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Ahmed HS. Uncover This Tech Term: Generative Adversarial Networks. Korean J Radiol 2024; 25:493-498. [PMID: 38627875 PMCID: PMC11058428 DOI: 10.3348/kjr.2023.1306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/04/2024] [Accepted: 02/11/2024] [Indexed: 05/01/2024] Open
Affiliation(s)
- H Shafeeq Ahmed
- Bangalore Medical College and Research Institute, Bangalore, India.
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Li Z, Li Z, Bilgic B, Lee HH, Ying K, Huang SY, Liao H, Tian Q. DIMOND: DIffusion Model OptimizatioN with Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2307965. [PMID: 38634608 DOI: 10.1002/advs.202307965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/09/2024] [Indexed: 04/19/2024]
Abstract
Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non-invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning-based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics-informed and self-supervised Deep learning entitled "DIMOND" is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self-supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications.
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Affiliation(s)
- Zihan Li
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Ziyu Li
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02129, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02129, USA
| | - Kui Ying
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, P. R. China
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02129, USA
| | - Hongen Liao
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Qiyuan Tian
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, P. R. China
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Feuerriegel GC, Weiss K, Tu Van A, Leonhardt Y, Neumann J, Gassert FT, Haas Y, Schwarz M, Makowski MR, Woertler K, Karampinos DC, Gersing AS. Deep-learning-based image quality enhancement of CT-like MR imaging in patients with suspected traumatic shoulder injury. Eur J Radiol 2024; 170:111246. [PMID: 38056345 DOI: 10.1016/j.ejrad.2023.111246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/21/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE To evaluate the diagnostic performance of CT-like MR images reconstructed with an algorithm combining compressed sense (CS) with deep learning (DL) in patients with suspected osseous shoulder injury compared to conventional CS-reconstructed images. METHODS Thirty-two patients (12 women, mean age 46 ± 14.9 years) with suspected traumatic shoulder injury were prospectively enrolled into the study. All patients received MR imaging of the shoulder, including a CT-like 3D T1-weighted gradient-echo (T1 GRE) sequence and in case of suspected fracture a conventional CT. An automated DL-based algorithm, combining CS and DL (CS DL) was used to reconstruct images of the same k-space data as used for CS reconstructions. Two musculoskeletal radiologists assessed the images for osseous pathologies, image quality and visibility of anatomical landmarks using a 5-point Likert scale. Moreover, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. RESULTS Compared to CT, all acute fractures (n = 23) and osseous pathologies were detected accurately on the CS only and CS DL images with almost perfect agreement between the CS DL and CS only images (κ 0.95 (95 %confidence interval 0.82-1.00). Image quality as well as the visibility of the fracture lines, bone fragments and glenoid borders were overall rated significantly higher for the CS DL reconstructions than the CS only images (CS DL range 3.7-4.9 and CS only range 3.2-3.8, P = 0.01-0.04). Significantly higher SNR and CNR values were observed for the CS DL reconstructions (P = 0.02-0.03). CONCLUSION Evaluation of traumatic shoulder pathologies is feasible using a DL-based algorithm for reconstruction of high-resolution CT-like MR imaging.
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Affiliation(s)
- Georg C Feuerriegel
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | | | - Anh Tu Van
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Yannik Leonhardt
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Jan Neumann
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; Musculoskeletal Radiology Section, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Florian T Gassert
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Yannick Haas
- Department of Trauma Surgery, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Markus Schwarz
- Department of Trauma Surgery, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Marcus R Makowski
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Klaus Woertler
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; Musculoskeletal Radiology Section, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Dimitrios C Karampinos
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Alexandra S Gersing
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; Department of Neuroradiology, University Hospital of Munich, LMU Munich, Munich, Germany.
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Feuerriegel GC, Weiss K, Kronthaler S, Leonhardt Y, Neumann J, Wurm M, Lenhart NS, Makowski MR, Schwaiger BJ, Woertler K, Karampinos DC, Gersing AS. Evaluation of a deep learning-based reconstruction method for denoising and image enhancement of shoulder MRI in patients with shoulder pain. Eur Radiol 2023; 33:4875-4884. [PMID: 36806569 PMCID: PMC10289918 DOI: 10.1007/s00330-023-09472-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/07/2023] [Accepted: 01/22/2023] [Indexed: 02/21/2023]
Abstract
OBJECTIVES To evaluate the diagnostic performance of an automated reconstruction algorithm combining MR imaging acquired using compressed SENSE (CS) with deep learning (DL) in order to reconstruct denoised high-quality images from undersampled MR images in patients with shoulder pain. METHODS Prospectively, thirty-eight patients (14 women, mean age 40.0 ± 15.2 years) with shoulder pain underwent morphological MRI using a pseudo-random, density-weighted k-space scheme with an acceleration factor of 2.5 using CS only. An automated DL-based algorithm (CS DL) was used to create reconstructions of the same k-space data as used for CS reconstructions. Images were analyzed by two radiologists and assessed for pathologies, image quality, and visibility of anatomical landmarks using a 4-point Likert scale. RESULTS Overall agreement for the detection of pathologies between the CS DL reconstructions and CS images was substantial to almost perfect (κ 0.95 (95% confidence interval 0.82-1.00)). Image quality and the visibility of the rotator cuff, articular cartilage, and axillary recess were overall rated significantly higher for CS DL images compared to CS (p < 0.03). Contrast-to-noise ratios were significantly higher for cartilage/fluid (CS DL 198 ± 24.3, CS 130 ± 32.2, p = 0.02) and ligament/fluid (CS DL 184 ± 17.3, CS 141 ± 23.5, p = 0.03) and SNR values were significantly higher for ligaments and muscle of the CS DL reconstructions (p < 0.04). CONCLUSION Evaluation of shoulder pathologies was feasible using a DL-based algorithm for MRI reconstruction and denoising. In clinical routine, CS DL may be beneficial in particular for reducing image noise and may be useful for the detection and better discrimination of discrete pathologies. Assessment of shoulder pathologies was feasible with improved image quality as well as higher SNR using a compressed sensing deep learning-based framework for image reconstructions and denoising. KEY POINTS • Automated deep learning-based reconstructions showed a significant increase in signal-to-noise ratio and contrast-to-noise ratio (p < 0.04) with only a slight increase of reconstruction time of 40 s compared to CS. • All pathologies were accurately detected with no loss of diagnostic information or prolongation of the scan time. • Significant improvements of the image quality as well as the visibility of the rotator cuff, articular cartilage, and axillary recess were detected.
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Affiliation(s)
- Georg C Feuerriegel
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany.
| | | | - Sophia Kronthaler
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Yannik Leonhardt
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Jan Neumann
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
- Musculoskeletal Radiology Section, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Markus Wurm
- Department of Trauma Surgery, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Nicolas S Lenhart
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Benedikt J Schwaiger
- Department of Neuroradiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Klaus Woertler
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
- Musculoskeletal Radiology Section, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Alexandra S Gersing
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
- Department of Neuroradiology, University Hospital of Munich, LMU Munich, Munich, Germany
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Pombo G, Gray R, Cardoso MJ, Ourselin S, Rees G, Ashburner J, Nachev P. Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models. Med Image Anal 2023; 84:102723. [PMID: 36542907 PMCID: PMC10591114 DOI: 10.1016/j.media.2022.102723] [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: 12/07/2021] [Revised: 11/21/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022]
Abstract
We describe CounterSynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesised counterfactuals with voxel-based morphometry, classification and regression of the conditioning attributes, and the Fréchet inception distance. Examining downstream discriminative performance in the context of engineered demographic imbalance and confounding, we use UK Biobank and OASIS magnetic resonance imaging data to benchmark CounterSynth augmentation against current solutions to these problems. We achieve state-of-the-art improvements, both in overall fidelity and equity. The source code for CounterSynth is available at https://github.com/guilherme-pombo/CounterSynth.
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Affiliation(s)
- Guilherme Pombo
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Robert Gray
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Geraint Rees
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - John Ashburner
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Parashkev Nachev
- UCL Queen Square Institute of Neurology, University College London, London, UK
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Ye X, Wang P, Li S, Zhang J, Lian Y, Zhang Y, Lu J, Guo H. Simultaneous superresolution reconstruction and distortion correction for single-shot EPI DWI using deep learning. Magn Reson Med 2023; 89:2456-2470. [PMID: 36705077 DOI: 10.1002/mrm.29601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 12/07/2022] [Accepted: 01/12/2023] [Indexed: 01/28/2023]
Abstract
PURPOSE Single-shot (SS) EPI is widely used for clinical DWI. This study aims to develop an end-to-end deep learning-based method with a novel loss function in an improved network structure to simultaneously increase the resolution and correct distortions for SS-EPI DWI. THEORY AND METHODS Point-spread-function (PSF)-encoded EPI can provide high-resolution, distortion-free DWI images. A distorted image from SS-EPI can be described as the convolution between a PSF function with a distortion-free image. The deconvolution process to recover the distortion-free image can be achieved with a convolution neural network, which also learns the mapping function between low-resolution SS-EPI and high-resolution reference PSF-EPI to achieve superresolution. To suppress the oversmoothing effect, we proposed a modified generative adversarial network structure, in which a dense net with gradient map guidance and a multilevel fusion block was used as the generator. A fractional anisotropy loss was proposed to utilize the diffusion anisotropy information among diffusion directions. In vivo brain DWI data were used to test the proposed method. RESULTS The results show that distortion-corrected high-resolution DWI images with restored structural details can be obtained from low-resolution SS-EPI images by taking advantage of the high-resolution anatomical images. Additionally, the proposed network can improve the quantitative accuracy of diffusion metrics compared with previously reported networks. CONCLUSION Using high-resolution, distortion-free EPI-DWI images as references, a deep learning-based method to simultaneously increase the perceived resolution and correct distortions for low-resolution SS-EPI was proposed. The results show that DWI image quality and diffusion metrics can be improved.
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Affiliation(s)
- Xinyu Ye
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Peipei Wang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Sisi Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Jieying Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yuan Lian
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yajing Zhang
- MR Clinical Science, Philips Healthcare, Suzhou, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI. FUTURE INTERNET 2022. [DOI: 10.3390/fi14120351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect abnormalities in brain images without an extensive manual feature extraction process. Generative adversarial network (GAN)-synthesized images have many applications in this field besides augmentation, such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. The existing literature was reviewed systematically to understand the role of GAN-synthesized dummy images in brain disease diagnosis. Web of Science and Scopus databases were extensively searched to find relevant studies from the last 6 years to write this systematic literature review (SLR). Predefined inclusion and exclusion criteria helped in filtering the search results. Data extraction is based on related research questions (RQ). This SLR identifies various loss functions used in the above applications and software to process brain MRIs. A comparative study of existing evaluation metrics for GAN-synthesized images helps choose the proper metric for an application. GAN-synthesized images will have a crucial role in the clinical sector in the coming years, and this paper gives a baseline for other researchers in the field.
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Liu K, Xi B, Sun H, Wang J, Chen C, Wen X, Zhang Y, Zeng M. The clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) for evaluation of uncooperative patients with brain diseases: comparison with the conventional T2-FLAIR with parallel imaging. Acta Radiol 2022; 64:1943-1949. [PMID: 36423247 DOI: 10.1177/02841851221139125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Background Satisfactory magnetic resonance imaging (MRI) of those patients with involuntary head motion due to brain diseases is essential in avoiding missed diagnosis and guiding treatment. Purpose To investigate the clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) in evaluating patients with involuntary head motion due to brain diseases, compared with the conventional T2-FLAIR with parallel imaging (PI-FLAIR). Material and Methods A total of 33 uncooperative patients with brain disease were prospectively enrolled. Two readers independently reviewed images acquired with ACS-SS-FLAIR and PI-FLAIR at a 3.0-T MR scanner. In the aspects of qualitative evaluation of image quality, overall image quality and lesion conspicuity of ACS-SS-FLAIR and PI-FLAIR were assessed and then statistically compared by paired Wilcoxon rank-sum test. For quantitative evaluation, the relative contrast of lesion-to-cerebral parenchyma were calculated and compared. Results Overall image quality scores of ACS-SS-FLAIR evaluated by two readers were 2.94 ± 0.24 and 2.91 ± 0.29, respectively, both of which were significantly higher than that of PI-FLAIR, respectively ( P < 0.001 and <0.001). Lesion conspicuity scores of were 2.74 ± 0.47 and 2.79 ± 0.44, both of which were significantly higher than that of PI-FLAIR, respectively ( P < 0.001 and <0.001). In the quantitative evaluation for image quality, the relative contrast of lesion-to-cerebral parenchyma was 0.34 ± 0.09 in the ACS-SS-FLAIR sequence, significantly larger than that in the PI-FLAIR sequence ( P = 0.001). Conclusion The ACS-SS-FLAIR sequence is clinically feasible in the MRI workup of those patients with involuntary head motion due to brain diseases, showing shorter image acquisition time and better image quality compared with conventional PI-FLAIR.
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Affiliation(s)
- Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
| | - Bin Xi
- Wuxi 9th People's Hospital Affiliated to Soochow University, Wuxi, PR China
| | - Haitao Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
| | - Jian Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
| | - Caizhong Chen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
| | - Xixi Wen
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, PR China
| | - Yunfei Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, PR China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
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Ali H, Biswas R, Ali F, Shah U, Alamgir A, Mousa O, Shah Z. The role of generative adversarial networks in brain MRI: a scoping review. Insights Imaging 2022; 13:98. [PMID: 35662369 PMCID: PMC9167371 DOI: 10.1186/s13244-022-01237-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/11/2022] [Indexed: 11/23/2022] Open
Abstract
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.
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Affiliation(s)
- Hazrat Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
| | - Rafiul Biswas
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Farida Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Uzair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Asma Alamgir
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Osama Mousa
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
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