Real-time CBCT Imaging and Motion Tracking via a Single Arbitrarily-angled X-ray Projection by a Joint Dynamic Reconstruction and Motion Estimation (DREME) Framework

TL;DR

The paper introduces DREME, a novel real-time CBCT imaging and motion estimation framework that operates from a single arbitrary-angle X-ray projection without relying on patient-specific prior knowledge, enabling accurate and fast intra-fractional motion management.

Contribution

DREME is the first framework to perform real-time CBCT imaging and motion estimation from a single arbitrary-angle X-ray projection without prior patient data.

Findings

Achieved ~1.5 ms inference time per projection.

Localized lung tumor center-of-mass with 1.2±0.9 mm error in phantom studies.

Real-time tumor localization with 1.8±1.6 mm accuracy in patient studies.

Abstract

Real-time cone-beam computed tomography (CBCT) provides instantaneous visualization of patient anatomy for image guidance, motion tracking, and online treatment adaptation in radiotherapy. While many real-time imaging and motion tracking methods leveraged patient-specific prior information to alleviate under-sampling challenges and meet the temporal constraint (< 500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy. To address this challenge, we developed a framework (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge. DREME incorporates a deep learning-based real-time CBCT imaging and motion estimation method into a dynamic CBCT reconstruction framework. The reconstruction framework reconstructs a dynamic sequence of CBCTs in a data-driven manner from a standard pre-treatment scan, without utilizing patient-specific knowledge. Meanwhile, a convolutional neural network-based motion encoder is jointly trained during the reconstruction to learn motion-related features relevant for real-time motion estimation, based on a single arbitrarily-angled x-ray projection. DREME was tested on digital phantom simulation and real patient studies. DREME accurately solved 3D respiration-induced anatomic motion in real time (~1.5 ms inference time for each x-ray projection). In the digital phantom study, it achieved an average lung tumor center-of-mass localization error of 1.2$\pm$0.9 mm (Mean$\pm$SD). In the patient study, it achieved a real-time tumor localization accuracy of 1.8$\pm$1.6 mm in the projection domain. DREME achieves CBCT and volumetric motion estimation in real time from a single x-ray projection at arbitrary angles, paving the way for future clinical applications in intra-fractional motion management.