The following abstract is drawn from a recently published paper in Academic Radiology. We invite you to read the full paper and join the conversation, become a member of the Radiology News community to share your thoughts, ask questions, and engage with others around this work.
Authors: Zhennong Chena,b , Quirin Strotzerc , Min Langd , Maryam Vejdani-Jahromic , Baihui Yue , Rehab Naeem Khalidc , Siyeop Yoona , Matthew Tivnana , Quanzheng Lia , Michael H. Levc , Rajiv Guptac , a,f
Abstract
Rationale and Objectives
To evaluate the clinical performance of a diffusion model-based motion correction algorithm for portable brain CT.
Materials and Methods
We retrospectively collected 67 portable brain CT scans with corresponding fixed CT scans acquired within ±2 days as reference. A pre-trained diffusion model was applied to correct motion artifacts in the portable scans. Each case yielded three volumes as follows: original (motion group), corrected (corrected group), and fixed (reference group). Images were reviewed in randomized order by three professional readers (one neuroradiologist, one neuroradiology fellow, and one radiology resident), with at least two weeks between sessions to reduce recall bias. Eight lesion types and four image quality metrics were scored using a 5-point Likert scale. ACR phantom testing was performed to assess compliance with diagnostic image quality standards.
Results
Corrected images significantly outperformed motion images in all image quality metrics (improvement: 0.33–0.79, p<0.001), except for sharpness (p = 0.34). Diagnostic confidence improved from 2.52 to 2.86. Lesion detectability remained comparable before and after correction, with no significant differences in agreement rates (McNemar’s p>0.10) or AUCs (DeLong’s p>0.06) across all lesion types. Agreement rates ranged from 0.866 to 0.985 in the corrected group against the reference, and AUCs from 0.788 to 0.964. The net reclassification index was 2.66%. Corrected images passed all ACR criteria in phantom testing.
Conclusion
The diffusion model-based algorithm effectively improves image quality and diagnostic confidence without compromising lesion detection, supporting its potential for clinical use in portable brain CT.
Read the full article:
Diffusion Model-Based Motion Correction in Portable Computed Tomography for Brain: A Human Observer Study – Academic Radiology
- a Center 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.)
- b School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou, China (Z.C.)
- c Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts (Q.S., M.V.J., R.N.K., M.H.L., R.G.)
- d Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts (M.L.)
- e Department of Radiology, The University of Chicago, Chicago, Illinois (B.Y.)
- f Department of Radiology, The Ohio State University, Columbus, Ohio (D.W.)
