Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach
<div><p>Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. This paper proposes an end-to-e...
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2021
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| _version_ | 1864513517683474432 |
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| author | Soan T. M. Duong (18460599) |
| author2 | Son Lam Phung (18460602) Abdesselam Bouzerdoum (17900021) Sui Paul Ang (18460605) Mark M. Schira (7817429) |
| author2_role | author author author author |
| author_facet | Soan T. M. Duong (18460599) Son Lam Phung (18460602) Abdesselam Bouzerdoum (17900021) Sui Paul Ang (18460605) Mark M. Schira (7817429) |
| author_role | author |
| dc.creator.none.fl_str_mv | Soan T. M. Duong (18460599) Son Lam Phung (18460602) Abdesselam Bouzerdoum (17900021) Sui Paul Ang (18460605) Mark M. Schira (7817429) |
| dc.date.none.fl_str_mv | 2021-03-26T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s21072314 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Correcting_Susceptibility_Artifacts_of_MRI_Sensors_in_Brain_Scanning_A_3D_Anatomy-Guided_Deep_Learning_Approach/25713783 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Information and computing sciences Machine learning susceptibility artifacts deep learning high-speed echo planar imaging reversed phase-encoding |
| dc.title.none.fl_str_mv | Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. This paper proposes an end-to-end deep learning framework, named TS-Net, for susceptibility artifact correction (SAC) in a pair of 3D EPI images with reversed phase-encoding directions. The proposed TS-Net comprises a deep convolutional network to predict a displacement field in three dimensions to overcome the limitation of existing methods, which only estimate the displacement field along the dominant-distortion direction. In the training phase, anatomical T1-weighted images are leveraged to regularize the correction, but they are not required during the inference phase to make TS-Net more flexible for general use. The experimental results show that TS-Net achieves favorable accuracy and speed trade-off when compared with the state-of-the-art SAC methods, i.e., TOPUP, TISAC, and S-Net. The fast inference speed (less than a second) of TS-Net makes real-time SAC during EPI image acquisition feasible and accelerates the medical image-processing pipelines.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s21072314" target="_blank">https://dx.doi.org/10.3390/s21072314</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_44c1b22c363fb4794108446d391bcecd |
| identifier_str_mv | 10.3390/s21072314 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25713783 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning ApproachSoan T. M. Duong (18460599)Son Lam Phung (18460602)Abdesselam Bouzerdoum (17900021)Sui Paul Ang (18460605)Mark M. Schira (7817429)Biomedical and clinical sciencesNeurosciencesInformation and computing sciencesMachine learningsusceptibility artifactsdeep learninghigh-speedecho planar imagingreversed phase-encoding<div><p>Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. This paper proposes an end-to-end deep learning framework, named TS-Net, for susceptibility artifact correction (SAC) in a pair of 3D EPI images with reversed phase-encoding directions. The proposed TS-Net comprises a deep convolutional network to predict a displacement field in three dimensions to overcome the limitation of existing methods, which only estimate the displacement field along the dominant-distortion direction. In the training phase, anatomical T1-weighted images are leveraged to regularize the correction, but they are not required during the inference phase to make TS-Net more flexible for general use. The experimental results show that TS-Net achieves favorable accuracy and speed trade-off when compared with the state-of-the-art SAC methods, i.e., TOPUP, TISAC, and S-Net. The fast inference speed (less than a second) of TS-Net makes real-time SAC during EPI image acquisition feasible and accelerates the medical image-processing pipelines.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s21072314" target="_blank">https://dx.doi.org/10.3390/s21072314</a></p>2021-03-26T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s21072314https://figshare.com/articles/journal_contribution/Correcting_Susceptibility_Artifacts_of_MRI_Sensors_in_Brain_Scanning_A_3D_Anatomy-Guided_Deep_Learning_Approach/25713783CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/257137832021-03-26T03:00:00Z |
| spellingShingle | Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach Soan T. M. Duong (18460599) Biomedical and clinical sciences Neurosciences Information and computing sciences Machine learning susceptibility artifacts deep learning high-speed echo planar imaging reversed phase-encoding |
| status_str | publishedVersion |
| title | Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach |
| title_full | Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach |
| title_fullStr | Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach |
| title_full_unstemmed | Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach |
| title_short | Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach |
| title_sort | Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach |
| topic | Biomedical and clinical sciences Neurosciences Information and computing sciences Machine learning susceptibility artifacts deep learning high-speed echo planar imaging reversed phase-encoding |