# High-fidelity, accelerated whole-brain submillimeter in-vivo diffusion MRI using gSlider-Spherical Ridgelets (gSlider-SR)

arXiv: https://arxiv.org/abs/1909.07925 · HTML: https://mlsanity.com/paper/1909.07925/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions](https://mlsanity.com/paper/2207.08975/) (2022)
- [A study of why we need to reassess full reference image quality assessment with medical images](https://mlsanity.com/paper/2405.19097/) (2024)
- [When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation](https://mlsanity.com/paper/2408.12897/) (2024)
- [Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning](https://mlsanity.com/paper/2407.15132/) (2024)
- [FiberGeoText: A Vision-Language Model for Population- Level Organization of Superficial White Matter](https://mlsanity.com/paper/2610.02755/) (2026)

## Abstract (arXiv)

Purpose: To develop an accelerated, robust, and accurate diffusion MRI acquisition and reconstruction technique for submillimeter whole human brain in-vivo scan on a clinical scanner. Methods: We extend the ultra-high resolution diffusion MRI acquisition technique, gSlider, by allowing under-sampling in q-space and Radio-Frequency (RF)-encoded data, thereby accelerating the total acquisition time of conventional gSlider. The novel method, termed gSlider-SR, compensates for the lack of acquired information by exploiting redundancy in the dMRI data using a basis of Spherical Ridgelets (SR), while simultaneously enhancing the signal-to-noise ratio. Using Monte-Carlo simulation with realistic noise levels and several acquisitions of in-vivo human brain dMRI data (acquired on a Siemens Prisma 3T scanner), we demonstrate the efficacy of our method using several quantitative metrics. Results: For high-resolution dMRI data with realistic noise levels (synthetically added), we show that gSlider-SR can reconstruct high-quality dMRI data at different acceleration factors preserving both signal and angular information. With in-vivo data, we demonstrate that gSlider-SR can accurately reconstruct 860 μm diffusion MRI data (64 diffusion directions at b = 2000 s/ mm2), at comparable quality as that obtained with conventional gSlider with four averages, thereby providing an eight-fold reduction in scan time (from 1 h 20 min to 10 min). Conclusion: gSlider-SR enables whole-brain high angular resolution dMRI at a submillimeter spatial resolution with a dramatically reduced acquisition time, making it feasible to use the proposed scheme on existing clinical scanners.