Quantitative evaluation of fiber tractography with a Delaunay triangulation–based interpolation approach

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

doi:10.1007/s11517-018-1932-y

Cited by 3 publications

(1 citation statement)

References 44 publications

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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%

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

Faiyaz¹,

Uddin²,

Schifitto³

2022

Preprint

View full text Add to dashboard Cite

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Comparison analysis of local angular interpolation methods in diffusion MRI

Alaya

Jribi

Ghorbel

et al. 2022

Computer Methods in Biomechanics and Biomedical Engineering: Im

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A Technical Note on the Application of Interpolation Method on Modeling of 3D Surface Rock Layer

Yin¹,

Zhou

2020

Advances in Civil Engineering

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The planning stages of dredging require comprehensive and detailed analyses. Identifying the dredging environment is one of the important points. A three-dimensional (3D) geological modeling technology has been shown to be a robust tool for representing and analyzing the conditions of geology. From a general perspective, a 3D model is established by some spatial surfaces. Based on a dredging project, this study investigated the estimation capability of an interpolation method of triangulation combined with BP neural network, for modeling a rock layer surface. The performance of the proposed model is compared with some conventional methods in the literature. The results showed that this interpolation method is effective to be employed for surface modeling of the rock layer.

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Quantitative evaluation of fiber tractography with a Delaunay triangulation–based interpolation approach

Cited by 3 publications

References 44 publications

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

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

Comparison analysis of local angular interpolation methods in diffusion MRI

A Technical Note on the Application of Interpolation Method on Modeling of 3D Surface Rock Layer

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