Virtual and real-world implementation of deep-learning-based image denoising model on projection domain in digital tomosynthesis and cone-beam computed tomography data

Jin, Shih-Chun; Chang, Li-Sheng; Wang, Yuhong; Chen, Jyh-Cheng; Tseng, Snow H.; Liu, Tse Ying

doi:10.1088/2057-1976/ac997d

Cited by 2 publications

(1 citation statement)

References 30 publications

(33 reference statements)

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“…Moreover, a combination of 2D and 3D concatenating convolutional encoder-decoder (CCE-3D) with a structural sensitive loss (SSL) was employed to denoise low-dose CBCT scans and remove artifacts in both projection and volume domains. This approach showed promising results in improving the quality of CBCT scans based on several metrics, such as PSNR and SSIM, and with greater improvements reported in the projection domain compared with the volume domain [57]. In addition, a CNN-based iterative reconstruction framework was integrated with a plug-and-play proximal gradient descent framework to leverage DL-based denoising algorithms and enhance CBCT reconstruction [56].…”

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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Section: Low Dosementioning

confidence: 99%