Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging
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Abstract (arXiv)
Purpose: To improve image quality and accelerate the acquisition of 3D MRF. Methods: Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low rank (LLR) constraint and a modified spiral-projection spatiotemporal encoding scheme termed tiny-golden-angle-shuffling (TGAS) were implemented for rapid whole-brain high-resolution quantitative mapping. The LLR regularization parameter and the number of subspace bases were tuned using retrospective in-vivo data and simulated examinations, respectively. B0 inhomogeneity correction using multi-frequency 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 can produce provide high quality 1-mm isotropic whole-brain quantitative maps in a total acquisition time of 1 minute 55 seconds, with higher-quality results than ones obtained from the previous approach in 6 minutes. The comparison of quantitative results indicates that neither the subspace reconstruction nor the TGAS trajectory induce bias for T1 and T2 mapping. High quality whole-brain MRF data were also obtained at 0.66-mm isotropic resolution in 4 minutes using the proposed technique, where the increased resolution was shown to improve visualization of subtle brain structures. Conclusion: The proposed TGAS-SPI-MRF with optimized spiral-projection trajectory and subspace reconstruction can enable high-resolution quantitative mapping with faster acquisition speed.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Aug 2021.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprintingcited by 3
- 2025Physics informed guided diffusion for accelerated multi-parametric MRI reconstructioncited by 2
- 2025Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstructioncited by 0
- 2026MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priorscited by 0
- 2026MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priors11 Mar 2026
- 2025Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction29 Jun 2025
- 2025Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction10 Jun 2025
- 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting29 Oct 2024