Deep Learning Based Methods For Image Reconstruction In Cardiac Ct And Cardiac Cine Mri
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Deep Learning Based Methods For Image Reconstruction In Cardiac Ct And Cardiac Cine Mri
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Author : Andreas Kofler
language : en
Publisher:
Release Date : 2021
Deep Learning Based Methods For Image Reconstruction In Cardiac Ct And Cardiac Cine Mri written by Andreas Kofler and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Deep Learning Based Methods For Image Reconstruction In Cardiac Ct And Cardiac Cine Mri
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Author : Andreas Kofler
language : en
Publisher:
Release Date : 2021
Deep Learning Based Methods For Image Reconstruction In Cardiac Ct And Cardiac Cine Mri written by Andreas Kofler and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Deep Learning Based Image Reconstruction In Abdominal And Cardiac Magnetic Resonance Imaging Mri
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Author : Chang Gao
language : en
Publisher:
Release Date : 2023
Deep Learning Based Image Reconstruction In Abdominal And Cardiac Magnetic Resonance Imaging Mri written by Chang Gao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
MRI plays an important role in abdominal and cardiac imaging due to its excellent soft tissue contrast and high image resolution. Despite the benefit of excellent image quality, MRI acquisition is intrinsically slow, causing patient discomfort and slowing down the clinical workflow, which hinders its broad clinical use. For decades, undersampling reconstruction techniques have been investigated to accelerate MRI acquisition. Traditional parallel imaging and compressed sensing methods either have limited acceleration capability or require extensive computational and time resources. While the recent development of deep learning achieved unprecedented performance in image reconstruction and image enhancement tasks, there are challenges remaining to be solved. One challenge is the potential loss of image details due to network over-smooths or over-regularization. Another challenge is that networks may struggle to generalize well to diverse MRI data acquired under different conditions. In medical imaging, high-quality diverse datasets are challenging to acquire, especially for rare or specialized MRI applications. Lastly, for non-Cartesian sampling, the reconstruction can be challenging due to the need for time-consuming interpolation of non-Cartesian k-space onto a Cartesian basis. The overall goal of the dissertation is to contribute to the development of deep learning-based accelerated image reconstruction techniques and investigate the challenges in network development as mentioned above. Specifically, we aim to develop deep learning networks to improve image quality and reduce artifacts and noise for the application of (1) undersampled radial MRI reconstruction in the abdomen (aims 1 and 2), and (2) ferumoxytol-enhanced cardiac cine MRI reconstruction (aim 3). In aim 1, I developed a generative adversarial network using paired undersampled and ground truth images to reduce streaking artifacts and preserve image sharpness. In aim 2, I developed a radial k-space prediction framework by training an attention-based transformer network on k-space data. By combining the acquired and predicted k-space data, the reconstructed images will have an improved signal-to-noise ratio and fewer streaking artifacts. In aim 3, I developed an unrolled spatiotemporal deep learning network for ferumoxytol-enhanced cardiac cine MRI reconstruction. The network was trained using non-contrast-enhanced bSSFP cine images and can be successfully generalized to ferumoxytol-enhanced images.
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.
Machine Learning For Medical Image Reconstruction
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Author : Florian Knoll
language : en
Publisher: Springer
Release Date : 2018-09-11
Machine Learning For Medical Image Reconstruction written by Florian Knoll and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-11 with Computers categories.
This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction.
Machine Learning For Medical Image Reconstruction
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Author : Florian Knoll
language : en
Publisher: Springer Nature
Release Date : 2019-10-24
Machine Learning For Medical Image Reconstruction written by Florian Knoll and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-24 with Computers categories.
This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.
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.
Machine Learning For Medical Image Reconstruction
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Author : Nandinee Haq
language : en
Publisher: Springer Nature
Release Date : 2022-09-22
Machine Learning For Medical Image Reconstruction written by Nandinee Haq and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-22 with Computers categories.
This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with MICCAI 2022, in September 2022, held in Singapore. The 15 papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
Deep Learning For Biomedical Image Reconstruction
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Author : Jong Chul Ye
language : en
Publisher: Cambridge University Press
Release Date : 2023-09-30
Deep Learning For Biomedical Image Reconstruction written by Jong Chul Ye and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-30 with Technology & Engineering categories.
Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. The background theory of deep learning is introduced step-by-step, and by incorporating modeling fundamentals this book explains how to implement deep learning in a variety of modalities, including X-ray, CT, MRI and others. Real-world examples demonstrate an interdisciplinary approach to medical image reconstruction processes, featuring numerous imaging applications. Recent clinical studies and innovative research activity in generative models and mathematical theory will inspire the reader towards new frontiers. This book is ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics.
Machine Learning For Medical Image Reconstruction
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Author : Nandinee Haq
language : en
Publisher: Springer Nature
Release Date : 2021-09-29
Machine Learning For Medical Image Reconstruction written by Nandinee Haq and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-29 with Computers categories.
This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.