Retrospective Motion Correction in Multishot MRI using Generative Adversarial Network
Autor: | Byoung-Dai Lee, Siddique Latif, Muhammad Asim, Junaid Qadir, Muhammad Usman |
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Rok vydání: | 2020 |
Předmět: |
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION lcsh:Medicine Information technology Article Motion (physics) 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine Motion artifacts Conjugate gradient method medicine Computer vision lcsh:Science Multidisciplinary medicine.diagnostic_test business.industry lcsh:R Magnetic resonance imaging Motion correction Spin echo lcsh:Q Artificial intelligence business 030217 neurology & neurosurgery |
Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020) |
ISSN: | 2045-2322 |
Popis: | Multishot Magnetic Resonance Imaging (MRI) is a promising data acquisition technique that can produce a high-resolution image with relatively less data acquisition time than the standard spin echo. The downside of multishot MRI is that it is very sensitive to subject motion and even small levels of motion during the scan can produce artifacts in the final magnetic resonance (MR) image, which may result in a misdiagnosis. Numerous efforts have focused on addressing this issue; however, all of these proposals are limited in terms of how much motion they can correct and require excessive computational time. In this paper, we propose a novel generative adversarial network (GAN)-based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. First CG-SENSE reconstruction is employed to reconstruct an image from the motion-corrupted k-space data and then the GAN-based proposed framework is applied to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establish that the proposed method is robust and reduces several-fold the computational time reported by the current state-of-the-art technique. |
Databáze: | OpenAIRE |
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