Patient-Specific CBCT Synthesis for Real-time Tumor Tracking in Surface-guided Radiotherapy

TL;DR

This paper introduces a patient-specific, real-time CBCT synthesis framework using generative modeling to improve tumor tracking in surface-guided radiotherapy, combining surface imaging and low-frequency X-ray projections.

Contribution

The study proposes a novel A-SI framework with a physics-integrated diffusion model for real-time volumetric image generation from surface and X-ray data, enhancing tumor localization accuracy.

Findings

High-fidelity real-time CBCT generation demonstrated in simulation with lung cancer patients.

Accurate tumor localization achieved with minimal imaging dose.

Framework shows potential for improved motion management in radiotherapy.

Abstract

We present a new imaging system to support real-time tumor tracking for surface-guided radiotherapy (SGRT). SGRT uses optical surface imaging (OSI) to acquire real-time surface topography images of the patient on the treatment couch. However, OSI cannot visualize internal anatomy. This study proposes an Advanced Surface Imaging (A-SI) framework to address this issue. In the proposed A-SI framework, a high-speed surface imaging camera consistently captures surface images during radiation delivery, and a CBCT imager captures single-angle X-ray projections at low frequency. The A-SI then utilizes a generative model to generate real-time volumetric images with full anatomy, referred to as Optical Surface-Derived cone beam computed tomography (OSD-CBCT), based on the real-time high-frequent surface images and the low-frequency collected single-angle X-ray projections. The generated OSD-CBCT can provide accurate tumor motion for precise radiation delivery. The A-SI framework uses a patient-specific generative model: physics-integrated consistency-refinement denoising diffusion probabilistic model (PC-DDPM). This model leverages patient-specific anatomical structures and respiratory motion patterns derived from four-dimensional CT (4DCT) during treatment planning. It then employs a geometric transformation module (GTM) to extract volumetric anatomy information from the single-angle X-ray projection. A simulation study with 22 lung cancer patients evaluated the A-SI framework supported by PC-DDPM. The results showed that the framework produced real-time OSD-CBCT with high reconstruction fidelity and precise tumor localization. This study demonstrates the potential of A-SI to enable real-time tumor tracking with minimal imaging dose, advancing SGRT for motion-associated cancers and interventional procedures.