ArticleBMC medical informatics and decision making2026
Optimizing acquisition time and injected dose in
Article in BMC medical informatics and decision making, 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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7 authors.
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Abstract
backgroundThis study aims to evaluate the effectiveness of deep learning algorithms in simulating standard acquisition time images from shortened acquisition times in
methodsWe evaluated 322 patients who underwent
resultsDeep learning significantly improved image quality scores in the half-dose group from 3.45 ± 0.48 to 4.33 ± 0.55 and enhanced kappa values from 0.76 to 0.82 (p < 0.05). Quantitative metrics demonstrated marked improvements, with SSIM values rising from 0.75 to 0.87 and PSNR from 37.65 dB to 41.37 dB in half-dose scenarios. Similarly, in the 30s model, SSIM increased from 0.73 to 0.84 and PSNR from 35.38 dB to 40.15 dB. Lesion detectability in small lesions improved by up to 16.3% in shortened acquisition conditions, effectively enhancing the detection of critical pathological features.
conclusionsThe application of deep learning models in PET/CT imaging may improve scan efficiency and reduce radiopharmaceutical usage while maintaining image quality without apparent loss of diagnostic information. These findings support the potential of AI-enhanced imaging protocols to reduce patient radiation exposure and improve clinical workflow efficiency.
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