用扩散模型提升光声成像质量，单次拍摄即可获得高清晰度图像。
Guidance-base Diffusion Models for Improving Photoacoustic Image Quality
- 利用传感器数据优化扩散逆过程，提升图像生成精度。
- 引入成像条件引导机制，显著改善单次拍摄图像质量。
- 适合需要快速、低成本高质量成像的医学诊断场景。
光声（PA）成像是利用超声传感器非破坏性、非侵入式地可视化体内微小血管结构的技术。在单次拍摄中，图像质量较差，需通过平均多次单次图像来提高质量，导致整体成像成本较高。本研究提出一种基于扩散模型的PA图像质量提升方法。该方法通过融合光声成像的传感器信息优化扩散反向过程，并引入成像条件信息作为引导，实现高质量图像的生成，从而显著降低对多帧平均的需求，提升成像效率与图像质量。
原文摘要 · Abstract (English)
Photoacoustic(PA) imaging is a non-destructive and non-invasive technology for visualizing minute blood vessel structures in the body using ultrasonic sensors. In PA imaging, the image quality of a single-shot image is poor, and it is necessary to improve the image quality by averaging many single-shot images. Therefore, imaging the entire subject requires high imaging costs. In our study, we propose a method to improve the quality of PA images using diffusion models. In our method, we improve the reverse diffusion process using sensor information of PA imaging and introduce a guidance method using imaging condition information to generate high-quality images.
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