Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, Ohio, USA.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Magn Reson Med. 2018 Apr;79(4):2392-2400. doi: 10.1002/mrm.26867. Epub 2017 Aug 13.
This work proposes new low rank approximation approaches with significant memory savings for large scale MR fingerprinting (MRF) problems.
We introduce a compressed MRF with randomized singular value decomposition method to significantly reduce the memory requirement for calculating a low rank approximation of large sized MRF dictionaries. We further relax this requirement by exploiting the structures of MRF dictionaries in the randomized singular value decomposition space and fitting them to low-degree polynomials to generate high resolution MRF parameter maps. In vivo 1.5T and 3T brain scan data are used to validate the approaches.
T , T , and off-resonance maps are in good agreement with that of the standard MRF approach. Moreover, the memory savings is up to 1000 times for the MRF-fast imaging with steady-state precession sequence and more than 15 times for the MRF-balanced, steady-state free precession sequence.
The proposed compressed MRF with randomized singular value decomposition and dictionary fitting methods are memory efficient low rank approximation methods, which can benefit the usage of MRF in clinical settings. They also have great potentials in large scale MRF problems, such as problems considering multi-component MRF parameters or high resolution in the parameter space. Magn Reson Med 79:2392-2400, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
本研究提出了新的低秩逼近方法，可显著节省大规模磁共振指纹成像（MRF）问题的内存。
我们引入了一种基于随机奇异值分解的压缩式 MRF，可显著降低计算大型 MRF 字典低秩逼近所需的内存。我们进一步利用随机奇异值分解空间中 MRF 字典的结构，并将其拟合到低阶多项式中，从而生成高分辨率的 MRF 参数图，以放宽这一要求。使用 1.5T 和 3T 体内脑扫描数据来验证这些方法。
T 1 、T 2 和离频图与标准 MRF 方法的结果吻合良好。此外，与 MRF-快速稳态进动序列相比，记忆节省高达 1000 倍，与 MRF-平衡稳态自由进动序列相比，记忆节省超过 15 倍。
所提出的基于随机奇异值分解和字典拟合的压缩式 MRF 是一种高效的低秩逼近方法，可使 MRF 在临床环境中的应用受益。它们在大规模 MRF 问题中也具有很大的潜力，例如考虑多分量 MRF 参数或参数空间中的高分辨率的问题。磁共振医学 79：2392-2400，2018。©2017 国际磁共振医学学会。
