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

TL;DR

This paper presents a deep learning framework for rapid volumetric image reconstruction using orthogonal x-ray projections, enabling precise target localization and anatomy assessment for proton FLASH radiotherapy.

Contribution

It introduces a novel deep learning-based method for fast 3D image generation from minimal x-ray projections tailored for proton FLASH therapy guidance.

Findings

Accurately reconstructed tumor and organ anatomy from two orthogonal x-ray projections.

Optimal image quality achieved with source angles of 135 and 225 degrees.

Framework enables fast, reliable volumetric imaging for treatment guidance.

Abstract

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.