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

Abstract

Purpose: Optical surface imaging presents radiation-dose-free and noninvasive approaches for image-guided radiotherapy, allowing continuous monitoring during treatment delivery. However, it falls short in cases where correlation of motion between body surface and internal tumor is complex, limiting the use of purely surface-guided surrogates for tumor tracking. Relying solely on surface-guided radiation therapy (SGRT) may not ensure accurate intra-fractional monitoring. This work aims to develop a data-driven framework, mitigating the limitations of SGRT in lung cancer radiotherapy by reconstructing volumetric CT images from surface images.

Methods and materials: We conducted a retrospective analysis involving 50 lung cancer patients who underwent radiotherapy and had 10-phase 4DCT scans during their treatment simulation. For each patient, we utilized nine phases of 4DCT images for patient-specific model training and validation, reserving one phase for testing purposes. Our approach employed a surface-to-volume image synthesis framework, harnessing cycle-consistency generative adversarial networks to transform surface images into volumetric representations. The framework was extensively validated using an additional 6-patient cohort with re-simulated 4DCT.

Results: The proposed technique has produced accurate volumetric CT images from the patient's body surface. In comparison to the ground truth CT images, those generated synthetically by the proposed method exhibited the GTV center of mass difference of 1.72±0.87 mm, the overall mean absolute error of 36.2±7.0 HU, structural similarity index measure of 0.94±0.02, and Dice score coefficient of 0.81±0.07. Furthermore, the robustness of the proposed framework was found to be linked to respiratory motion.

Conclusion: The proposed approach provides a novel solution to overcome the limitation of SGRT for lung cancer radiotherapy, which can potentially enable real-time volumetric imaging during radiation treatment delivery for accurate tumor tracking without radiation-induced risk. This data-driven framework offers a comprehensive solution to tackle motion management in radiotherapy, without necessitating the rigid application of first principles modeling for organ motion.