ArticleAcademic radiology2026
Diffusion Model-Based Motion Correction in Portable Computed Tomography for Brain: A Human Observer Study.
Article in Academic radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
RATIONALE AND
objectivesTo evaluate the clinical performance of a diffusion model-based motion correction algorithm for portable brain CT. MATERIALS AND
methodsWe 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.
resultsCorrected 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.
conclusionThe 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.
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