SMILR - Subspace MachIne Learning Reconstruction

作者：Siddharth Iyer, Christopher M. Sandino, Mahmut Yurt, Xiaozhi Cao, Congyu Liao, Sophie Schauman, Kawin Setsompop · 发表于：Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 年份：2023 · DOI：10.58530/2022/3466 · 被引用次数：2 · 研究领域：Advanced MRI Techniques and Applications、Advanced Neuroimaging Techniques and Applications、Medical Imaging Techniques and Applications

Recent developments in spatiotemporal MRI techniques enable whole-brain multi-parametric mapping in incredibly short acquisition times through highly-efficient k-space encoding, subspace reconstruction and carefully-designed regularization. However, this comes at the cost of long reconstruction times making such methods difficult to integrate into clinical practice. This abstract proposes a framework denoted SMILR (pronounced smile-r) to reduce the reconstruction times of subspace methods from multiple hours to a few minutes through machine learning. To evaluate performance, the framework is applied to multi-axis spiral projection MRF (denoted SPI-MRF) where it achieves improved reconstruction over conventional subspace reconstruction with locally low-rank at ~16-20x faster speed.