Optimization of Proton CT Detector System and Image Reconstruction Algorithm for On-Line Proton Therapy

Area of Science:

• Medical Physics
• Imaging Technology
• Computational Science

Background:

• Proton computed tomography (pCT) is crucial for accurate proton range verification in image-guided proton therapy.
• Current pCT systems require optimization for image quality and reconstruction efficiency.
• Proton starvation and under-sampling can degrade pCT image quality.

Purpose of the Study:

• To optimize a pCT system's geometric parameters for proton range verification.
• To validate a compressed sensing (CS)-based image reconstruction algorithm for pCT.
• To demonstrate the potential of the optimized pCT system for on-line proton therapy.

Main Methods:

• Developed a new pCT scanner using the GEANT4 simulation toolkit.
• Optimized detector thickness and inter-detector distance via GEANT4 simulations.
• Employed a total variation-based CS convex optimization algorithm for image reconstruction.

Main Results:

• Achieved high-quality pCT image reconstruction with 72 projections in 20 iterations.
• The CS algorithm successfully avoided streaks and noise caused by under-sampling.
• Images reconstructed with the CS algorithm showed superior quality compared to other methods.

Conclusions:

• The optimized pCT system and CS reconstruction algorithm enable high-quality imaging for on-line proton therapy.
• This approach facilitates image-guided proton therapy without additional imaging dose.
• The developed system and algorithm offer significant potential for clinical application in proton therapy.