Distilling Vision Transformers for no-reference Perceptual CT Image Quality Assessment

Image quality assessment of CT scans is of utmost importance in balancing radiation dose and image quality. Nonetheless, estimating the image quality of CT scans is a highly subjective task that cannot be adequately captured by a single quantitative metric. In this work, we present a novel vision Transformer network for no-reference CT image quality assessment. Our network combines convolutional operations and multi-head self-attention mechanisms by adding a powerful convolutional stem in the beginning of the traditional ViT network. To enhance the performance and efficiency of the network, we introduce a distillation methodology, comprised of two sequential steps. In Step I, we construct a "teacher ensemble network" by training five Vision Transformer networks using a five-fold division schema. In Step II, we train a single vision Transformer, referred to as the "student network", by using the teacher's predictions as new labels. The student network is also optimized using the original labeled dataset. The effectiveness of the proposed model is evaluated on the task of predicting image quality scores from low-dose abdominal CT images from the LDCTIQAC2023 Grand Challenge. Our model demonstrates remarkable performance, ranking 6th during the testing phase of the challenge. Additionally, our experiments highlight the effectiveness of incorporating a convolutional stem in the ViT architecture and the distillation methodology.