A strategy of CT exam protocol to standardize groups of scanners using automated noise assessment and noise prediction for CT radiotherapy simulation.

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Abstract

Standardizing clinical CT images enables consistent analysis of image data for target delineation, radiomics, and machine learning in personalized medicine. The process of managing the scanned protocols in various CT scanners for radiotherapy simulation is underexplored in the literature. This study uses noise evaluation and prediction models to harmonize CT protocols across scanner models and manufacturers, ensuring reliable data for radiotherapy planning. A global noise index (GNI) was calculated from 1,581 clinical CT exams obtained on five scanners (three from vendor P and two from vendor S: Sp and Sc). Exams were categorized by anatomical site. GNI was assessed (I) within the same model, (II) between models from the same manufacturer, and (III) across manufacturers. One-way ANOVA (I, III) and Student t-tests (II) evaluated significance (p < 0.05). Predictive models were created and validated with 90 further exams, establishing a reference GNI (GNIref) for future optimization. GNI showed minor variations among P-type and between Sp and Sc scanners, but S scanners differed from P. Predictive model error ranged from 0.8 to 1.5 Hounsfield Units (HU). GNI differences between S and P scanners were <1 HU for head, neck, and paraspinal protocols, but S scanners had 1.5-2 HU higher GNI for the abdomen, pelvis, breast, and lungs. Scanners of the same model show slight variation; minor noise differences exist between manufacturers. Predictive modeling can estimate CT noise and support protocol optimization. The reference GNI of an anatomical site can be derived from sufficient CT exams, with or without the predictive mode; a 1-2 HU difference in GNIref is achievable if the protocol is properly translated.&#xD;&#xD;&#xD;&#xD;&#xD;&#xD;&#xD;&#xD;&#xD.

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