Deep Learning-Based Beamlet Model for Generic X-Ray Beam Dose Calculation

TL;DR

This paper introduces a versatile deep learning model that accurately predicts X-ray dose distributions across various imaging systems using beamlet decomposition and dual U-Net architectures, significantly reducing computation time.

Contribution

A novel generic deep learning approach combining beamlet decomposition with dual U-Net networks for adaptable, fast, and accurate dose calculation across multiple X-ray imaging modalities.

Findings

Achieved approximately 1.2% relative dose error compared to Monte Carlo simulations.

Reduced dose calculation time by a factor of 130.

Demonstrated model versatility across different X-ray systems without retraining.

Abstract

Modeling the absorbed dose during X-ray imaging is essential for optimizing radiation exposure. Monte Carlo simulations (MCS) are the gold standard for precise 3D dose estimation but require significant computation time. Deep learning offers faster dose prediction but often lacks generality, as models are typically trained for specific anatomical sites and beam geometries. The aim in this work was proposing a generic deep-learning approach for dose calculation that can be used for multiple X-ray imaging systems. This article proposes a versatile approach combining beamlet decomposition with deep learning, where the X-ray beam is broken down into beamlets. By using a sampling approach, various beam shapes can be generated, reducing learning complexity. The model learns the dose response of a beamlet for different energies and patient properties, making it adaptable to new system geometries without altering the learning model. In this work, we propose combining two U-Net networks (1D+3D) trained on different body parts to predict the dose of a beamlet regardless of its orientation and energy. Results have shown that the deep learning-based dose engine achieved a relative dose error of approximately 1.2+/-3.87% compared to the reference dose. For a more realistic simulation in cone-beam CT, dose results exhibited a relative error within the beam of 5% compared to a full MCS. The convergence of the proposed method was faster compared to MCS, with a speedup of 130 times for equivalent dose results. The versatility of the proposed solution allows for the simulation of multiple X-ray systems without the need to retrain the deep learning model with new beam specificities. The same trained model is capable of calculating the 3D dose within the patient for helical CT, cone-beam CT, fan-beam CT, or any collimated beam shape.