对于零射击低剂量CT图像消噪的扩散概率先验
Xuan Liu1, Yaoqin Xie2, Chenbin Liu3
1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.
Medical physics
|October 16, 2024
概括
这项研究引入了一种新的无监督方法,用于使用扩散模型消除低剂量计算机断层扫描 (CT) 图像的噪音. 该技术实现了最先进的结果,而不需要配对低剂量和正常剂量的CT图像进行训练.
科学领域:
- 医疗图像计算 医疗图像计算
- 深度学习 (Deep Learning) 是一种深度学习.
- 图像删除 图像删除
- 扩散模型 扩散模型
背景情况:
- 消除低剂量计算机断层扫描 (CT) 图像在医学成像中至关重要.
- 监督深度学习方法需要难以获得的配对低剂量和正常剂量的CT图像.
- 现有的无监督方法往往需要大型数据集或专门的获取协议.
研究的目的:
- 开发一种新的无监督方法,用于低剂量的CT图像消噪.
- 通过仅在正常剂量CT图像上进行训练来实现零射击消噪.
主要方法:
- 利用级联无条件扩散模型生成高质量的正常剂量CT图像.
- 将低剂量CT图像集成到扩散模型的反向过程中作为概率.
- 使用的代后期最大估计 (MAP) 估计与适应系数调整以适应噪声水平.
主要成果:
- 提出的无监督方法的性能优于最先进的无监督和监督深度学习方法.
- 达到高峰信号噪声比 (PSNR) 值:在腹部CT上达到45.02 dB,在胸部CT上达到35.35 dB.
- 显著超过Noise2Sim无监督算法的0.39dB (腹部) 和0.85dB (胸部).
结论:
- 一种基于扩散模型的新型无监督低剂量CT消毒方法成功开发.
- 该方法有效地解决了数据稀缺问题,只在正常剂量的CT图像上进行训练.
- 这种方法提供了卓越的定性和定量排污性能.
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