Apr 29, 2026Hao Wen, Jingsu KangImage Segmentation Magnetic Resonance Imaging
College of Science, China Agricultural University, Beijing, China · Tianjin Medical University, Tianjin, China
Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?
Apr 24, 2026Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier +2Domain Generalization Vision Foundation Model Adaptation
1Dublin City University, Ireland · University College Dublin, Ireland
ECGFlowCMR: Pretraining with ECG-Generated Cine CMR Helps Cardiac Disease Classification and Phenotype Prediction
Jan 28, 2026Xiaocheng Fang, Zhengyao Ding, Guangkun Nie +9Synthetic Data Pretraining Medical Diagnosis
State Key Laboratory of General Artificial Intelligence, Peking University · School of Intelligence Science and Technology, Peking University, Beijing, China · Polytechnic Institute of Zhejiang University, Zhejiang University, Hangzhou, China +4
End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
Sep 15, 2025Yihong Chen, Jiancheng Yang, Deniz Sayin Mercadier +34D Reconstruction 3D Reconstruction
School of Computer and Communication Science, EPFL · ELLIS Institute Finland, Aalto University, Finland · University of North Carolina at Charlotte +1

Jun 3, 2026, cs.CVJ/K move · Enter open · S save

Motion-Guided Causal Disentanglement for Robust Multi-View Cine Cardiac MRI Diagnosis

Chuankai Xu, Cristiane De Carvalho Singulane, Mohammad Abuannadi, Stephen Chandler+9

University of Virginia, Charlottesville, VA, USA · University of Chicago, Chicago, IL, USA · Ohio State University, Columbus, OH, USA+5

Disentangled Representation Learning Causal Representation Learning Multi-View Learning Medical Image Classification Magnetic Resonance Imaging Medical Image Segmentation+1

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Multi-view cardiac magnetic resonance (CMR) imaging provides complementary anatomical information and is widely used for noninvasive disease assessment. Recent transformer-based models have demonstrated strong representation learning capabilities for CMR analysis; however, they typically learn unified latent embeddings that entangle view-specific anatomical variations with disease-related features. Such entanglement biases classifiers toward structural attributes rather than view-invariant pathological patterns. This issue is exacerbated in low-data regimes, particularly for underrepresented cardiac conditions, where limited samples increase the susceptibility to shortcut learning and view-dependent decision boundaries. To address this, we propose a Motion-Guided View--Disease Disentanglement framework MoViD built upon a ViT-MAE backbone. The model explicitly factorizes latent representations into view-specific and disease-discriminative components using dual-branch supervised contrastive objectives and a gradient-reversal adversarial constraint that minimizes disease leakage into the view embedding. Additionally, an annotation-free temporal motion feature, derived from inter-frame difference maps, is introduced to localize the beating heart region and suppress background artifacts. A focal reweighting mechanism is incorporated into the contrastive loss to mitigate class imbalance. We evaluate the framework on a private clinical venous thrombosis dataset and two public benchmarks (M&Ms, M&Ms2). Across disease classification and cardiac segmentation tasks, our approach consistently outperforms standard transformer baselines and demonstrates competitive performance against large-scale pretrained foundation models, validating the efficacy of structural disentanglement in medical image analysis.

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