Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach

https://doi.org/10.1016/j.compmedimag.2015.02.008Get rights and content

Highlights

  • Introduce spatial adaptivity in the NLM-based regularization.

  • Demonstrate necessity and efficacy of introducing the spatial adaptivity.

  • Achieve superior reconstruction for low-contrast objects and subtle structures.

  • Systematic validation of the strategy with phantoms and clinical patient data.

Abstract

To reduce radiation dose in X-ray computed tomography (CT) imaging, one common strategy is to lower the tube current and exposure time settings during projection data acquisition. However, this strategy would inevitably increase the projection data noise, and the resulting image by the conventional filtered back-projection (FBP) method may suffer from excessive noise and streak artifacts. The well-known edge-preserving nonlocal means (NLM) filtering can reduce the noise-induced artifacts in the FBP reconstructed image, but it sometimes cannot completely eliminate the artifacts, especially under the very low-dose circumstance when the image is severely degraded. Instead of taking NLM filtering, we proposed a NLM-regularized statistical image reconstruction scheme, which can effectively suppress the noise-induced artifacts and significantly improve the reconstructed image quality. From our previous investigation on NLM-based strategy, we noted that using a spatially invariant filtering parameter in the regularization was rarely optimal for the entire field of view (FOV). Therefore, in this study we developed a novel strategy for designing spatially variant filtering parameters which are adaptive to the local characteristics of the image to be reconstructed. This adaptive NLM-regularized statistical image reconstruction method was evaluated with low-contrast phantoms and clinical patient data to show (1) the necessity in introducing the spatial adaptivity and (2) the efficacy of the adaptivity in achieving superiority in reconstructing CT images from low-dose acquisitions.

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.

References (42)

  • Z. Li et al.

    Adaptive nonlocal means filtering based on local noise level for CT denoising

    Med Phys

    (2014)
  • J. Ma et al.

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

    Med Phys

    (2011)
  • H. Zhang et al.

    Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: a survey

    (2014)
  • J. Wang et al.

    Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray CT

    IEEE Trans Med Imaging

    (2006)
  • J.B. Thibault et al.

    A three-dimensional statistical approach to improved image quality for multislice helical CT

    Med Phys

    (2007)
  • J. Wang et al.

    Iterative image reconstruction for CBCT using edge-preserving prior

    Med Phys

    (2009)
  • J. Tang et al.

    Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms

    Phys Med Biol

    (2009)
  • X. Jia et al.

    GPU-based iterative cone-beam CT reconstruction using tight frame regularization

    Phys Med Biol

    (2011)
  • Cited by (39)

    • Weighted adaptive non-local dictionary for low-dose CT reconstruction

      2021, Signal Processing
      Citation Excerpt :

      In [18], an image reconstructed by FBP was used as a reference to calculate the weighting coefficients for improving the LDCT image quality. In [16,19], the weighting coefficients were computed on the current reconstructing image to further improve the reconstruction accuracy. NLM has proven very effective in general, but it fails in the case that an image patch cannot match similar patches from itself or a database.

    • Fractal heat conduction model of semi-coke bed in waste heat recovery heat exchanger

      2020, Journal of Cleaner Production
      Citation Excerpt :

      Six different diameters of semi-coke were obtained by the sieve method, and they are 3mm/9mm/19mm/37mm/55mm/65 mm, respectively. X-ray CT is the efficient tool to observe the internal structures of the object in industry and medicine (Peng et al., 2011; Tian et al., 2012; Zhao and Peng, 2017; Zhang et al., 2015). The internal structure of semi-coke bed was obtained by the spiral CT machine for the first time, and the CT images were segmented by image binarization method to obtain the bit binary images.

    • Modeling of fractal heat conduction of semi-coke bed in waste heat recovery steam generator for hydrogen production

      2019, International Journal of Hydrogen Energy
      Citation Excerpt :

      According to the simplification above, the simplified model is shown in Fig. 6. When the X-ray was discovered, the non-destructive CT technology was widely used in the industry and medicine [21–23]. Due to the high-resolution and non-destruction of X-ray, the internal structures of thick samples can be easily obtained [24,25].

    • An adaptive regularization method for low-dose CT reconstruction from CT transmission data in Poisson–Gaussian noise

      2019, Optik
      Citation Excerpt :

      Thus several researchers have proposed various valid approximations, either using a Gaussian distribution [7,18] or using a simple Poisson approximation [19,20]. In addition to the quanta noise, the works in [19,21–24] also considered the other principal source of noise, system electronic noise, which was modeled as Gaussian distribution. Reported results showed that the quality of low-dose CT reconstruction could be improved.

    View all citing articles on Scopus
    View full text