Deep Neural Networks For Cardiovascular Magnetic Resonance Imaging
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Deep Neural Networks For Cardiovascular Magnetic Resonance Imaging
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Author : Vahid Ghodrati Kouzehkonan
language : en
Publisher:
Release Date : 2022
Deep Neural Networks For Cardiovascular Magnetic Resonance Imaging written by Vahid Ghodrati Kouzehkonan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Magnetic Resonance Imaging (MRI) is a powerful diagnostic imaging modalities known to provide high soft-tissue contrast and spatial resolution. Much of the versatility of MRI stems from the fact that the signal from different tissue types can be weighted differently through manipulation of the sequence in which radiofrequency (RF) and gradient events are played out during the data acquisition phase. However, data acquisition for most MRI measurements is sequential, limiting its speed and increasing its susceptibility to motion artifacts. This is particularly the case for cardiovascular applications, where cardiac and respiratory motion complicate all aspects of the data acquisition and signal processing pathways. Moreover, following data acquisition and image reconstruction, clinically relevant post-processing may require substantial time and effort, increasing the burden on clinical centers and medical staff. Thus, general algorithms should be customized to accelerate image acquisition, image reconstruction and image post-processing with the goal of expanding the speed, scope and reliability of cardiovascular MRI applications. This dissertation describes several deep learning-based methods applying tailored image reconstruction, respiratory motion correction, blood vessel segmentation, and instance T1 mapping calculation.
Brain Tumor Mri Image Segmentation Using Deep Learning Techniques
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Author : Jyotismita Chaki
language : en
Publisher: Academic Press
Release Date : 2021-11-27
Brain Tumor Mri Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-27 with Science categories.
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
Current And Future Role Of Artificial Intelligence In Cardiac Imaging Volume Ii
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Author : Steffen Erhard Petersen
language : en
Publisher: Frontiers Media SA
Release Date : 2023-10-30
Current And Future Role Of Artificial Intelligence In Cardiac Imaging Volume Ii written by Steffen Erhard Petersen and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-30 with Medical categories.
Our first Research Topic entitled “Current and Future Role of Artificial Intelligence in Cardiac Imaging” provided comprehensive reviews of the recent advances and potential impact of AI for a range of cardiac imaging applications and remains available as an e-book to download at no cost. Since this first set of publications, the field has moved at pace and it is timely to now invite further up-to-date and topical reviews but importantly original research articles via our Research Topic “Current and Future Role of Artificial Intelligence in Cardiac Imaging 2.0”.
Quantitative Imaging Qi And Artificial Intelligence Ai In Cardiovascular Diseases
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Author : Sebastian Kelle
language : en
Publisher: Frontiers Media SA
Release Date : 2023-05-17
Quantitative Imaging Qi And Artificial Intelligence Ai In Cardiovascular Diseases written by Sebastian Kelle and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-17 with Medical categories.
Current And Future Role Of Artificial Intelligence In Cardiac Imaging
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Author : Steffen Erhard Petersen
language : en
Publisher: Frontiers Media SA
Release Date : 2020-10-09
Current And Future Role Of Artificial Intelligence In Cardiac Imaging written by Steffen Erhard Petersen and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-09 with Medical categories.
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Deep Learning Image Synthesis For Mri
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Author : Evan Masataka Masutani
language : en
Publisher:
Release Date : 2022
Deep Learning Image Synthesis For Mri written by Evan Masataka Masutani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Since its invention in the 1970s, magnetic resonance imaging (MRI) has contributed greatly to our understanding of the human body in health and disease. MRI images anatomy and physiology with high spatiotemporal resolution and without ionizing radiation. Due to these factors, MRI is particularly well suited to studying the heart, and cardiac MRI is considered the clinical gold-standard for assessment of cardiac morphology, flow, and function. However, interpretation of cardiac MRI is highly dependent on image quality and often requires extensive manual annotation and visual analysis. Convolutional neural networks (CNNs), a form of deep learning and artificial intelligence, have potential to revolutionize medical imaging. Broadly, CNNs comprise a series of trainable weights called layers, which iteratively learn the features required to perform a given task. Currently, CNNs are being explored for a variety of computer vision tasks, such as classification, localization, and segmentation, but have untapped potential. Specifically, their ability to perform image synthesis is unknown. Given these challenges in MRI and the untapped potential of CNNs, I asked: can we use deep learning to perform image synthesis for MRI? Using this question as the bedrock for my dissertation, I set out to solve progressively more challenging problems in cardiovascular MRI using CNNs, building towards the ultimate task of automatically quantifying cardiac function and biomechanics. In aim 1, I asked whether existing CNNs can enhance low-resolution cardiac images. That is, can CNNs perform image super-resolution of steady-state free precession (SSFP)? Specifically, I asked which CNN architectures are suitable for this task and how well they perform relative to conventional image upscaling methods. In aim 2, I asked whether I could upgrade the CNN architectures from aim 1 to isolate and remove background signal from 4D Flow MRI. That is, can CNNs perform phase-error correction of 4D Flow MRI acquisitions via synthesis of the background static vector field? To achieve this, I asked what architectural modifications are necessary to infer these multi-component volumetric vector fields. I then compared CNN-based phase error correction with existing manual segmentation-based methods. In aim 3, I asked whether I could further upgrade my phase-error correction CNN from aim 2 to predict intracardiac blood flow from videos of the beating heart. That is, can CNNs infer dynamic blood flow velocity fields from cardiac cine SSFP images? Specifically, I asked how I could incorporate spatiotemporal information and anatomical boundaries into this new architecture I call Triton-Net. I then measured the correlation between the synthesized flow fields and 4D Flow MRI measurements. Lastly, I asked whether I could use these flow values to detect left ventricular outflow obstruction. Finally, in aim 4, I asked whether I could refine Triton-Net to evaluate local myocardial function. That is, could I add explicit physical constraints into Triton-Net to infer dynamic myocardial velocity and strain tensor fields from cardiac cine SSFP images? Realizing that myocardial contraction is periodic, I explored how I may encode net-zero displacement and strain constraints into the Triton-Net architecture, resulting in a heavily modified deep learning synthetic strain (DLSS) CNN. I then characterized DLSS strain in a healthy population and asked whether I could use DLSS strain to identify wall motion abnormalities in an ischemic heart disease population. Lastly, I compared DLSS classification performance against the consensus visual assessment of four cardiothoracic radiologist readers.
New Advances In Magnetic Resonance Imaging
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Author : Denis Larrivee
language : en
Publisher: BoD – Books on Demand
Release Date : 2024-02
New Advances In Magnetic Resonance Imaging written by Denis Larrivee and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02 with Medical categories.
Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics
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Author : Le Lu
language : en
Publisher: Springer Nature
Release Date : 2019-09-19
Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics written by Le Lu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-19 with Computers categories.
This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
Statistical Atlases And Computational Models Of The Heart Multi Disease Multi View And Multi Center Right Ventricular Segmentation In Cardiac Mri Challenge
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Author : Esther Puyol Antón
language : en
Publisher: Springer Nature
Release Date : 2022-01-14
Statistical Atlases And Computational Models Of The Heart Multi Disease Multi View And Multi Center Right Ventricular Segmentation In Cardiac Mri Challenge written by Esther Puyol Antón and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-14 with Computers categories.
This book constitutes the proceedings of the 12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021, as well as the M&Ms-2 Challenge: Multi-Disease, Multi-View and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. The 25 regular workshop papers included in this volume were carefully reviewed and selected after being revised. They deal with cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, artificial intelligence, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods. In addition, 15 papers from the M&MS-2 challenge are included in this volume. The Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge (M&Ms-2) is focusing on the development of generalizable deep learning models for the Right Ventricle that can maintain good segmentation accuracy on different centers, pathologies and cardiac MRI views. There was a total of 48 submissions to the workshop.
Simultaneous Multiparametric And Multidimensional Cardiovascular Magnetic Resonance Imaging
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Author : Aleksandra Radjenovic
language : en
Publisher: Frontiers Media SA
Release Date : 2023-06-30
Simultaneous Multiparametric And Multidimensional Cardiovascular Magnetic Resonance Imaging written by Aleksandra Radjenovic and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Medical categories.