Adapting low-dose CT denoisers for texture preservation using zero-shot local noise-level matching

On enhancing the image quality of low-dose computed tomography (LDCT), various denoising methods have achieved meaningful improvements. However, they commonly produce over-smoothed results; the denoised images tend to be more blurred than the normal-dose targets (NDCTs). Furthermore, many recent denoising methods employ deep learning(DL)-based models, which require a vast amount of CT images (or image pairs).

Our goal is to address the problem of over-smoothed results and design an algorithm that works regardless of the need for a large amount of training dataset to achieve plausible denoising results. Over-smoothed images negatively affect the diagnosis and treatment since radiologists had developed clinical experiences with NDCT. Besides, a large-scale training dataset is often not available in clinical situations. To overcome these limitations, we propose locally-adaptive noise-level matching (LANCH), emphasizing the output should retain the same noise-level and characteristics to that of the NDCT without additional training.

We represent the NDCT image as the pixel-wisely weighted sum of an over-smoothed output from off-the-shelf denoiser (OSD) and the difference between the LDCT image and the OSD output. Herein, LANCH determines a 2D ratio map (i.e., pixel-wise weight matrix) by locally matching the noise-level of output and NDCT, where the LDCT-to-NDCT device flux (mAs) ratio reveals the NDCT noise-level. Thereby, LANCH can preserve important details in LDCT, and enhance the sharpness of the noise-free regions. Note that LANCH can enhance any LDCT denoisers without additional training data (i.e., zero-shot).

The proposed method is applicable to any OSD denoisers, reporting significant texture plausibility development over the baseline denoisers in quantitative and qualitative manners. It is surprising that the denoising accuracy achieved by our method with zero-shot denoiser was comparable or superior to that of the best training-based denoisers; our result showed 1% and 33% gains in terms of SSIM and DISTS, respectively. Reader study with experienced radiologists shows significant image quality improvements, a gain of + 1.18 on a five-point mean opinion score scale.

In this paper, we propose a technique to enhance any low-dose CT denoiser by leveraging the fundamental physical relationship between the x-ray flux and noise variance. Our method is capable of operating in a zero-shot condition, which means that only a single low-dose CT image is required for the enhancement process. We demonstrate that our approach is comparable or even superior to supervised DL-based denoisers that are trained using numerous CT images. Extensive experiments illustrate that our method consistently improves the performance of all tested LDCT denoisers.