Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI

ilustraciones (principalmente a color), diagramas

Autores:: Murcia Tapias, Al-yhuwert

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI
dc.title.translated.eng.fl_str_mv	Enriqueciendo la información de la resonancia magnética estructural mediante asociaciones interescala con la resonancia magnética ponderada por difusión
title	Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI
spellingShingle	Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Resonancia magnética en imágenes Magnetic resonance imaging MRI synthesis Structural MRI Fractional anisotropy Diffusion weighted imaging Image-to-image translation Generative adversarial networks Sı́ntesis de resonancia magnética Resonancia magnética estructural Anisotropı́a fraccional Imágenes ponderadas en difusión Traducción de imagen a imagen Redes generativas adversarias Anisotropía Modelos de Redes Neurales Anisotropy Neural Network Simulation
title_short	Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI
title_full	Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI
title_fullStr	Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI
title_full_unstemmed	Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI
title_sort	Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI
dc.creator.fl_str_mv	Murcia Tapias, Al-yhuwert
dc.contributor.advisor.none.fl_str_mv	Romero Castro, Eduardo
dc.contributor.author.none.fl_str_mv	Murcia Tapias, Al-yhuwert
dc.contributor.researchgroup.spa.fl_str_mv	Computer Imaging and Medical Aplications Laboratory - Cim@lab
dc.contributor.supervisor.none.fl_str_mv	Giraldo Franco, Diana Lorena
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Resonancia magnética en imágenes Magnetic resonance imaging MRI synthesis Structural MRI Fractional anisotropy Diffusion weighted imaging Image-to-image translation Generative adversarial networks Sı́ntesis de resonancia magnética Resonancia magnética estructural Anisotropı́a fraccional Imágenes ponderadas en difusión Traducción de imagen a imagen Redes generativas adversarias Anisotropía Modelos de Redes Neurales Anisotropy Neural Network Simulation
dc.subject.lemb.spa.fl_str_mv	Resonancia magnética en imágenes
dc.subject.lemb.eng.fl_str_mv	Magnetic resonance imaging
dc.subject.proposal.eng.fl_str_mv	MRI synthesis Structural MRI Fractional anisotropy Diffusion weighted imaging Image-to-image translation Generative adversarial networks
dc.subject.proposal.spa.fl_str_mv	Sı́ntesis de resonancia magnética Resonancia magnética estructural Anisotropı́a fraccional Imágenes ponderadas en difusión Traducción de imagen a imagen Redes generativas adversarias
dc.subject.umls.spa.fl_str_mv	Anisotropía Modelos de Redes Neurales
dc.subject.umls.eng.fl_str_mv	Anisotropy Neural Network Simulation
description	ilustraciones (principalmente a color), diagramas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-02T21:46:42Z
dc.date.available.none.fl_str_mv	2024-07-02T21:46:42Z
dc.date.issued.none.fl_str_mv	2024
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
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dc.type.content.spa.fl_str_mv	Text
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status_str	publishedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/86355
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.repo.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86355 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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Cox, Simon Taylor-Robinson, and Mark McPhail. Magnetic resonance imaging: Principles and techniques: Lessons for clinicians. Journal of Clinical and Experimental Hepatology, 5, 08 2015. Viviana Lo Buono, Rosanna Palmeri, Francesco Corallo, Cettina Allone, Deborah Pria, Placido Bramanti, and Silvia Marino. Diffusion tensor imaging of white matter degeneration in early stage of Alzheimer’s disease: a review. International Journal of Neuroscience, 130(3):243–250, 2020. PMID: 31549530. Woo-Suk Tae, Byung Ham, Sung-Bom Pyun, Shin-Hyuk Kang, and Byung-Jo Kim. Current clinical applications of diffusion-tensor imaging in neurological disorders. Journal of clinical neurology (Seoul, Korea), 14, 02 2018. Chantel D. Mayo, Erin L. Mazerolle, Lesley Ritchie, John D. Fisk, and Jodie R. Gawryluk. Longitudinal changes in microstructural white matter metrics in alzheimer’s disease. NeuroImage: Clinical, 13:330–338, 2017. Josue Dalboni, Ivanei Bramati, Gabriel Coutinho, Fernanda Moll, and Ranganatha Sitaram. Fractional anisotropy changes in parahippocampal cingulum due to alzheimer’s disease. Scientific Reports, 10, 02 2020. Vince D. Calhoun and Jing Sui. Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(3):230–244, 2016. Brain Connectivity in Psychopathology. Yun Wang, Chenxiao Xu, Ji-Hwan Park, Seonjoo Lee, Yaakov Stern, Shinjae Yoo, Jong Hun Kim, Hyoung Seop Kim, and Jiook Cha. Diagnosis and prognosis of Alzheimer’s disease using brain morphometry and white matter connectomes. NeuroImage: Clinical, 23:101859, January 2019. Xianjin Dai, Yang Lei, Yabo Fu, Walter J. Curran, Tian Liu, Hui Mao, and Xiaofeng Yang. Multimodal MRI Synthesis Using Unified Generative Adversarial Networks. Medical physics, 47(12):6343–6354, December 2020. Yingxue Pang, Jianxin Lin, Tao Qin, and Zhibo Chen. 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Generative adversarial networks, 2014. Rongguang Wang, Vishnu Bashyam, Zhijian Yang, Fanyang Yu, Vasiliki Tassopoulou, Sai Spandana Chintapalli, Ioanna Skampardoni, Lasya P. Sreepada, Dushyant Sahoo, Konstantina Nikita, Ahmed Abdulkadir, Junhao Wen, and Christos Davatzikos. Applications of generative adversarial networks in neuroimaging and clinical neuroscience. NeuroImage, 269:119898, 2023. Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets, 2014. Karissa Chan, Pejman Jabehdar Maralani, Alan R. Moody, and April Khademi. Synthesis of diffusion-weighted mri scalar maps from flair volumes using generative adversarial networks. Frontiers in Neuroinformatics, 17, 2023. Xuan Gu, Hans Knutsson, Markus Nilsson, and Anders Eklund. Generating diffusion mri scalar maps from t1 weighted images using generative adversarial networks. In Michael Felsberg, Per-Erik Forssén, Ida-Maria Sintorn, and Jonas Unger, editors, Image Analysis, pages 489–498, Cham, 2019. 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Springer International Publishing. Yunbi Liu, Ling Yue, Shifu Xiao, Wei Yang, and Mingxia Liu. Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages. Medical Image Analysis, 75:102266, 10 2021. Al yhuwert Murcia Tapias, Diana L. Giraldo, and Eduardo Romero. Synthesizing fractional anisotropy maps from T1-weighted magnetic resonance images using a simplified generative adversarial network. In Barjor S. Gimi and Andrzej Krol, editors, Medical Imaging 2024: Clinical and Biomedical Imaging, volume 12930, page 129302P. International Society for Optics and Photonics, SPIE, 2024. R. C. Petersen, P. S. Aisen, L. A. Beckett, M. C. Donohue, A. C. Gamst, D. J. Harvey, C. R. Jack, W. J. Jagust, L. M. Shaw, A. W. Toga, J. Q. Trojanowski, and M. W. Weiner. Alzheimer’s disease neuroimaging initiative (adni) clinical characterization. Neurology, 74(3):201–209, 2010. 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Very deep convolutional networks for large-scale image recognition, 2015. Alain Horé and Djemel Ziou. Image quality metrics: Psnr vs. ssim. In 2010 20th International Conference on Pattern Recognition, pages 2366–2369, 2010. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011. B. Efron. Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1):1 – 26, 1979. Sergey Kastryulin, Jamil Zakirov, Nicola Pezzotti, and Dmitry V. Dylov. Image quality assessment for magnetic resonance imaging. IEEE Access, 11:14154–14168, 2023. Huanqing Yang, Hua Xu, Qingfeng Li, Yan Jin, Weixiong Jiang, Jinghua Wang, Yina Wu, Wei Li, Cece Yang, Xia Li, Shifu Xiao, Feng Shi, and Tao Wang. Study of brain morphology change in alzheimer’s disease and amnestic mild cognitive impairment compared with normal controls. General Psychiatry, 32(2), 2019.
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
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dc.format.extent.spa.fl_str_mv	vii, 34 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Medicina - Maestría en Ingeniería Biomédica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Medicina
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Romero Castro, Eduardocdde36df751c8bac46785c53d50fefca600Murcia Tapias, Al-yhuwert316b8e4354c103f45ed218f4ded2d5e5Computer Imaging and Medical Aplications Laboratory - Cim@labGiraldo Franco, Diana Lorena2024-07-02T21:46:42Z2024-07-02T21:46:42Z2024https://repositorio.unal.edu.co/handle/unal/86355Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones (principalmente a color), diagramasUnimodal MRI provides a unique channel of information specific to the organ under examination, but it tends to restrict the amount of information necessary for accurate diagnoses. Conversely, in multimodal MRI different tissue structures are highlighted, thereby enriching the information about processes affecting an organ and hence improving the diagnostic precision. Nevertheless, in clinical settings the availability of multiple MR modalities and the scanning time for every patient is limited. To address this challenge, image-to-image translation techniques can be used to synthesize different brain image modalities and provide enriched complementary information about the organ. The image translation task is often done with the use of Generative Adversarial Networks (GANs) which is a computationally expensive approach. This work presents the synthesis of diffusion-derived fractional anisotropy maps (FA) from T1-weighted brain Magnetic Resonance Images using a simplified GAN-based architecture that enrich the structural information while reducing the computational cost associated with training the generative model. Furthermore, to prove that the latent information of the generative network is inherently enriched by both input and target image modalities, a classification task of three stages of the Alzheimer’s disease spectrum (healthy, mild cognitive impairment and mild dementia) was performed. Brain magnetic resonance images from the ADNI database were employed. Paired T1 and FA slices in axial, coronal, and sagittal views were utilized for the synthesis task. For the classification task, T1 slices in the same orientations were used. We evaluated the synthesis task by comparing the performance of the proposed GAN architecture against two state-of-the-art networks: Pix2pix and CycleGAN. Using almost 70% less parameters than those used in Pix2pix, the proposed method showed competitive results in mean PSNR (20.21 ± 1.38) and SSIM (0.65 ± 0.07) when compared to Pix2pix (PSNR: 20.46 ± 1.46, SSIM: 0.66 ± 0.07), outperforming quality metrics achieved by CycleGAN (PSNR: 18.65 ± 1.31, SSIM: 0.61 ± 0.08). For the classification task, a Support Vector Machine (SVM) classifier was trained with the latent information of the proposed generative network. A boost in classification was demonstrated when comparing the enriched (multimodal) latent information with non-enriched (unimodal) information (Texto tomado de la fuente).La resonancia magnética unimodal proporciona un canal único de información especı́fica del órgano examinado, pero tiende a restringir la cantidad de información necesaria para diagnósticos precisos. En contraposición, en la resonancia magnética multimodal se destacan diferentes estructuras de tejido, lo cual enriquece la información sobre los procesos que afectan un órgano y por tanto mejora la precisión diagnóstica. Sin embargo, en entornos clı́nicos, la disponibilidad de múltiples modalidades de resonancia magnética y el tiempo de exploración para cada paciente son limitados. Para abordar este desafı́o, se pueden utilizar técnicas de traducción de imagen a imagen para sintetizar diferentes modalidades de imágenes cerebrales que proporcionen información complementaria enriquecida sobre el órgano. Esta tarea de generación de imágenes depende en gran medida del uso de Redes Generativas Adversarias (GAN), caracterizadas por su alta demanda computacional. Este trabajo presenta la sı́ntesis de mapas de anisotropı́a fraccional (FA) derivados de la imagen de difusión a partir de imágenes de resonancia magnética cerebral ponderada en T1, utilizando una arquitectura simplificada basada en GAN que enriquece la información estructural al tiempo que reduce el costo computacional asociado al entrenamiento del modelo generativo. Adicionalmente, con el fin de demostrar que la información latente de la red generativa está naturalmente enriquecida por ambas imágenes de entrada y de salida, se realizó una tarea de clasificación de tres de los estadios del espectro de la enfermedad de Alzheimer (sano, deterioro cognitivo moderado, demencia leve). Se utilizaron imágenes de resonancia magnética cerebral de la base de datos ADNI. Cortes pareados de T1 y FA en vistas axial, coronal y sagital fueron empleados en la tarea de sı́ntesis. Para la tarea de clasificación se usaron cortes de T1 en los mismos planos. La tarea de sı́ntesis fue evaluada comparando la arquitectura propuesta con dos redes del estado del arte: Pix2pix y CycleGAN. Utilizando casi un 70% menos de parámetros que los utilizados en Pix2pix, el método propuesto mostró resultados competitivos en PSNR (20.21 ± 1.38) y SSIM (0.65 ± 0.07) en comparación con Pix2pix (PSNR: 20.46 ± 1.46, SSIM: 0.66 ± 0.07), superando las métricas de calidad alcanzadas por CycleGAN (PSNR: 18.65 ± 1.31, SSIM: 0.61 ± 0.08). Para la tarea de clasificación, se entrenó un clasificador de máquina de soporte vectorial (SVM) utilizando la información latente de la red generativa propuesta. Se demostró una mejora en la clasificación cuando se comparó la información latente enriquecida (multimodal) con la información no enriquecida (unimodal) (Texto tomado de la fuente).MaestríaMagister en Ingeniería BiomédicaImágenes médicasMedicina.Sede Bogotávii, 34 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Medicina - Maestría en Ingeniería BiomédicaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaResonancia magnética en imágenesMagnetic resonance imagingMRI synthesisStructural MRIFractional anisotropyDiffusion weighted imagingImage-to-image translationGenerative adversarial networksSı́ntesis de resonancia magnéticaResonancia magnética estructuralAnisotropı́a fraccionalImágenes ponderadas en difusiónTraducción de imagen a imagenRedes generativas adversariasAnisotropíaModelos de Redes NeuralesAnisotropyNeural Network SimulationEnriching the structural MRI information by cross-scale associations with the diffusion-weighted MRIEnriqueciendo la información de la resonancia magnética estructural mediante asociaciones interescala con la resonancia magnética ponderada por difusiónTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TMOzlem Coskun. Magnetic resonance imaging and safety aspects. Toxicology and Industrial Health, 27(4):307–313, 2011. PMID: 21112927.M. Jenkinson and M. Chappell. Introduction to Neuroimaging Analysis. Oxford neuroimaging primers. Oxford University Press, 2018.M Symms, H R Jäger, K Schmierer, and T A Yousry. A review of structural magnetic resonance neuroimaging. Journal of Neurology, Neurosurgery & Psychiatry, 75(9):1235– 1244, 2004.Clifford Jack, David Bennett, Kaj Blennow, Maria Carrillo, Billy Dunn, Samantha Haeberlein, David Holtzman, William Jagust, Frank Jessen, Jason Karlawish, Enchi Liu, Jose Molinuevo, Thomas Montine, Creighton Phelps, Katherine Rankin, Christopher Rowe, Philip Scheltens, Eric Siemers, Heather Snyder, and Nina Silverberg. Nia-aa research framework: Toward a biological definition of alzheimer’s disease. Alzheimer’s Dementia, 14:535–562, 04 2018.Vijay Grover, Joshua Tognarelli, Mary Crossey, I. Cox, Simon Taylor-Robinson, and Mark McPhail. Magnetic resonance imaging: Principles and techniques: Lessons for clinicians. Journal of Clinical and Experimental Hepatology, 5, 08 2015.Viviana Lo Buono, Rosanna Palmeri, Francesco Corallo, Cettina Allone, Deborah Pria, Placido Bramanti, and Silvia Marino. Diffusion tensor imaging of white matter degeneration in early stage of Alzheimer’s disease: a review. International Journal of Neuroscience, 130(3):243–250, 2020. PMID: 31549530.Woo-Suk Tae, Byung Ham, Sung-Bom Pyun, Shin-Hyuk Kang, and Byung-Jo Kim. Current clinical applications of diffusion-tensor imaging in neurological disorders. Journal of clinical neurology (Seoul, Korea), 14, 02 2018.Chantel D. Mayo, Erin L. Mazerolle, Lesley Ritchie, John D. Fisk, and Jodie R. Gawryluk. Longitudinal changes in microstructural white matter metrics in alzheimer’s disease. NeuroImage: Clinical, 13:330–338, 2017.Josue Dalboni, Ivanei Bramati, Gabriel Coutinho, Fernanda Moll, and Ranganatha Sitaram. 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General Psychiatry, 32(2), 2019.BibliotecariosEstudiantesInvestigadoresMaestrosProveedores de ayuda financiera para estudiantesLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86355/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1101690075.2024.pdf1101690075.2024.pdfTesis de Maestría en Ingenieria-Biomédica - Enriching the Structural MRI informationapplication/pdf3575079https://repositorio.unal.edu.co/bitstream/unal/86355/2/1101690075.2024.pdf4add8c33002d44f8505898bbbc85022eMD52THUMBNAIL1101690075.2024.pdf.jpg1101690075.2024.pdf.jpgGenerated Thumbnailimage/jpeg4461https://repositorio.unal.edu.co/bitstream/unal/86355/3/1101690075.2024.pdf.jpged3fe14fd0f3449a41afc45250e8f6c1MD53unal/86355oai:repositorio.unal.edu.co:unal/863552024-08-25 23:12:09.867Repositorio Institucional Universidad Nacional de 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Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI

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