Angle prediction model when the imaging plane is tilted about z-axis

Fang, Zheng; Ye, Bichao; Yuan, Bingan; Wang, Tingjun; Zhong, Shuo; Shunren, Li,; Zheng, Jianyi

doi:10.1007/s11227-022-04595-0

Cited by 3 publications

(2 citation statements)

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“…Consequently, the robustness of these models warrants further scrutiny to ensure their ability to generalize well across various human anatomies. CNN architectures, known for their stable convergence and versatility, demonstrate a wide range of applications for artifact reduction through adapting different vision backbones [32] and incorporating diverse architectural components such as attention blocks [24]. However, in terms of multi-scale information fusion, they are inferior to U-Nets and their variants (e.g., U-Net++ [137]), which demonstrate a fast convergence in supervised learning due to the internal architectural connections between different layers enhancing the multi-resolution information fusion [7].…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…Training 3D models using a multi-task learning objective improved the quality of CBCTs by producing high-quality synthetic CT (sCT) scans from noisy and artifact-ridden scans for segmenting organs-at-risk (OARs) [30]. Lately, using InceptionV3 [31] as a backbone has proven beneficial in reducing the artifacts observed in CBCT short scans due to the misalignment of the detection plane around the z-axis [32].…”

Section: Applications Based On Cnnsmentioning

confidence: 99%

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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: Discussion and Recommendationsmentioning

confidence: 99%