Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach
Introduction
The usage of X-ray computed tomography (CT) has increased dramatically since its introduction in the 1970s. It was estimated that 76 million CT scans were performed across the hospitals and clinics of the United States in 2013 [1]. The consequential radiation dose is significant and the potential radiation risks are receiving growing concerns [2]. Since the radiation risks typically decrease with the reduced radiation dose, many techniques and strategies have been proposed for dose reduction on the CT examinations [3], [4], [5]. One cost-effective and commonly used way is to acquire CT projection data with a lower milliampere-second (mAs) protocol [6]. However, the use of the standard filtered backprojection (FBP) method (equipped on most of commercial CT scanners) to reconstruct the low-dose acquisitions frequently produces inferior results with excessive noise and streak artifacts. Many projection or image domain denoising methods were proposed to improve the quality of the FBP-reconstructed low-dose CT images. The low-pass filters have the drawback that while removing the noise, they may also blur other high-frequency components including edges and fine structures, which could be critical in clinical assessment. Some more sophisticated edge-preserving filters can mitigate this drawback to some extent. For instance, the nonlocal means (NLM) filter was successfully applied to FBP-reconstructed low-dose CT images for noise reduction [7]. Based on the success, several strategies were proposed to achieve further improvement, such as using large-scale neighborhood [8], considering local noise level [9], and exploiting a previous normal-dose CT image [10]. Despite all these efforts, it is still observed that the NLM filtering strategies sometimes cannot completely remove the noise and streak artifacts, especially under the desired circumstance for as low as possible on the radiation dose.
On the other hand, many statistical image reconstruction (SIR) methods [11], which take into account the statistical properties of the low-dose projection data and accommodate the imaging geometry, have been shown to be superior in suppressing the noise and streak artifacts as compared to the NLM filtering strategies. The SIR strategy has recently become an endeavor for almost all major vendors of clinical CT systems [12], [13], [14]. The SIR approach is typically formulated by an objective function consisting of a data-fidelity term and a regularization (or equivalently, penalty) term where the penalized weighted least-squares (PWLS) is one of the commonly used objective function [11]. The penalty (or regularization) in the PWLS criterion plays a critical role for successful image reconstruction [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]. Among these studies, several explored the NLM-based regularization for PWLS image reconstruction of low-dose CT [24], [25], [26], [27], [28]. For instance, Ma et al. [24] proposed a previous normal-dose scan induced NLM regularization to improve the follow-up low-dose CT scans reconstruction. A temporal NLM regularization [25], [26] was also investigated for four-dimensional CT and cone-beam CT reconstruction, where the reconstruction of current frame image utilizing two neighboring frame images. However, the previous normal-dose CT image or neighboring frame images may not be readily available for some applications. Therefore, in our previous study [27], [28], a NLM-based generic regularization was explored using the currently available low-dose scan, wherein the regularization exploits a one-step-late (OSL) strategy to estimate the associated weighting coefficients. The NLM-regularized statistical image reconstruction scheme demonstrated promising reconstructions from low-dose data of relatively high-contrast phantoms [27], [28]. For clinical applications where the CT images contain not only high-contrast objects but also low-contrast objects and subtle structures, the reconstruction scheme could be problematic due to the use of a spatially invariant filtering parameter h in the regularization. A spatially invariant denoising may be too strong for some regions (blurring much) while too weak for other regions (filtering little) across the entire field of view (FOV) [8]. To address this issue, in this study we developed a novel strategy in designing adaptive filtering parameters for the NLM-based regularization by considering local characteristics of the to-be-reconstructed image, and the resulting new name is called adaptive NLM-based regularization.
The remainder of this paper is presented as follows. Section 2 explicitly illustrates the framework of the proposed adaptive NLM-regularized statistical image reconstruction algorithm, and further describes the associated issues about the algorithm implementation and performance evaluation. Section 3 reports the experimental results using both phantom and patient datasets. Finally, discussions on and conclusions from the experimental results are presented in Section 4.
Section snippets
Review of the NLM methodology
The NLM method was proposed as a non-iterative and edge-preserving filter for image de-noising [29], [30]. It reduces image noise by replacing each pixel's intensity with a weighted average of its neighbors according to similarity. Although the similarity comparison could be performed between any two pixels within the entire image, it is typically limited to a fixed neighboring window area (called search-window (SW), e.g., 17 × 17, in 2D case) of target pixel in practice for computation
Results
In this work, three categories of projection data were utilized to validate the performance of the proposed adaptive NLM-regularized statistical image reconstruction method (referred to as PWLS-adaptiveNLM) for X-ray CT imaging from low-dose acquisitions. For comparison purpose, the standard FBP reconstruction, the FBP reconstruction followed by NLM filtering (referred to as FBP + NLM filtering), the NLM-regularized statistical image reconstruction with constant filtering parameter (referred to
Discussions and conclusions
In this study, we proposed and validated an adaptive NLM-regularized statistical image reconstruction method for X-ray CT from low-dose acquisitions. One motivation of this work is that the traditional NLM filtering methods sometimes cannot completely remove the noise and streak artifacts in the low-dose CT images, especially when the streak artifacts are very severe. The NLM-regularized statistical image reconstruction method can mitigate this problem, partially due to the explicit statistical
Conflict of interest statement
No conflict of interest was declared by the authors.
Acknowledgements
This work was partly supported by the NIH/NCI under grants #CA082402 and #CA143111. JM was partially supported by the NSF of China under grants # 81371544, #81000613 and #81101046. JW was supported in part by grants from the Cancer Prevention and Research Institute of Texas (RP110562-P2 and RP130109), a grant from the American Cancer Society (RSG-13-326-01-CCE) and a grant from NIH (R01EB020366). HL was supported in part by the NSF of China grants #81230035 and #81071220.
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