Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation
White matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a diffusion maps clustering method applied to di...
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Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16874188_v2008_n1_p_Wassermann http://hdl.handle.net/20.500.12110/paper_16874188_v2008_n1_p_Wassermann |
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paper:paper_16874188_v2008_n1_p_Wassermann2023-06-08T16:26:40Z Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation Wassermann, Demián Diffusion Image segmentation Magnetic resonance imaging Neurology Diffusion maps Q-ball imaging segmentation White matter fiber clustering Medical imaging White matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a diffusion maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles. It is well known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent high-angular resolution diffusion imaging (HARDI) such as Q-Ball imaging (QBI) has been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations. In this paper,we use a spherical harmonic ODF representation as input to the diffusion maps clustering method. We first show the advantage of using diffusion maps clustering over classical methods such as N-Cuts and Laplacian eigenmaps. In particular, our ODF diffusion maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation, and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptive scale-space parameter. We also show that our ODF diffusion maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters. Finally, we show results on a real-brain dataset where we segment well-known fiber bundles. Fil:Wassermann, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2008 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16874188_v2008_n1_p_Wassermann http://hdl.handle.net/20.500.12110/paper_16874188_v2008_n1_p_Wassermann |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Diffusion Image segmentation Magnetic resonance imaging Neurology Diffusion maps Q-ball imaging segmentation White matter fiber clustering Medical imaging |
spellingShingle |
Diffusion Image segmentation Magnetic resonance imaging Neurology Diffusion maps Q-ball imaging segmentation White matter fiber clustering Medical imaging Wassermann, Demián Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation |
topic_facet |
Diffusion Image segmentation Magnetic resonance imaging Neurology Diffusion maps Q-ball imaging segmentation White matter fiber clustering Medical imaging |
description |
White matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a diffusion maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles. It is well known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent high-angular resolution diffusion imaging (HARDI) such as Q-Ball imaging (QBI) has been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations. In this paper,we use a spherical harmonic ODF representation as input to the diffusion maps clustering method. We first show the advantage of using diffusion maps clustering over classical methods such as N-Cuts and Laplacian eigenmaps. In particular, our ODF diffusion maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation, and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptive scale-space parameter. We also show that our ODF diffusion maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters. Finally, we show results on a real-brain dataset where we segment well-known fiber bundles. |
author |
Wassermann, Demián |
author_facet |
Wassermann, Demián |
author_sort |
Wassermann, Demián |
title |
Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation |
title_short |
Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation |
title_full |
Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation |
title_fullStr |
Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation |
title_full_unstemmed |
Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation |
title_sort |
diffusion maps clustering for magnetic resonance q-ball imaging segmentation |
publishDate |
2008 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16874188_v2008_n1_p_Wassermann http://hdl.handle.net/20.500.12110/paper_16874188_v2008_n1_p_Wassermann |
work_keys_str_mv |
AT wassermanndemian diffusionmapsclusteringformagneticresonanceqballimagingsegmentation |
_version_ |
1768543487144230912 |