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

Authors

Hua-Chieh Shao
Tielige Mengke
Tinsu Pan
You Zhang

Publication Date

1-21-2025

Journal

Physics in Medicine and Biology

Abstract

Objective: 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 500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy.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 dynamic reconstruction and motion estimation (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge.

Approach: 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 requiring patient-specific prior 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 simulations and real patient studies.

Main Results: DREME accurately solved 3D respiration-induced anatomical motion in real time (∼1.5 ms inference time for each x-ray projection). For the digital phantom studies, it achieved an average lung tumor center-of-mass localization error of 1.2 ± 0.9 mm (Mean ± SD). For the patient studies, it achieved a real-time tumor localization accuracy of 1.6 ± 1.6 mm in the projection domain.

Significance. 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. In addition, it can be used for dose tracking and treatment assessment, when combined with real-time dose calculation.

Keywords

Cone-Beam Computed Tomography, Movement, Humans, Image Processing, Computer-Assisted, Time Factors, Lung Neoplasms, Phantoms, Imaging, Deep Learning

DOI

10.1088/1361-6560/ada519

PMID

39746309

PMCID

PMC11747166

PubMedCentral® Posted Date

1-21-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes