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Updated: Jan 13, 2026
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Published on: May 10, 2012
脳用ポータブルCTにおける拡散モデルベースのモーション補正：人間の観察者研究
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.).
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.
