@article{Lv:2021:10.3390/diagnostics11010061,
author = {Lv, J and Wang, C and Yang, G},
doi = {10.3390/diagnostics11010061},
journal = {Diagnostics},
title = {PIC-GAN: a parallel imaging coupled generative adversarial network for accelerated multi-channel MRI reconstruction},
url = {http://dx.doi.org/10.3390/diagnostics11010061},
volume = {11},
year = {2021}
}

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TY  - JOUR
AB  - 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 ×10−5) (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 c
AU  - Lv,J
AU  - Wang,C
AU  - Yang,G
DO  - 10.3390/diagnostics11010061
PY  - 2021///
SN  - 2075-4418
TI  - PIC-GAN: a parallel imaging coupled generative adversarial network for accelerated multi-channel MRI reconstruction
T2  - Diagnostics
UR  - http://dx.doi.org/10.3390/diagnostics11010061
UR  - http://hdl.handle.net/10044/1/86442
VL  - 11
ER  - 

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