Prof. Cunfeng Wei Profile

Prof. Cunfeng Wei

at Institute of High Energy Physics

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Proceedings Article | 1 April 2024 Poster + Paper

Spectral photon-counting CT for quantification of iodized oil in mouse tumors

Yuedong Liu, Qiong Xu, Xuan Zhou, Cunfeng Wei, Long Wei

Proceedings Volume 12925, 129252O (2024) https://doi.org/10.1117/12.3006178

KEYWORDS: Tumors, Tissues, X-ray computed tomography, Cancer detection, Sensors, Image fusion, Diffusion, Attenuation, Quantitative analysis, X-rays

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Spectral Photon-Counting CT (SPCCT) has become the focus of researchers, because of its energy resolution ability. SPCCT has broad application prospects, especially in the field of medical imaging. In medicine, iodized oil is often used as an antitumor drug, and its diffusion and retention time in tumors greatly affect the effect of tumor treatment. It's becoming an increasing concern for researchers, therefore, it is necessary to quantify the distribution and content of iodized oil in the tumor. In this study, the distribution and content of iodized oil in tumors of living mice were quantitatively analyzed with SPCCT, and an effective and convenient method based on SPCCT was established. First, using iodized oil and water as the base materials, the method of material decomposition was carried out to obtain the decomposed image of iodized oil. Then the content of iodized oil was calculated from the decomposed images, and the change of iodized oil content with time was quantitatively analyzed. The experimental results show that the quantification method of iodized oil based on SPCCT established in this study could effectively quantify the change in the distribution and content of iodized oil over a long period, indicating the potential of SPCCT in the application of drug quantitative analysis.

Proceedings Article | 1 April 2024 Poster + Paper

SGM-based sparsity reconstruction under non-standard geometry of robotic CT

Xuan Zhou, Qiong Xu, Yuedong Liu, Cunfeng Wei, Long Wei

Proceedings Volume 12925, 1292524 (2024) https://doi.org/10.1117/12.3006170

KEYWORDS: Image restoration, Robotics, Computed tomography, Education and training, Data modeling, Sensors, Image processing, Computer simulations, Statistical modeling, Image quality

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Robotic CT is a novel imaging platform built on two manipulators with great flexibility and convenience. But it suffers from limited mechanical motion accuracy, which brings artifacts into images. Acquiring true geometry parameters is critical for accurate image reconstruction. While it’s impractical to monitor all geometry positions in practice. Down-sampling the projection number and using sparsity reconstruction offers a feasible way of solving this problem. Score-based generative model (SGM) is a powerful generative model able to produce directional samples guided by prior information. Through combining prior data and generated data, images quality can be significantly improved. In this work, we trained a score-based generative model using images from two real CT scan datasets. In sampling of score-based net, prior sparsity projection was added through cone-beam projection and image reconstruction. Full projection under non-standard geometry was simulated by adding deviation into standard circular geometry. We compared performance of several algorithms on sparsity and full data, under true and ideal geometry. Image was evaluated by typical indexes and visible details. Images of SART under ideal geometry showed severe artifacts, with strip artifacts in soft tissue. Compared with images under ideal geometry, SGM-based sparsity reconstruction showed visual fuzzier image but with higher index, which improved by 59.0% and 41.1% for PSNR and SSIM. Compared with sparsity reconstruction under true geometry using SART, SGM-based method showed clearer image and higher indexes, with 11.4% and 24.7% improvement of PSNR and SSIM. SGM-based sparsity reconstruction showed great potential in sparsity reconstruction under non-standard geometry.

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