Dr. Wenchao Cao Profile

Dr. Wenchao Cao

at Thomas Jefferson University

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Author

Publications (4)

Proceedings Article | 29 March 2024 Presentation + Paper

Metal artifact reduction for CT-guided interventional oncology procedures (MARIO): demonstration of a deep learning-based approach for metal artifact reduction for cryoablation procedures

Wenchao Cao, Andrew Missert, Ahmad Parvinian, Daniel Adamo, Brian Welch, Matthew Callstrom, Christopher Favazza

Proceedings Volume 12928, 129281I (2024) https://doi.org/10.1117/12.3006552

KEYWORDS: Metals, Ablation, Education and training, Tissues, Image processing, Computed tomography, Data modeling, Oncology, Visualization, Deep learning

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SPIE Journal Paper | 31 January 2022

Hepatic dual-contrast CT imaging: slow triple kVp switching CT with CNN-based sinogram completion and material decomposition

Wenchao Cao, Nadav Shapira, Andrew Maidment, Heiner Daerr, Peter B. Noël

JMI, Vol. 9, Issue 01, 014003, (January 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.1.014003

KEYWORDS: Switching, X-ray computed tomography, Iodine, Gadolinium, Computed tomography, Sensors, Liver, Scanners, Image processing, X-rays

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Purpose: Dual-contrast protocols are a promising clinical multienergy computed tomography (CT) application for focal liver lesion detection and characterization. One avenue to enable multienergy CT is the introduction of photon-counting detectors (PCD). Although clinical translation is highly desired because of the diagnostic benefits of PCDs, it will still be a decade or more before they are broadly available. In our work, we investigated an alternative solution that can be implemented on widely used conventional CT systems (single source and integrating detector) to perform multimaterial spectral decomposition for dual-contrast imaging. Approach: We propose to slowly alternate the x-ray tube voltage between 3 kVp levels so each kVp level covers a few degrees of gantry rotation. This leads to the challenge of sparsely sampled projection data in each energy level. Performing the material decomposition (MD) in the sinogram domain is not directly possible as the projection images of the three energy levels are not angularly aligned. In order to overcome this challenge, we developed a convolutional neural network (CNN) framework for sparse sinogram completion (SC) and MD. To evaluate the feasibility of the slow kVp switching scheme, simulation studies of an abdominal phantom, which included liver lesions, were conducted. Results: The line-integral SC network yielded sinograms with a pixel-wise RMSE < 0.05 of the line-integrals compared to the ground truth. This provided acceptable image quality up to a switching angle of 9 deg per kVp. The MD network we developed allowed us to differentiate iodine and gadolinium in the sinogram domain. The average relative quantification errors for iodine and gadolinium were below 10%. Conclusions: We developed a slow triple kVp switching data acquisition scheme and a CNN-based data processing pipeline. Results from a digital phantom validation illustrate the potential for future applications of dual-contrast agent protocols on practically available single-energy CT systems.

Proceedings Article | 15 February 2021 Presentation + Paper

Dual-contrast decomposition of dual-energy CT using convolutional neural networks

Nadav Shapira, Wenchao Cao, Leening Liu, Tim Leiner, Maarten L. Smits, Peter Noël

Proceedings Volume 11595, 115951D (2021) https://doi.org/10.1117/12.2581333

KEYWORDS: Convolutional neural networks, Liver, Computed tomography, Network architectures, Magnetic resonance imaging, Iodine, Gadolinium, Feature extraction, Diagnostics, Computer simulations

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

GAN-based sinogram completion for slow triple kVp switching CT

Wenchao Cao, Nadav Shapira, Peter Noël

Proceedings Volume 11595, 115952O (2021) https://doi.org/10.1117/12.2580735

KEYWORDS: Switching, Sensors, Photodetectors, Modulation, Image quality, Gallium nitride, Computer simulations

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