Abstract
With the rapid increase of CT usage, radiation dose across patient populations is also increasing. Therefore, it is desirable to reduce the CT radiation dose. However, the reduction in dose also incurs additional noise and with the degraded image quality, diagnostic performance can be compromised. Existing routine dosimetric quantities are usually based on absorbed dose within cylindrical phantoms and do not appropriately represent the actual patient dose. More comprehensive dose metrics such as effective dose require estimation of patient-specific dose at an organ level. Unfortunately, currently available systems are quite far from achieving this goal as well as limited by a number of manual adjustments, time-consuming and inefficient procedures. To overcome all these challenges in achieving the goal of patient safety through reduced dose without compromising image quality, we devise a fully-automated, end-to-end deep learning-based solution to perform real-time, patient-specific, organ-level dosimetric 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-Net model estimates the patient-specific mean dose in real-time for six different organs. Our experimental evaluation on real patient data demonstrates the effectiveness of our Scout-Net model not only in real-time dose estimation (only 11 ms on average per scan), but also as a potential tool for optimizing CT radiation dose in specific patients.
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Notes
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https://github.com/DIDSR/MCGPU, version 1.3, accessed on August 10, 2020.
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Imran, AAZ. et al. (2021). Personalized CT Organ Dose Estimation from Scout Images. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_47
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