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Enriching the structural MRI information by cross-scale associations with the diffusion-weighted MRI ; Enriqueciendo la información de la resonancia magnética estructural mediante asociaciones interescala con la resonancia magnética ponderada por difusión
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- المؤلفون: Murcia Tapias, Al-yhuwert
- الموضوع:
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- نوع التسجيلة:
master thesis- اللغة:
English - الموضوع:
- معلومة اضافية
- Contributors: Romero Castro, Eduardo; Computer Imaging and Medical Aplications Laboratory - Cim@lab; Giraldo Franco, Diana Lorena
- بيانات النشر: Universidad Nacional de Colombia
Bogotá - Medicina - Maestría en Ingeniería Biomédica
Facultad de Medicina
Bogotá, Colombia
Universidad Nacional de Colombia - Sede Bogotá - الموضوع: 2024
- نبذة مختصرة : ilustraciones (principalmente a color), diagramas ; Unimodal 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 ...
- File Description: vii, 34 páginas; application/pdf
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- Rights: Atribución-NoComercial 4.0 Internacional ; http://creativecommons.org/licenses/by-nc/4.0/ ; info:eu-repo/semantics/openAccess
- الرقم المعرف: edsbas.9839B112
- Contributors:
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