Comparison analysis of local angular interpolation methods in diffusion MRI

Alaya, Inès; Jribi, Majdi; Ghorbel, Faouzi; Kraiem, Tarek

doi:10.1080/21681163.2021.2024089

Cited by 1 publication

(1 citation statement)

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“…Harnessing high angular resolution(HAR) with reduced number of gradients in diffusion data in MR that retains clinical features is an important and challenging problem in the field [1]. Interpolation techniques to estimate DWI signals for Fiber oreientation density(fod) estimation and tractography with dMRI has shown promising result in simulation and invivo [2]- [4]. We hypothesized that if the upsampling procedure can be guided through a Delanuy Triangulation sampling scheme, the nearest neighbor geometry could potentially be generalized in terms of diffusion signals.…”

Section: Introductionmentioning

confidence: 99%

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

Faiyaz¹,

Uddin²,

Schifitto³

2022

Preprint

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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 upsampling 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.

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Section: Introductionmentioning

confidence: 99%