用子空间能量模型加速多对比度MRI，大幅减少扫描时间。
Accelerating Quantitative MRI using Subspace Multiscale Energy Model (SS-MuSE)
- 将能量模型扩展到子空间正则化框架，联合优化多对比度图像。
- 在3D多对比度重建中实现高效计算，避免高内存与大数据依赖。
- 适合临床3D MRI加速，尤其对高分辨率成像有显著优势。
多对比度MRI通过获取不同对比权重的图像，用于组织类型区分或定量映射。然而，三维采集方案获取多对比度所需扫描时间过长。尽管深度学习方法已广泛应用于2D及2D+time问题加速，但其高内存需求、计算耗时和大训练数据要求使其难以应用于大规模体积重建。为此，本文将可插拔的多尺度能量模型（MuSE）推广至正则化子空间恢复框架，联合在子空间中正则化3D多对比度空间因子。显式的能量函数形式使我们能采用可变分裂优化方法，实现高效计算恢复。
原文摘要 · Abstract (English)
Multi-contrast MRI methods acquire multiple images with different contrast weightings, which are used for the differentiation of the tissue types or quantitative mapping. However, the scan time needed to acquire multiple contrasts is prohibitively long for 3D acquisition schemes, which can offer isotropic image resolution. While deep learning-based methods have been extensively used to accelerate 2D and 2D + time problems, the high memory demand, computation time, and need for large training data sets make them challenging for large-scale volumes. To address these challenges, we generalize the plug-and-play multi-scale energy-based model (MuSE) to a regularized subspace recovery setting, where we jointly regularize the 3D multi-contrast spatial factors in a subspace formulation. The explicit energy-based formulation allows us to use variable splitting optimization methods for computationally efficient recovery.
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