Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , |
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. |
id |
SBEB-1_a9a0c5772b262aca93b498357ad4a215 |
---|---|
oai_identifier_str |
oai:scielo:S2446-47402017000300247 |
network_acronym_str |
SBEB-1 |
network_name_str |
Research on Biomedical Engineering (Online) |
repository_id_str |
|
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 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000300247 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000300247 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2446-4740.02017 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
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) instacron:SBEB |
instname_str |
Sociedade Brasileira de Engenharia Biomédica (SBEB) |
instacron_str |
SBEB |
institution |
SBEB |
reponame_str |
Research on Biomedical Engineering (Online) |
collection |
Research on Biomedical Engineering (Online) |
repository.name.fl_str_mv |
Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB) |
repository.mail.fl_str_mv |
||rbe@rbejournal.org |
_version_ |
1752126288754114560 |