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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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اقرأ أكثر حفظ في قائمتي
  • 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
    • Relation:
      Ozlem 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. 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. Image-to-image translation: Methods and applications. 2021.; Henri Hoyez, Cédric Schockaert, Jason Rambach, Bruno Mirbach, and Didier Stricker. Unsupervised image-to-image translation: A review. Sensors, 22(21), 2022.; Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, October 2017.; Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation with conditional adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5967–5976, 2017.; Lei Wang, Wei Chen, Wenjia Yang, Fangming Bi, and Fei Yu. A state-of-the-art review on image synthesis with generative adversarial networks. IEEE Access, PP:1–1, 03 2020.; Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 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. Springer International Publishing.; Haoyu Lan, the Alzheimer Disease Neuroimaging Initiative, Arthur W. Toga, and Farshid Sepehrband. Three-dimensional self-attention conditional gan with spectral normalization for multimodal neuroimaging synthesis. Magnetic Resonance in Medicine, 86(3):1718–1733, 2021.; Jake McNaughton, Justin Fernandez, Samantha Holdsworth, Benjamin Chong, Vickie Shim, and Alan Wang. Machine learning for medical image translation: A systematic review. Bioengineering, 10(9), 2023.; Karim Armanious, Chenming Jiang, Marc Fischer, Thomas Küstner, Tobias Hepp, Konstantin Nikolaou, Sergios Gatidis, and Bin Yang. Medgan: Medical image translation using gans. Computerized Medical Imaging and Graphics, 79:101684, 2020.; Lingke Kong, Chenyu Lian, Detian Huang, Zhenjiang Li, Yanle Hu, and Qichao Zhou. Breaking the dilemma of medical image-to-image translation, 2021.; Seong-Jin Son, Bo yong Park, Kyoungseob Byeon, and Hyunjin Park. Synthesizing diffusion tensor imaging from functional mri using fully convolutional networks. Computers in Biology and Medicine, 115:103528, 2019.; Benoit Anctil-Robitaille, Antoine Théberge, Pierre-Marc Jodoin, Maxime Descoteaux, Christian Desrosiers, and Hervé Lombaert. Manifold-aware synthesis of high-resolution diffusion from structural imaging. Frontiers in Neuroimaging, 1, 2022.; Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation, 2015.; Dor Bank, Noam Koenigstein, and Raja Giryes. Autoencoders, 2021.; Abel Gonzalez-Garcia, Joost van de Weijer, and Yoshua Bengio. Image-to-image translation for cross-domain disentanglement, 2018.; Apoorva Sikka, Skand Vishwanath Peri, and Deepti R. Bathula. MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net for Multi-modal Alzheimer’s Classification. In Ali Gooya, Orcun Goksel, Ipek Oguz, and Ninon Burgos, editors, Simulation and Synthesis in Medical Imaging, pages 80–89, Cham, 2018. 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.; Nicholas J Tustison, Brian B Avants, Philip A Cook, Yuanjie Zheng, Alexander Egan, Paul A Yushkevich, and James C Gee. N4itk: improved n3 bias correction. IEEE transactions on medical imaging, 29(6):1310—1320, June 2010.; Juan Iglesias, Cheng-Yi Liu, Paul Thompson, and Z. Tu. Robust brain extraction across datasets and comparison with publicly available methods. IEEE transactions on medical imaging, 30:1617–34, 09 2011.; William Penny, Karl Friston, John Ashburner, Stefan Kiebel, and T. Nichols. Statistical Parametric Mapping: The Analysis of Functional Brain Images. 01 2007.; Jacob C Reinhold, Blake E Dewey, Aaron Carass, and Jerry L Prince. Evaluating the impact of intensity normalization on MR image synthesis. In Medical Imaging 2019: Image Processing, volume 10949, page 109493H. International Society for Optics and Photonics, 2019.; Jelle Veraart, Dmitry S. Novikov, Daan Christiaens, Benjamin Ades-aron, Jan Sijbers, and Els Fieremans. Denoising of diffusion mri using random matrix theory. NeuroImage, 142:394–406, 2016.; Jesper Andersson and Stamatios Sotiropoulos. An integrated approach to correction for off-resonance effects and subject movement in diffusion mr imaging. NeuroImage, 125, 10 2015.; Stephen M Smith, Mark Jenkinson, Mark W Woolrich, Christian F Beckmann, Timothy E J Behrens, Heidi Johansen-Berg, Peter R Bannister, Marilena De Luca, Ivana Drobnjak, David E Flitney, Rami K Niazy, James Saunders, John Vickers, Yongyue Zhang, Nicola De Stefano, J Michael Brady, and Paul M Matthews. Advances in functional and structural mr image analysis and implementation as fsl. NeuroImage, 23 Suppl 1:S208—19, 2004.; Jelle Veraart, Jan Sijbers, Stefan Sunaert, Alexander Leemans, and Ben Jeurissen. Weighted linear least squares estimation of diffusion mri parameters: Strengths, limitations, and pitfalls. NeuroImage, 81:335–346, 2013.; P.J. Basser, J. Mattiello, and D. Lebihan. Estimation of the effective self-diffusion tensor from the nmr spin echo. Journal of Magnetic Resonance, Series B, 103(3):247–254, 1994.; J.-D. Tournier, R. E. Smith, D. Raffelt, R. Tabbara, T. Dhollander, M. Pietsch, E. Christiaens, B. Jeurissen, C. Yeh, R. F. N. van den Berg, D. A. Porter, M. P. J. van Osch, S. Jbabdi, A. Tax, T. E. Nichols, E. J. W. van Nunen, J. A. De Backer, D. L. Flanagan, G. Calamante, C. Connell, and A. Connelly. Mrtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 202:116137, 2019.; Mark Jenkinson, Peter Bannister, Michael Brady, and Stephen Smith. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2):825–841, 2002.; Douglas N. Greve and Bruce Fischl. Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1):63–72, 2009.; Karen Simonyan and Andrew Zisserman. 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.; https://repositorio.unal.edu.co/handle/unal/86355; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/
    • Rights:
      Atribución-NoComercial 4.0 Internacional ; http://creativecommons.org/licenses/by-nc/4.0/ ; info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.9839B112