Junqi Ma

East China Normal University

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Xinyue Zhang

Beijing University of Chinese Medicine

Zhenhao Zhang

North China University of Science and Technology

Jupeng Li

Beijing Jiaotong University

Yue Wang

China United Network Communications Group (China)

Yanlin Wang

Sun Yat-sen University

Leilei Huang

East China Normal University

Xiaodi Zhou

Fudan University

Chunqi Shi

East China Normal University

Yanlin Wang

Peking University

Chenmeng Li

Institute of Information Engineering

Cooperative Institutions

The Ohio State University

University of Science and Technology of China

Peking University

East China Normal University

Chinese Academy of Sciences

Zhejiang University

Beijing University of Posts and Telecommunications

University of Chinese Academy of Sciences

Sun Yat-sen University

Institute of Microelectronics

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An iterative sinogram metal artifact reducdion based on UNet

2021 IEEE International Conference on Big Data (Big Data) (2023)

Zichong An Xuemei Zhu Xiangling Fu Junqi Ma Chenyi Guo

In the practice of dentistry, oral dental CT images are frequently used to assist doctors in diagnosis. Filtered back projection (FBP) technique is widely employed in practice for the reconstruction of CT images obtained from X-ray calculations. However, when metal objects occur in a patient's oral cavity, the CT images would show density discontinuities due to the metals' "X-ray absorption coefficient is much larger than human tissues. When the FBP algorithm is applied to CT images with metals, severe metal artifacts would be obtained, which significantly reconstructed images. Therefore, metal artifact reduction (MAR) work is becoming an important problem in dentistry image processing. In this paper, we propose a novel iterative sinogram metal artifact reduction model (IS-MARM) to solve the problem. Inspired by the Diffusion model, we propose a new method to reduce metal artifacts and interpolate new data in sinogram of dentistry images iteratively. This approach reduces the difficulty of model learning and achieves good results. Secondly, we proposed a new simple method of iterative data generating to simulate real-world metals in CT sinogram images. Finally, we have demonstrated the effectiveness of our method through experiments on dental CT MAR work.

Artifact (error)

10.1109/bigdata59044.2023.10386169

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A Wearable Augmented Reality Navigation System for Surgical Telementoring Based on Microsoft HoloLens

Annals of Biomedical Engineering (2020)

Peng Liu Chenmeng Li Changlin Xiao Zeshu Zhang Junqi Ma

Surgical procedures

Optical head-mounted display

10.1007/s10439-020-02538-5

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A Buck DC-DC Achieving High Efficiency in an Ultra-wide-load Range from 0.001 to 2A

2022 International Conference on Microwave and Millimeter Wave Technology (ICMMT) (2024)

Junqi Ma Xiaodi Zhou Chunqi Shi Leilei Huang Runxi Zhang

Buck converter

10.1109/icmmt61774.2024.10671962

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Optimal Training Positive Sample Size Determination for Deep Learning Caries Recognition Model Establishment with Cbct Images

Yanlin Wang Gang Li Xinyue Zhang Yue Wang Jupeng Li

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10.2139/ssrn.4781668

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Optimal Training Positive Sample Size Determination for Deep Learning with a Validation on CBCT Image Caries Recognition

Diagnostics (2024)

Yanlin Wang Gang Li Xinyue Zhang Yue Wang Zhenhao Zhang

Objectives: During deep learning model training, it is essential to consider the balance among the effects of sample size, actual resources, and time constraints. Single-arm objective performance criteria (OPC) was proposed to determine the optimal positive sample size for training deep learning models in caries recognition. Methods: An expected sensitivity (PT) of 0.6 and a clinically acceptable sensitivity (P0) of 0.5 were applied to the single-arm OPC calculation formula, yielding an optimal training set comprising 263 carious teeth. U-Net, YOLOv5n, and CariesDetectNet were trained and validated using clinically self-collected cone-beam computed tomography (CBCT) images that included varying quantities of carious teeth. To assess performance, an additional dataset was utilized to evaluate the accuracy of caries detection by both the models and two dental radiologists. Results: When the number of carious teeth reached approximately 250, the models reached the optimal performance levels. U-Net demonstrated superior performance, achieving accuracy, sensitivity, specificity, F1-Score, and Dice similarity coefficients of 0.9929, 0.9307, 0.9989, 0.9590, and 0.9435, respectively. The three models exhibited greater accuracy in caries recognition compared to dental radiologists. Conclusions: This study demonstrated that the positive sample size of CBCT images containing caries was predictable and could be calculated using single-arm OPC.

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10.3390/diagnostics14182080

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