Improving Low-Dose Pediatric Abdominal CT by Using Convolutional Neural Networks.

PURPOSE:To evaluate the efficacy of convolutional neural networks (CNNs) to improve the image quality of low-dose pediatric abdominal CT images. MATERIALS AND METHODS:Images from 11 pediatric abdominal CT examinations acquired between June and July 2018 were reconstructed with filtered back projection (FBP) and an iterative reconstruction (IR) algorithm. A residual CNN was trained using the FBP image as the input and the difference between FBP and IR as the target such that the network was able to predict the residual image and simulate the IR. CNN-based postprocessing was applied to 20 low-dose pediatric image datasets acquired between December 2016 and December 2017 on a scanner limited to reconstructing FBP images. The FBP and CNN images were evaluated based on objective image noise and subjective image review by two pediatric radiologists. For each of five features, readers rated images on a five-point Likert scale and also indicated their preferred series. Readers also indicated their "overall preference" for CNN versus FBP. Preference and Likert scores were analyzed for individual and combined readers. Interreader agreement was assessed. RESULTS:The CT number remained unchanged between FBP and CNN images. Image noise was reduced by 31% for CNN images (P < .001). CNN was preferred for overall image quality for individual and combined readers. For combined Likert scores, at least one of the two score types (Likert or binary preference) indicated a significant favoring of CNN over FBP for low contrast, image noise, artifacts, and high contrast, whereas the reverse was true for spatial resolution. CONCLUSION:FBP images can be improved in image space by a well-trained CNN, which may afford a reduction in dose or improvement in image quality on scanners limited to FBP reconstruction.© RSNA, 2019.