Mesoscale myelin-water fraction and T1/T2/PD mapping through optimized 3D ViSTa-MRF and stochastic reconstruction with preconditioning
Congyu Liao1,2, Xiaozhi Cao1,2, Siddharth Srinivasan Iyer1,3, Zihan Zhou4, Yunsong Liu5, Justin Haldar5, Mahmut Yurt1,2, Ting Gong6, Zhe Wu7, Hongjian He4, Jianhui Zhong4,8, Adam Kerr1,9, and Kawin Setsompop1,2
1Department of Radiology, Stanford University, Stanford, CA, United States, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 4Center for Brain Imaging Science and Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, 5Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, United States, 6Centre for Medical Imaging Computing, Department of Computer Science, University College London, London, United Kingdom, 7Techna Institute, University Health Network, Toronto, ON, Canada, 8Department of Imaging Sciences, University of Rochester, Rochester, NY, United States, 9Stanford Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, United States


In this work, we developed ViSTa-MRF, which combined Visualization of Short Transverse relaxation time component (ViSTa) technique with MR Fingerprinting (MRF), to achieve high-fidelity whole-brain myelin-water fraction (MWF) and T1/T2/PD mapping at sub-millimeter isotropic resolution on a clinical 3T scanner. To achieve fast acquisition and memory-efficient reconstruction, the ViSTa-MRF sequence leverages an optimized 3D tiny-golden-angle-shuffling (TGAS) spiral-projection acquisition and stochastic subspace reconstruction with optimized k-space diagonal preconditioning. With the proposed ViSTa-MRF method, high-fidelity direct MWF mapping was achieved without a need for multi-compartment fitting.


Myelin-Water Fraction (MWF)-mapping has shown great potential in characterizing brain’s myelination processes(1). Conventional MWF-mapping utilizes a multi-echo spin-echo sequence with multi-compartment fitting to extract the shorter relaxation time of myelin-water(2). However, such fitting is ill-conditioned and prone to noise. To improve MWF-mapping, the ViSTa technique(3,4) that employed a specifically configured double-inversion-recovery was proposed to suppress the long T1-component for direct visualization of short-T1 myelin-water components. Our previous work(5) incorporated ViSTa into 3D-MR fingerprinting (ViSTa-MRF) with subspace reconstruction, to improve the SNR of the ViSTa and accelerate MWF-mapping, which enables whole-brain 1mm3 MWF and T1/T2/PD maps in ~10 minutes.

Building on the previous work, we push the ViSTa-MRF to the mesoscale and develop approaches to improve the fidelity of ViSTa-MRF method: (i) A modified spiral-projection spatiotemporal-encoding scheme termed tiny-golden-angle-shuffling(TGAS)(6) was implemented to maximize the sampling-incoherency for higher accelerations. (ii) To mitigate fat artifact, a non-selective water-excitation hard pulse(7) was employed for data acquisition. (iii) To achieve robustness to B1+ inhomogeneity, B1+ variations were simulated into the dictionary and incorporated into the subspace reconstruction, and (iv) Stochastic primal-dual hybrid-gradient algorithm(8) with optimized k-space diagonal-preconditioning was implemented for memory-efficient subspace reconstruction of very large mesoscale whole-brain MRF data. We demonstrated that the proposed method could achieve high-fidelity whole-brain 0.88- and 0.66-mm isotropic resolution in 9.6 and 22.8 minutes on a 3T clinical scanner.


Pulse sequence: Figure1(A) shows the diagram of the ViSTa-MRF sequence, where each acquisition-group consists of multiple ViSTa-blocks and one MRF-block. A water-exciting rectangular(WE-Rect) hard pulse(7) is employed for data acquisition, where the RF duration is set to 2.3ms so that the first zero-crossing of its sinc-shaped frequency response is at the main fat-frequency. In each ViSTa-block, a double-inversion-recovery is performed, with the first subsequent signal time-point labeled the “ViSTa signal”. Through extended-phase-graph(EPG) simulation(9), Fig.1(B) shows that the myelin-water signal is preserved in the ViSTa signal, while the white-matter(WM), gray-matter(GM) and CSF are suppressed, which enables direct myelin-water imaging. Figure1(C) shows the ViSTa-MRF signal-curves with good signal-separability between different tissue-types. To increase the emphasis on the encoding of the short-T1 signal, the sequence repeats the ViSTa-block multiple-times with different spatial-encodings(Fig1.(C)). After the ViSTa-blocks, 500-time-point FISP-MRF data are acquired. Between the acquisition blocks, a BIR-4 90°-saturation-pulse with a waiting time of 380ms is used to achieve steady-state longitudinal-magnetization.

Acquisitions: 3D-spiral-projection imaging(SPI) with TGAS was used for ViSTa-MRF acquisition at 0.88- and 0.66-mm whole-brain on a 3T GE Premier scanner with a 48-channel head-neck coil: FOV:220×220×220mm3, TR/TE=12/1.8ms with a 7.0ms spiral-readout. Twenty-four acquisition-groups with 12 ViSTa-blocks and 72 acquisition-groups with 8 ViSTa-blocks were acquired for 0.88-mm and 0.66-mm cases, respectively, where the spiral-interleaves were designed to rotate around three axes by TGAS(Fig.2(A)). This resulted in scan times of 24s×24=9.6minutes for 0.88mm-iso and 19s×72=22.8minutes for 0.66mm-iso datasets.

Reconstruction: The ViSTa-MRF dictionary with B1+-variations was generated using EPG, and the first eight principal components were selected as the temporal bases Φ (Fig2(A)). The ViSTa-MRF time-series x is expressed as $$$x=Φc$$$, where c are the temporal coefficient-maps. Figure2(B) shows the flowchart of the subspace reconstruction with locally-low-rank constraint, which could be described as:
$$ min_{c}\bf\parallel MFS\phi c\parallel + \lambda_{1}\bf\parallel c\parallel_{*} [1] $$

where S contains coil sensitivities, F is the NUFFT operator and M is the undersampling-pattern. We implemented a novel algorithm in SigPy(10) that combined stochastic primal-dual hybrid-gradient(8) with optimized k-space diagonal-preconditioning(11) to solve Eq.[1], where at each iteration, a random coil/TR/group was chosen for memory-efficient processing. With such reconstruction, the subproblem per coil and group can be resolved on a GPU with 32GB-VRAM while previously the problem did not fit >1TB of RAM.
The reconstructed c is then used to generate the time-series with voxel-by-voxel B1+-correction for estimating T1/T2/PD maps(Fig.2(C)), while the quantitative MWF-map is derived from the reconstructed first time-point ViSTa image and the PD map.


Figure3(A) shows a representative time-resolved MRF-volume after subspace reconstruction using original fermi-pulse and the WE-Rect pulse. Figure3(B) shows the comparison between a fully-sampled standard 2D-ViSTa sequence and ViSTa-MRF with subspace reconstruction, where the results are highly consistent, demonstrating the feasibility in leveraging the joint-spatiotemporal encoding information for highly-accelerated ViSTa-MRF data. Figure3(C) shows T1/T2/MWF maps with and without B1+-correction as well as corresponding B1+-maps. With B1+-correction, the estimated T2 and MWF maps are more uniform compared to the results without B1+ correction.

Figure4(A) and 5(A) shows whole-brain 0.88mm-iso and 0.66mm-iso T1/T2/PD/ViSTa and MWF maps in three orthogonal views, respectively, where the MWF values shown in Fig.4(B) from ViSTa-MRF across four WM-regions are consistent with the literature results(3). In comparison with the 0.88mm-iso results, the higher resolution in the 0.66-mm dataset can aid in better visualization of subtle brain structures such as small sulci and the periventricular space as indicated by the red arrows in Fig5(B).

Discussion and conclusion

In this work, we developed an optimized 3D ViSTa-MRF pulse sequence together with a novel stochastic subspace reconstruction to achieve whole-brain mesoscale MWF and T1/T2/PD mapping in a single scan. The results demonstrate that the proposed method enables high-quality multi-parametric brain mapping at sub-millimeter resolution on a clinical 3T scanner.


This study is supported in part by GE Healthcare research funds and NIH R01EB020613, R01MH116173, R01EB019437, U01EB025162, P41EB030006.


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Fig1. (A) Sequence diagram of 3D ViSTa-MRF. (B) extended phase graph (EPG) simulation of the first time-point ViSTa signal. The myelin-water signal with short-T1 is preserved in the ViSTa signal while the white-matter (WM), gray-matter (GM) and CSF are suppressed, which enables direct myelin-water imaging. (C)Simulated signal curves of myelin-water, WM, GM and CSF for the ViSTa-MRF sequence. FA: flip angle

Fig2. (A) Spiral-projection sampling with TGAS trajectory and temporal components obtained from B1+ incorporated dictionary. (B) The flowchart of model-based stochastic subspace reconstruction. (C) Template matching and B1+ correction process for T1/T2/PD quantification and MWF estimation. The quantitative MWF-map is derived from the reconstructed first time-point ViSTa image I(ViSTa) and the PD map I(PD), where S(myelin-water) is the simulated signal-intensity using nominal T1 and T2 values of myelin-water (T1/T2 =120/20ms).

Fig3. (A) Comparison of a representative MRF volume using non-selective fermi pulse and the WE-Rect pulse. As yellow arrows indicate, the fat artifacts are mitigated using the WE-Rect pulse. (B) The comparison between a fully-sampled standard 2D ViSTa sequence and 2D ViSTa-MRF with subspace reconstruction. (C) reconstructed T1, T2, and MWF without (first row) and with (second row) B1+ correction as well as corresponding B1+ map. With B1+-correction, the estimated T2 and MWF maps in regions indicated by the red arrows are more uniform compared to the results without B1+ correction.

Fig4. (A) Whole-brain 0.88-mm iso T1/T2/PD/ViSTa and MWF maps in three orthogonal views. (B) MWF comparison between the proposed ViSTa-MRF method and the literature values in splenium, forceps Major/minor, and genu corpus callosum regions.

Figure 5. (A) Whole-brain 0.66mm-iso T1/T2/PD/ViSTa and MWF maps in three orthogonal views. (B) T1 and ViSTa comparison between 0.88- and 0.66-mm data. The higher resolution in the 0.66-mm dataset can aid in better visualization of subtle brain structures such as small sulci and the periventricular space as indicated by the red arrows.

Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)