用低秩流形模型实现自由呼吸心脏MRI的无监督运动校正
Motion-compensated cardiac MRI using low-rank diffeomorphic flow (DMoCo)
- 通过低秩参数化速度场，联合建模心脏不同心动周期的形变
- 在真实数据上比现有方法更优，重建质量显著提升
- 适合做心脏动态成像的科研与临床人员参考
我们提出一种无监督运动补偿图像重建算法，用于自由呼吸、非门控的3D心脏磁共振成像。将每个运动相位对应的图像体积表示为单一静态图像模板的形变。核心贡献在于提出一种低秩模型，用于紧凑地联合表示一组微分同胚（diffeomorphisms），这些形变由连接参考模板相位到各运动相位的路径上的参数化速度场积分得到。不同相位的速度场采用低秩形式表达，静态模板与低秩运动模型参数直接从k空间数据中无监督学习获得。相比当前的运动分辨与运动补偿算法，该更受约束的运动模型在自由呼吸3D电影心肌MRI中展现出更优的恢复性能。
原文摘要 · Abstract (English)
We introduce an unsupervised motion-compensated image reconstruction algorithm for free-breathing and ungated 3D cardiac magnetic resonance imaging (MRI). We express the image volume corresponding to each specific motion phase as the deformation of a single static image template. The main contribution of the work is the low-rank model for the compact joint representation of the family of diffeomorphisms, parameterized by the motion phases. The diffeomorphism at a specific motion phase is obtained by integrating a parametric velocity field along a path connecting the reference template phase to the motion phase. The velocity field at different phases is represented using a low-rank model. The static template and the low-rank motion model parameters are learned directly from the k-space data in an unsupervised fashion. The more constrained motion model is observed to offer improved recovery compared to current motion-resolved and motion-compensated algorithms for free-breathing 3D cine MRI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。