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Noordman CR, Yakar D, Bosma J, Simonis FFJ, Huisman H. Complexities of deep learning-based undersampled MR image reconstruction. Eur Radiol Exp 2023; 7:58. [PMID: 37789241 PMCID: PMC10547669 DOI: 10.1186/s41747-023-00372-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 08/01/2023] [Indexed: 10/05/2023] Open
Abstract
Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction methods. The literature in this field shows exponential growth, both in volume and complexity, as the capabilities of machine learning in solving inverse problems such as image reconstruction are explored. We review the latest developments, aiming to assist researchers and radiologists who are developing new methods or seeking to provide valuable feedback. We shed light on key concepts by exploring the technical intricacies of MR image reconstruction, highlighting the importance of raw datasets and the difficulty of evaluating diagnostic value using standard metrics.Relevance statement Increasingly complex algorithms output reconstructed images that are difficult to assess for robustness and diagnostic quality, necessitating high-quality datasets and collaboration with radiologists.Key points• Deep learning-based image reconstruction algorithms are increasing both in complexity and performance.• The evaluation of reconstructed images may mistake perceived image quality for diagnostic value.• Collaboration with radiologists is crucial for advancing deep learning technology.
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Affiliation(s)
- Constant Richard Noordman
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands.
| | - Derya Yakar
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Joeran Bosma
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands
| | | | - Henkjan Huisman
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, 7030, Norway
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Krueger F, Aigner CS, Hammernik K, Dietrich S, Lutz M, Schulz-Menger J, Schaeffter T, Schmitter S. Rapid estimation of 2D relative B 1 + -maps from localizers in the human heart at 7T using deep learning. Magn Reson Med 2023; 89:1002-1015. [PMID: 36336877 DOI: 10.1002/mrm.29510] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/11/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE Subject-tailored parallel transmission pulses for ultra-high fields body applications are typically calculated based on subject-specific B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps of all transmit channels, which require lengthy adjustment times. This study investigates the feasibility of using deep learning to estimate complex, channel-wise, relative 2D B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps from a single gradient echo localizer to overcome long calibration times. METHODS 126 channel-wise, complex, relative 2D B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps of the human heart from 44 subjects were acquired at 7T using a Cartesian, cardiac gradient-echo sequence obtained under breath-hold to create a library for network training and cross-validation. The deep learning predicted maps were qualitatively compared to the ground truth. Phase-only B 1 + $$ {\mathrm{B}}_1^{+} $$ -shimming was subsequently performed on the estimated B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps for a region of interest covering the heart. The proposed network was applied at 7T to 3 unseen test subjects. RESULTS The deep learning-based B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps, derived in approximately 0.2 seconds, match the ground truth for the magnitude and phase. The static, phase-only pulse design performs best when maximizing the mean transmission efficiency. In-vivo application of the proposed network to unseen subjects demonstrates the feasibility of this approach: the network yields predicted B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps comparable to the acquired ground truth and anatomical scans reflect the resulting B 1 + $$ {\mathrm{B}}_1^{+} $$ -pattern using the deep learning-based maps. CONCLUSION The feasibility of estimating 2D relative B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps from initial localizer scans of the human heart at 7T using deep learning is successfully demonstrated. Because the technique requires only sub-seconds to derive channel-wise B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps, it offers high potential for advancing clinical body imaging at ultra-high fields.
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Affiliation(s)
- Felix Krueger
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany.,Technische Universität Berlin, Biomedical Engineering, Berlin, Germany
| | | | - Kerstin Hammernik
- Technical University of Munich, Munich, Germany.,Imperial College London, London, United Kingdom
| | | | - Max Lutz
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
| | - Jeanette Schulz-Menger
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Experimental Clinical Research Center, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany.,Department of Cardiology and Nephrology, HELIOS Hospital Berlin-Buch, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany.,Technische Universität Berlin, Biomedical Engineering, Berlin, Germany.,Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Sebastian Schmitter
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany.,Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA.,Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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