Application of Convolutional Neural Network for Evaluating CT Dose Using Image Noise Classification: A Phantom Study

Abstract

It is well known that there is a trade-off relationship between image noise and exposure dose in X-ray computed tomography (CT) examination. Therefore, CT dose level was evaluated by using the CT image noise property. Although noise power spectrum (NPS) is a common measure for evaluating CT image noise property, it is difficult to evaluate noise performance directly on clinical CT images, because NPS requires CT image samples with uniform exposure area for the evaluation. In this study, various noise levels of CT phantom images were classified for estimating dose levels of CT images using convolutional neural network (CNN). CT image samples of water phantom were obtained with a combination of mAs value (50, 100, 200 mAs) and X-ray tube voltage (80, 100, 120 kV). The CNN was trained and tested for classifying various noise levels of CT image samples by keeping 1) a constant kV and 2) a constant mAs. In addition, CT dose levels (CT dose index: CTDI) for all exposure conditions were estimated by using regression approach of the CNN. Classification accuracies for various noise levels were very high (more than 99.9%). The CNN-estimated dose level of CT images was highly correlated (r=0.998) with the actual CTDI. CT image noise level classification using CNN can be useful for the estimation of CT radiation dose.