HeartFormer: Semantic-Aware Dual-Structure Transformers for 3D Four-Chamber Cardiac Point Cloud Reconstruction

TL;DR

HeartFormer introduces a novel dual-structure transformer framework for detailed 3D cardiac point cloud reconstruction from cine MRI data, addressing the limitations of traditional 2D imaging.

Contribution

This work presents the first geometric deep learning framework for multi-class 3D cardiac reconstruction from MRI, including a new dataset and state-of-the-art performance.

Findings

Outperforms existing methods in accuracy and robustness

Provides high-fidelity 3D cardiac reconstructions

Establishes a new benchmark with HeartCompv1 dataset

Abstract

We present the first geometric deep learning framework based on point cloud representation for 3D four-chamber cardiac reconstruction from cine MRI data. This work addresses a long-standing limitation in conventional cine MRI, which typically provides only 2D slice images of the heart, thereby restricting a comprehensive understanding of cardiac morphology and physiological mechanisms in both healthy and pathological conditions. To overcome this, we propose \textbf{HeartFormer}, a novel point cloud completion network that extends traditional single-class point cloud completion to the multi-class. HeartFormer consists of two key components: a Semantic-Aware Dual-Structure Transformer Network (SA-DSTNet) and a Semantic-Aware Geometry Feature Refinement Transformer Network (SA-GFRTNet). SA-DSTNet generates an initial coarse point cloud with both global geometry features and substructure geometry features. Guided by these semantic-geometry representations, SA-GFRTNet progressively refines the coarse output, effectively leveraging both global and substructure geometric priors to produce high-fidelity and geometrically consistent reconstructions. We further construct \textbf{HeartCompv1}, the first publicly available large-scale dataset with 17,000 high-resolution 3D multi-class cardiac meshes and point-clouds, to establish a general benchmark for this emerging research direction. Extensive cross-domain experiments on HeartCompv1 and UK Biobank demonstrate that HeartFormer achieves robust, accurate, and generalizable performance, consistently surpassing state-of-the-art (SOTA) methods. Code and dataset will be released upon acceptance at: https://github.com/10Darren/HeartFormer.