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

Abstract

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.

Systematic review of reconstruction techniques for accelerated quantitative MRI

“…Model‐based dictionaries $$$ \boldsymbol{D}\in {\mathbb{C}}^{n^c\times {n}^d} $$$ consist of $$$ {n}^d $$$ dictionary items, each with the expected signal evolution for the given acquisition settings and specific qMRI parameters 110‐136 . More details about the generation of the dictionaries are presented in Section 4.1.2.1.…”

“…Most of the works use alternate direction method of multipliers with variable splitting for solving subspace constrained reconstruction problems. 112,115,116,118,121,122,126,128,130,[139][140][141][142]144,149,[162][163][164][165][166]169,183,187 Other solvers include the nonlinear conjugate gradient descent with backtracking line search, 120 the orthogonal matching pursuit method, 143,190 the linear conjugate gradient algorithm 123 and the fast iterative shrinkage-thresholding algorithm. 135…”

“…Subspace constrained reconstruction has been used jointly with a spatially sparse representation of 𝝈 using the wavelet transform, 110-112,119,120,163 total variation, 117,119,123,124,129,143,145,155,162,165 finite difference, 164 Fourier representation of images in the temporal direction, 165 low-rank priors, 112,115,126,128,[135][136][137][138][139]141,142,145,[166][167][168][169][170][171][172] and deep learning priors. 173 Alternative ways to exploit the low-rank nature of the signal.…”

Low‐field MRI: A report on the 2022 ISMRM workshop

“…He provided examples from field strengths ranging from 6.5 mT to 0.55T. [36][37][38][39][40] Session 5 described hardware for low-field MRI. Charlotte Sappo provided an overview of coil design for low-field MRI.…”

“…Mark Griswold made an argument for MR fingerprinting at low field, which allows imaging in transient magnetization states, longer data collection times during a period of relaxation and/or diffusion processes, and pattern matching as a nonlinear filter to reduce sensitivity to low SNR. He provided examples from field strengths ranging from 6.5 mT to 0.55T 36–40 …”