Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Chen, Geng; Dong, Bin; Zhang, Yong; Lin, Weili; Shen, Dinggang; Yap, Pew Thian

doi:10.3389/fninf.2018.00057

Cited by 9 publications

(10 citation statements)

References 35 publications

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“…In DMRI, patch-based methods have a wide range of applications, including denoising (Wiest-Daesslé et al, 2007, 2008; Descoteaux et al, 2008; Yap et al, 2014), atlas building (Saghafi et al, 2017; Kim et al, 2017; Yang et al, 2017), fiber orientation estimation (Chen et al, 2016b; Ye et al, 2016), resolution enhancement (Chen et al, 2018), statistical group comparison (Chen et al, 2015), etc. Our x – q space patch matching strategy can be extended for these applications to better leverage the directional nature of DMRI data for performance improvements.…”

Section: Discussionmentioning

confidence: 99%

Noise reduction in diffusion MRI using non-local self-similar information in jointx−qspace

Chen

Shen

et al. 2019

Medical Image Analysis

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Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the in-ability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x – q space. Specifically, we define for each point in the x – q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.

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Section: Discussionmentioning

confidence: 99%

Noise reduction in diffusion MRI using non-local self-similar information in jointx−qspace

Chen

Shen

et al. 2019

Medical Image Analysis

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“…Chen et al [36, 37] proposed to improve NLM by considering the similar information in both spatial domain and diffusion wavevector domain. This idea was further employed to improve atlas building [38] and resolution enhancement [39].…”

Section: Discussionmentioning

confidence: 99%

Multi-channel framelet denoising of diffusion-weighted images

Chen

Zhang

et al. 2019

PLoS ONE

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Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with the signal attenuation is the reduction of signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance in image noise reduction. However, TV denoising can result in stair-casing effects due to the inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of diffusion-weighted (DW) images. Specifically, we employ the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders, which will help avoid stair-casing effects. Instead of denoising each DW image separately, we collaboratively denoise groups of DW images acquired with adjacent gradient directions. In addition, we introduce a very efficient method for solving an ℓ0 denoising problem that involves only thresholding and solving a trivial inverse problem. We demonstrate the effectiveness of our method qualitatively and quantitatively using synthetic and real data.

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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].…”

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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Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Cited by 9 publications

References 35 publications

Noise reduction in diffusion MRI using non-local self-similar information in jointx−qspace

Noise reduction in diffusion MRI using non-local self-similar information in jointx−qspace

Multi-channel framelet denoising of diffusion-weighted images

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

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