Denoising Low-Dose CT Images Using Multiframe Blind Source Separation and Block Matching Filter

Hasan, Ahmed M.; Melli, Ali; Wahid, Khan A.; Babyn, Paul

doi:10.1109/trpms.2018.2810221

Cited by 35 publications

(18 citation statements)

References 35 publications

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“…Further processing is required for fusing the information from all the images to one single image. Several approaches [16][17][18][19][20][21][22][23][24][25] that have been designed to remove both AWGN and Poisson noise from multiframe image datasets, are inspired from algorithms that are originally designed for single-frame image denoising.…”

Section: Introductionmentioning

confidence: 99%

Poisson Noise Removal Using Multi-Frame 3D Block Matching

Bodduna

Weickert

2019

2019 8th European Workshop on Visual Information Processing (EUVIP)

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The 3D block matching (BM3D) filter belongs to the stateof-the-art techniques for eliminating additive white Gaussian noise from single-frame images. There exist four multi-frame extensions of BM3D as of today. In this work, we combine these extensions with a variance stabilising transformation (VST) for eliminating Poisson noise. Our evaluation reveals that the extension which retains the original noise model of the noisy images and additionally has a comprehensive connectivity of 2D and temporal image information at both pixel and patch levels, gives the best results. Additionally, we find a surprising change in performance of one the four extensions due to the specific application of the VST. Finally, we also introduce a simple low-pass filtering as a preprocessing step for the best performing extension. This can give rise to a significant additional improvement of 0.94 dB in the output according to the peak signal to noise ratio.Index Terms-Multi-frame denoising, non-local patch methods, Poisson noise, 3D block matching

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Section: Introductionmentioning

confidence: 99%

Poisson Noise Removal Using Multi-Frame 3D Block Matching

Bodduna

Weickert

2019

2019 8th European Workshop on Visual Information Processing (EUVIP)

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“…While it may be ideal to process the entire volume using a 3D neural network, there are practical constraints associated with 3D networks [60][61][62][63][64][65] . Conventional denoising methods for 3D CT images based on non-local means 66 or block matching filter 67 showed that a multi-slice approach is able to leverage inter-slice spatial dependencies with small growth in computational complexity. Hence, we jointly reconstruct Z ∈ Z + adjacent slices as a compromise between 2D and 3D processing.…”

Section: Methodsmentioning

confidence: 99%

A residual dense network assisted sparse view reconstruction for breast computed tomography

Tseng

Vedantham

et al. 2020

Sci Rep

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To develop and investigate a deep learning approach that uses sparse-view acquisition in dedicated breast computed tomography for radiation dose reduction, we propose a framework that combines 3D sparse-view cone-beam acquisition with a multi-slice residual dense network (MS-RDN) reconstruction. Projection datasets (300 views, full-scan) from 34 women were reconstructed using the FDK algorithm and served as reference. Sparse-view (100 views, full-scan) projection data were reconstructed using the FDK algorithm. The proposed MS-RDN uses the sparse-view and reference FDK reconstructions as input and label, respectively. Our MS-RDN evaluated with respect to fully sampled FDK reference yields superior performance, quantitatively and visually, compared to conventional compressed sensing methods and state-of-the-art deep learning based methods. The proposed deep learning driven framework can potentially enable low dose breast CT imaging.

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“…However, decreasing the radiation dose leads to extra noise and artifacts in a reconstructed image, degrading the diagnostic information. Therefore, Many works that can remove noise and improve the quality of low-dose CT (LDCT) images have been proposed in the past decades, which can be generally divided into three categories: 1) sinogram domain filtering [3]- [6], 2) iterative reconstruction [7]- [10], and 3) image processing [11]- [15].…”

Section: Introductionmentioning

confidence: 99%

Single Low-Dose CT Image Denoising Using a Generative Adversarial Network With Modified U-Net Generator and Multi-Level Discriminator

Chi

et al. 2020

IEEE Access

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Low-dose CT (LDCT) images have been widely applied in the medical imaging field due to the potential risk of exposing patients to X-ray radiations. Given the fact that reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures or false lesions derived from noise. In this paper, we propose a generative adversarial network (GAN) with novel architecture and loss function for restoring the LDCT image. Firstly, the inception-residual block and residual mapping are incoporated in the U-Net structure. The modified U-Net is applied as the generator of the GAN network so that the noise feature can be eliminated during the forward propagation. Secondly, a novel multi-level joint discriminator is designed by concatenating multiple convolutional neural networks (CNNs) where the output of each deconvolutional layer in the generator is compared with the corresponding down-sampled ground truth image. The adversarial training can be sensitive to noise and artifacts in different scales with this discriminator. Thirdly, we novely define a loss function consisting of the least square adversarial loss, VGG based perceptual loss, MSE based pixel loss and the noise loss, so that the differences in pixel, visual perception and noise distribution are comprehensively considered to optimize the network. Experimental results on both simulated and official simulated clinical images have demonstrated that the proposed method can provide superior performance to the state-of-the-art methods in noise removal, structure preservation and false lesions elimination. INDEX TERMS Low-dose CT image denoising, deep learning, generative adversarial network, inception block, residual mapping, joint loss.

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Denoising Low-Dose CT Images Using Multiframe Blind Source Separation and Block Matching Filter

Cited by 35 publications

References 35 publications

Poisson Noise Removal Using Multi-Frame 3D Block Matching

Poisson Noise Removal Using Multi-Frame 3D Block Matching

A residual dense network assisted sparse view reconstruction for breast computed tomography

Single Low-Dose CT Image Denoising Using a Generative Adversarial Network With Modified U-Net Generator and Multi-Level Discriminator

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