Development of a denoising convolutional neural network-based algorithm for metal artifact reduction in digital tomosynthesis for arthroplasty: A phantom study

Gomi, Tsutomu; Sakai, Rina; Hara, Hidetake; Watanabe, Yusuke; Mizukami, Shinya

doi:10.1371/journal.pone.0222406

Cited by 6 publications

(8 citation statements)

References 37 publications

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“…The usefulness of image quality improvement for reducing noise and metal artifacts in DT using deep learning has recently been reported [ 4 , 31 ]. Although noise and radiation-dose reductions using deep learning in the DT of the breast are possible, no studies have reported on the reduction of radiation dose related to MAR.…”

Section: Discussionmentioning

confidence: 99%

“…A metal object can produce beam hardening, partial volume, aliasing, small-angle scatter, under-range data acquisition electrons, or overflow of the dynamic range in the reconstruction process. A previous study on digital tomosynthesis (DT) proposed several artifact compensation approaches to minimizing metal artifacts [ 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Dual-energy DT (DE-DT) has recently become available. One of the inherent capabilities of DE-DT is the generation of synthesized monochromatic images obtained at different energy levels (keV) from a single data acquisition [ 3 , 4 ]. By generating monochromatic images at higher energy levels (e.g., 140 keV), beam-hardening artifacts can be suppressed.…”

Section: Introductionmentioning

confidence: 99%

“…The final DT image was then reconstructed from the completed projection using the conventional filtered back projection (FBP) algorithm [ 32 ]. Compared with the previous MAR approaches for DT [ 2 , 3 , 4 ], the whole framework was trained efficiently for the complementary learning of prior processed images according to the method of each network so that the prior image generation and deep projection completion procedures can be learned in a collaborative manner and benefit from each other. Our recommended MAR algorithm (combination of hybrid GAN: cycle-GAN_pix2pix_MPN [CGpM-MAR]) is described in the Methods section.…”

Section: Introductionmentioning

confidence: 99%

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Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks

et al. 2021

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In this study, a novel combination of hybrid generative adversarial networks (GANs) comprising cycle-consistent GAN, pix2pix, and (mask pyramid network) MPN (CGpM-metal artifact reduction [MAR]), was developed using projection data to reduce metal artifacts and the radiation dose during digital tomosynthesis. The CGpM-MAR algorithm was compared with the conventional filtered back projection (FBP) without MAR, FBP with MAR, and convolutional neural network MAR. The MAR rates were compared using the artifact index (AI) and Gumbel distribution of the largest variation analysis using a prosthesis phantom at various radiation doses. The novel CGpM-MAR yielded an adequately effective overall performance in terms of AI. The resulting images yielded good results independently of the type of metal used in the prosthesis phantom (p < 0.05) and good artifact removal at 55% radiation-dose reduction. Furthermore, the CGpM-MAR represented the minimum in the model with the largest variation at 55% radiation-dose reduction. Regarding the AI and Gumbel distribution analysis, the novel CGpM-MAR yielded superior MAR when compared with the conventional reconstruction algorithms with and without MAR at 55% radiation-dose reduction and presented features most similar to the reference FBP. CGpM-MAR presents a promising method for metal artifact and radiation-dose reduction in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks

et al. 2021

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“…This can be attributed to the lack of new networks for extracting important features from training images, which is a result of the small amount of noise variation data used in training them [18,19]. In previous studies, the reconstruction of the learning mechanism for a specific purpose was achieved by optimizing the learning model by transfer learning [20][21][22].…”

Section: Discussionmentioning

confidence: 99%

Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography

Usui

Ogawa

Goto

et al. 2021

Vis. Comput. Ind. Biomed. Art

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To minimize radiation risk, dose reduction is important in the diagnostic and therapeutic applications of computed tomography (CT). However, image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance. Deep learning approaches with convolutional neural networks (CNNs) have been proposed for natural image denoising; however, these approaches might introduce image blurring or loss of original gradients. The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images. To simulate a low-dose CT image, a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function. An abdominal CT of 100 images obtained from a public database was adopted, and simulated dose-reduction images were created from the original dose at equal 10-step dose-reduction intervals with a final dose of 1/100. These images were denoised using the denoising network structure of CNN (DnCNN) as the general CNN model and for transfer learning. To evaluate the image quality, image similarities determined by the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were calculated for the denoised images. Significantly better denoising, in terms of SSIM and PSNR, was achieved by the DnCNN than by other image denoising methods, especially at the ultra-low-dose levels used to generate the 10% and 5% dose-equivalent images. Moreover, the developed CNN model can eliminate noise and maintain image sharpness at these dose levels and improve SSIM by approximately 10% from that of the original method. In contrast, under small dose-reduction conditions, this model also led to excessive smoothing of the images. In quantitative evaluations, the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model.

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A Convolutional Neural Network for Small Sample's Ring Structured Light Denoising

Lin

Han

Wang

et al. 2021

2021 33rd Chinese Control and Decision Conference (CCDC)

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Development of a denoising convolutional neural network-based algorithm for metal artifact reduction in digital tomosynthesis for arthroplasty: A phantom study

Cited by 6 publications

References 37 publications

Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks

Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks

Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography

A Convolutional Neural Network for Small Sample's Ring Structured Light Denoising

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