Chen Zhennong, Yoon Siyeop, Strotzer Quirin, Khalid Rehab Naeem, Tivnan Matthew, Li Quanzheng, Gupta Rajiv, Wu Dufan
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, United States.
Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, United States.
Comput Med Imaging Graph. 2025 Jan;119:102478. doi: 10.1016/j.compmedimag.2024.102478. Epub 2024 Dec 13.
Portable head CT images often suffer motion artifacts due to the prolonged scanning time and critically ill patients who are unable to hold still. Image-domain motion correction is attractive for this application as it does not require CT projection data. This paper describes and evaluates a generative model based on conditional diffusion to correct motion artifacts in portable head CT scans. This model was trained to find the motion-free CT image conditioned on the paired motion-corrupted image. Our method utilizes histogram equalization to resolve the intensity range discrepancy of skull and brain tissue and an advanced Elucidated Diffusion Model (EDM) framework for faster sampling and better motion correction performance. Our EDM framework is superior in correcting artifacts in the brain tissue region and across the entire image compared to CNN-based methods and standard diffusion approach (DDPM) in a simulation study and a phantom study with known motion-free ground truth. Furthermore, we conducted a reader study on real-world portable CT scans to demonstrate improvement of image quality using our method.
由于扫描时间较长以及重症患者无法保持静止，便携式头部CT图像经常会出现运动伪影。图像域运动校正对于此应用很有吸引力，因为它不需要CT投影数据。本文描述并评估了一种基于条件扩散的生成模型，用于校正便携式头部CT扫描中的运动伪影。该模型经过训练，以根据配对的运动受损图像找到无运动的CT图像。我们的方法利用直方图均衡化来解决颅骨和脑组织的强度范围差异，并使用先进的阐释扩散模型（EDM）框架实现更快的采样和更好的运动校正性能。在具有已知无运动真实情况的模拟研究和体模研究中，与基于卷积神经网络的方法和标准扩散方法（DDPM）相比，我们的EDM框架在校正脑组织区域和整个图像中的伪影方面表现更优。此外，我们对真实世界的便携式CT扫描进行了读者研究，以证明使用我们的方法可提高图像质量。
