用低秩张量U-Net加速心脏MRI，减少扫描时间与伪影。
Low-Rank Conjugate Gradient-Net for Accelerated Cardiac MR Imaging
- 基于共轭梯度数据一致性求解时空基底，结合U-Net正则化。
- 在多种解剖结构与欠采样条件下实现快速高质量重建。
- 适合需要快速心脏成像的临床场景，尤其对呼吸受限患者友好。
心血管疾病仍是全球死亡和致残的主要原因。高质量影像对疾病诊断和预后至关重要，而心脏磁共振成像（CMR）因采集时间长、需多次屏气，常导致患者不适并产生图像伪影。本文提出一种低秩张量U-Net方法（LowRank-CGNet），可快速重建高度欠采样的数据，适用于多种解剖结构、对比度及欠采样伪影。模型利用共轭梯度数据一致性求解空间与时间基底，并通过U-Net进一步正则化基向量。当前性能优于标准U-Net，但略逊于传统压缩感知方法。未来计划通过增大U-Net规模、延长训练时间及动态调整张量秩来提升性能。
原文摘要 · Abstract (English)
Cardiovascular diseases (CVDs) remain the leading cause of mortality and morbidity worldwide. Both diagnosis and prognosis of these diseases benefit from high-quality imaging, which cardiac magnetic resonance imaging provides. CMR imaging requires lengthy acquisition times and multiple breath-holds for a complete exam, which can lead to patient discomfort and frequently results in image artifacts. In this work, we present a Low-rank tensor U-Net method (LowRank-CGNet) that rapidly reconstructs highly undersampled data with a variety of anatomy, contrast, and undersampling artifacts. The model uses conjugate gradient data consistency to solve for the spatial and temporal bases and employs a U-Net to further regularize the basis vectors. Currently, model performance is superior to a standard U-Net, but inferior to conventional compressed sensing methods. In the future, we aim to further improve model performance by increasing the U-Net size, extending the training duration, and dynamically updating the tensor rank for different anatomies.
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