基于几何蒸馏和特征自适应的磁共振成像重建

朵琳; 任勇; 许渤雨; 杨新

doi:10.13700/j.bh.1001-5965.2023.0323

基于几何蒸馏和特征自适应的磁共振成像重建

doi: 10.13700/j.bh.1001-5965.2023.0323

昆明理工大学信息工程与自动化学院，昆明 650500

基金项目:

国家自然科学基金(61962032)

详细信息

通讯作者:
E-mail：duolin2003@126.com

中图分类号: TP391；R319
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出版历程
- 收稿日期: 2023-06-07
- 录用日期: 2023-08-18
- 网络出版日期: 2023-09-12
- 整期出版日期: 2025-06-30

MRI reconstruction based on geometry distillation and feature adaptation

Faculty of Information Engineering and Automation，Kunming University of Science and Technology，Kunming 650500，China

Funds:

National Natural Science Foundation of China (61962032)

More Information

Corresponding author: E-mail：duolin2003@126.com

摘要

摘要:
虽然现有基于深度学习的压缩感知磁共振成像(CS-MRI)方法已经取得了较好的效果，但这些方法的可解释性仍然面临挑战，并且从理论分析到网络设计的过渡并不够自然。为解决上述问题，提出深度双域几何蒸馏特征自适应网络(DDGD-FANet)。该深度展开网络将磁共振成像重建优化问题迭代展开成3个子部分：数据一致性模块、双域几何蒸馏模块和自适应网络模块，不仅可以补偿重建图像丢失的上下文信息，恢复更多的纹理细节，还可以去除全局伪影，进一步提高重建效果。在公开数据集使用3种不同的采样模式进行实验，结果表明：DDGD-FANet在3种采样模式下均取得了更高的峰值信噪比和结构相似性指数，在笛卡儿10%压缩感知(CS)比率下，峰值信噪比较迭代收缩阈值算法(ISTA-Net+)、快速ISTA(FISTA)-Net和DGDN模型分别提高了5.01 dB、4.81 dB和3.34 dB。
- 磁共振成像 /
- 图像重建 /
- 深度学习 /
- 几何蒸馏 /
- 自适应网络 /
- 注意力机制
Abstract:
Although the existing compressed sensing-magnetic resonance imaging (CS-MRI) methods based on deep learning have achieved good results, the interpretability of these methods still faces challenges, and the transition from theoretical analysis to network design is not natural enough. In order to solve the above problems, this paper proposed a deep dual-domain geometry distillation feature adaptive network (DDGD-FANet). The deep unfolding network iteratively expanded the MRI reconstruction optimization problem into three sub-modules: data consistency module, dual-domain geometry distillation module, and adaptive network module. It could compensate for the lost context information of the reconstructed image, restore more texture details, remove global artifacts, and further improve the reconstruction effect. Three different sampling modes were used in the public dataset. The results show that DDGD-FANet achieves a higher peak signal-to-noise ratio and structural similarity index in all three sampling modes. At the Cartesian 10% compressed sensing(CS )ratio, the peak signal-to-noise ratio is increased by 5.01 dB, 4.81 dB, and 3.34 dB, respectively, higher than that of iterative shrinkage-thresholding algorithm (ISTA)-Net +, fast ISTA (FISTA)-Net, and DGDN models.
- magnetic resonance imaging /
- image reconstruction /
- deep learning /
- geometry distillation /
- adaptive network /
- attention mechanism

HTML全文

图 1 DDGD-FANet结构

Figure 1. Structure of DDGD-FANet

下载: 全尺寸图片幻灯片

图 2 自适应网络结构

Figure 2. Structure of adaptive network

下载: 全尺寸图片幻灯片

图 3 大脑数据集上使用不同采样模式的重建效果对比

Figure 3. Comparison of reconstruction effects using different sampling patterns in brain dataset

下载: 全尺寸图片幻灯片

图 4 FAF末尾使用激活函数对网络性能影响

Figure 4. Effect of activation functions at end of FAF on network performance

下载: 全尺寸图片幻灯片

表 1 不同CS比率笛卡儿采样模式下大脑数据集重建效果对比

Table 1. Comparison of reconstruction effects in different CS ratios of Cartesian sampling pattern in brain dataset

掩码类型	方法	PSNR/dB			SSIM
掩码类型	方法	CS比率为10%	CS比率为20%	CS比率为30%	CS比率为10%	CS比率为20%	CS比率为30%
	Zero-filling^[32]	23.84	26.40	31.14	0.574 3	0.661 1	0.786 5
	PBDW^[10]	26.82	32.41	35.05	0.735 5	0.866 0	0.911 6
	PANO^[11]	28.98	34.62	36.75	0.789 7	0.893 3	0.922 2
	DC-CNN^[24]	30.04	35.75	38.48	0.823 5	0.920 5	0.949 3
笛卡儿	ISTA-Net^[22]	30.28	36.58	39.17	0.825 6	0.933 4	0.956 8
	ISTA-Net+^[22]	30.86	37.03	39.63	0.845 5	0.935 5	0.959 7
	FISTA-Net^[23]	31.06	37.29	39.86	0.844 4	0.939 6	0.960 7
	DGDN^[25]	32.53	38.31	40.61	0.886 3	0.949 9	0.965 6
	DDGD-FANet	35.87	39.55	41.41	0.932 0	0.959 3	0.970 1

下载: 导出CSV

表 2 不同CS比率伪径向和二维随机采样模式下大脑数据集建效果对比

Table 2. Comparison of reconstruction effects in different CS ratios of pseudo-radial and 2D random sampling pattern in brain dataset

掩码类型	方法	PSNR/dB
掩码类型	方法	CS比率为10%	CS比率为20%	CS比率为30%
伪径向	Zero-filling^[32]	26.64	30.32	32.93
	PBDW^[10]	32.48	36.53	38.95
	PANO^[11]	33.61	37.01	39.29
	DC-CNN^[24]	34.32	38.43	40.75
	ISTA-Net^[22]	34.70	38.74	41.01
	ISTA-Net+^[22]	34.83	38.75	41.00
	FISTA-Net^[23]	35.10	39.05	41.19
	DGDN^[25]	35.91	39.33	41.34
	DDGD-FANet	37.08	39.67	41.54
二维随机	Zero-filling^[32]	27.71	29.36	31.13
	PBDW^[10]	35.68	38.49	40.83
	PANO^[11]	36.60	39.57	42.03
	DC-CNN^[24]	37.65	40.81	43.40
	ISTA-Net^[22]	38.22	41.40	43.97
	ISTA-Net+^[22]	38.41	41.66	44.20
	FISTA-Net^[23]	38.36	41.75	44.29
	DGDN^[25]	38.85	41.91	44.38
	DDGD-FANet	39.25	42.13	44.53

下载: 导出CSV

表 3 本文模型消融实验结果

Table 3. Results of the proposed model in ablation experiment

模型	ADN		PSNR/dB	SSIM
模型	AM	FAF	PSNR/dB	SSIM
DGDN^[25]			32.53	0.8863
DGDN^[25]	√	√	33.58	0.9021
DGDN^[25]+DDGD			34.55	0.9165
	√		34.70	0.9176
		√	35.67	0.9302
	√	√	35.87	0.9320

下载: 导出CSV

表 4 数据一致性层消融实验结果

Table 4. Ablation experiment results of data consistency layer

模型	PSNR/dB	SSIM
DDGD-FANet_NODC	34.73	0.9215
DDGD-FANet	35.87	0.9320

下载: 导出CSV

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