SU-F-18C-01: Minimum Detectability Analysis for Comprehensive Sized Based Optimization of Image Quality and Radiation Dose Across CT Protocols.

PURPOSE: This work involved a comprehensive modeling of task-based performance of CT across a wide range of protocols. The approach was used for optimization and consistency of dose and image quality within a large multi-vendor clinical facility. METHODS: 150 adult protocols from the Duke University Medical Center were grouped into sub-protocols with similar acquisition characteristics. A size based image quality phantom (Duke Mercury Phantom) was imaged using these sub-protocols for a range of clinically relevant doses on two CT manufacturer platforms (Siemens, GE). The images were analyzed to extract task-based image quality metrics such as the Task Transfer Function (TTF), Noise Power Spectrum, and Az based on designer nodule task functions. The data were analyzed in terms of the detectability of a lesion size/contrast as a function of dose, patient size, and protocol. A graphical user interface (GUI) was developed to predict image quality and dose to achieve a minimum level of detectability. RESULTS: Image quality trends with variations in dose, patient size, and lesion contrast/size were evaluated and calculated data behaved as predicted. The GUI proved effective to predict the Az values representing radiologist confidence for a targeted lesion, patient size, and dose. As an example, an abdomen pelvis exam for the GE scanner, with a task size/contrast of 5-mm/50-HU, and an Az of 0.9 requires a dose of 4.0, 8.9, and 16.9 mGy for patient diameters of 25, 30, and 35 cm, respectively. For a constant patient diameter of 30 cm, the minimum detected lesion size at those dose levels would be 8.4, 5, and 3.9 mm, respectively. CONCLUSION: The designed CT protocol optimization platform can be used to evaluate minimum detectability across dose levels and patient diameters. The method can be used to improve individual protocols as well as to improve protocol consistency across CT scanners.

Duke Scholars

Author Ehsan Samei Radiology