[Design and Implementation of Automated QA Software for Cone Beam CT Image]

Area of Science:

• Medical Imaging
• Radiology
• Image Processing

Background:

• Cone beam CT (CBCT) is crucial for dental and medical imaging.
• Current image quality assurance (QA) methods can be time-consuming and prone to errors.
• Standardized QA is essential for reliable diagnostic imaging.

Purpose of the Study:

• To develop and validate an automated software for CBCT image quality assurance.
• To enhance the speed and accuracy of QA procedures.
• To provide a reliable tool for evaluating CBCT performance using standardized phantoms.

Main Methods:

• Designed and implemented automated QA software utilizing feature extraction techniques.
• Employed Canny algorithm and Hough transform for automated phantom positioning and Region of Interest (ROI) selection.
• Developed algorithms for calculating key image quality metrics: HU accuracy, spatial linearity, and HU uniformity.

Main Results:

• The software successfully automated the positioning of Catphan500 phantoms.
• Accurate selection of ROIs within different phantom modules was achieved.
• Experimental validation demonstrated the software's feasibility in assessing CBCT image quality parameters.

Conclusions:

• The developed automated software offers a significant improvement in CBCT image quality assurance.
• The software provides a fast and accurate method for evaluating essential image quality metrics.
• This tool can aid in maintaining and improving the diagnostic quality of CBCT systems.