Deep Learning-based Low Dose CT Imaging

We developed a machine-learning-based method generate good quality low dose CT using a residual block concept and a self-attention strategy with a cycle-consistent adversarial network framework. A fully convolution neural network with residual blocks and attention gates is used in the generator to enable end-to-end transformation. We have collected CT images from 30 patients treated with frameless brain stereotactic radiosurgery (SRS) for this study. These full dose images were used to generate projection data, which were then added with noise to simulate the low mAs scanning scenario. Low dose CT images were reconstructed from this noise-contaminated projection data, and were fed into our network along with the original full dose CT images for training. The performance of our network was evaluated by quantitatively comparing the high quality CT images generated by our method with the original full dose images. When mAs is reduced to 0.5% of the original CT scan, the mean square error of the CT images obtained by our method is similar to 1.6%, with respective to the original full dose images. The proposed method successfully improved the noise, CNR and non-uniformity level to be close to those of full dose CT images, and outperforms a state-of-art iterative reconstruction method. Dosimetric studies shows that the average differences of DVH metrics are less than 0.1 Gy (p>0.05). These quantitative results strongly indicate that the denoised low dose CT images using our method maintains image accuracy and quality, and are accurate enough for dose calculation in current CT simulation of brain SRS treatment. This study also demonstrates the great potential for low dose CT in the process of simulation and treatment planning.