David Larson

Publication Details

  • System for Verifiable CT Radiation Dose Optimization Based on Image Quality. Part I. Optimization Model RADIOLOGY Larson, D. B., Wang, L. L., Podberesky, D. J., Goske, M. J. 2013; 269 (1): 167-176

    Abstract:

    To develop and validate a mathematical radiation dose optimization model for computed tomography (CT) of the chest, abdomen, and pelvis.This quality improvement project was determined not to constitute human subject research. A model for measuring water-equivalent diameter (DW) based on the topogram was developed and validated on each axial section in eight CT examinations of the chest, abdomen, and pelvis (500 images). A model for estimating image noise and size-specific dose estimates (SSDEs) using image and metadata was developed and validated in 16 examinations of anthropomorphic phantoms. A model to quantify radiologist image quality preferences was developed and applied to evaluations of 32 CT examinations of the abdomen and pelvis by 10 radiologists. The scanners' dose modulation algorithms were modeled and incorporated into an application capable of prediction of image noise and SSDE over a range of patient sizes. With use of the application, protocol techniques were recommended to achieve specific image noise targets. Comparisons were evaluated by using two-tailed nonpaired and paired t tests. Results: The mean difference between topogram- and axial-based DW estimates was -3.5% ± 2.2 (standard deviation). The mean difference between estimated and measured image noise and volume CT dose index on the anthropomorphic phantoms was -6.9% ± 5.5 and 0.8% ± 1.8, respectively. A three-dimensional radiologist image quality preference model was developed. For the prediction model validation studies, mean differences between predicted and actual effective tube current-time product, SSDE, and estimated image noise were -0.9% ± 9.3, -1.8% ± 10.6, and -0.5% ± 4.4, respectively.CT image quality and radiation dose can be mathematically predicted and optimized on the basis of patient size and radiologist-specific image noise target curves.

    View details for DOI 10.1148/radiol.13122320

    View details for Web of Science ID 000325000700019

    View details for PubMedID 23784878

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