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Considering the potential radiation effect on patients in computed tomography (CT) imaging, it is desirable to reduce the radiation dose. Reduction in dose incurs degradation in image quality and possible reduced diagnostic performance. CT image quality needs to be maintained at standards sufficient for effective clinical reading. Therefore, the dose reduction should be guided by the potential image quality. Unfortunately, currently available CT image quality assessment (IQA) tools are based on maintaining a uniform image quality or use the CT exams themselves to retrospectively determine image quality. A robust and comprehensive IQA metric should represent the image quality of each patient at the organ level, and before the CT exams. Towards this objective, we devise a fully-automated, end-to-end deep learning-based solution to perform real-time, patient-specific, organ-level image quality prediction of CT scans. Leveraging the 2D scout (frontal and lateral) images of the actual patients, which are routinely acquired prior to the CT scan, our proposed Scout-IQA model estimates the patient-specific mean noise in real-time for six different organs. Our experimental evaluation on real patient data demonstrates the effectiveness of our Scout model not only in real-time noise estimation (only 6 ms on average per scan), but also as a potential tool for optimizing CT radiation dose in individual patients.
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