Defect Detection Probability as a Metric for CT Image Quality Assessment

Type: Project

Status: running

Supervisors: Linda-Sophie Schneider

This project focuses on using defect detection probability within CT (Computed Tomography) images as a metric for assessing image quality. Key steps include:

  • Establishing a data preparation pipeline to insert defects into CT volumes sourced from CAD files.
  • Simulating CT scans to replicate imaging processes.
  • Developing a defect detection neural network to analyze CT images and determine the probability of defect presence.
  • Utilizing the defect detection probability as a quantitative metric for evaluating the quality of CT images, with potential integration of trajectory optimization techniques.