Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians
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 summari...
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| Published in | Journal of magnetic resonance imaging Vol. 53; no. 4; pp. 1015 - 1028 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.04.2021
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. 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 will 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. 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. 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.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. |
| Author | Knoll, Florian Lin, Dana J. Johnson, Patricia M. Lui, Yvonne W. |
| AuthorAffiliation | 1 Department of Radiology, NYU School of Medicine / NYU Langone Health 2 New York University School of Medicine, Center for Biomedical Imaging |
| AuthorAffiliation_xml | – name: 1 Department of Radiology, NYU School of Medicine / NYU Langone Health – name: 2 New York University School of Medicine, Center for Biomedical Imaging |
| Author_xml | – sequence: 1 givenname: Dana J. surname: Lin fullname: Lin, Dana J. organization: NYU School of Medicine/NYU Langone Health – sequence: 2 givenname: Patricia M. surname: Johnson fullname: Johnson, Patricia M. organization: New York University School of Medicine – sequence: 3 givenname: Florian surname: Knoll fullname: Knoll, Florian organization: New York University School of Medicine – sequence: 4 givenname: Yvonne W. orcidid: 0000-0002-9984-9164 surname: Lui fullname: Lui, Yvonne W. email: yvonne.lui@nyulangone.org organization: NYU School of Medicine/NYU Langone Health |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32048372$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1002/mrm.27771 10.1109/TMI.2018.2820120 10.1371/journal.pone.0223141 10.1109/TMI.2018.2858752 10.1148/radiol.2018180445 10.1002/mrm.1910380414 10.1109/TMI.2018.2833635 10.1002/mrm.21391 10.1016/j.media.2019.04.009 10.1002/mrm.27178 10.3174/ajnr.A5543 10.1088/1361-6560/ab0606 10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S 10.1148/radiol.2017170700 10.1109/TMI.2018.2865356 10.1002/jmri.26534 10.1016/j.zemedi.2018.12.003 10.1109/TMI.2017.2760978 10.1109/ISBI.2016.7493320 10.1002/mrm.10171 10.1002/mrm.27420 10.1102/1470-7330.2005.0018 10.1002/mrm.27896 10.1109/TIP.2003.819861 10.1118/1.596065 10.4236/ojmi.2017.74018 10.1002/mrm.27106 10.1109/CVPR.2009.5206848 10.1002/mrm.27201 10.3389/fneur.2019.00869 10.1109/TMI.2018.2887072 10.2967/jnumed.117.198051 10.1109/TMI.2016.2551324 10.1088/1361-6560/aac71a 10.1097/00005382-199001000-00011 10.1002/mrm.27134 10.1148/rg.2017170077 10.4236/ojmi.2017.73010 10.1088/1361-6560/aac763 10.1148/radiol.2018180547 10.1016/j.acra.2018.02.018 10.1007/s00259-019-04374-9 10.1109/TMI.2017.2785879 10.1109/TMI.2018.2863670 10.1038/nature25988 10.1002/mrm.27355 10.1088/0031-9155/60/20/8047 10.1038/nature14539 10.1109/TPAMI.2015.2439281 10.1002/mrm.26977 10.1002/mp.12122 10.1038/s41568-018-0016-5 10.1007/s11604-018-0796-2 |
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| References | 1987; 14 2018; 286 2019; 290 2015; 521 2019; 55 2019; 10 2018; 289 2017; 44 2019; 37 2019; 14 2009 2018; 80 2019; 38 2018; 63 1999; 42 2017; 07 2016; 38 2016; 35 2007; 58 2018; 25 2018; 18 2002; 47 2018; 39 2019; 82 2019; 81 2018; 5 2017; 37 2015; 60 2019; 64 2019; 46 2018; 555 2004; 13 2005; 5 2019; 49 2019 1985 2018 2019; 29 1997; 38 2016 2018; 38 2018; 37 2018; 59 1990; 5 2018; 79 Liu F (e_1_2_11_51_1) 2018; 5 e_1_2_11_55_1 e_1_2_11_30_1 e_1_2_11_57_1 e_1_2_11_36_1 e_1_2_11_13_1 e_1_2_11_34_1 e_1_2_11_53_1 e_1_2_11_11_1 e_1_2_11_29_1 e_1_2_11_6_1 e_1_2_11_27_1 e_1_2_11_4_1 e_1_2_11_48_1 e_1_2_11_2_1 Lecun Y (e_1_2_11_19_1) 1985 e_1_2_11_60_1 e_1_2_11_20_1 e_1_2_11_45_1 Fuin N (e_1_2_11_33_1) 2019 e_1_2_11_47_1 e_1_2_11_24_1 e_1_2_11_41_1 e_1_2_11_8_1 e_1_2_11_22_1 e_1_2_11_43_1 e_1_2_11_17_1 e_1_2_11_15_1 Zhang C (e_1_2_11_62_1) 2019 e_1_2_11_38_1 Sun J (e_1_2_11_9_1) 2016 e_1_2_11_50_1 e_1_2_11_10_1 Chen F (e_1_2_11_32_1) 2019 e_1_2_11_31_1 e_1_2_11_56_1 e_1_2_11_58_1 e_1_2_11_14_1 e_1_2_11_35_1 e_1_2_11_52_1 e_1_2_11_12_1 e_1_2_11_7_1 e_1_2_11_28_1 e_1_2_11_5_1 e_1_2_11_26_1 e_1_2_11_3_1 e_1_2_11_49_1 Chaudhari AS (e_1_2_11_59_1) 2019 e_1_2_11_61_1 e_1_2_11_21_1 e_1_2_11_44_1 e_1_2_11_46_1 e_1_2_11_25_1 e_1_2_11_40_1 e_1_2_11_23_1 e_1_2_11_42_1 e_1_2_11_18_1 e_1_2_11_16_1 e_1_2_11_37_1 e_1_2_11_39_1 Kobler E (e_1_2_11_54_1) 2018 |
| References_xml | – year: 1985 – volume: 521 start-page: 436 issue: 7553 year: 2015 end-page: 444 article-title: Deep learning publication-title: Nature – volume: 80 start-page: 2139 issue: 5 year: 2018 end-page: 2154 article-title: Super‐resolution musculoskeletal MRI using deep learning publication-title: Magn Reson Med – start-page: 10 year: 2016 end-page: 18 article-title: ADMM‐net for compressive sensing MRI publication-title: Adv Neural Inf Process Syst – volume: 5 issue: 1 year: 2018 article-title: A deep learning approach for (18)F‐FDG PET attenuation correction publication-title: Eur J Nucl Med Mol Imaging Phys – volume: 46 start-page: 2700 issue: 13 year: 2019 end-page: 2707 article-title: Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning publication-title: Eur J Nucl Med Mol Imaging – volume: 49 start-page: 939 issue: 4 year: 2019 end-page: 954 article-title: Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI publication-title: J Magn Reson Imaging – volume: 44 start-page: 902 issue: 3 year: 2017 end-page: 913 article-title: Hybrid ZTE/Dixon MR‐based attenuation correction for quantitative uptake estimation of pelvic lesions in PET/MRI publication-title: Med Phys – volume: 25 start-page: 1472 issue: 11 year: 2018 end-page: 1480 article-title: Deep learning in radiology publication-title: Acad Radiol – start-page: 248 year: 2009 end-page: 255 – volume: 82 start-page: 2299 issue: 6 year: 2019 end-page: 2313 article-title: K‐space deep learning for reference‐free EPI ghost correction publication-title: Magn Reson Med – volume: 35 start-page: 1344 issue: 5 year: 2016 end-page: 1351 article-title: Q‐space deep learning: Twelve‐fold shorter and model‐free diffusion MRI scans publication-title: IEEE Trans Med Imaging – volume: 82 start-page: 1452 issue: 4 year: 2019 end-page: 1461 article-title: Network accelerated motion estimation and reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model publication-title: Magn Reson Med – volume: 38 start-page: 1633 issue: 7 year: 2019 end-page: 1642 article-title: MR image reconstruction using deep density priors publication-title: IEEE Trans Med Imaging – volume: 81 start-page: 439 issue: 1 year: 2019 end-page: 453 article-title: Scan‐specific robust artificial‐neural‐networks for space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imaging publication-title: Magn Reson Med – volume: 59 start-page: 852 issue: 5 year: 2018 end-page: 858 article-title: Zero‐echo‐time and Dixon deep pseudo‐CT (ZeDD CT): Direct generation of pseudo‐CT images for pelvic PET/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI publication-title: J Nucl Med – volume: 79 start-page: 3055 issue: 6 year: 2018 end-page: 3071 article-title: Learning a variational network for reconstruction of accelerated MRI data publication-title: Magn Reson Med – volume: 5 start-page: 67 issue: 1 year: 1990 end-page: 76 article-title: Computer‐aided diagnosis in chest radiology publication-title: J Thorac Imaging – year: 2019 article-title: Data‐driven self‐calibration and reconstruction for non‐Cartesian wave‐encoded single‐shot fast spin echo using deep learning publication-title: J Magn Reson Imaging – start-page: 6687 year: 2018 end-page: 6691 – volume: 80 start-page: 2188 issue: 5 year: 2018 end-page: 2201 article-title: KIKI‐net: Cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images publication-title: Magn Reson Med – volume: 60 start-page: 8047 issue: 20 year: 2015 end-page: 8065 article-title: Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): Application to PET/MR brain imaging publication-title: Phys Med Biol – volume: 80 start-page: 1440 issue: 4 year: 2018 end-page: 1451 article-title: Zero TE‐based pseudo‐CT image conversion in the head and its application in PET/MR attenuation correction and MR‐guided radiation therapy planning publication-title: Magn Reson Med – volume: 555 start-page: 487 issue: 7697 year: 2018 end-page: 492 article-title: Image reconstruction by domain‐transform manifold learning publication-title: Nature – year: 2019 – volume: 58 start-page: 1182 issue: 6 year: 2007 end-page: 1195 article-title: Sparse MRI: The application of compressed sensing for rapid MR imaging publication-title: Magn Reson Med – volume: 18 start-page: 500 issue: 8 year: 2018 end-page: 510 article-title: Artificial intelligence in radiology publication-title: Nat Rev Cancer – volume: 14 start-page: 538 issue: 4 year: 1987 end-page: 548 article-title: Image feature analysis and computer‐aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography publication-title: Med Phys – volume: 37 start-page: 1310 issue: 6 year: 2018 end-page: 1321 article-title: DAGAN: Deep de‐aliasing generative adversarial networks for fast compressed sensing MRI reconstruction publication-title: IEEE Trans Med Imaging – volume: 07 start-page: 100 issue: 03 year: 2017 end-page: 111 article-title: Performance evaluation of super‐resolution methods using deep‐learning and sparse‐coding for improving the image quality of magnified images in chest radiographs publication-title: Open J Med Imaging – volume: 80 start-page: 1189 issue: 3 year: 2018 end-page: 1205 article-title: Deep learning with domain adaptation for accelerated projection‐reconstruction MR publication-title: Magn Reson Med – volume: 14 issue: 10 year: 2019 article-title: Attenuation correction using 3D deep convolutional neural network for brain 18F‐FDG PET/MR: Comparison with atlas, ZTE and CT based attenuation correction publication-title: PLoS One – volume: 29 start-page: 86 issue: 2 year: 2019 end-page: 101 article-title: A gentle introduction to deep learning in medical image processing publication-title: Z Med Phys – volume: 55 start-page: 136 year: 2019 end-page: 147 article-title: Automatic CNN‐based detection of cardiac MR motion artefacts using k space data augmentation and curriculum learning publication-title: Med Image Anal – volume: 13 start-page: 600 issue: 4 year: 2004 end-page: 612 article-title: Image quality assessment: From error visibility to structural similarity publication-title: IEEE Trans Image Process – volume: 47 start-page: 1202 issue: 6 year: 2002 end-page: 1210 article-title: Generalized autocalibrating partially parallel acquisitions (GRAPPA) publication-title: Magn Reson Med – volume: 07 start-page: 180 issue: 04 year: 2017 end-page: 195 article-title: Super‐resolution imaging of mammograms based on the super‐resolution convolutional neural network publication-title: Open J Med Imaging – volume: 38 start-page: 295 issue: 2 year: 2016 end-page: 307 article-title: Image super‐resolution using deep convolutional networks publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 38 start-page: 591 issue: 4 year: 1997 end-page: 603 article-title: Simultaneous acquisition of spatial harmonics (SMASH): Fast imaging with radiofrequency coil arrays publication-title: Magn Reson Med – volume: 63 issue: 12 year: 2018 article-title: Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images publication-title: Phys Med Biol – volume: 38 start-page: 280 issue: 1 year: 2018 end-page: 290 article-title: Convolutional recurrent neural networks for dynamic MR image reconstruction publication-title: IEEE Trans Med Imaging – volume: 63 issue: 13 year: 2018 article-title: Deep learning for undersampled MRI reconstruction publication-title: Phys Med Biol – volume: 5 start-page: 17 year: 2005 end-page: 19 article-title: Computer aided detection (CAD): An overview publication-title: Cancer Imaging – volume: 38 start-page: 394 issue: 2 year: 2019 end-page: 405 article-title: MoDL: Model‐based deep learning architecture for inverse problems publication-title: IEEE Trans Med Imaging – volume: 37 start-page: 491 issue: 2 year: 2018 end-page: 503 article-title: A deep cascade of convolutional neural networks for dynamic MR image reconstruction publication-title: IEEE Trans Med Imaging – volume: 64 issue: 7 year: 2019 article-title: Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain (18)F‐FDG PET publication-title: Phys Med Biol – volume: 37 start-page: 1488 issue: 6 year: 2018 end-page: 1497 article-title: Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss publication-title: IEEE Trans Med Imaging – volume: 37 start-page: 1289 issue: 6 year: 2018 end-page: 1296 article-title: Image reconstruction is a new frontier of machine learning publication-title: IEEE Trans Med Imaging – volume: 289 start-page: 366 issue: 2 year: 2018 end-page: 373 article-title: Variable‐density single‐shot fast spin‐echo MRI with deep learning reconstruction by using variational networks publication-title: Radiology – volume: 39 start-page: 1776 issue: 10 year: 2018 end-page: 1784 article-title: Deep learning in neuroradiology publication-title: AJNR Am J Neuroradiol – volume: 81 start-page: 116 issue: 1 year: 2019 end-page: 128 article-title: Assessment of the generalization of learned image reconstruction and the potential for transfer learning publication-title: Magn Reson Med – start-page: 1636 year: 2019 end-page: 1640 – volume: 290 start-page: 590 issue: 3 year: 2019 end-page: 606 article-title: Convolutional neural networks for radiologic images: A radiologist's guide publication-title: Radiology – volume: 10 start-page: 869 year: 2019 article-title: Applications of deep learning to neuro‐imaging techniques publication-title: Front Neurol – volume: 37 start-page: 73 issue: 1 year: 2019 end-page: 80 article-title: Improvement of image quality at CT and MRI using deep learning publication-title: Jpn J Radiol – volume: 42 start-page: 952 issue: 5 year: 1999 end-page: 962 article-title: SENSE: Sensitivity encoding for fast MRI publication-title: Magn Reson Med – volume: 37 start-page: 2113 issue: 7 year: 2017 end-page: 2131 article-title: Deep learning: A primer for radiologists publication-title: Radiographics – volume: 38 start-page: 167 issue: 1 year: 2019 end-page: 179 article-title: Deep generative adversarial neural networks for compressive sensing MRI publication-title: IEEE Trans Med Imaging – start-page: 514 year: 2016 end-page: 517 – volume: 286 start-page: 676 issue: 2 year: 2018 end-page: 684 article-title: Deep learning MR imaging‐based attenuation correction for PET/MR imaging publication-title: Radiology – ident: e_1_2_11_41_1 doi: 10.1002/mrm.27771 – ident: e_1_2_11_35_1 doi: 10.1109/TMI.2018.2820120 – ident: e_1_2_11_48_1 doi: 10.1371/journal.pone.0223141 – ident: e_1_2_11_14_1 doi: 10.1109/TMI.2018.2858752 – ident: e_1_2_11_11_1 doi: 10.1148/radiol.2018180445 – ident: e_1_2_11_27_1 doi: 10.1002/mrm.1910380414 – ident: e_1_2_11_8_1 doi: 10.1109/TMI.2018.2833635 – ident: e_1_2_11_28_1 doi: 10.1002/mrm.21391 – ident: e_1_2_11_40_1 doi: 10.1016/j.media.2019.04.009 – ident: e_1_2_11_58_1 doi: 10.1002/mrm.27178 – ident: e_1_2_11_7_1 doi: 10.3174/ajnr.A5543 – ident: e_1_2_11_52_1 doi: 10.1088/1361-6560/ab0606 – ident: e_1_2_11_26_1 doi: 10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S – ident: e_1_2_11_50_1 doi: 10.1148/radiol.2017170700 – ident: e_1_2_11_30_1 doi: 10.1109/TMI.2018.2865356 – start-page: 10 year: 2016 ident: e_1_2_11_9_1 article-title: ADMM‐net for compressive sensing MRI publication-title: Adv Neural Inf Process Syst – ident: e_1_2_11_17_1 doi: 10.1002/jmri.26534 – ident: e_1_2_11_20_1 doi: 10.1016/j.zemedi.2018.12.003 – ident: e_1_2_11_38_1 doi: 10.1109/TMI.2017.2760978 – ident: e_1_2_11_10_1 doi: 10.1109/ISBI.2016.7493320 – ident: e_1_2_11_25_1 doi: 10.1002/mrm.10171 – ident: e_1_2_11_34_1 doi: 10.1002/mrm.27420 – ident: e_1_2_11_4_1 doi: 10.1102/1470-7330.2005.0018 – ident: e_1_2_11_42_1 doi: 10.1002/mrm.27896 – ident: e_1_2_11_61_1 doi: 10.1109/TIP.2003.819861 – ident: e_1_2_11_2_1 doi: 10.1118/1.596065 – ident: e_1_2_11_57_1 doi: 10.4236/ojmi.2017.74018 – volume-title: Proceedings of Cognitiva year: 1985 ident: e_1_2_11_19_1 – year: 2019 ident: e_1_2_11_32_1 article-title: Data‐driven self‐calibration and reconstruction for non‐Cartesian wave‐encoded single‐shot fast spin echo using deep learning publication-title: J Magn Reson Imaging – ident: e_1_2_11_60_1 doi: 10.1002/mrm.27106 – ident: e_1_2_11_53_1 doi: 10.1109/CVPR.2009.5206848 – ident: e_1_2_11_13_1 doi: 10.1002/mrm.27201 – ident: e_1_2_11_21_1 doi: 10.3389/fneur.2019.00869 – ident: e_1_2_11_31_1 doi: 10.1109/TMI.2018.2887072 – ident: e_1_2_11_44_1 doi: 10.2967/jnumed.117.198051 – ident: e_1_2_11_22_1 doi: 10.1109/TMI.2016.2551324 – ident: e_1_2_11_23_1 doi: 10.1088/1361-6560/aac71a – ident: e_1_2_11_3_1 doi: 10.1097/00005382-199001000-00011 – ident: e_1_2_11_43_1 doi: 10.1002/mrm.27134 – start-page: 1636 volume-title: 52nd Asilomar conference on signals, systems and computers year: 2019 ident: e_1_2_11_62_1 – ident: e_1_2_11_18_1 doi: 10.1148/rg.2017170077 – ident: e_1_2_11_56_1 doi: 10.4236/ojmi.2017.73010 – ident: e_1_2_11_49_1 doi: 10.1088/1361-6560/aac763 – volume-title: A variational neural network for accelerating free‐breathing whole‐heart coronary MR angiography year: 2019 ident: e_1_2_11_33_1 – ident: e_1_2_11_16_1 doi: 10.1148/radiol.2018180547 – ident: e_1_2_11_6_1 doi: 10.1016/j.acra.2018.02.018 – ident: e_1_2_11_47_1 doi: 10.1007/s00259-019-04374-9 – ident: e_1_2_11_36_1 doi: 10.1109/TMI.2017.2785879 – ident: e_1_2_11_39_1 doi: 10.1109/TMI.2018.2863670 – ident: e_1_2_11_37_1 doi: 10.1038/nature25988 – volume: 5 start-page: 24 issue: 1 year: 2018 ident: e_1_2_11_51_1 article-title: A deep learning approach for (18)F‐FDG PET attenuation correction publication-title: Eur J Nucl Med Mol Imaging Phys – start-page: 6687 volume-title: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) year: 2018 ident: e_1_2_11_54_1 – ident: e_1_2_11_12_1 doi: 10.1002/mrm.27355 – ident: e_1_2_11_46_1 doi: 10.1088/0031-9155/60/20/8047 – ident: e_1_2_11_15_1 doi: 10.1038/nature14539 – volume-title: J Magn Reson Imaging year: 2019 ident: e_1_2_11_59_1 – ident: e_1_2_11_55_1 doi: 10.1109/TPAMI.2015.2439281 – ident: e_1_2_11_29_1 doi: 10.1002/mrm.26977 – ident: e_1_2_11_45_1 doi: 10.1002/mp.12122 – ident: e_1_2_11_5_1 doi: 10.1038/s41568-018-0016-5 – ident: e_1_2_11_24_1 doi: 10.1007/s11604-018-0796-2 |
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| Snippet | Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep‐learning models for data... Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data... Artificial intelligence (AI) shows tremendous promise in the field of medical imaging with recent breakthroughs applying deep learning models for data... |
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| SubjectTerms | Algorithms Artificial Intelligence Computational neuroscience Data acquisition Deep learning Humans Image acquisition Image classification Image processing Image Processing, Computer-Assisted Image quality Image reconstruction Image segmentation Learning algorithms Machine learning Magnetic resonance imaging Medical imaging MRI Neuroimaging Radiography Therapeutic applications |
| Title | Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians |
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