谷歌浏览器插件
订阅小程序
在清言上使用

Low-rank inversion reconstruction for through-plane accelerated radial MR fingerprinting applied to relaxometry at 0.35 T

MAGNETIC RESONANCE IN MEDICINE(2022)

引用 1|浏览5
暂无评分
摘要
Purpose To reduce scan time, methods to accelerate phase-encoded/non-Cartesian MR fingerprinting (MRF) acquisitions for variable density spiral acquisitions have recently been developed. These methods are not applicable to MRF acquisitions, wherein a single k-space spoke is acquired per frame. Therefore, we propose a low-rank inversion method to resolve MRF contrast dynamics from through-plane accelerated Cartesian/radial measurements applied to quantitative relaxation-time mapping on a 0.35T system. Methods An algorithm was implemented to reconstruct through-plane aliased low-rank images describing the contrast dynamics occurring because of the transient-state MRF acquisition. T-1 and T-2 times from accelerated acquisitions were compared with those from unaccelerated linear reconstructions in a standardized system phantom and within in vivo brain and prostate experiments on a hybrid 0.35T MRI/linear accelerator. Results No significant differences between T-1 and T-2 times for the accelerated reconstructions were observed compared to fully sampled acquisitions (p = 0.41 and p = 0.36, respectively). The mean absolute errors in T-1 and T-2 were 5.6% and 2.9%, respectively, between the full and accelerated acquisitions. The SDs in T-1 and T-2 decreased with the advanced accelerated reconstruction compared with the unaccelerated reconstruction (p = 0.02 and p = 0.03, respectively). The quality of the T-1 and T-2 maps generated with the proposed approach are comparable to those obtained using the unaccelerated data sets. Conclusions Through-plane accelerated MRF with radial k-space coverage was demonstrated at a low field strength of 0.35 T. This method enabled 3D T-1 and T-2 mapping at 0.35 T with a 3-min scan.
更多
查看译文
关键词
CAIPIRINHA,low-field,MR fingerprinting,non-Cartesian,parallel imaging,quantitative imaging
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要