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Conference Paper: Direct Pathology Detection and Characterization from MR K-Space Data Using Deep Learning
Title | Direct Pathology Detection and Characterization from MR K-Space Data Using Deep Learning |
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Authors | |
Issue Date | 2020 |
Publisher | International Society of Magnetic Resonance Imaging (ISMRM) . |
Citation | Proceedings of the 28th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), Virtual Conference, 8-14 August 2020, paper no. 3530 How to Cite? |
Abstract | Present MRI diagnosis comprises two steps: (i) reconstruction of multi-slice 2D or 3D images from k-space data; and (ii) pathology identification from images. In this study, we propose a strategy of direct pathology detection and characterization from MR k-space data through deep learning. This concept bypasses the traditional MR image reconstruction prior to pathology diagnosis, and presents an alternative MR diagnostic paradigm that may lead to potentially more powerful new tools for automatic and effective pathology screening, detection and characterization. Our simulation results demonstrated that this image-free strategy could detect brain tumors and their sizes/locations with high sensitivity and specificity. |
Description | Digital Poster Session: Acquisition, Reconstruction & Analysis: ML: Post Processing, Analysis, & Applications: Machine Learning: Disease, Diagnosis, Pathology & Treatment - paper no. 3530 |
Persistent Identifier | http://hdl.handle.net/10722/289406 |
DC Field | Value | Language |
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dc.contributor.author | Xiao, L | - |
dc.contributor.author | Liu, Y | - |
dc.contributor.author | Zeng, P | - |
dc.contributor.author | Lyu, M | - |
dc.contributor.author | Ma, X | - |
dc.contributor.author | Leong, TL | - |
dc.contributor.author | Wu, EX | - |
dc.date.accessioned | 2020-10-22T08:12:12Z | - |
dc.date.available | 2020-10-22T08:12:12Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Proceedings of the 28th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), Virtual Conference, 8-14 August 2020, paper no. 3530 | - |
dc.identifier.uri | http://hdl.handle.net/10722/289406 | - |
dc.description | Digital Poster Session: Acquisition, Reconstruction & Analysis: ML: Post Processing, Analysis, & Applications: Machine Learning: Disease, Diagnosis, Pathology & Treatment - paper no. 3530 | - |
dc.description.abstract | Present MRI diagnosis comprises two steps: (i) reconstruction of multi-slice 2D or 3D images from k-space data; and (ii) pathology identification from images. In this study, we propose a strategy of direct pathology detection and characterization from MR k-space data through deep learning. This concept bypasses the traditional MR image reconstruction prior to pathology diagnosis, and presents an alternative MR diagnostic paradigm that may lead to potentially more powerful new tools for automatic and effective pathology screening, detection and characterization. Our simulation results demonstrated that this image-free strategy could detect brain tumors and their sizes/locations with high sensitivity and specificity. | - |
dc.language | eng | - |
dc.publisher | International Society of Magnetic Resonance Imaging (ISMRM) . | - |
dc.relation.ispartof | Proceedings of International Society of Magnetic Resonance in Medicine (ISMRM) Virtual Conference, 2020 | - |
dc.title | Direct Pathology Detection and Characterization from MR K-Space Data Using Deep Learning | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Leong, TL: tlleong@hku.hk | - |
dc.identifier.email | Wu, EX: ewu@eee.hku.hk | - |
dc.identifier.authority | Leong, TL=rp02483 | - |
dc.identifier.authority | Wu, EX=rp00193 | - |
dc.identifier.hkuros | 316615 | - |
dc.identifier.spage | 3530 | - |
dc.identifier.epage | 3530 | - |