Poster + Paper
29 March 2024 Surface-based volumetric image reconstruction for image-guided procedures using a data-driven framework
Author Affiliations +
Conference Poster
Abstract
The advent of computed tomography significantly improves patients’ health regarding diagnosis, prognosis, and treatment planning and image-guided radiotherapy. However, tomographic imaging cannot achieve real-time imaging and the imaging escalates concomitant radiation doses to patients, inducing potential secondary cancer by 4%. We demonstrate the feasibility of a data-driven approach to synthesize volumetric images using patients’ surface images, which can be obtained from a zero-dose surface imaging system. This study includes 500 computed tomography (CT) image sets from 50 patients. Compared to the ground truth CT, the synthetic images result in the evaluation metric values of 26.9 ± 4.1 Hounsfield units, 39.1 ± 1.0 dB, and 0.97 ± 0.01 regarding the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure. This approach provides a data integration solution that can potentially enable real-time imaging, which is free of radiation-induced risk and could be applied to image-guided medical procedures.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Chih-Wei Chang, Shaoyan Pan, Zhen Tian, Tonghe Wang, Marian Axente, Joseph Shelton, Tian Liu, Justin Roper, and Xiaofeng Yang "Surface-based volumetric image reconstruction for image-guided procedures using a data-driven framework", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 1292822 (29 March 2024); https://doi.org/10.1117/12.3006510
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KEYWORDS
Radiotherapy

Computed tomography

3D image processing

Anatomy

Image restoration

3D image reconstruction

3D modeling

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