  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.
