基于PCAU-Net的快速多通道磁共振成像方法

doi:10.11938/cjmr20222992

摘要/Abstract

摘要：

多通道磁共振成像方法采用多个接收线圈同时欠采样k空间以加快成像速度，并基于后处理算法重建图像，但在较高加速因子时，其图像重建质量仍然较差．本文提出了一种基于PCAU-Net的快速多通道磁共振成像方法，将单通道实数U型卷积神经网络拓展到多通道复数卷积神经网络，设计了一种结构不对称的U型网络结构，通过在解码部分减小网络规模以降低模型的复杂度．PCAU-Net网络在跳跃连接前增加了1×1卷积，以实现跨通道信息交互．输入和输出之间利用残差连接为误差的反向传播提供捷径．实验结果表明，使用规则和随机采样模板，在不同加速因子时，相比常规的GRAPPA重建算法和SPIRiT重建方法，本文提出的PCAU-Net方法可高质量重建出磁共振复数图像，并且相比于PCU-Net方法，PCAU-Net减少了模型参数、缩短了训练时间．

关键词: 多线圈, 快速磁共振成像, 深度学习, 复数卷积, 不对称神经网络

Abstract:

Multi-channel magnetic resonance imaging methods utilize multiple receiving coils to simultaneously under-sample k-space for fast magnetic resonance imaging. Post-processing algorithms were often used to restore the missing information for under-sampled images. However, the quality of reconstructed images is far from satisfying, especially at higher acceleration factor. In this backdrop, a fast multi-channel magnetic resonance imaging method based on parallel complex asymmetric U-Net (PCAU-Net) was proposed, which extends single-channel real-valued U-shaped convolutional neural network to multi-channel complex convolutional neural network. A U-shaped network with an asymmetric structure was designed to reduce the model complexity by reducing the network size in decoding. A 1×1 convolution block was added before skip-connection for PCAU-Net to share cross-channel information. The residual connection between the input and output of the network facilitates loss backpropagation. The experimental results show that by using regular and random sampling templates, the proposed PCAU-Net method can reconstruct the complex magnetic resonance images with high quality compared with the conventional generalized auto-calibrating partially parallel acquisitions (GRAPPA) reconstruction algorithm and iterative self-consistent parallel imaging reconstruction (SPIRiT) algorithm at different acceleration factors. Moreover, compared with the parallel complex U-Net (PCU-Net) method, PCAU-Net reduces model parameters and shortens the training time.

Key words: multi-coils, fast magnetic resonance imaging, deep learning, complex convolution, asymmetric neural network

中图分类号:

TP391.4

施伟成,金朝阳,叶铮. 基于PCAU-Net的快速多通道磁共振成像方法[J]. 波谱学杂志, 2023, 40(1): 39-51.

SHI Weicheng,JIN Zhaoyang,YE Zheng. Fast Multi-channel Magnetic Resonance Imaging Based on PCAU-Net[J]. Chinese Journal of Magnetic Resonance, 2023, 40(1): 39-51.

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参考文献 29

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量化指标	双线性插值	反卷积
TRE	8.98×10^-4	9.02×10^-4
SSIM	0.9787	0.9795
PSNR	32.1041	32.2539
参数量/kB	25359	36249

量化指标		GRAPPA
TRE	R (N=3, C=32)=2.5	1.10×10^-3	8.08×10^-4	7.64×10^-4
	R (N=5, C=32)=3.3	1.80×10^-3	9.44×10^-4	8.98×10^-4
	R (N=7, C=32)=4.0	2.40×10^-3	9.73×10^-4	9.33×10^-4
	R (N=9, C=32)=4.6	2.60×10^-3	1.70×10^-3	1.40×10^-3
	R (N=12, C=32)=5.0	2.90×10^-3	1.70×10^-3	1.70×10^-3
SSIM	R (N=3, C=32)=2.5	0.9645	0.9758	0.9787
	R (N=5, C=32)=3.3	0.9123	0.9673	0.9701
	R (N=7, C=32)=4.0	0.8634	0.9669	0.9688
	R (N=9, C=32)=4.6	0.8337	0.9627	0.9543
	R (N=12, C=32)=5.0	0.8125	0.9422	0.9424
PSNR	R (N=3, C=32)=2.5	30.4921	33.0363	33.5118
	R (N=5, C=32)=3.3	26.0461	31.6888	32.1041
	R (N=7, C=32)=4.0	23.6166	31.4050	31.7779
	R (N=9, C=32)=4.6	22.9207	29.5721	30.7301
	R (N=12, C=32)=5.0	22.5559	28.7280	28.7139

量化指标	R (N=3, C=32) = 2.5	R (N=5, C=32) = 3.3	R (N=7, C=32) = 4.0	R (N=9, C=32) = 4.6	R (N=12, C=32) = 5.0
TRE	91%	86%	86%	79%	48%
SSIM	88%	84%	75%	44%	53%
PSNR	91%	86%	86%	61%	52%

量化指标		SPIRiT
TRE	R = 2.5	1.10×10^-3	7.8081×10^-4	7.7575×10^-4
	R = 3.3	1.10×10^-3	8.3336×10^-4	8.1667×10^-4
	R = 4.0	1.50×10^-3	8.7133×10^-4	8.9446×10^-4
	R = 4.6	1.40×10^-3	9.4146×10^-4	9.0757×10^-4
	R = 5.0	1.90×10^-3	9.8781×10^-4	9.9610×10^-4
SSIM	R = 2.5	0.9629	0.9656	0.9727
	R = 3.3	0.9588	0.9642	0.9694
	R = 4.0	0.9327	0.9632	0.9720
	R = 4.6	0.9419	0.9596	0.9593
	R = 5.0	0.9051	0.9536	0.9660
PSNR	R = 2.5	33.2773	33.2802	33.9417
	R = 3.3	32.7259	32.9076	33.5339
	R = 4.0	29.0483	32.7743	33.5606
	R = 4.6	27.8330	32.3175	32.4758
	R = 5.0	26.6143	31.8950	32.1532