Robust learning-based x-ray image denoising—potential pitfalls, their analysis and solutions

Abstract

Purpose: Since guidance based on X-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the X-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures. Method: We present a robust learning-based denoising strategy involving model- based simulations of low-dose X-ray images during the training phase. The method also utilizes a data-driven normalization step - based on an X-ray imaging model - to stabilize the mixed signal-dependent noise associated with X-ray images. We thoroughly analyze the method's sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose X-ray images on the denoising results. Results: A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band co efficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy. Conclusion: The proposed learning-based denoising strategy provides scope for significant X-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training ph

Unsupervised learning of robust models for cardiac and photon-counting x-ray CT denoising

“…Deep learning based denoising methods for X-ray imaging are often specific to the distribution of data used for model training. 4,12 We propose a simple normalization strategy for multi-channel CT data to improve model generalization from a fixed training set:…”

“…Furthermore, trained models may be brittle: failing to generalize to closely related data sets (e.g. differing reconstruction kernels, 4 noise levels 12 ), limiting model reuse and computational advantages. In multi-channel CT, label generation is difficult for retrospective cardiac CT data where dose modulation yields variable image quality by cardiac phase and each phase is only acquired at a single dose level.…”