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Diffusion Model-Based Motion Correction in Portable Computed Tomography for Brain: A Human Observer Study
Zhennong Chen1, Quirin Strotzer2, Min Lang3
1Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts (Z.C., S.Y., M.T., Q.L., D.W.); School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou, China (Z.C.).
Academic Radiology
|January 7, 2026
Summary
A new diffusion model algorithm enhances portable brain CT image quality and diagnostic confidence. It effectively corrects motion artifacts without negatively impacting lesion detection, showing promise for clinical use.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Radiology
- Computed Tomography
Background:
- Motion artifacts are a significant challenge in portable brain CT imaging.
- Existing correction methods may compromise diagnostic accuracy.
- Portable CT offers accessibility but is prone to motion-related image degradation.
Purpose of the Study:
- To evaluate a diffusion model-based algorithm for correcting motion artifacts in portable brain CT.
- To assess the impact of motion correction on image quality and diagnostic performance.
- To determine the clinical utility of AI-driven motion correction in portable CT.
Main Methods:
- Retrospective analysis of 67 portable brain CT scans compared to fixed CT scans.
- Application of a pre-trained diffusion model for motion artifact correction.
- Reader studies evaluating image quality metrics and lesion detectability using Likert scales and AUC analysis.
Main Results:
- Corrected portable CT images showed significant improvement in image quality metrics (p<0.001) compared to uncorrected scans.
- Diagnostic confidence increased post-correction (2.52 to 2.86).
- Lesion detectability and diagnostic agreement remained comparable to reference scans, with no significant compromise.
Conclusions:
- The diffusion model-based algorithm effectively enhances portable brain CT image quality and diagnostic confidence.
- The algorithm demonstrates potential for clinical adoption without sacrificing diagnostic accuracy for lesion detection.
- AI-powered motion correction offers a viable solution for improving portable CT imaging.
