Journal ArticleDOI
Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.
TLDR
This article is an introductory overview aimed at clinical radiologists with no experience in deep‐learning‐based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.Abstract:
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.read more
Citations
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Journal ArticleDOI
CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions
Thomas Küstner,Niccolo Fuin,Kerstin Hammernik,Aurelien Bustin,Haikun Qi,Reza Hajhosseiny,Pier Giorgio Masci,Radhouene Neji,Radhouene Neji,Daniel Rueckert,René M. Botnar,René M. Botnar,Claudia Prieto,Claudia Prieto +13 more
TL;DR: A novel 4D (3D + time) deep learning-based reconstruction network, termed 4D CINENet, for prospectively undersampled 3D Cartesian CINE imaging, which outperforms iterative reconstructions in visual image quality and contrast and finds good agreement in LV function.
Journal ArticleDOI
Medical imaging and nuclear medicine: a Lancet Oncology Commission.
Hedvig Hricak,Hedvig Hricak,May Abdel-Wahab,May Abdel-Wahab,May Abdel-Wahab,Rifat Atun,Miriam Mikhail Lette,Diana Paez,James A. Brink,Lluis Donoso-Bach,Guy Frija,Monika Hierath,Ola Holmberg,Pek-Lan Khong,Jason S. Lewis,Jason S. Lewis,Geraldine McGinty,Geraldine McGinty,Wim J.G. Oyen,Wim J.G. Oyen,Lawrence N. Shulman,Zachary J. Ward,Andrew M. Scott +22 more
TL;DR: In this article, a global assessment of imaging and nuclear medicine resources identified substantial shortages in equipment and workforce, particularly in low-income and middle-income countries (LMICs), and proposed actions and investments that would enhance access to imaging equipment, workforce capacity, digital technology, radiopharmaceuticals, and research and training programmes in LMICs, to produce massive health and economic benefits and reduce the burden of cancer globally.
Journal ArticleDOI
Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.
Akshay S. Chaudhari,Christopher M. Sandino,Elizabeth K. Cole,David B. Larson,Garry E. Gold,Shreyas S. Vasanawala,Matthew P. Lungren,Brian A. Hargreaves,Curtis P. Langlotz +8 more
TL;DR: A framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices is provided.
Journal ArticleDOI
Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
TL;DR: This survey aims to review deep learning based MR image reconstruction works from 2016- June 2020 and will discuss merits, limitations and challenges associated with such methods, as a starting point for researchers interested in contributing to this field.
Book
Compressive Imaging: Structure, Sampling, Learning
Ben Adcock,Anders C. Hansen +1 more
TL;DR: An in-depth treatment of compressive imaging, with an eye to the next decade of imaging research, and using both empirical and mathematical insights, examines the potential benefits and the pitfalls of these latest approaches.
References
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