Portable head CT motion artifact correction via diffusion-based generative model.

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Zhennong Chen, Siyeop Yoon, Quirin Strotzer, Rehab Naeem Khalid, Matthew Tivnan, Quanzheng Li, Rajiv Gupta, Dufan Wu
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引用次数: 0

Abstract

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.

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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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