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The advent of computed tomography significantly improves patient healthregarding diagnosis, prognosis, and treatment planning and verification.However, tomographic imaging escalates concomitant radiation doses to patients,inducing potential secondary cancer. We demonstrate the feasibility of adata-driven approach to synthesize volumetric images using patient surfaceimages, which can be obtained from a zero-dose surface imaging system. Thisstudy includes 500 computed tomography (CT) image sets from 50 patients.Compared to the ground truth CT, the synthetic images result in the evaluationmetric values of 26.9 Hounsfield units, 39.1dB, and 0.965 regarding the meanabsolute error, peak signal-to-noise ratio, and structural similarity indexmeasure. This approach provides a data integration solution that canpotentially enable real-time imaging, which is free of radiation-induced riskand could be applied to image-guided medical procedures.

Data-Driven Volumetric Image Generation from Surface Structures using a Patient-Specific Deep Leaning Model