Deep Interactive Denoiser (DID) for X-Ray Computed Tomography
This work provides a method for clinicians to interactively adjust noise-resolution tradeoffs in LDCT images, improving the adaptability of deep learning denoisers for diverse diagnostic and interventional needs.
The paper addresses the limitations of deep learning-based denoisers in low-dose computed tomography (LDCT) by introducing a lightweight optimization process during testing. This process enables the generation of multiple image candidates with varying noise-resolution tradeoffs in real-time, allowing users to interactively select the most suitable image for different clinical tasks.
Low dose computed tomography (LDCT) is desirable for both diagnostic imaging and image guided interventions. Denoisers are openly used to improve the quality of LDCT. Deep learning (DL)-based denoisers have shown state-of-the-art performance and are becoming one of the mainstream methods. However, there exists two challenges regarding the DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs which sometimes are needed for different clinical tasks; 2) the model generalizability might be an issue when the noise level in the testing images is different from that in the training dataset. To address these two challenges, in this work, we introduce a lightweight optimization process at the testing phase on top of any existing DL-based denoisers to generate multiple image candidates with different noise-resolution tradeoffs suitable for different clinical tasks in real-time. Consequently, our method allows the users to interact with the denoiser to efficiently review various image candidates and quickly pick up the desired one, and thereby was termed as deep interactive denoiser (DID). Experimental results demonstrated that DID can deliver multiple image candidates with different noise-resolution tradeoffs, and shows great generalizability regarding various network architectures, as well as training and testing datasets with various noise levels.