Optimized multi‐axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole‐brain high‐isotropic‐resolution quantitative imaging

Abstract

Purpose: To improve image quality and accelerate the acquisition of 3D MR fingerprinting (MRF). Methods: Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low-rank constraint and a modified spiral-projection spatiotemporal encoding scheme called tiny golden-angle shuffling were implemented for rapid whole-brain high-resolution quantitative mapping. Reconstruction parameters such as the locally low-rank regularization parameter and the subspace rank were tuned using retrospective in vivo data and simulated examinations. B 0 inhomogeneity correction using multifrequency interpolation was incorporated into the subspace reconstruction to further improve the image quality by mitigating blurring caused by off-resonance effect. Results: The proposed MRF acquisition and reconstruction framework yields high-quality 1-mm isotropic whole-brain quantitative maps in 2 min at better quality compared with 6-min acquisitions of prior approaches. The proposed method was validated to not induce bias in T 1 and T 2 mapping. High-quality whole-brain MRF data were also obtained at 0.66-mm isotropic resolution in 4 min using the proposed technique, where the increased resolution was shown to improve visualization of subtle brain structures. Conclusions: The proposed tiny golden-angle shuffling, MRF with optimized spiral-projection trajectory and subspace reconstruction enables high-resolution quantitative mapping in ultrafast acquisition time.

3D MR fingerprinting using Seiffert spirals

“…Although they compare well with our proposed trajectory, there are small differences in image quality in which the Seiffert trajectory shows improved performance. However, it should be noted that different reconstruction parameters and k‐space trajectories were used between this study and Cao et al, 19 potentially creating differences in performance. Although speculative, we believe that the Seiffert trajectory should perform with even fewer TRs, as shown by the strong performance in the brain simulations.…”

“…The calculated 2D spiral trajectory is shown in Figure 2A. To allow for comparison with previous methods, a multi‐axis 2D spiral acquisition was designed using the tiny golden‐angle scheme as proposed by Cao et al 8,19 The acquisition used 30 groups, with every 10 groups (a set) having a different rotation axis ( xy , xz , yz ). The rotation angles for the first set were and , where j is the TR number of an imaging segment and i is the imaging segment number.…”

Optimized three‐dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping

“…For further increasing the spatial resolution, we could optimize the sequence by the following potential approaches. (1) Higher gradient amplitude could be used to reach higher kmax at each radial spoke; (2) Reducing the full width at half maximum by optimizing the radial sampling k‐space to achieve higher incoherence at each MRF time point; (3) Increasing radial spokes; however, the scan time will also be increased; therefore, advanced reconstruction methods, such as subspace reconstruction for 3D highly accelerated MRF data (Cao et al, 2022 ; Hu et al, 2021 ; Zhao et al, 2018 ) should be utilized to reduce the scan time; (4) Increasing the sampling efficiency using radial cone trajectory (Johnson, 2017 ) or modified radial trajectories by deep learning (Peng et al, 2022 ), while maintaining the acquisition quality at central k‐space for ultrashort T2* components. However, the 3D whole brain MPF and MWF maps obtained from our proposed method can still provide the valuable data in clinical friendly scanning time, such as possibility of earlier detection of lesions, which might be implemented as an additional routine clinical scanning.…”

“…Magnetic resonance fingerprinting (MRF) is a fast multiparametric mapping technique, which can simultaneously generate multiple tissue parameters in a single scan (Ma et al, 2013 ). In MRF, each signal evolution or fingerprint was matched with precalculated dictionary to identify tissue parameters, including T1, T2, T2*, and proton density (PD) (Boyacioglu et al, 2021 ; Cao et al, 2017 , 2019 , 2022 ; Chen et al, 2019 ; Chen, Panda, et al, 2019 ; Jiang et al, 2015 ; Korzdorfer et al, 2019 ; Liao et al, 2017 , 2018 ). With a multiple component analysis method, it is possible to estimate tissue fraction, such as WM, gray matter (GM), and myelin‐water fraction (MWF) (Chen et al, 2019 ; Cui et al, 2021 ; Liao et al, 2018 ; Nagtegaal et al, 2020 ).…”

Deep Learning-Based Reconstruction for Cardiac MRI: A Review

“…While most DL-based reconstruction methods can outperform conventional reconstruction methods in terms of image quality and computation time, these methods oftentimes come at the expense of increased computational cost, requiring expensive GPUs for both training and inference. This limitation is a clinical bottleneck, especially for 3D non-Cartesian applications [ 124 ], such as 4D-Flow [ 77 ], Dynamic Contrast-Enhanced MRI (DCE) [ 48 ], and MR Fingerprinting [ 125 ]. Possible solutions to reduce the computation cost of these types of reconstructions include coil compression [ 126 , 127 , 128 ], stochastic gradient descent [ 48 , 129 ], and randomized sketching algorithms [ 130 , 131 , 132 , 133 ].…”