# Anatomically informed deep learning framework for generating fast, low-dose synthetic CBCT for prostate radiotherapy

## TL;DR

This paper introduces a deep learning framework that generates fast, low-dose synthetic CT images for prostate radiotherapy, improving patient safety and treatment efficiency.

## Contribution

A novel deep learning framework that generates high-fidelity synthetic CBCT from sparse projections and a reference CT without retraining.

## Key findings

- The framework produces high-quality volumetric reconstructions in real-time.
- It generalizes across patients without requiring retraining.
- It reduces imaging dose and treatment time while preserving anatomical details.

## Abstract

Precise patient positioning and daily anatomical verification are crucial in external beam radiotherapy to ensure accurate dose delivery and minimize harm to healthy tissues. However, Current image-guided radiotherapy techniques struggle to balance high-quality volumetric anatomical visualization and rapid low-dose imaging. Addressing this, reconstructing volumetric images from ultra-sparse X-ray projections holds promise for significantly reducing patient radiation exposure and potentially enabling real-time anatomy verification. Here, we present a novel DL-based framework that generates synthetic volumetric cone-beam CT in real-time from two orthogonal projection views and a reference planning CT for prostate cancer patients. Our model learns the mapping between 2D and 3D domains and generalizes across patients without retraining. We demonstrate that our framework produces high-fidelity volumetric reconstructions in real-time, potentially supporting clinical workflows without hardware modifications. This approach could reduce imaging dose and treatment time while preserving comprehensive anatomical information, offering a pathway for safer, more efficient prostate radiotherapy workflows.

The online version contains supplementary material available at 10.1038/s41598-025-23781-7.