Improved 3D Whole Heart Geometry from Sparse CMR Slices

TL;DR

This paper presents a novel combination of algorithms to correct respiratory motion artifacts in sparse CMR slices and transform them into dense, accurate 3D heart segmentations, validated on synthetic data.

Contribution

It introduces a combined approach using SSA, STN, and LTN for motion correction and segmentation densification in cardiac imaging, demonstrating superior performance on synthetic datasets.

Findings

SSA-LTN achieved 94.0% Dice score and 4.7 mm Hausdorff distance.

STN effectively corrected topological errors with minimal performance impact.

SSA improved performance over other models.

Abstract

Cardiac magnetic resonance (CMR) imaging and computed tomography (CT) are two common non-invasive imaging methods for assessing patients with cardiovascular disease. CMR typically acquires multiple sparse 2D slices, with unavoidable respiratory motion artefacts between slices, whereas CT acquires isotropic dense data but uses ionising radiation. In this study, we explore the combination of Slice Shifting Algorithm (SSA), Spatial Transformer Network (STN), and Label Transformer Network (LTN) to: 1) correct respiratory motion between segmented slices, and 2) transform sparse segmentation data into dense segmentation. All combinations were validated using synthetic motion-corrupted CMR slice segmentation generated from CT in 1699 cases, where the dense CT serves as the ground truth. In 199 testing cases, SSA-LTN achieved the best results for Dice score and Huasdorff distance (94.0% and 4.7 mm respectively, average over 5 labels) but gave topological errors in 8 cases. STN was effective as a plug-in tool for correcting all topological errors with minimal impact on overall performance (93.5% and 5.0 mm respectively). SSA also proves to be a valuable plug-in tool, enhancing performance over both STN-based and LTN-based models. The code for these different combinations is available at https://github.com/XESchong/STACOM2024.