DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE
Autor: | Dongmyung Shin, Jongho Lee, Hongjun An, Hyeong-Geol Shin, Juhyung Park, Woojin Jung, Sooyeon Ji, Se-Hong Oh |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Computer science
Cognitive Neuroscience Phase (waves) Diaphragmatic breathing Deep neural network 050105 experimental psychology lcsh:RC321-571 03 medical and health sciences 0302 clinical medicine FOS: Electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Respiration-induced B0 fluctuations lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry business.industry Deep learning 05 social sciences Image and Video Processing (eess.IV) Multi slice Pattern recognition Electrical Engineering and Systems Science - Image and Video Processing Neurology Phase error in GRE Breathing Artificial intelligence business 030217 neurology & neurosurgery |
Zdroj: | NeuroImage, Vol 224, Iss, Pp 117432-(2021) |
ISSN: | 1095-9572 |
Popis: | Respiration-induced B$_0$ fluctuation corrupts MRI images by inducing phase errors in k-space. A few approaches such as navigator have been proposed to correct for the artifacts at the expense of sequence modification. In this study, a new deep learning method, which is referred to as DeepResp, is proposed for reducing the respiration-artifacts in multi-slice gradient echo (GRE) images. DeepResp is designed to extract the respiration-induced phase errors from a complex image using deep neural networks. Then, the network-generated phase errors are applied to the k-space data, creating an artifact-corrected image. For network training, the computer-simulated images were generated using artifact-free images and respiration data. When evaluated, both simulated images and in-vivo images of two different breathing conditions (deep breathing and natural breathing) show improvements (simulation: normalized root-mean-square error (NRMSE) from 7.8% to 1.3%; structural similarity (SSIM) from 0.88 to 0.99; ghost-to-signal-ratio (GSR) from 7.9% to 0.6%; deep breathing: NRMSE from 13.9% to 5.8%; SSIM from 0.86 to 0.95; GSR 20.2% to 5.7%; natural breathing: NRMSE from 5.2% to 4.0%; SSIM from 0.94 to 0.97; GSR 5.7% to 2.8%). Our approach does not require any modification of the sequence or additional hardware, and may therefore find useful applications. Furthermore, the deep neural networks extract respiration-induced phase errors, which is more interpretable and reliable than results of end-to-end trained networks. 19 pages |
Databáze: | OpenAIRE |
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