Purpose:
To develop and evaluate an image-domain
noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT
images and to implement this method in a time frame consistent with clinical workflow.
Methods:
A computationally efficient technique for local noise estimation directly from CT
images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution,
noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice.
Results:
The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the shape and peak frequency of the noise power spectrum better than commercial smoothing kernels, and indicate that the spatial resolution at low contrast levels is not significantly degraded. Both the subjective evaluation using the ACR phantom and the objective evaluation on a low-contrast detection task using a CHO model observer demonstrate an improvement on low-contrast performance. The GPU implementation can process and transfer 300 slice images within 5 min. On patient data, the adaptive NLM algorithm provides more effective denoising of CT data throughout a volume than standard NLM, and may allow significant lowering of radiation dose. After a two week pilot study of lower dose
CT urography and CT enterography exams, both GI and GU radiology groups elected to proceed with permanent implementation of adaptive NLM in their GI and GU CT practices.
Conclusions:
This work describes and validates a computationally efficient technique for noise map estimation directly from CT
images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose.

https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.4851635

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

A comprehensive survey on impulse and Gaussian denoising filters for digital images

We propose a novel approach for image space adaptive sampling and filtering in Monte Carlo rendering. We use an iterative scheme composed of three steps. First, we adaptively distribute samples in the image plane. Second, we denoise the image using a non-linear filter. Third, we estimate the residual per-pixel error of the filtered rendering, and the error estimate guides the sample distribution in the next iteration. The effectiveness of our approach hinges on the use of a state of the art image denoising technique, which we extend to an adaptive rendering framework. A key idea is to split the Monte Carlo samples into two buffers. This improves denoising performance and facilitates variance and error estimation. Our method relies only on the Monte Carlo samples, allowing us to handle arbitrary light transport and lens effects. In addition, it is robust to high noise levels and complex image content. We compare our approach to a state of the art adaptive rendering technique based on adaptive bandwidth selection and demonstrate substantial improvements in terms of both numerical error and visual quality. Our framework is easy to implement on top of standard Monte Carlo renderers and it incurs little computational overhead.

Adaptive rendering with non-local means filtering

Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide-spread prevalence, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, we propose the weighted image patch-based FCM (WIPFCM) algorithm for image segmentation. In this algorithm, we use image patches to replace pixels in the fuzzy clustering, and construct a weighting scheme to able the pixels in each image patch to have anisotropic weights. Thus, the proposed algorithm incorporates local spatial information embedded in the image into the segmentation process, and hence improve its robustness to noise. We compared the novel algorithm to several state-of-the-art segmentation approaches in synthetic images and clinical brain MR studies. Our results show that the proposed WIPFCM algorithm can effectively overcome the impact of noise and substantially improve the accuracy of image segmentations.

Fuzzy c-means clustering with weighted image patch for image segmentation

Low-dose X-ray computed tomography (LDCT) imaging is highly recommended for use in the clinic because of growing concerns over excessive radiation exposure. However, the CT images reconstructed by the conventional filtered back-projection (FBP) method from low-dose acquisitions may be severely degraded with noise and streak artifacts due to excessive X-ray quantum noise, or with view-aliasing artifacts due to insufficient angular sampling. In 2005, the nonlocal means (NLM) algorithm was introduced as a non-iterative edge-preserving filter to denoise natural images corrupted by additive Gaussian noise, and showed superior performance. It has since been adapted and applied to many other image types and various inverse problems. This paper specifically reviews the applications of the NLM algorithm in LDCT image processing and reconstruction, and explicitly demonstrates its improving effects on the reconstructed CT image quality from low-dose acquisitions. The effectiveness of these applications on LDCT and their relative performance are described in detail.

Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review

In the paper, we propose a robust and fast image denoising method. The approach integrates both 'Non-Local means algorithm and Laplacian Pyramid. Given an image to be denoised, we first decompose it into Laplacian pyramid. Exploiting the redundancy property of Laplacian pyramid, we then perform non-local means on every level image of Laplacian pyramid. Essentially, we use the similarity of image features in Laplacian pyramid to act as weight to denoise image. Since the features extracted in Laplacian pyramid are localized in spatial position and scale, they are much more able to describe image, and computing the similarity between them is more reasonable and more robust. Also, based on the efficient Summed Square Image (SSI) scheme and Fast Fourier Transform (FFT), we present an accelerating algorithm to break the bottleneck of non-local means algorithm -similarity computation of compare windows. After speedup, our algorithm is fifty times faster than original non-local means algorithm. Experiments demonstrated the effectiveness of our algorithm.

https://cs.nju.edu.cn/ywguo/webs/paperdownload/A_Robust_and_Fast_Non-local_Algorithm_for_Image_Denoising.pdf

A Robust and Fast Non-local Means Algorithm for Image Denoising

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A Robust and Fast Non-local Means Algorithm for Image Denoising