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Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction

MRI reconstruction is one of the critical processes of MRI machines, along with the acquisition. Due to a slow processing time of signal acquiring, parallel imaging and reconstruction techniques are applied for acceleration. To accelerate the acquisition process, fewer raw data are sampled simultane...

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Autores principales: Karnjanapreechakorn, Sarattha, Kusakunniran, Worapan, Siriapisith, Thanongchai, Saiviroonporn, Pairash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044365/
https://www.ncbi.nlm.nih.gov/pubmed/35494819
http://dx.doi.org/10.7717/peerj-cs.934
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author Karnjanapreechakorn, Sarattha
Kusakunniran, Worapan
Siriapisith, Thanongchai
Saiviroonporn, Pairash
author_facet Karnjanapreechakorn, Sarattha
Kusakunniran, Worapan
Siriapisith, Thanongchai
Saiviroonporn, Pairash
author_sort Karnjanapreechakorn, Sarattha
collection PubMed
description MRI reconstruction is one of the critical processes of MRI machines, along with the acquisition. Due to a slow processing time of signal acquiring, parallel imaging and reconstruction techniques are applied for acceleration. To accelerate the acquisition process, fewer raw data are sampled simultaneously with all RF coils acquisition. Then, the reconstruction uses under-sampled data from all RF coils to restore the final MR image that resembles the fully sampled MR image. These processes have been a traditional procedure inside the MRI system since the invention of the multi-coils MRI machine. This paper proposes the deep learning technique with a lightweight network. The deep neural network is capable of generating the high-quality reconstructed MR image with a high peak signal-to-noise ratio (PSNR). This also opens a high acceleration factor for MR data acquisition. The lightweight network is called Multi-Level Pooling Encoder–Decoder Net (MLPED Net). The proposed network outperforms the traditional encoder–decoder networks on 4-fold acceleration with a significant margin on every evaluation metric. The network can be trained end-to-end, and it is a lightweight structure that can reduce training time significantly. Experimental results are based on a publicly available MRI Knee dataset from the fastMRI competition.
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spelling pubmed-90443652022-04-28 Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction Karnjanapreechakorn, Sarattha Kusakunniran, Worapan Siriapisith, Thanongchai Saiviroonporn, Pairash PeerJ Comput Sci Artificial Intelligence MRI reconstruction is one of the critical processes of MRI machines, along with the acquisition. Due to a slow processing time of signal acquiring, parallel imaging and reconstruction techniques are applied for acceleration. To accelerate the acquisition process, fewer raw data are sampled simultaneously with all RF coils acquisition. Then, the reconstruction uses under-sampled data from all RF coils to restore the final MR image that resembles the fully sampled MR image. These processes have been a traditional procedure inside the MRI system since the invention of the multi-coils MRI machine. This paper proposes the deep learning technique with a lightweight network. The deep neural network is capable of generating the high-quality reconstructed MR image with a high peak signal-to-noise ratio (PSNR). This also opens a high acceleration factor for MR data acquisition. The lightweight network is called Multi-Level Pooling Encoder–Decoder Net (MLPED Net). The proposed network outperforms the traditional encoder–decoder networks on 4-fold acceleration with a significant margin on every evaluation metric. The network can be trained end-to-end, and it is a lightweight structure that can reduce training time significantly. Experimental results are based on a publicly available MRI Knee dataset from the fastMRI competition. PeerJ Inc. 2022-03-30 /pmc/articles/PMC9044365/ /pubmed/35494819 http://dx.doi.org/10.7717/peerj-cs.934 Text en © 2022 Karnjanapreechakorn et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Karnjanapreechakorn, Sarattha
Kusakunniran, Worapan
Siriapisith, Thanongchai
Saiviroonporn, Pairash
Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction
title Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction
title_full Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction
title_fullStr Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction
title_full_unstemmed Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction
title_short Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction
title_sort multi-level pooling encoder–decoder convolution neural network for mri reconstruction
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044365/
https://www.ncbi.nlm.nih.gov/pubmed/35494819
http://dx.doi.org/10.7717/peerj-cs.934
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