A blurring correction method suitable to analyze quantitative x-ray images derived from energy-resolving photon counting detector.
Phys Med Biol
; 69(7)2024 Mar 26.
Article
em En
| MEDLINE
| ID: mdl-38452379
ABSTRACT
Objective.The purpose of this study is to propose a novel blurring correction method that enables accurate quantitative analysis of the object edge when using energy-resolving photon counting detectors (ERPCDs). Although the ERPCDs have the ability to generate various quantitative analysis techniques, such as the derivations of effective atomic number (Zeff) and bone mineral density values, at the object edge in these quantitative images, accurate quantitative information cannot be obtained. This is because image blurring prevents the gathering of accurate primary x-ray attenuation information.Approach.We developed the following procedure for blurring correction. A 5 × 5 pixels masking region was set as the processing area, and the pixels affected by blurring were extracted from the analysis of pixel value distribution. The blurred pixel values were then corrected to the proper values estimated by analyzing minimum and/or maximum values in the set mask area. The suitability of our correction method was verified by a simulation study and an experiment using a prototype ERPCD.Main results. WhenZeffimage of aluminum objects (Zeff= 13) were analyzed without applying our correction method, regardless of raw data or correction data applying a conventional edge enhancement method, the properZeffvalues could not be derived for the object edge. In contrast, when applying our correction method, 82% of pixels affected by blurring were corrected and the properZeffvalues were calculated for those pixels. As a result of investigating the applicability limits of our method through simulation, it was proven that it works effectively for objects with 4 × 4 pixels or more.Significance. Our method is effective in correcting image blurring when the quantitative image is calculated based on multiple images. It will become an in-demand technology for putting a quantitative diagnosis into actual medical examinations.
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2024
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