Geometry calibration and image reconstruction for carbon-nanotube-based multisource and multidetector CT

Area of Science:

• Medical Imaging
• Physics
• Engineering

Background:

• Conventional intraoperative computed tomography (CT) systems suffer from long scan times and large physical footprints, compromising image quality and surgical workflow.
• These limitations hinder real-time surgical guidance and necessitate improvements in CT system design and performance for intraoperative applications.

Purpose of the Study:

• To develop and validate a novel intraoperative CT system addressing the limitations of conventional systems.
• To significantly reduce scan time, improve image quality, and enhance surgeon maneuverability during surgical procedures.

Main Methods:

• Proposed a compact intraoperative CT system utilizing eight carbon-nanotube (CNT)-based X-ray sources and 16 detector modules.
• Implemented a novel geometry calibration method for each subsystem using a custom phantom with polytetrafluoroethylene beads.
• Employed filtered-backprojection reconstruction after rebinning projection data using estimated calibration parameters.

Main Results:

• The proposed system achieved an 1/8 reduction in scan time due to a 45° gantry rotation and efficient CNT X-ray sources.
• Geometry calibration significantly reduced artifacts, evidenced by lower mean-squared error (MSE) and higher structure similarity (SSIM) values.
• Simulations and real experiments confirmed the improved accuracy and image quality of the calibrated intraoperative CT system.

Conclusions:

• The novel intraoperative CT system with CNT X-ray sources effectively overcomes the limitations of conventional systems.
• The developed geometry calibration and reconstruction methods are crucial for enhancing image quality and reducing artifacts in intraoperative CT imaging.