Convolutional neural network–based metal and streak artifacts reduction in dental CT images with sparse‐view sampling scheme

Kim, Seong-Jun; Ahn, Junhyun; Kim, Byeongjoon; Kim, Chulhong; Baek, Jongduk

doi:10.1002/mp.15884

Cited by 10 publications

(12 citation statements)

References 59 publications

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“…For CNN training, we utilized a U-Net structure 30 that has a large receptive field due to its several max pooling layers, which can effectively capture the features of globally distributed streak artifacts. 5,35 However, since the transposed convolution layers in the original U-Net 7 can introduce checkerboard artifacts 36 in the reconstructed images,bilinear upsampling layers were utilized instead of the transposed convolution layers. The network was trained to reduce the mean squared error (MSE) loss between the output of the network and the target image.…”

Section: Implementation Detailsmentioning

confidence: 99%

Sparsier2Sparse: Self‐supervised convolutional neural network‐based streak artifacts reduction in sparse‐view CT images

Kim

Lee

et al. 2023

Medical Physics

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BackgroundSparse‐view computed tomography (CT) has attracted a lot of attention for reducing both scanning time and radiation dose. However, sparsely‐sampled projection data generate severe streak artifacts in the reconstructed images. In recent decades, many sparse‐view CT reconstruction techniques based on fully‐supervised learning have been proposed and have shown promising results. However, it is not feasible to acquire pairs of full‐view and sparse‐view CT images in real clinical practice.PurposeIn this study, we propose a novel self‐supervised convolutional neural network (CNN) method to reduce streak artifacts in sparse‐view CT images.MethodsWe generate the training dataset using only sparse‐view CT data and train CNN based on self‐supervised learning. Since the streak artifacts can be estimated using prior images under the same CT geometry system, we acquire prior images by iteratively applying the trained network to given sparse‐view CT images. We then subtract the estimated steak artifacts from given sparse‐view CT images to produce the final results.ResultsWe validated the imaging performance of the proposed method using extended cardiac‐torso (XCAT) and the 2016 AAPM Low‐Dose CT Grand Challenge dataset from Mayo Clinic. From the results of visual inspection and modulation transfer function (MTF), the proposed method preserved the anatomical structures effectively and showed higher image resolution compared to the various streak artifacts reduction methods for all projection views.ConclusionsWe propose a new framework for streak artifacts reduction when only the sparse‐view CT data are given. Although we do not use any information of full‐view CT data for CNN training, the proposed method achieved the highest performance in preserving fine details. By overcoming the limitation of dataset requirements on fully‐supervised‐based methods, we expect that our framework can be utilized in the medical imaging field.

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Section: Implementation Detailsmentioning

confidence: 99%

Sparsier2Sparse: Self‐supervised convolutional neural network‐based streak artifacts reduction in sparse‐view CT images

Kim

Lee

et al. 2023

Medical Physics

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“…The experimental results of the three typical simulated cases (figure 5) are shown in figures 6-8 respectively. In each case, the original constructed image, the Ground Truth, the result of the Projection-correction (Oh et al 2018), LI-MAR (Kalender et al 1987), FS-MAR (Meyer et al 2012), CNN+NMAR (Kim et al 2022) and the proposed method are illustrated to show the comparative results. The ROI area in rectangle is shown in a higher resolution at the down corner of each image to better observe the difference of these results.…”

Section: Experiments On Simulated Datamentioning

confidence: 99%

A new dental CBCT metal artifact reduction method based on a dual-domain processing framework

Tang,

Lin,

Jiang

et al. 2023

Phys. Med. Biol.

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Objective: Cone beam computed tomography (CBCT) has been wildly used in clinical treatment of dental diseases. However, patients often have metallic implants in mouth, which will lead to severe metal artifacts in the reconstructed images. To reduce metal artifacts in dental CBCT images, which have a larger amount of data and a limited field of view (FOV) compared to CT images, a new dental CBCT metal artifact reduction method based on a projection correction and a CNN based image post-processing model is proposed in this paper.Approach: The proposed method consists of three stages: (1) volume reconstruction and metal segmentation in the image domain, using the forward projection to get the metal masks in the projection domain; (2) linear interpolation in the projection domain and reconstruction to build a linear interpolation (LI) corrected volume; (3) take the LI corrected volume as prior and perform the prior based beam hardening correction in the projection domain, and (4) combine the constructed projection corrected volume and LI-volume slice-by-slice in the image domain by two concatenated U-Net based models (CNN1 and CNN2). Simulated and clinical dental CBCT cases are used to evaluate the proposed method. The normalized root means square difference (NRMSD) and the structural similarity index (SSIM) are used for the quantitative evaluation of the method. Main results: The proposed method outperforms the frequency domain fusion method (FS-MAR) and a state-of-art CNN based method on the simulated dataset and yields the best NRMSD and SSIM of 4.0196 and 0.9924, respectively. Visual results on both simulated and clinical images also illustrate that the proposed method can effectively reduce metal artifacts.Significance: This study demonstrated that the proposed dual-domain processing framework is suitable for metal artifact reduction in dental CBCT images.

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“…Image domain methods are more generally applicable to the clinical image processing chain and have been evaluated for niche MAR applications [24][25][26][27][28][29] . Huang et al proposed a CNN method for cervical CT images used in brachytherapy 24 .…”

Section: Introductionmentioning

confidence: 99%

“…Huang et al proposed a CNN method for cervical CT images used in brachytherapy 24 . Various researchers have introduced deep learning-based approaches for dental CT since metal artifact is a severe problem in this application 25,27,28,30 . Wang et al employed a conditional generative adversarial network (cGAN) for MAR in ear images of cochlear implants recipients 31 .…”

Section: Introductionmentioning

confidence: 99%

Metal artifact reduction for CT-guided interventional oncology procedures (MARIO): demonstration of a deep learning-based approach for metal artifact reduction for cryoablation procedures

Cao,

Missert,

Parvinian

et al. 2024

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling

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Computed tomography (CT) is routinely used to guide cryoablation procedures. Notably, CT-guidance provides 3D localization of cryoprobes and can be used to delineate frozen tissue during ablation. However, metal-induced artifacts from ablation probes can make accurate probe placement challenging and degrade the ice ball conspicuity, which in combination could lead to undertreatment of potentially curable lesions. An image domain metal artifact simulation framework was developed and validated for deep-learning-based metal artifact reduction for interventional oncology (MARIO). Metal probes and resulting artifacts were segmented from 19 phantom image sets and inserted into 19 different sets of patient CT images to simulate artifacts. This dataset was used to optimize a U-Net type model. Due to unique traits of probe artifacts, we employed custom augmentation techniques and loss functions for model optimization. An ablation study compared performance with and without these additional factors. The combined strategies improved quantitative metrics by 40.95% over baseline training. Augmentations also increased generalizability. Patient cases showed MARIO substantially reduced artifacts while preserving anatomical details. In a reader study, scores from three board-certified radiologists were significantly higher for MARIO processed images compared to the original images across all metrics (all p<0.0001).

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Convolutional neural network–based metal and streak artifacts reduction in dental CT images with sparse‐view sampling scheme

Cited by 10 publications

References 59 publications

Sparsier2Sparse: Self‐supervised convolutional neural network‐based streak artifacts reduction in sparse‐view CT images

Sparsier2Sparse: Self‐supervised convolutional neural network‐based streak artifacts reduction in sparse‐view CT images

A new dental CBCT metal artifact reduction method based on a dual-domain processing framework

Metal artifact reduction for CT-guided interventional oncology procedures (MARIO): demonstration of a deep learning-based approach for metal artifact reduction for cryoablation procedures

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