Task-specific self-supervision for CT image denoising

CT image quality is largely reliant on radiation dose, which causes a trade-off between image quality and dose, affecting the subsequent image-based diagnostic and treatment performances. Deep learning approaches for low-dose CT image denoising require access to large training sets and specifically the reference full-dose images, which can be difficult to obtain. Self-supervised learning enables learning with reduced reference data burden. Currently available self-supervised CT denoising works are either dependent on foreign domains or pretexts that are not very task-relevant, requiring lots of adjustments (architectural, loss function, training parameters, etc.) and additional skills to perform the downstream denoising task. To tackle the aforementioned challenges, we propose a novel self-supervised pretraining approach, namely Self-Supervised Window-Leveling for Image DeNoising (SSWL-IDN), leveraging an innovative, task-relevant, simple yet effective surrogate–prediction of the window-leveled equivalent. SSWL-IDN leverages residual learning and a hybrid loss combining perceptual loss and MSE, all incorporated in a VAE framework. Our extensive (in- and cross-domain) experimentation demonstrates the effectiveness of SSWL-IDN in aggressive denoising of CT (abdomen and chest) images acquired at two different dose levels (5% and 25%), without requiring any kind of architectural or downstream training adjustments. We have made our code publicly available at https://github.com/ayaanzhaque/SSWL-IDN.