CBCT of a Moving Sample From X-Rays and Multiple Videos

Area of Science:

• Medical Imaging
• Computational Anatomy
• Biophysics

Background:

• Current dense volumetric modeling methods often require moving X-ray devices, limiting their application to static samples.
• Handling moving samples in volumetric modeling presents significant challenges, particularly in clinical settings where low-dose and cost-effective solutions are desired.

Purpose of the Study:

• To develop a novel method for dense volumetric modeling of moving samples, such as body parts, using a fixed X-ray device.
• To achieve low-cost, low-dose 3-D imaging by integrating readily available clinical equipment.

Main Methods:

• Combined a video-based surface motion capture system with a single, low-cost, low-dose fixed planar X-ray device to capture sample motion and attenuation data.
• Employed Bayesian inference to reconstruct a dense attenuation volume from planar radioscopic images of the moving sample, accounting for noise and limited prior information.

Main Results:

• Successfully reconstructed dense volumetric attenuation models from a limited number of radiographic views over time.
• Demonstrated the feasibility of the proposed strategy on both synthetic and in-situ data.

Conclusions:

• The proposed method offers a viable solution for low-cost, low-dose 3-D imaging of moving samples, particularly in clinical environments.
• The integration of motion capture and Bayesian inference enables robust volumetric modeling even with limited data and high noise levels.