Deep Interactive Denoiser (DID) for X-Ray Computed Tomography

Abstract

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.

arXiv commentunder review

References in corpus (16)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Instance Normalization: The Missing Ingredient for Fast Stylization
• Fully Convolutional Networks for Semantic Segmentation
• Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
• Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
• Neural Tangent Kernel: Convergence and Generalization in Neural Networks
• Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT
• Deep Image Prior
• Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
• Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
• A constrained, total-variation minimization algorithm for low-intensity X-ray CT
• Accurate Image Super-Resolution Using Very Deep Convolutional Networks
• Low-Dose CT Image Denoising Using Parallel-Clone Networks
• Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks