Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction
To accelerate the data acquisition speed of magnetic resonance imaging (MRI) and improve the reconstructed MR images’ quality, we propose a parallel MRI reconstruction model (SPIRiT-Net), which combines the iterative self-consistent parallel imaging reconstruction model (SPIRiT) with the cascaded co...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2024/7006156 |
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| author | Jizhong Duan Xinmin Ren |
| author_facet | Jizhong Duan Xinmin Ren |
| author_sort | Jizhong Duan |
| collection | DOAJ |
| description | To accelerate the data acquisition speed of magnetic resonance imaging (MRI) and improve the reconstructed MR images’ quality, we propose a parallel MRI reconstruction model (SPIRiT-Net), which combines the iterative self-consistent parallel imaging reconstruction model (SPIRiT) with the cascaded complex convolutional neural networks (CCNNs). More specifically, this model adopts the SPIRiT model for reconstruction in the k-space domain and the cascaded CCNNs with dense connection for reconstruction in the image domain. Meanwhile, this model introduces the data consistency layers for better reconstruction in both the image domain and the k-space domain. The experimental results on two clinical knee datasets as well as the fastMRI brain dataset under different undersampling patterns show that the SPIRiT-Net model achieves better reconstruction performance in terms of visual effect, peak signal-to-noise ratio, and structural similarity over SPIRiT, Deepcomplex, and DONet. It will be beneficial to the diagnosis of clinical medicine. |
| format | Article |
| id | doaj-art-3e0783f113d04f54ae82b256e1bc44ce |
| institution | OA Journals |
| issn | 1751-9683 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-3e0783f113d04f54ae82b256e1bc44ce2025-08-20T02:08:23ZengWileyIET Signal Processing1751-96832024-01-01202410.1049/2024/7006156Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI ReconstructionJizhong Duan0Xinmin Ren1Faculty of Information Engineering and AutomationFaculty of Information Engineering and AutomationTo accelerate the data acquisition speed of magnetic resonance imaging (MRI) and improve the reconstructed MR images’ quality, we propose a parallel MRI reconstruction model (SPIRiT-Net), which combines the iterative self-consistent parallel imaging reconstruction model (SPIRiT) with the cascaded complex convolutional neural networks (CCNNs). More specifically, this model adopts the SPIRiT model for reconstruction in the k-space domain and the cascaded CCNNs with dense connection for reconstruction in the image domain. Meanwhile, this model introduces the data consistency layers for better reconstruction in both the image domain and the k-space domain. The experimental results on two clinical knee datasets as well as the fastMRI brain dataset under different undersampling patterns show that the SPIRiT-Net model achieves better reconstruction performance in terms of visual effect, peak signal-to-noise ratio, and structural similarity over SPIRiT, Deepcomplex, and DONet. It will be beneficial to the diagnosis of clinical medicine.http://dx.doi.org/10.1049/2024/7006156 |
| spellingShingle | Jizhong Duan Xinmin Ren Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction IET Signal Processing |
| title | Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction |
| title_full | Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction |
| title_fullStr | Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction |
| title_full_unstemmed | Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction |
| title_short | Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction |
| title_sort | improved complex convolutional neural network based on spirit and dense connection for parallel mri reconstruction |
| url | http://dx.doi.org/10.1049/2024/7006156 |
| work_keys_str_mv | AT jizhongduan improvedcomplexconvolutionalneuralnetworkbasedonspiritanddenseconnectionforparallelmrireconstruction AT xinminren improvedcomplexconvolutionalneuralnetworkbasedonspiritanddenseconnectionforparallelmrireconstruction |