Abstract:
Despite the significant clinical benefits of computed tomography (CT) in providing diagnostic information for a broad range of diseases, concerns have been raised regarding the potential cancer risk induced by CT radiation exposure. In that regard, optimizing CT protocols and minimizing radiation dose have become the core problem for the CT community. To develop strategies to optimize radiation dose, it is crucial to effectively characterize CT image quality. Such image quality estimates need to be prospective to ensure that optimization can be performed before the scan is initiated. The purpose of this study was to establish a phantombased methodology to predict quantum noise in CT images as a first step in our image quality prediction. Quantum noise was measured using a variable-sized phantom under clinical protocols. The mathematical relationship between noise and water-equivalent-diameter (Dw) was further established. The prediction was achieved by ascribing a noise value to a patient according to the patient’s water-equivalent- diameter. The prediction accuracy was evaluated in anthropomorphic phantoms across a broad range of sizes, anatomy, and reconstruction algorithms. The differences between the measured and predicted noise were within 10% for anthropomorphic phantoms across all sizes and anatomy. This study proposed a practically applicable technique to predict noise in CT images. With a prospective estimation of image quality level, the scanning parameters can then by adjusted to ensure optimized imaging performance. © 2014 SPIE.
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