High-fidelity, high-isotropic resolution diffusion imaging through gSlider acquisition with B1+ & T1 corrections and integrated DeltaB0/Rx shim array
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Abstract (arXiv)
Purpose: B1+ and T1 corrections and dynamic multi-coil shimming approaches were proposed to improve the fidelity of high isotropic resolution Generalized slice dithered enhanced resolution (gSlider) diffusion imaging. Methods: An extended reconstruction incorporating B1+ inhomogeneity and T1 recovery information was developed to mitigate slab-boundary artifacts in short-TR gSlider acquisitions. Slab-by-slab dynamic B0 shimming using a multi-coil integrated DeltaB0/Rx shim-array, and high in-plane acceleration (Rinplane=4) achieved with virtual-coil GRAPPA were also incorporated into a 1 mm isotropic resolution gSlider acquisition/reconstruction framework to achieve an 8-11 fold reduction in geometric distortion compared to single-shot EPI. Results: The slab-boundary artifacts were alleviated by the proposed B1+ and T1 corrections compared to the standard gSlider reconstruction pipeline for short-TR acquisitions. Dynamic shimming provided >50% reduction in geometric distortion compared to conventional global 2nd order shimming. 1 mm isotropic resolution diffusion data show that the typically problematic temporal and frontal lobes of the brain can be imaged with high geometric fidelity using dynamic shimming. Conclusions: The proposed B1+ and T1 corrections and local-field control substantially improved the fidelity of high isotropic resolution diffusion imaging, with reduced slab-boundary artifacts and geometric distortion compared to conventional gSlider acquisition and reconstruction. This enabled high-fidelity whole-brain 1 mm isotropic diffusion imaging with 64 diffusion-directions in 20 minutes using a 3T clinical scanner.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Nov 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
4 papers.
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- 2021SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI14 Nov 2021
- 2021SRDTI: Deep learning-based super-resolution for diffusion tensor MRI17 Feb 2021