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Artificial Intelligence In Diffusion Mri
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Book Synopsis Artificial Intelligence in Diffusion MRI by : Mohammad Shehab
Download or read book Artificial Intelligence in Diffusion MRI written by Mohammad Shehab and published by Springer Nature. This book was released on 2019-11-20 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the use of artificial intelligence to address a specific problem in the brain – the orientation distribution function. It discusses three aspects: (i) Preparing, enhancing and evaluating one of the cuckoo search algorithms (CSA); (ii) Describing the problem: Diffusion-weighted magnetic resonance imaging (DW-MRI) is used for non-invasive investigations of anatomical connectivity in the human brain, while Q-ball imaging (QBI) is a diffusion MRI reconstruction technique based on the orientation distribution function (ODF), which detects the dominant fiber orientations; however, ODF lacks local estimation accuracy along the path. (iii) Evaluating the performance of the CSA versions in solving the ODF problem using synthetic and real-world data. This book appeals to both postgraduates and researchers who are interested in the fields of medicine and computer science.
Book Synopsis Computational Diffusion MRI by : Elisenda Bonet-Carne
Download or read book Computational Diffusion MRI written by Elisenda Bonet-Carne and published by Springer. This book was released on 2019-05-17 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI’18), which was held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain on September 20, 2018. It presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find papers on a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as harmonisation and frontline applications in research and clinical practice. The respective papers constitute invited works from high-profile researchers with a specific focus on three topics that are now gaining momentum within the diffusion MRI community: i) machine learning for diffusion MRI; ii) diffusion MRI outside the brain (e.g. in the placenta); and iii) diffusion MRI for multimodal imaging. The book shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. It includes rigorous mathematical derivations, a wealth of full-colour visualisations, and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics alike.
Book Synopsis Artificial Intelligence in Medical Imaging by : Erik R. Ranschaert
Download or read book Artificial Intelligence in Medical Imaging written by Erik R. Ranschaert and published by Springer. This book was released on 2019-01-29 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Book Synopsis Computational Diffusion MRI by : Suheyla Cetin-Karayumak
Download or read book Computational Diffusion MRI written by Suheyla Cetin-Karayumak and published by Springer Nature. This book was released on 2022-12-01 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the International Workshop on Computational Diffusion MRI, CDMRI 2022, which was held 22 September 2022, in conjunction with MICCAI 2022. The 12 full papers included were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: Data processing, Signal representations, Tractography and WM pathways.
Book Synopsis Computational Diffusion MRI by : Muge Karaman
Download or read book Computational Diffusion MRI written by Muge Karaman and published by Springer. This book was released on 2024-02-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 14th International Workshop, CDMRI 2023, held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Vancouver, BC, Canada, on October 8, 2023. The 17regular papers presented in this book were carefully reviewed and selected from 19 submissions. These contributions cover various aspects, including preprocessing, signal modeling, tractography, bundle segmentation, and clinical applications. Many of these studies employ novel machine learning implementations, highlighting the evolving landscape of techniques beyond the more traditional physics-based algorithms.
Download or read book Diffusion MRI written by Derek K Jones and published by Oxford University Press. This book was released on 2010-11-11 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: Professor Derek Jones, a world authority on diffusion MRI, has assembled most of the world's leading scientists and clinicians developing and applying diffusion MRI to produce an authorship list that reads like a "Who's Who" of the field and an essential resource for those working with diffusion MRI. Destined to be a modern classic, this definitive and richly illustrated work covers all aspects of diffusion MRI from basic theory to clinical application. Oxford Clinical Neuroscience is a comprehensive, cross-searchable collection of resources offering quick and easy access to eleven of Oxford University Press's prestigious neuroscience texts. Joining Oxford Medicine Online these resources offer students, specialists and clinical researchers the best quality content in an easy-to-access format.
Book Synopsis Computational Diffusion MRI by : Elisenda Bonet-Carne
Download or read book Computational Diffusion MRI written by Elisenda Bonet-Carne and published by Springer. This book was released on 2020-12-31 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI 2019), held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), which took place in Shenzhen, China on October 17, 2019. This book presents the latest advances in the rapidly expanding field of diffusion MRI. It shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning about computational techniques in diffusion MRI. The book includes rigorous mathematical derivations, a wealth of rich, full-colour visualisations and extensive clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics. Readers will find contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice. This edition includes invited works from high-profile researchers with a specific focus on three new and important topics that are gaining momentum within the diffusion MRI community, including diffusion MRI signal acquisition and processing strategies, machine learning for diffusion MRI, and diffusion MRI outside the brain and clinical applications.
Book Synopsis Artificial Intelligence for Medical Image Analysis of NeuroImaging Data by : Nianyin Zeng
Download or read book Artificial Intelligence for Medical Image Analysis of NeuroImaging Data written by Nianyin Zeng and published by Frontiers Media SA. This book was released on 2020-07-03 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advanced analysis of diffusion MRI data by : Xuan Gu
Download or read book Advanced analysis of diffusion MRI data written by Xuan Gu and published by Linköping University Electronic Press. This book was released on 2019-11-19 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive imaging modality which can measure diffusion of water molecules, by making the MRI acquisition sensitive to diffusion. Diffusion MRI provides unique possibilities to study structural connectivity of the human brain, e.g. how the white matter connects different parts of the brain. Diffusion MRI enables a range of tools that permit qualitative and quantitative assessments of many neurological disorders, such as stroke and Parkinson. This thesis introduces novel methods for diffusion MRI data analysis. Prior to estimating a diffusion model in each location (voxel) of the brain, the diffusion data needs to be preprocessed to correct for geometric distortions and head motion. A deep learning approach to synthesize diffusion scalar maps from a T1-weighted MR image is proposed, and it is shown that the distortion-free synthesized images can be used for distortion correction. An evaluation, involving both simulated data and real data, of six methods for susceptibility distortion correction is also presented in this thesis. A common problem in diffusion MRI is to estimate the uncertainty of a diffusion model. An empirical evaluation of tractography, a technique that permits reconstruction of white matter pathways in the human brain, is presented in this thesis. The evaluation is based on analyzing 32 diffusion datasets from a single healthy subject, to study how reliable tractography is. In most cases only a single dataset is available for each subject. This thesis presents methods based on frequentistic (bootstrap) as well as Bayesian inference, which can provide uncertainty estimates when only a single dataset is available. These uncertainty measures can then, for example, be used in a group analysis to downweight subjects with a higher uncertainty.
Book Synopsis Diffusion Tensor Imaging Unboxing by : Antonio Senra Filho
Download or read book Diffusion Tensor Imaging Unboxing written by Antonio Senra Filho and published by Antonio Senra Filho. This book was released on 2023-11-04 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: This guide is designed for researchers who still do not have or have little contact with the diffusion weighted imaging modality (DTI) in MRI. After a few decades of research and development, is now known that the DTI images are extremely powerful for several uses in clinical routine. However, along with these advances of this imaging modality, there are several computational tools for image reconstruction and its visualization. Do not be concerned about what is the first step to understand the DTI imaging modality, because this guide have the intention to summarize the main tools and a brief discussion of what is important to DTI image for modern medicine.
Book Synopsis Computational Diffusion MRI by : Noemi Gyori
Download or read book Computational Diffusion MRI written by Noemi Gyori and published by Springer Nature. This book was released on 2021-09-29 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers papers presented at the Workshop on Computational Diffusion MRI, CDMRI 2020, held under the auspices of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), which took place virtually on October 8th, 2020, having originally been planned to take place in Lima, Peru. This book presents the latest developments in the highly active and rapidly growing field of diffusion MRI. While offering new perspectives on the most recent research challenges in the field, the selected articles also provide a valuable starting point for anyone interested in learning computational techniques for diffusion MRI. The book includes rigorous mathematical derivations, a large number of rich, full-colour visualizations, and clinically relevant results. As such, it is of interest to researchers and practitioners in the fields of computer science, MRI physics, and applied mathematics. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice.
Book Synopsis Microstructure Imaging in the Human Brain with Advanced Diffusion MRI and Machine Learning by : Noémi G. Győri
Download or read book Microstructure Imaging in the Human Brain with Advanced Diffusion MRI and Machine Learning written by Noémi G. Győri and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Radiomics and Radiogenomics in Neuro-oncology by : Hassan Mohy-ud-Din
Download or read book Radiomics and Radiogenomics in Neuro-oncology written by Hassan Mohy-ud-Din and published by Springer Nature. This book was released on 2020-02-24 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the development of tools that can automate the analysis and synthesis of neuro-oncologic imaging.
Book Synopsis Advancing White Matter Tractometry of the Brain Using Diffusion MRI and Machine Learning by : Bramsh Qamar Chandio
Download or read book Advancing White Matter Tractometry of the Brain Using Diffusion MRI and Machine Learning written by Bramsh Qamar Chandio and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human brain contains billions of axons that bundle together in tracts and fasciculi. These can be reconstructed in vivo by collecting diffusion MRI data and deploying tractography algorithms. The outputs of tractography algorithms are called tractograms. These tractograms are represented digitally using streamlines, which are representations of 3D curves traversing the brain. Diffusion MRI and tractography provide crucial information about brain connectivity and microstructural changes due to underlying conditions such as Alzheimer's, Parkinson's, and Schizophrenia disease. However, often generated whole-brain tractograms have millions of streamlines with many false positives and anatomically implausible streamlines. Therefore, tractograms require novel processing pipelines that can reduce such issues and provide anatomically relevant outcomes. For example, a) bundle segmentation methods extract anatomically relevant streamlines and white matter tracts/bundles from the whole-brain tractograms. b) bundle registration methods are used to create common spaces across subjects, and c) statistical methods can then be applied to study microstructural changes in groups and populations along the length of the bundles. This process of quantifying microstructural changes due to a disease or condition along the length of the digitally reconstructed white matter tracts is called tractometry.In this dissertation, we introduced new methods to advance tractometry using machine learning and functional data analysis approaches. For the problem of bundle segmentation and streamline filtering, we introduced the auto-calibrated RecoBundles method that precisely extracts bundles from tractograms with only one reference exemplar. We also developed an unsupervised method, FiberNeat, that filters out spurious streamlines from bundles in latent space. To solve the registration problem, a novel method, BundleWarp, was created for the nonlinear registration of white matter bundles where users can control the amount of deformations with a single free regularization parameter (Lambda). In the category of tractometry methods, we created a publicly available advanced tractometry pipeline called BUndle ANalytics (BUAN). BUAN provides a completely automatic, end-to-end streamline-based solution that connects bundle segmentation, registration, analysis of bundle anatomy, and bundle shape analysis. BUAN reports the exact locations of population differences along the length of the tracts. BUAN also includes metrics and methods for quality assurance of extracted white matter tracts in large populations. Furthermore, in BUAN 2.0, instead of treating points on the streamlines as independent observations in statistical analysis, we proposed using functional data analysis (FDA) methods where each streamline is considered a function. This dissertation moves beyond the standard processing of brain images to a tractography-based analysis of the brain tissue microstructure and connectivity by introducing robust, fast, and simple-to-use algorithms. Results are shown on Parkinson's disease data from Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's disease from Alzheimer's Disease Neuroimaging Initiative phase 3 (ADNI3) datasets. The methods developed as part of this dissertation are made publicly available through DIPY.org.
Book Synopsis Brain Tumor MRI Image Segmentation Using Deep Learning Techniques by : Jyotismita Chaki
Download or read book Brain Tumor MRI Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and published by Academic Press. This book was released on 2021-11-27 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more. The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation. Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation Covers research Issues and the future of deep learning-based brain tumor segmentation
Book Synopsis Machine Learning for Medical Image Reconstruction by : Farah Deeba
Download or read book Machine Learning for Medical Image Reconstruction written by Farah Deeba and published by Springer Nature. This book was released on 2020-10-21 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
Book Synopsis Computational Diffusion MRI by : Enrico Kaden
Download or read book Computational Diffusion MRI written by Enrico Kaden and published by Springer. This book was released on 2018-04-02 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as frontline applications in neuroscience research and clinical practice. These proceedings contain the papers presented at the 2017 MICCAI Workshop on Computational Diffusion MRI (CDMRI’17) held in Québec, Canada on September 10, 2017, sharing new perspectives on the most recent research challenges for those currently working in the field, but also offering a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. This book includes rigorous mathematical derivations, a large number of rich, full-colour visualisations and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics.
