  Proton FLASH therapy leverages ultra-high dose-rate radiation to enhance the
sparing of organs at risk without compromising tumor control probability. To
prepare for the delivery of high doses to targets, we aim to develop a deep
learning-based image-guide framework to enable fast volumetric image
reconstruction for accurate target localization before FLSAH beam delivery. 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 evaluation. We investigated volumetric
image reconstruction using four kV projection pairs with different source
angles. Thirty lung patients were identified from the institutional database,
and each patient contains a four-dimensional computed tomography dataset with
ten respiratory phases. The retrospective patient study indicated that the
proposed framework could reconstruct patient 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
degrees yielded the optimal volumetric images. The proposed framework has been
demonstrated to reconstruct volumetric images with accurate lesion locations
from two orthogonal x-ray projections. The embedded WET module can be used to
detect potential proton beam-specific patient anatomy variations. The framework
can deliver fast volumetric image generation and can potentially guide
treatment delivery systems for proton FLASH therapy.
