K-SVD for HARDI denoising

Vishal Patel; Yonggang Shi; Paul M. Thompson; Arthur W. Toga

doi:10.1109/ISBI.2011.5872757

K-SVD for HARDI denoising

Vishal Patel, Yonggang Shi, Paul M. Thompson, Arthur W. Toga

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm. We analyze its characteristics using both simulated and biological data and compare its performance with existing methods. Our results show that K-SVD provides robust and effective noise reduction and is practical for use in high-volume applications.

Original language	English
Title of host publication	2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI'11
Pages	1805-1808
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2011.5872757
State	Published - 2011
Externally published	Yes
Event	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States Duration: 30 Mar 2011 → 2 Apr 2011

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/Territory	United States
City	Chicago, IL
Period	30/03/11 → 2/04/11

Keywords

Magnetic resonance imaging
algorithms
brain
diffusion tensor imaging
noise reduction

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10.1109/ISBI.2011.5872757

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title = "K-SVD for HARDI denoising",

abstract = "Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm. We analyze its characteristics using both simulated and biological data and compare its performance with existing methods. Our results show that K-SVD provides robust and effective noise reduction and is practical for use in high-volume applications.",

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author = "Vishal Patel and Yonggang Shi and Thompson, {Paul M.} and Toga, {Arthur W.}",

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language = "English",

isbn = "9781424441280",

series = "Proceedings - International Symposium on Biomedical Imaging",

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note = "2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 ; Conference date: 30-03-2011 Through 02-04-2011",

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Patel, V, Shi, Y, Thompson, PM & Toga, AW 2011, K-SVD for HARDI denoising. in 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11., 5872757, Proceedings - International Symposium on Biomedical Imaging, pp. 1805-1808, 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11, Chicago, IL, United States, 30/03/11. https://doi.org/10.1109/ISBI.2011.5872757

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AU - Shi, Yonggang

AU - Thompson, Paul M.

AU - Toga, Arthur W.

PY - 2011

Y1 - 2011

N2 - Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm. We analyze its characteristics using both simulated and biological data and compare its performance with existing methods. Our results show that K-SVD provides robust and effective noise reduction and is practical for use in high-volume applications.

AB - Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm. We analyze its characteristics using both simulated and biological data and compare its performance with existing methods. Our results show that K-SVD provides robust and effective noise reduction and is practical for use in high-volume applications.

KW - Magnetic resonance imaging

KW - algorithms

KW - brain

KW - diffusion tensor imaging

KW - noise reduction

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ER -

K-SVD for HARDI denoising

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Keywords

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