Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT

Choi, Dahim; Kim, Won-Jin; Han, Mina; Baek, Jongduk; Choi, Jang Hwan

doi:10.1007/s00138-021-01240-3

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…Therefore, if a network is trained to improve the perceptual quality, it inevitably suffers the loss of the PSNR and SSIM values, which can be explained by the perception-distortion trade-off [75]. This observation also confirms that previous study results [76], [77] that commonly used quantitative measures, such as PSNR and SSIM, are not strongly correlated with radiologists' visual perception of image quality and demonstrated that a more reliable evaluation metric alternative than PSNR and SSIM is needed in the evaluation of medical image quality.…”

Section: Utsupporting

confidence: 88%

MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography

Jeon

Kim

Choi

2023

IEEE Trans. Radiat. Plasma Med. Sci.

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Low-dose computed tomography (LDCT) is crucial due to the risk of radiation exposure to patients. However, the high noise level in LDCT images may reduce the image quality, leading to a less accurate diagnosis. Deep learning technology, especially supervised methods, has recently been widely accepted as a powerful tool for LDCT image denoising tasks. However, supervised methods require numerous paired datasets of LDCT and high-quality pristine CT images, which are rarely available in real-world clinical scenarios. This study presents an unsupervised learning-based framework called MM-Net, consisting of two training steps for a volumetric LDCT denoising task. In the two-step training approach, we first train the initial denoising network multi-scale attention U-Net (MSAU-Net) in a self-supervised manner to predict the noise-suppressed center slice with a neighboring multi-slice input. The second training step aims to train the U-Net-based final denoiser based on the pre-trained MSAU-Net to improve the image quality by introducing new multi-patch and multi-mask matching loss. Qualitative visual inspection and quantitative measures across texturally different domains of clinical and animal data reveal that the proposed MM-Net outperformed all competing state-ofthe-art unsupervised algorithms. The unsupervised method also achieved denoising performance comparable to the representative supervised methods trained with ground truth images.

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Section: Utsupporting

confidence: 88%

MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography

Jeon

Kim

Choi

2023

IEEE Trans. Radiat. Plasma Med. Sci.

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“…However, SSIM shows a monotonically decreasing trend as the dose decreases. Similarly, previous studies reported that SSIM and PSNR did not show a strong correlation with radiologists' opinions of diagnostic image quality in the evaluation of magnetic resonance [46] and CT [47] images. This result indicates that although the SSIM metric has been widely accepted in the community, it is necessary to be careful in interpreting the results of SSIM-based diagnostic imaging evaluation.…”

Section: Full-referencementioning

confidence: 54%

No-reference perceptual CT image quality assessment based on a self-supervised learning framework

Lee

Kim

Choi

et al. 2022

Mach. Learn.: Sci. Technol.

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Accurate image quality assessment (IQA) is crucial to optimize computed tomography (CT) image protocols while keeping the radiation dose as low as reasonably achievable. In the medical domain, IQA is based on how well an image provides a useful and efficient presentation necessary for physicians to make a diagnosis. Moreover, IQA results should be consistent with radiologists’ opinions on image quality, which is accepted as the gold standard for medical IQA. As such, the goals of medical IQA are greatly different from those of natural IQA. In addition, the lack of pristine reference images or radiologists’ opinions in a real-time clinical environment makes IQA challenging. Thus, no-reference IQA (NR-IQA) is more desirable in clinical settings than full-reference IQA (FR-IQA). Leveraging an innovative self-supervised training strategy for object detection models by detecting virtually inserted objects with geometrically simple forms, we propose a novel NR-IQA method, named deep detector IQA (D2IQA), that can automatically calculate the quantitative quality of CT images. Extensive experimental evaluations on clinical and anthropomorphic phantom CT images demonstrate that our D2IQA is capable of robustly computing perceptual image quality as it varies according to relative dose levels. Moreover, when considering the correlation between the evaluation results of IQA metrics and radiologists’ quality scores, our D2IQA is marginally superior to other NR-IQA metrics and even shows performance competitive with FR-IQA metrics.

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“…Training models inspired by self-supervised learning approaches for inpainting and denoising Poisson and Gaussian noise have shown promising results in removing low-dose artifacts [58]. Similarly, models optimized for removing Gaussian noise and addressing view aliasing artifacts through 2D iterations with 3D kernels have been developed [59]. Furthermore, researchers combined a non-subsampled contourlet transform (NSCT) and a Sobel filter with U-Net architectures, referred to as NCS-Unet, to improve the quality of low-dose CBCT scans by enhancing both low-and high-frequency components [60].…”

Section: Low Dosementioning

confidence: 99%

Artifact Reduction in 3D and 4D Cone-Beam Computed Tomography Images With Deep Learning: A Review

Amirian,

Barco,

Herzig

et al. 2024

IEEE Access

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Deep learning based approaches have been used to improve image quality in cone-beam computed tomography (CBCT), a medical imaging technique often used in applications such as imageguided radiation therapy, implant dentistry or orthopaedics. In particular, while deep learning methods have been applied to reduce various types of CBCT image artifacts arising from motion, metal objects, or lowdose acquisition, a comprehensive review summarizing the successes and shortcomings of these approaches, with a primary focus on the type of artifacts rather than the architecture of neural networks, is lacking in the literature. In this review, the data generation and simulation pipelines, and artifact reduction techniques are specifically investigated for each type of artifact. We provide an overview of deep learning techniques that have successfully been shown to reduce artifacts in 3D, as well as in time-resolved (4D) CBCT through the use of projection-and/or volume-domain optimizations, or by introducing neural networks directly within the CBCT reconstruction algorithms. Research gaps are identified to suggest avenues for future exploration. One of the key findings of this work is an observed trend towards the use of generative models including GANs and score-based or diffusion models, accompanied with the need for more diverse and open training datasets and simulations. INDEX TERMS Cone-beam Computed Tomography (CBCT), Deep Learning, Artifacts.

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Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT

Cited by 3 publications

References 44 publications

MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography

MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography

No-reference perceptual CT image quality assessment based on a self-supervised learning framework

Artifact Reduction in 3D and 4D Cone-Beam Computed Tomography Images With Deep Learning: A Review

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