# Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging

arXiv: https://arxiv.org/abs/2401.01662 · HTML: https://mlsanity.com/paper/2401.01662/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data](https://mlsanity.com/paper/2012.08931/) (2020)
- [A microstructure estimation Transformer inspired by sparse representation for diffusion MRI](https://mlsanity.com/paper/2205.06450/) (2022)
- [Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI](https://mlsanity.com/paper/1902.00386/) (2019)
- [Estimation of Tissue Microstructure Using a Deep Network Inspired by a Sparse Reconstruction Framework](https://mlsanity.com/paper/1704.01246/) (2017)
- [Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network](https://mlsanity.com/paper/1903.09272/) (2019)
- [Towards learned optimal q-space sampling in diffusion MRI](https://mlsanity.com/paper/2009.03008/) (2020)

## Built on by (most cited first, 1 of 1)

- [Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset](https://mlsanity.com/paper/2412.06666/) (2024)

## Abstract (arXiv)

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the textitin vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation regularization. The experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise.