Article: [A multi-channel input convolutional neural network for artifact reduction in quantitative susceptibility mapping].
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
2023 Volume 42, Issue 12, Page(s) 1799–1806
Abstract: Objective: To develop a deep learning-based QSM reconstruction method for reducing artifacts to improve the accuracy of magnetic susceptibility results.: Methods: To eliminate artifacts caused by susceptibility interfaces with gigantic differences, ... ...
| Abstract | Objective: To develop a deep learning-based QSM reconstruction method for reducing artifacts to improve the accuracy of magnetic susceptibility results. Methods: To eliminate artifacts caused by susceptibility interfaces with gigantic differences, we propose a multi-channel input convolutional neural network for artifact reduction (MAR-CNN) for solving the dipole inversion problem in QSM. In this neural network, the original tissue field was first separated into two components, which were subsequently imported as additional channels into a multi-channel 3D U-Net. MAR-CNN was compared with 3 conventional model-based methods, namely truncated k-space deconvolution (TKD), morphology enabled dipole inversion (MEDI), and improved sparse linear equation and least squares method (iLSQR), and with a deep learning method (QSMnet). High-frequency error norm, peak signal-to-noise ratio, normalized root mean squared error, and structure similarity index were reported for quantitative comparisons. Results: Experiments on healthy volunteers demonstrated that the results obtained using MAR-CNN had superior peak signal-to-noise ratio (43.12±1.19) and normalized root mean squared error (51.98± 3.65) to those of TKD, MEDI, iLSQR and QSMnet. MAR-CNN outperformed QSMnet reconstruction on all the 4 quantitative metrics with significant differences ( Conclusion: The proposed MAR-CNN for artifact reduction is capable of improving the accuracy of deep learning- based QSM reconstruction to effectively reduce artifacts. |
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| MeSH term(s) | Humans ; Artifacts ; Image Processing, Computer-Assisted/methods ; Neural Networks, Computer ; Signal-To-Noise Ratio ; Algorithms ; Magnetic Resonance Imaging |
| Language | Chinese |
| Publishing date | 2023-01-18 |
| Publishing country | China |
| Document type | English Abstract ; Journal Article |
| ZDB-ID | 2250951-3 |
| ISSN | 1673-4254 |
| ISSN | 1673-4254 |
| DOI | 10.12122/j.issn.1673-4254.2022.12.07 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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