Edge Response and Defect Detectability in Flat Panel Digital Radiography

Abstract

Defect detectability studies are used in nondestructive testing to ascertain the reliability of the method of inspection. In digital radiography, with the growing prevalence of automation of quality control processes by image processing and machine learning, a threshold detection criterion based on quantifiable data from the digital radiograph could be explored. The use of the parameter contrast-to-noise ratio (CNR) of defect signal as a probability of detection (POD) threshold criterion is explored in this paper. A stainless steel block containing artificial defects of known dimensions and location is radiographed by a flat panel detector, and an empirical POD curve is constructed. Before the POD study, the edge response of the flat panel system is studied to ensure noninterference of adjacent defect signals, gain insights about the lateral spread of the defect signal, and provide information to choose the region of interest for CNR calculation. The effect of noise on the POD using CNR as the threshold criterion is also included in the present study. The use of CNR-based POD models for digital radiography to aid the comparison and development of automatic defect detection models is also discussed.