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Proton FLASH therapy leverages ultra-high dose-rate radiation to enhance thesparing of organs at risk without compromising tumor control probability. Toprepare for the delivery of high doses to targets, we aim to develop a deeplearning-based image-guide framework to enable fast volumetric imagereconstruction for accurate target localization before FLSAH beam delivery. Theproposed framework comprises four modules, including orthogonal kV x-rayprojection acquisition, DL-based volumetric image generation, image qualityanalyses, and water equivalent thickness evaluation. We investigated volumetricimage reconstruction using four kV projection pairs with different sourceangles. Thirty lung patients were identified from the institutional database,and each patient contains a four-dimensional computed tomography dataset withten respiratory phases. The retrospective patient study indicated that theproposed framework could reconstruct patient volumetric anatomy, includingtumors and organs at risk from orthogonal x-ray projections. Considering allevaluation metrics, the kV projections with source angles of 135 and 225degrees yielded the optimal volumetric images. The proposed framework has beendemonstrated to reconstruct volumetric images with accurate lesion locationsfrom two orthogonal x-ray projections. The embedded WET module can be used todetect potential proton beam-specific patient anatomy variations. The frameworkcan deliver fast volumetric image generation and can potentially guidetreatment delivery systems for proton FLASH therapy.

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