Angular upsampling in diffusion MRI using contextual HemiHex sub-sampling in q-space

TL;DR

This paper introduces a geometrically optimized regression method called HemiHex subsampling for upsampling diffusion MRI data, effectively leveraging q-space geometry to improve high angular resolution imaging with fewer gradient directions.

Contribution

The study proposes a novel HemiHex subsampling technique combined with nearest neighbor regression to enhance dMRI upsampling by incorporating q-space geometry, addressing limitations of previous methods.

Findings

Improved upsampling accuracy over previous regression methods

Effective use of q-space geometry in diffusion MRI

Enhanced preservation of clinical features in high angular resolution data

Abstract

Artificial Intelligence (Deep Learning(DL)/ Machine Learning(ML)) techniques are widely being used to address and overcome all kinds of ill-posed problems in medical imaging which was or in fact is seemingly impossible. Reducing gradient directions but harnessing high angular resolution(HAR) diffusion data in MR that retains clinical features is an important and challenging problem in the field. While the DL/ML approaches are promising, it is important to incorporate relevant context for the data to ensure that maximum prior information is provided for the AI model to infer the posterior. In this paper, we introduce HemiHex (HH) subsampling to suggestively address training data sampling on q-space geometry, followed by a nearest neighbor regression training on the HH-samples to finally upsample the dMRI data. Earlier studies has tried to use regression for up-sampling dMRI data but yields performance issues as it fails to provide structured geometrical measures for inference. Our proposed approach is a geometrically optimized regression technique which infers the unknown q-space thus addressing the limitations in the earlier studies.