Deep learning-based Fast Volumetric Image Generation for Image-guided Proton FLASH Radiotherapy.

Objective: FLASH radiotherapy leverages ultra-high dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. This may allow dose escalation, toxicity mitigation, or both. To prepare for the ultra-high dose-rate delivery, we aim to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for proton FLASH beam delivery. Approach: The proposed framework comprises four modules, including orthogonal kV x-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a four-dimensional computed tomography (CT) dataset with ten respiratory phases. Leave-phase-out cross-validation was performed to investigate the DL model's robustness for each patient. Main results: The proposed framework reconstructed patients' volumetric anatomy, including tumors and organs at risk from orthogonal x-ray projections. Considering all evaluation metrics, the kV projections with source angles of 135° and 225° yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were 75±22 HU, 19±3.7 dB, 0.938±0.044, and -1.3%±4.1%. Significance: The proposed framework has been demonstrated to reconstruct volumetric images with a high degree of accuracy using two orthogonal x-ray projections. The embedded WET module can be used to detect potential proton beam-specific patient anatomy variations. This framework can rapidly deliver volumetric images to potentially guide proton FLASH therapy treatment delivery systems.