用扩散模型提升介入室低质CT图像，达到诊断级清晰度。
Towards Diagnostic Quality Flat-Panel Detector CT Imaging Using Diffusion Models
- 用去噪扩散模型修复平板探测器CT的伪影和模糊。
- 修复后图像与多排CT相当，且不漏诊出血。
- 适合需要快速影像评估的卒中介入场景。
接受机械取栓术的患者通常需在术前和术后进行多排探测器CT（MDCT）扫描。介入室中的平板探测器CT（FDCT）因显著伪影，图像质量远低于MDCT。然而，仅使用FDCT可避免患者移动至MDCT室，节省时间。已有研究评估了单独使用FDCT的潜力及其带来的时效优势。本研究提出采用去噪扩散概率模型（DDPM）提升FDCT图像质量，使其接近MDCT水平。临床医生通过问卷对FDCT、MDCT及模型生成图像进行诊断评估。结果表明，DDPM有效消除了多数伪影，提升了解剖结构可见性，且未影响出血检测能力，前提是输入的FDCT图像质量不过于低下。代码已公开于GitHub。
原文摘要 · Abstract (English)
Patients undergoing a mechanical thrombectomy procedure usually have a multi-detector CT (MDCT) scan before and after the intervention. The image quality of the flat panel detector CT (FDCT) present in the intervention room is generally much lower than that of a MDCT due to significant artifacts. However, using only FDCT images could improve patient management as the patient would not need to be moved to the MDCT room. Several studies have evaluated the potential use of FDCT imaging alone and the time that could be saved by acquiring the images before and/or after the intervention only with the FDCT. This study proposes using a denoising diffusion probabilistic model (DDPM) to improve the image quality of FDCT scans, making them comparable to MDCT scans. Clinicans evaluated FDCT, MDCT, and our model's predictions for diagnostic purposes using a questionnaire. The DDPM eliminated most artifacts and improved anatomical visibility without reducing bleeding detection, provided that the input FDCT image quality is not too low. Our code can be found on github.
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