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噪音辅助混合注意网络用于低剂量PET和CT消噪

Hengzhi Xue¹, Yudong Yao^1,2, Yueyang Teng^1,3

¹College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.

Medical physics

|October 21, 2024

概括

这项研究引入了噪音辅助混合注意网络 (NAHANet),以从低剂量扫描中创建高质量的全剂量PET和CT图像. 这种人工智能方法可以减少辐射暴露,同时保持瘤识别的诊断准确性.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

定子发射断层扫描 (PET) 和计算机断层扫描 (CT) 对于癌症诊断和治疗规划至关重要.
全剂量扫描增加了辐射风险,而低剂量扫描损害了图像质量,阻碍了精确的瘤检测.

研究的目的:

开发一个噪音辅助混合注意网络 (NAHANet) 来从低剂量数据中重建高质量的全剂量PET和CT图像.
为了减少患者的辐射暴露,同时保持瘤识别的诊断性能.

主要方法:

NAHANet采用了两个分支架构:一个噪声特征预测分支 (NFPB) 和一个级联重建分支.
在重建分支中,NFPB产生噪声特征,这些特征与低剂量图像特征在级联噪声特征融合块 (NFFB) 中融合.
在超低剂量PET成像挑战和低剂量CT大挑战数据集上进行了实验.

主要成果:

与现有方法相比,NAHANet在图像重建方面表现出卓越的性能.
在CT数据集上,NAHANet将PSNR提高了4.1dB,SSIM提高了0.06,与低剂量CT相比,rMSE降低了5.46.
在PET数据集上,NAHANet提高了PSNR3.37dB和SSIM0.02,同时与低剂量PET相比减少了rMSE9.04.

结论:

拟议的基于变压器的NAHANet有效地使用混合注意力和噪声特征融合来消除低剂量的PET和CT图像.
该方法显著提高了低剂量医学成像的图像质量和诊断潜力.
NAHANet为减少医学成像中的辐射暴露提供了一个有前途的解决方案,同时保持诊断效率.

关键词:

图像去色化图像去色化低剂量CT (LDCT) 的使用.低剂量PET (LDPET) 的使用变压器变压器变压器变压器

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