Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography

The study of brain vascular dynamic patterns in infants, through dynamic angio MRI (TRANCE-MRI) images, is relevant to identify pathologies associated with brain flow and perfusion. However, several drawbacks arise while using these types of images for diagnosis, such as noisy images and difficultie...

Descripción completa

Detalles Bibliográficos
Autores: Herrera Montes de Oca, Daniela, Ochoa Ruíz, Gilberto, Stephan Otto, Christian, González Mendoza, Miguel, Munuera del Cerro, Josep, Mata Miquel, Cristian|||0000-0003-4768-5062
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/449644
Acceso en línea:https://hdl.handle.net/2117/449644
https://dx.doi.org/10.1007/s13755-025-00406-x
Access Level:acceso abierto
Palabra clave:Medical informatics
Medicina--Informàtica
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria
id ES_10e19230fc50a6e040ebc5bb7c7b5937
oai_identifier_str oai:upcommons.upc.edu:2117/449644
network_acronym_str ES
network_name_str España
repository_id_str
spelling Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiographyHerrera Montes de Oca, DanielaOchoa Ruíz, GilbertoStephan Otto, ChristianGonzález Mendoza, MiguelMunuera del Cerro, JosepMata Miquel, Cristian|||0000-0003-4768-5062Medical informaticsMedicina--InformàticaÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeriaThe study of brain vascular dynamic patterns in infants, through dynamic angio MRI (TRANCE-MRI) images, is relevant to identify pathologies associated with brain flow and perfusion. However, several drawbacks arise while using these types of images for diagnosis, such as noisy images and difficulties in the quantification of the vessel patterns. Depending on the patient, specialists can use manual procedures to analyze the images and segment the veins during the image analysis. Image acquisition in infants is often affected by motion artifacts, variable contrast due to short acquisition times, and scanner hardware limitations, which together increase noise and reduce vessel visibility. Furthermore, this fact poses serious challenges for both the use of AI tools as well as the analysis and diagnosis of professionals. The goal of this research is to assess automatic denoising pipelines for enhancing image quality to aid visual analysis and automated vessel segmentation for improved quantification through vessel feature extraction. As a result of this research, an entire pipeline is presented as a solution. For denoising the images, we have explored the combination of conventional techniques with unsupervised techniques based on deep learning. The outcomes were subjectively assessed by experts and quantitatively by non-reference image quality evaluators. Using Noise2Void and PPN2V GMM produced the best outcomes, according to the scores. However, employing a combination of traditional methods and deep learning-based methods, the vessels showed a reduction in noise in the central and most dense areas, according to qualitative results. A model was trained using noisy images for segmentation. Then it was put to the test using both denoised and noisy images. The findings demonstrated an improvement of 9.4% in the dice score and nearly 16% in the Hausdorff distance when the model was trained using noisy images and segmentation was obtained using denoised images.Peer ReviewedSpringer20262026-01-0120252025-12-24journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/449644https://dx.doi.org/10.1007/s13755-025-00406-xreponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4496442026-05-27T15:37:01Z
dc.title.none.fl_str_mv Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
spellingShingle Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
Herrera Montes de Oca, Daniela
Medical informatics
Medicina--Informàtica
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria
title_short Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title_full Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title_fullStr Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title_full_unstemmed Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
title_sort Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography
dc.creator.none.fl_str_mv Herrera Montes de Oca, Daniela
Ochoa Ruíz, Gilberto
Stephan Otto, Christian
González Mendoza, Miguel
Munuera del Cerro, Josep
Mata Miquel, Cristian|||0000-0003-4768-5062
author Herrera Montes de Oca, Daniela
author_facet Herrera Montes de Oca, Daniela
Ochoa Ruíz, Gilberto
Stephan Otto, Christian
González Mendoza, Miguel
Munuera del Cerro, Josep
Mata Miquel, Cristian|||0000-0003-4768-5062
author_role author
author2 Ochoa Ruíz, Gilberto
Stephan Otto, Christian
González Mendoza, Miguel
Munuera del Cerro, Josep
Mata Miquel, Cristian|||0000-0003-4768-5062
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Medical informatics
Medicina--Informàtica
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria
topic Medical informatics
Medicina--Informàtica
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria
description The study of brain vascular dynamic patterns in infants, through dynamic angio MRI (TRANCE-MRI) images, is relevant to identify pathologies associated with brain flow and perfusion. However, several drawbacks arise while using these types of images for diagnosis, such as noisy images and difficulties in the quantification of the vessel patterns. Depending on the patient, specialists can use manual procedures to analyze the images and segment the veins during the image analysis. Image acquisition in infants is often affected by motion artifacts, variable contrast due to short acquisition times, and scanner hardware limitations, which together increase noise and reduce vessel visibility. Furthermore, this fact poses serious challenges for both the use of AI tools as well as the analysis and diagnosis of professionals. The goal of this research is to assess automatic denoising pipelines for enhancing image quality to aid visual analysis and automated vessel segmentation for improved quantification through vessel feature extraction. As a result of this research, an entire pipeline is presented as a solution. For denoising the images, we have explored the combination of conventional techniques with unsupervised techniques based on deep learning. The outcomes were subjectively assessed by experts and quantitatively by non-reference image quality evaluators. Using Noise2Void and PPN2V GMM produced the best outcomes, according to the scores. However, employing a combination of traditional methods and deep learning-based methods, the vessels showed a reduction in noise in the central and most dense areas, according to qualitative results. A model was trained using noisy images for segmentation. Then it was put to the test using both denoised and noisy images. The findings demonstrated an improvement of 9.4% in the dice score and nearly 16% in the Hausdorff distance when the model was trained using noisy images and segmentation was obtained using denoised images.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-12-24
2026
2026-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/449644
https://dx.doi.org/10.1007/s13755-025-00406-x
url https://hdl.handle.net/2117/449644
https://dx.doi.org/10.1007/s13755-025-00406-x
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403534550630400
score 15,812429

Similares en LA Referencia