mlsanityData to 14 Oct 2026

Multi-Modality Cardiac Image Computing: A Survey

arXiv 2208.12881 ↗First posted 26 Aug 2022eess.IV cs.CVcited by 19

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Abstract (arXiv)

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, textiteither combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 57 of its 323 references that are in the map. First posted on arXiv 26 Aug 2022.

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57 of 323 references.

References · 57

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38 more
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Citing papers per year: 2022: 1; 2023: 3; 2024: 8; 2025: 3; 2026: 4.

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  1. 2024DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistencycited by 53
  2. 2022MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Imagescited by 22
  3. 2023Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inferencecited by 18
  4. 2024Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Surveycited by 14
  5. 2023Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentationcited by 7
  6. 2023Medical Image Segmentation with Domain Adaptation: A Surveycited by 5
  7. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  8. 2024An Uncertainty-Aware Generalization Framework for Cardiovascular Image Segmentationcited by 3
  9. 2025CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MRcited by 3
  10. 2025Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modelingcited by 3
  11. 2026CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRIcited by 3
  12. 2024Personalized Topology-Informed Localization of Standard 12-Lead ECG Electrode Placement from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twinscited by 2
7 more
  1. 2024A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiographycited by 1
  2. 2025DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalitiescited by 1
  3. 2024Multi-view Cardiac Image Segmentation via Trans-Dimensional Priorscited by 0
  4. 2024Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Predictioncited by 0
  5. 2026Learning from Acquisition: Metadata-driven Multimodal Pre-training for Cardiac MRIcited by 0
  6. 2026When Fusion Fails: Corruption-Aware Rebalanced Fusion for Multi-Modal Medical Image Segmentationcited by 0
  7. 2026Anatomy-Aligned Surface Field Learning for Myocardial Reconstruction from Sparse Short-Axis Cine MRIcited by 0