Convolutional neural network model to improve image processing on a C-arm imaging

Jindai Huang; Jingli Zhang; Dianchen Zhang

doi:10.1117/12.3029754

21 June 2024 Convolutional neural network model to improve image processing on a C-arm imaging

Jindai Huang, Jingli Zhang, Dianchen Zhang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671Y (2024) https://doi.org/10.1117/12.3029754
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

In the field of medical imaging, C-arm systems play a pivotal role in surgeries, especially interventional surgeries. However, the current C-arm imaging system cannot adapt to the application scenarios due to algorithm limitations. Therefore, in order to improve the quality of diagnostic and therapeutic interventions, it is necessary to improve the quality of C-arm imaging and to fulfill the various requirements of different scenarios by improving the image processing techniques associated with C-arm imaging. This article explores how to improve C-arm image processing using convolutional neural network (CNN)-related algorithms. CNNs are known for their ability to extract complex features from images, which can be used for contrast enhancement, noise reduction, artifact removal, and resolution enhancement. Through a comprehensive review of state-of-the-art applications and technologies, this study proposes an innovative method of applying CNN for image processing in C-arm systems, improving image quality and thus providing potential advancements in clinical diagnosis and surgery. In terms of real-time image enhancement, integrating CNN into the C-arm imaging pipeline has demonstrated promising results, providing a reference for medical imaging learning and research. This article discusses the methods, experimental results, and implications of incorporating CNN into C-arm imaging, emphasizing that this technology can greatly improve image quality and indicating the potential of artificial intelligence technology in C-arm imaging applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jindai Huang, Jingli Zhang, and Dianchen Zhang "Convolutional neural network model to improve image processing on a C-arm imaging", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671Y (21 June 2024); https://doi.org/10.1117/12.3029754

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KEYWORDS

Image processing

Education and training

Imaging systems

Data modeling

Image quality

Convolutional neural networks

Denoising

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