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Article

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

by 1, 2,* and 3,4,*
1
School of Computer and Control Engineering, Yantai University, Yantai 264005, China
2
Human Phenome Institute, Fudan University, Shanghai 201203, China
3
Cardiovascular Research Centre, Royal Brompton Hospital, London SW3 6NP, UK
4
National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK
*
Authors to whom correspondence should be addressed.
Diagnostics 2021, 11(1), 61; https://doi.org/10.3390/diagnostics11010061
Received: 5 November 2020 / Revised: 28 December 2020 / Accepted: 29 December 2020 / Published: 2 January 2021
(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)
In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an “end-to-end” reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ×105) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods. View Full-Text
Keywords: MRI reconstruction; parallel imaging; generative adversarial network; multi-channel MRI reconstruction; parallel imaging; generative adversarial network; multi-channel
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MDPI and ACS Style

Lv, J.; Wang, C.; Yang, G. PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction. Diagnostics 2021, 11, 61. https://doi.org/10.3390/diagnostics11010061

AMA Style

Lv J, Wang C, Yang G. PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction. Diagnostics. 2021; 11(1):61. https://doi.org/10.3390/diagnostics11010061

Chicago/Turabian Style

Lv, Jun, Chengyan Wang, and Guang Yang. 2021. "PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction" Diagnostics 11, no. 1: 61. https://doi.org/10.3390/diagnostics11010061

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