Real-Time Patient-Specific CT Dose Estimation using a Deep Convolutional Neural Network

Due to the potential risk of ionizing radiation, the assessment of the administered radiation dose is an important topic in CT. However, dosimetric quantities that are routinely evaluated in CT only refer to the absorbed dose within cylindrical phantoms and do not appropriately represent the actual patient dose. While Monte Carlo (MC), the gold standard for patient-specific dose estimation, is too slow to be applied routinely, faster alternatives are usually far less accurate. To overcome this drawback, we developed the deep dose estimation (DDE) algorithm. DDE uses a deep convolutional neural network to reproduce MC dose estimates given only a CT image and a first-order dose estimate as two-channel input. To learn the corresponding mapping, DDE was trained on artificial data generated from whole-body clinical CT scans of 15 patients. For each patient 60 circular CT scans were simulated for 20 different z-positions (pelvis, abdomen, thorax) and 3 different acquisition settings. The total number of 900 data sets was divided into 720 training data sets (12 patients) and 180 validation data sets (3 patients). Each scan was reconstructed on a 256 x 256 x 48 voxel grid with an isotropic voxel size of 2 mm. In addition a first-order dose estimate as well as a MC dose estimate was calculated for every data set. Using these data, the network's open parameters were determined by minimizing the mean relative error between the output and the MC dose estimate. Evaluating the MRE on the validation data set yields deviations of 3.0 % on average with respect to the ground truth and processing times of about 250 ms per data set. Thus, DDE is able to achieve similar accuracy as MC while performing orders of magnitude faster..