Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database

The valuable structure features in full-dose computed tomography (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT image adaptively may result in poor preservation of details/textures in LdCT image. This paper aims to explore a novel prior knowledge retrieval and representation paradigm, called adaptive prior features assisted restoration algorithm, for the purpose of better restoration of the low-dose lung CT images by capturing local features from FdCT scans adaptively. The innovation lies in the construction of an offline training database and the online patch-search scheme integrated with the principal components analysis (PCA). Specifically, the offline training database is composed of 3-D patch samples extracted from existing full-dose lung scans. For online patch-search, 3-D patches with structure similar to the noisy target patch are first selected from the database as the training samples. Then, PCA is applied on the training samples to retrieve their local prior principal features adaptively. By employing the principal features to decompose the noisy target patch and using an adaptive coefficient shrinkage technique for inverse transformation, the noise of the target patch can be efficiently removed and the detailed texture can be well preserved. The effectiveness of the proposed algorithm was validated by CT scans of patients with lung cancer. The results show that it can achieve a noticeable gain over some state-of-the-art methods in terms of noise suppression and details/textures preservation.

I. Introduction

Nowadays, the risk of radiation induced genetic, cancerous and other diseases is of a significant concern to patients and operators [1]–[3]. Minimizing X-ray exposure to patients has been one of the major efforts in the CT fields. A simple and cost-effective method to reduce radiation exposure is to lower the X-ray tube current and/or exposure time (mAs) during data acquisition. However the low mAs acquisition protocols could be highly detrimental to image quality, resulting in images with serious noise and streak artifacts [4]. To address this problem, various noise-reduction strategies have been proposed, including statistic-based iterative reconstruction (SIR) [5], [6], sinogram domain denoising [7]–[9] and image domain denoising [10]–[12].