Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

Detalhes bibliográficos
Autor(a) principal: Senra Filho,Antonio Carlos da Silva
Data de Publicação: 2017
Outros Autores: Salmon,Carlos Ernesto Garrido, Santos,Antonio Carlos dos, Murta Junior,Luiz Otávio
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Research on Biomedical Engineering (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000300247
Resumo: Abstract Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.
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spelling Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filterAnomalous diffusionImage enhancementDiffusion Tensor ImagingSpatial filteringAbstract Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.Sociedade Brasileira de Engenharia Biomédica2017-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000300247Research on Biomedical Engineering v.33 n.3 2017reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.02017info:eu-repo/semantics/openAccessSenra Filho,Antonio Carlos da SilvaSalmon,Carlos Ernesto GarridoSantos,Antonio Carlos dosMurta Junior,Luiz Otávioeng2018-08-02T00:00:00Zoai:scielo:S2446-47402017000300247Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2018-08-02T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false
dc.title.none.fl_str_mv Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter
title Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter
spellingShingle Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter
Senra Filho,Antonio Carlos da Silva
Anomalous diffusion
Image enhancement
Diffusion Tensor Imaging
Spatial filtering
title_short Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter
title_full Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter
title_fullStr Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter
title_full_unstemmed Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter
title_sort Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter
author Senra Filho,Antonio Carlos da Silva
author_facet Senra Filho,Antonio Carlos da Silva
Salmon,Carlos Ernesto Garrido
Santos,Antonio Carlos dos
Murta Junior,Luiz Otávio
author_role author
author2 Salmon,Carlos Ernesto Garrido
Santos,Antonio Carlos dos
Murta Junior,Luiz Otávio
author2_role author
author
author
dc.contributor.author.fl_str_mv Senra Filho,Antonio Carlos da Silva
Salmon,Carlos Ernesto Garrido
Santos,Antonio Carlos dos
Murta Junior,Luiz Otávio
dc.subject.por.fl_str_mv Anomalous diffusion
Image enhancement
Diffusion Tensor Imaging
Spatial filtering
topic Anomalous diffusion
Image enhancement
Diffusion Tensor Imaging
Spatial filtering
description Abstract Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.
publishDate 2017
dc.date.none.fl_str_mv 2017-09-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
dc.source.none.fl_str_mv Research on Biomedical Engineering v.33 n.3 2017
reponame:Research on Biomedical Engineering (Online)
instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)
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