Low-dose computed tomography image restoration using previous normal-dose scan

Abstract

Purpose: In current computed tomography (CT) examinations, the associated x-ray radiation dose is of a significant concern to patients and operators. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) or kVp parameter (or delivering less x-ray energy to the body) as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and the noise would propagate into the CT image if no adequate noise control is applied during image reconstruction. Since a normal-dose high diagnostic CT image scanned previously may be available in some clinical applications, such as CT perfusion imaging and CT angiography (CTA), this paper presents an innovative way to utilize the normal-dose scan as a priori information to induce signal restoration of the current low-dose CT image series. Methods: Unlike conventional local operations on neighboring image voxels, nonlocal means (NLM) algorithm utilizes the redundancy of information across the whole image. This paper adapts the NLM to utilize the redundancy of information in the previous normal-dose scan and further exploits ways to optimize the nonlocal weights for low-dose image restoration in the NLM framework. The resulting algorithm is called the previous normal-dose scan induced nonlocal means (ndiNLM). Because of the optimized nature of nonlocal weights calculation, the ndiNLM algorithm does not depend heavily on image registration between the current low-dose and the previous normal-dose CT scans. Furthermore, the smoothing parameter involved in the ndiNLM algorithm can be adaptively estimated based on the image noise relationship between the current low-dose and the previous normal-dose scanning protocols. Results: Qualitative and quantitative evaluations were carried out on a physical phantom as well as clinical abdominal and brain perfusion CT scans in terms of accuracy and resolution properties. The gain by the use of the previous normal-dose scan via the presented ndiNLM algorithm is noticeable as compared to a similar approach without using the previous normal-dose scan. Conclusions: For low-dose CT image restoration, the presented ndiNLM method is robust in preserving the spatial resolution and identifying the low-contrast structure. The authors can draw the conclusion that the presented ndiNLM algorithm may be useful for some clinical applications such as in perfusion imaging, radiotherapy, tumor surveillance, etc.

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

“…The low-dose images are denoised by finding similar patches in the standard-dose image. The NLM-type algorithm suggested for CT perfusion imaging and angiography in [61] registers the standard-dose image to the low-dose image. The low-dose image is then denoised using patches extracted from the standard-dose image.…”

Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior

“…High-quality CT measurements for a patient acquired in previous scans can be utilized as prior knowledge to facilitate the subsequent image reconstruction, for instance, in the cases of lowdose scan and sparse-view measurements [13,15,26,27,28,31,33,35,38]. However, due to the inverse effect of the mismatched regions between the desired-and prior-image, using prior image without any misalignment reduction would lead to blur or even loss of the original structures in mismatched regions [33].…”

“…Inspired by the PICCS algorithm introduced by Chen et al [15], in this paper, we propose a ndiTV prior by incorporating the ndiNLM filter proposed by Ma et al [38], which is expressed as follows:…”

“…In this paper, based on the recent studies about sparse-view and low-dose CT image reconstructions [11,12,13,28,34,35,36,37,38,39], we propose a normal-dose image induced total variation prior (ndiTV) under the penalized weighted least-square (PWLS) criteria [10], which is referred to ''PWLS-ndiTV'' for simplicity, aiming to relieve the requirement of misalignment reduction operation of the PICCS algorithm. The novelty of the present PWLS-ndiTV approach is twofold.…”

Progressive cone beam CT dose control in image‐guided radiation therapy

“…Their utilization is beneficial for processing the image at fraction k + 1. While image enhancement using NLM has also been used in diagnostic CT imaging, 45,46 our scheme distinguishes itself by its dedicated design for IGRT, where a number of prior CBCT images are readily available.…”