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.

翻译：心脏磁共振成像与计算机断层扫描是评估心血管疾病患者的两种常用无创成像方法。心脏磁共振通常采集多个稀疏二维切片，切片间存在不可避免的呼吸运动伪影；而计算机断层扫描虽能获取各向同性的密集数据，但需使用电离辐射。本研究探索了切片偏移算法、空间变换网络与标签变换网络的组合方法，旨在实现两个目标：1）校正分割切片间的呼吸运动；2）将稀疏分割数据转换为密集分割。所有组合方案均通过1699例基于计算机断层扫描生成的合成运动伪影心脏磁共振切片分割数据进行验证，其中密集计算机断层扫描数据作为金标准。在199例测试数据中，切片偏移算法-标签变换网络组合在Dice系数与豪斯多夫距离指标上取得最佳结果（五类标签平均值分别为94.0%与4.7毫米），但在8例中出现拓扑错误。空间变换网络作为插件工具能有效修正所有拓扑错误，且对整体性能影响极小（指标分别为93.5%与5.0毫米）。切片偏移算法同样被证明是有效的插件工具，可提升基于空间变换网络和基于标签变换网络模型的性能。相关组合方案的代码已发布于https://github.com/XESchong/STACOM2024。