论文标题
快速数据驱动的MRI抽样模式的大规模问题的学习模式
Fast Data-Driven Learning of MRI Sampling Pattern for Large Scale Problems
论文作者
论文摘要
目的：提出了一种快速数据驱动的优化方法，称为“偏置”子集选择（Bass），用于学习有效的采样模式（SP），目的是减少在大维平行MRI中的扫描时间。方法：当笛卡尔完全采样的特定解剖结构的K空间数据可用于训练并指定重建方法时，低音是适用的，在恢复未采样点时，学习哪些K空间点与特定解剖结构和重建更相关。基于低级别和稀疏性，使用四种平行MRI的重建方法对低音进行了测试，从而可以免费选择SP。测试了两个数据集，这是用于高分辨率成像的大脑图像之一，另一个用于软骨定量映射的膝盖图像。结果：低音，其计算成本低和快速收敛，SPS的SPS比当前最佳贪婪方法快100倍。考虑到相同的扫描时间，我们学到的SP超过了可变密度和泊松磁盘SP提供的重建质量，高达45 \％。可选地，扫描时间几乎可以减半，而不会损失重建质量。结论：与当前方法相比，低音可用于使用较大的SP和较大数据集来快速学习各种重建方法的有效SP。这使得更好地选择了针对特定MRI问题的有效采样重建对。
Purpose: A fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of reducing scan time in large-dimensional parallel MRI. Methods: BASS is applicable when Cartesian fully-sampled k-space data of specific anatomy is available for training and the reconstruction method is specified, learning which k-space points are more relevant for the specific anatomy and reconstruction in recovering the non-sampled points. BASS was tested with four reconstruction methods for parallel MRI based on low-rankness and sparsity that allow a free choice of the SP. Two datasets were tested, one of the brain images for high-resolution imaging and another of knee images for quantitative mapping of the cartilage. Results: BASS, with its low computational cost and fast convergence, obtained SPs 100 times faster than the current best greedy approaches. Reconstruction quality increased up to 45\% with our learned SP over that provided by variable density and Poisson disk SPs, considering the same scan time. Optionally, the scan time can be nearly halved without loss of reconstruction quality. Conclusion: Compared with current approaches, BASS can be used to rapidly learn effective SPs for various reconstruction methods, using larger SP and larger datasets. This enables a better selection of efficacious sampling-reconstruction pairs for specific MRI problems.
