视觉语言模型辅助CT通过文本导向扩散和保真性维护进行无声化
Ye Shen1, Ningning Liang1, Ailong Cai1
1Department of Henan Key Laboratory of Imaging and Intelligent Processing, Information Engineering University, Zhengzhou, China.
Journal of X-ray science and technology
|March 12, 2026
概括
一个新的视觉语言模型辅助CT排泄 (VLD) 框架减少了计算机断层扫描 (CT) 扫描中的噪音,而不需要配对数据. 这种方法可以提高图像质量和诊断准确度,使患者的成像更安全.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 在计算机断层扫描 (CT) 和光子计数CT (PCCT) 中降低辐射剂量对于患者的安全至关重要.
- 低剂量CT成像引入噪音,降低图像质量,并可能影响诊断准确性.
- 目前的无声化方法通常需要配对数据或依赖于特定的噪声假设,限制了临床适用性.
研究的目的:
- 引入一种新的视觉语言模型辅助CT排泄 (VLD) 框架,以有效地减少CT图像噪声.
- 利用视觉语言模型的语义理解来改善CT图像恢复.
- 为了保持低剂量CT图像的诊断准确性和结构完整性.
主要方法:
- 开发了一个视觉语言模型辅助的CT Denoising (VLD) 框架,利用语义指导.
- 采用由多式视觉语言模型衍生的语义理解指导的扩散模型.
- 实施了三域一致性框架,以逐步改进图像细节和结构保存.
主要成果:
- VLD方法在模拟CT和真实PCCT数据上展示了高质量的重建.
- 在特定条件下,达到0.95dB和1.21dB的平均峰值信号噪声比改善.
- 在模拟实验中,超越了需要配对数据的WGAN和FBPConvNet等现有方法.
- 向新的成像场景展示了强大的概括能力.
结论:
- VLD框架有效地减少CT图像中的噪声,同时保持诊断质量.
- 利用视觉语言模型为先进的医学图像拒绝提供了一个有希望的方向.
- 拟议的方法为低剂量CT消毒提供了强大的和可通用的解决方案,克服了以前方法的局限性.
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