CT sinogram‐consistency learning for metal‐induced beam hardening correction

Park, Hyoung Suk; Lee, Sung Min; Kim, Hwa Pyung; Seo, Jin Keun; Chung, Yong Eun

doi:10.1002/mp.13199

Cited by 110 publications

(74 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The average mean squared error between the required residual projection images and the estimated images from included artifact projection (with noise) image input can be implemented as the loss function q to learn the trainable parameters δ (SGDM with the weight decay of 0.0001, momentum of 0.9, initial learning rate of 0.1, and hyper-parameters mini-batch size and epochs) in the DnCNN [19]. With regard to δ , mini-batch size and epochs are hyper-parameters that affect MAR [23, 24]. We evaluated the optimal parameter, and we have applied the parameters in the “Evaluation” section below.…”

Section: Methodsmentioning

confidence: 99%

“…Deep learning approaches have successfully been employed recently, in pattern recognition and image processing methods, including image denoising [19], image super-resolution [20], and low-dose CT reconstruction [21, 22]. For instance, a convolutional neural network (CNN) has been implemented for artifact reduction in medical imaging [23, 24], and the CNN has been employed to get rid-off the residual errors from MAR. Even though these previous studies showed that the CNN could enhance MAR effectively, no study has been conducted on MAR using DT.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

The present study aimed to develop a denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm for decreasing metal objects in digital tomosynthesis (DT) for arthroplasty by using projection data. For metal artifact reduction (MAR), we implemented a DnCNN-MARHR algorithm based on a training network (mini-batch stochastic gradient descent algorithm with momentum) to estimate the residual reference (140 keV virtual monochromatic [VM]) and object (70 kV with metal artifacts) images. For this, we used projection data and subtracted the estimated residual images from the object images, involving hybrid and subjectively reconstructed image usage (back projection and maximum likelihood expectation maximization [MLEM]). The DnCNN-MARHR algorithm was compared with the dual-energy material decomposition reconstruction algorithm (DEMDRA), VM, MLEM, established and commonly used filtered back projection (FBP), and a simultaneous algebraic reconstruction technique-total variation (SART-TV) with MAR processing. MAR was compared using artifact index (AI) and texture analysis. Artifact spread functions (ASFs) for images that were out-of-plane and in-focus were evaluated using a prosthesis phantom. The overall performance of the DnCNN-MARHR algorithm was adequate with regard to the ASF, and the derived images showed better results, without being influenced by the metal type (AI was almost equal to the best value for the DEMDRA). In the ASF analysis, the DnCNN-MARHR algorithm generated better MAR compared with that obtained employing usual algorithms for reconstruction using MAR processing. In addition, comparison of the difference (mean square error) between DnCNN-MARHR and the conventional algorithm resulted in the smallest VM. The DnCNN-MARHR algorithm showed the best performance with regard to image homogeneity in the texture analysis. The proposed algorithm is particularly useful for reducing artifacts in the longitudinal direction, and it is not affected by tissue misclassification.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The current DL‐based MAR methods mainly seek to patch metal trace in corrupted sinograms, and then use filtered back‐projection (FBP) 24,25 to reconstruct the patched sinograms to CT images. For example, Ghani et al 26 used a ten‐layer fully convolutional network (FCN) 27 to patch metal trace; Park et al 28 used a U‐Net 29 like network to reduce metal artifacts; Zhang et al 30 proposed a multistep convolutional neural network (MSCNN) for MAR, and used an FCN to patch metal trace followed by a postprocessing step to suppress secondary artifacts; Lin et al 31 applied a U‐Net–like network to patch metal trace and employed the network for metal trace inpainting to reduce secondary artifacts. These DL‐based metal trace inpainting methods aimed to reduce more artifacts and incur less secondary artifacts than traditional sinogram restoration methods due to their better detail recovering capability.…”

Section: Introductionmentioning

confidence: 99%

An irregular metal trace inpainting network for x‐ray CT metal artifact reduction

Peng

et al. 2020

Medical Physics

View full text Add to dashboard Cite

Purpose: Metal implants in the patient's body can generate severe metal artifacts in x-ray computed tomography (CT) images. These artifacts may cover the tissues around the metal implants in CT images and even corrupt the tissue regions, thus affecting disease diagnosis using these images. Previous deep learning metal trace inpainting methods used both valid pixels of uncorrupted areas and invalid pixels of corrupted areas to patch metal trace (i.e., the holes of removed metal-corrupted regions). Such methods cannot recover fine details well and often suffer information mismatch due to interference of invalid pixels, thus incurring considerable secondary artifacts. In this paper, we develop a new irregular metal trace inpainting network for reducing metal artifacts. Methods: We develop a new deep learning network to patch irregular metal trace in metal-corrupted sinograms to reduce metal artifacts for isometric fan-beam CT. Our new method patches irregular metal trace in CT sinograms using only valid pixels, avoiding interference from invalid pixels. Furthermore, to enable the inpainting network to recover as many details as possible, we design an auxiliary inpainting network to suppress the probable secondary artifacts in CT images to assist fine detail restoration. The image produced by the auxiliary network is then projected onto a sinogram via a forward projection (FP) algorithm and is fused with the sinogram predicted by the inpainting network in order to predict the final recovered sinogram. Our entire network is trained end-to-end to extract cross-domain information between the sinogram domain and CT image domain. Results: We compare our proposed method with two traditional and four deep learning-based metal trace inpainting methods, and with an iterative reconstruction method on four datasets: dental fillings (panoramic and local perspectives), hip prostheses, and spine fixations. We use both quantitative and qualitative indices to evaluate our method, and the analyses suggest that our method reduces the most metal artifacts and produces the best quality CT images. Additionally, our proposed method takes 0.1512 s on average to process a CT slice, which meets the clinical requirement. Conclusions: This paper proposes a new deep learning network to patch irregular metal trace in corrupted sinograms to reduce metal artifacts. Our method restores more fine details in irregular metal trace and has a superior capability on metal artifact reduction compared with state-of-the-art methods.

show abstract

“…Gjesteby et al [17] applied a combination of deep learning and normalized metal artifact reduction (NMAR) on a reconstructed image to correct the region with serious artifacts. Park et al [18] used U-Net to correct an inconsistent sinogram and eliminate beam-hardening factors triggered by main metals along the metal trace in a sinogram. Zhang et al [19] proposed an open MAR framework called CNN-MAR based on the deep learning model to reduce metal artifacts in CT images.…”

Section: Introductionmentioning

confidence: 99%

CT Metal Artifact Reduction based on Virtual Generated Artifacts Using Modified pix2pix

Xie

Gao

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Metal artifacts introduce challenges in image-guided diagnosis or accurate dose calculations. This study aims to reduce metal artifacts from the spinal brace by using virtual generated artifacts through convolutional neural networks and to compare the performance of this approach with two other methods, namely, linear interpolation metal artifact reduction (LIMAR) and normalized metal artifact reduction (NMAR) .Method: A total of 3,600-slice CT images of 60 vertebral metastases patients were selected. The spinal cord center was marked in each image, metal masks were added to two sides of the marker to generate artifact-insert CT images, and the CT values of the metal parts were copied to original CT images to obtain reference CT images. These images were divided into training (3,000 slices) and test (600) sets. The modified U-Net and pix2pix architecture was applied to understand the relationship between the reference and artifact-insert images. The mean absolute error (MAE), mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were calculated between the reference CT images and the predicted CT through LIMAR, NMAR, U-Net, and pix2pix. The CT values of organs from different images were compared. Radiotherapy treatment plans for vertebral metastases were designed, and dose calculation was performed. The dose distribution in different types of images was also compared.Results: The MAE values between the reference images and those images generated by LIMAR, NMAR, U-Net, and pix2pix were 15.02, 16.16, 6.12, and 6.48 HU, respectively, and the corresponding PSNR values were 15.37, 152.70, 158.93, and 65.14 dB, respectively. Pix2pix restored more texture than U-Net according to the visual comparison. The average CT values in the artifact-insert images of the liver, spleen, and left and right kidneys were all significantly higher than those of the reference images (p<0.05). The average CT values of the organs in images processed by the four methods showed no significant differences from those of the organs in the reference images. The mean dose of planned target volume in the artifact-insert images was significantly lower than that in the reference CT images. The average γ passing rate (1%, 1 mm) of the artifact-insert images was significantly lower than that of the reference images (95.9±1.4% vs. 99.2±1.4%, p<0.05).Conclusions: U-Net and pix2pix deep learning networks can remarkably reduce metal artifacts and improve critical structure visualization compared with LIMAR and NMAR according to the simulation data of artifact-insert images in the spinal brace. Pix2pix can restore more texture with the help of a discriminator. Metal artifacts increase the dose calculation uncertainty in radiotherapy. The dose calculated through images obtained by U-Net and pix2pix was identical with that calculated through reference images.

show abstract

CT sinogram‐consistency learning for metal‐induced beam hardening correction

Cited by 110 publications

References 23 publications

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

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

An irregular metal trace inpainting network for x‐ray CT metal artifact reduction

CT Metal Artifact Reduction based on Virtual Generated Artifacts Using Modified pix2pix

Contact Info

Product

Resources

About