Monte Carlo simulation of inverse geometry x-ray fluoroscopy using a modified MC-GPU framework.

Area of Science:

• Medical Imaging Physics
• Computational Imaging
• Radiological Physics

Background:

• Scanning-Beam Digital X-ray (SBDX) offers low-dose fluoroscopy using inverse geometry x-ray beam scanning.
• Rapid modeling of complex inverse geometry systems is crucial for technological advancement.

Purpose of the Study:

• To develop a Monte Carlo (MC) simulation tool based on the MC-GPU framework for rapid modeling of inverse geometry x-ray systems.
• To validate the simulation tool by comparing its predictions with experimental data for geometric accuracy, scatter fraction, and dose reduction.

Main Methods:

• Modified MC-GPU to simulate a 2D array of focal spot positions with individually adjustable x-ray outputs.
• Evaluated tomosynthesis reconstructions using simulated images of spheres to assess geometric accuracy and blurring.
• Simulated x-ray scatter fraction for different SBDX detector geometries and compared with experimental measurements.
• Modeled dose reduction using regional adaptive exposure (RAE) scanning in an anthropomorphic phantom and compared with experimental dose measurements.

Main Results:

• The simulation's artifact spread function agreed with experimental data within 1.6% (rRMSD).
• Simulated x-ray scatter fraction (2.1-4.5%) closely matched experimental measurements (2.8-6.4%) for the SBDX prototype.
• Simulated dose reduction with RAE scanning (46% for kerma-area-product, 61% for integral kerma) aligned well with experimental findings (45% and 57%, respectively).

Conclusions:

• The developed MC simulation tool accurately predicts key performance metrics for inverse geometry x-ray systems.
• This tool can effectively estimate tomographic blur, detected scatter, and dose distributions during the development of SBDX technologies.
• The simulation facilitates efficient design and optimization of low-dose inverse geometry x-ray imaging systems.