CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI

2026-05-15 · Source: cs.CV updates on arXiv.org · Field: Science & Research — Health & Medical Research, Mathematics & Computational Sciences, Engineering & Applied Sciences · Depth: Expert, long

Summary

CineMesh4D is a new end-to-end 4D (3D+t) pipeline designed for patient-specific whole-heart mesh reconstruction directly from multi-view 2D cine MRI. This method addresses the challenge of sparse 3D cardiac anatomy sampling and the tight coupling of cardiac shape and motion. Unlike previous approaches that often reconstruct only subsets of chambers or single cardiac phases, CineMesh4D reconstructs the entire heart across the full cardiac cycle. It introduces a differentiable rendering loss, inspired by the Beer–Lambert law, for 3D+t whole-heart mesh supervision from sparse 2D cine MRI contours. Additionally, a dual-context temporal block fuses global and local cardiac temporal information to capture high-dimensional sequential patterns. Quantitative and qualitative evaluations show CineMesh4D surpasses existing methods in reconstruction quality and motion consistency, offering a practical solution for personalized real-time cardiac assessment.

Key takeaway

For AI Scientists and Machine Learning Engineers developing cardiac imaging solutions, CineMesh4D offers a robust framework for generating personalized 4D whole-heart models from standard cine MRI. You should consider integrating differentiable rendering losses and dual-context temporal blocks into your pipelines to improve reconstruction accuracy and temporal consistency, especially when working with sparse 2D input data for complex 3D+t anatomical structures.

Key insights

CineMesh4D reconstructs dynamic 3D+t whole-heart meshes from sparse 2D cine MRI using differentiable rendering and dual-context temporal modeling.

Principles

• Sparse 2D views can supervise 3D+t mesh reconstruction.
• Dual-context temporal modeling improves motion consistency.
• End-to-end image-to-mesh mapping is robust.

Method

CineMesh4D uses a U-Net for feature extraction, a MeshVAE for anatomical variability, a dual-context temporal block for motion, and a Beer–Lambert inspired differentiable rendering loss for contour-guided mesh optimization.

In practice

• Use differentiable rendering for 2D-to-3D supervision.
• Integrate global and local temporal contexts for dynamic modeling.
• Pretrain modality-specific encoders/decoders for efficiency.

Topics

• CineMesh4D
• 4D Whole-Heart Reconstruction
• Sparse Cine MRI
• Differentiable Rendering
• Dual-Context Temporal Block

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.