基于生成对抗网络的膝关节模型构建与局部比吸收率估计

doi:10.11938/cjmr20233053

摘要/Abstract

摘要：

局部比吸收率（SAR）是衡量高场磁共振成像安全性的重要指标．目前主要的方法是对扫描获得的磁共振图像进行组织分割，从而构建个体特异性模型，对其进行电磁仿真以计算局部SAR．针对仿真中膝关节模型长度影响局部SAR估计准确度的问题，本文提出了一种基于条件生成对抗网络（CGAN）的膝关节磁共振图像分割与视野扩展方法，将膝关节图像简化归类为肌肉、脂肪和骨骼三种组织，通过CGAN进行像素的语义分割，采用注意力机制以提高分割的准确度，并且沿头-足方向在图像两端生成扩展区域，构建出更长的模型．实验中对所提方法以及各种对比方法得到的膝关节模型进行电磁仿真，计算它们与人工标注模型的局部SAR的相对误差，结果验证了所提方法可以获得相对精确的膝关节局部SAR估计．

关键词: 磁共振成像, 局部比吸收率, 条件生成对抗网络, 膝关节, 图像分割

Abstract:

The local specific absorption rate (SAR) is an essential metric when assessing the safety of high-field magnetic resonance imaging (MRI). Usually, the subject-specific model is created from segmented magnetic resonance (MR) images, followed by SAR estimates. However, the length of knee joint model affects the accuracy of local SAR estimation in simulation. To address the issue, this paper proposed a knee joint MR image segmentation and field-of-view extension method based on conditional generative adversarial nets (CGAN). The image of knee joint was classified into images of three tissues, namely, muscle, fat, and bone. And pixel semantic segmentation was performed using CGAN with attention mechanism to improve accuracy. The method also generated extension areas at both ends of the image along the head-foot direction to construct a longer model. The knee joint models by using the proposed method and various comparison methods were electromagnetically simulated, and the relative error of their local SAR compared to the manually annotated model was calculated. Results verified that the proposed method can achieve a relatively accurate estimation of local SAR in the knee joint.

Key words: magnetic resonance imaging, local specific absorption rate, conditional generative adversarial nets, knee joint, image segmentation

中图分类号:

任宏晋, 马岩, 肖亮. 基于生成对抗网络的膝关节模型构建与局部比吸收率估计[J]. 波谱学杂志, 2023, 40(4): 410-422.

REN Hongjin, MA Yan, XIAO Liang. Knee Joint Model Construction and Local Specific Absorption Rate Estimation Based on Generative Adversarial Networks[J]. Chinese Journal of Magnetic Resonance, 2023, 40(4): 410-422.

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分割方法	评价指标	肌肉	脂肪	骨骼
所提方法	DCC	0.8798	0.9135	0.9022
	TPR	0.8915	0.9198	0.9114
U-Net	DCC	0.8472	0.9040	0.9006
	TPR	0.8841	0.8785	0.8921
Attention U-Net	DCC	0.8578	0.9022	0.9058
	TPR	0.9059	0.8611	0.8991

	所提方法	方法1	方法2	方法3
平均值	0.0696	0.2932	0.0783	0.1066
标准差	0.0542	0.1206	0.0527	0.0525

模型	Duke左腿	Duke右腿	Ella左腿	Ella右腿
230 mm-全组织	1.4973	1.2078	1.0765	1.3145
230 mm-简化	1.4404	1.3555	1.1862	1.4363
230 mm-肌肉	1.4500	1.3261	1.1587	1.3676
230 mm-两端重复外推	1.5249	1.3940	1.2387	1.4592
150 mm-简化	1.9746	1.9019	1.5773	1.8345