Gain Correction for an X-ray Imaging System With a Movable Flat Panel Detector and Intrinsic Localization Crosshair

Abstract

Gain calibration for X-ray imaging systems with a movable flat panel detector and an intrinsic crosshair is a challenge due to the geometry-dependent heel effect and crosshair artifact. This study aims to develop a gain correction method for such systems by implementing the Multi-Acquisition Gain Image Correction technique. Flood field images containing crosshair and heel effect were acquired in 4 different flat panel detector positions at fixed exposure parameters. The crosshair region was automatically detected using common image processing algorithms and removed by a simple interpolation procedure, resulting in a crosshair-removed image. A large kernel-based correction was then used to remove the heel effect. Mask filters corresponding to each crosshair region were applied to the resultant heel effect-removed images to invalidate the pixels of the original crosshair region. Finally, a seamless gain map was composed with corresponding valid pixels from the processed images either by the sequential replacement or by the selective averaging techniques developed in this study. Quantitative evaluation was performed based on normalized noise power spectrum and detective quantum efficiency improvement factor for the flood field images corrected by the MultiAcquisition Gain Image Correction-based gain maps. For comparison purposes, a single crosshair-removed gain map was also tested. As a result, it was demonstrated that the Multi-Acquisition Gain Image Correction technique achieved better image quality than the crosshair-removed technique, showing lower normalized noise power spectrum values over most of spatial frequencies. The improvement was more obvious at the priori-crosshair region of the gain map. The mean detective quantum efficiency improvement factor was 1.09 + 0.06, 2.46 + 0.32, and 3.34 + 0.36 in the priori-crosshair region and 2.35 + 0.31, 2.33 + 0.31, and 3.09 + 0.34 in the normal region, for crosshair-removed, Multi-Acquisition Gain Image Correction-sequential replacement, and Multi-Acquisition Gain Image Correction-selective averaging techniques, respectively. Therefore, this study indicates that the introduced Multi-Acquisition Gain Image Correction technique is an appropriate method for gain calibration of an imaging system associated with a moving flat panel detector and an intrinsic crosshair.

In situ X-ray area detector flat-field correction at an operating photon energy without flat illumination

“…by the beamstop) have significantly different responses from exposed pixels when used again after a change in the setup, and will display an artifact such as an apparent shadowing on the detector. In medical imaging using similar detectors, it is known that simply translating the detector results in erroneous apparent shadowing of an imaged object (Park & Sharp, 2015), also resulting in the need for flat-field corrections (FFCs). The limitations of these detectors when used in medical and industrial imaging are well known, with numerous approaches taken to correct for them (Park et al, 2014;Cao & Peter, 2008;Hofmann et al, 2011).…”

Consecutive Short-Scan CT for Geological Structure Analog Models with Large Size on In-Situ Stage

“…The main experimental parameters are shown in Table 1 . The offset and gain corrections of the LAD are accomplished in advance to avoid dark field noise and response non-uniformity of the detecting units [ 37 – 39 ]. Geometrical calibrations such as focus to detector distance, center of rotation, and pixel size are also necessary to measure for an accurate image reconstruction.…”

Research on Fast Imaging Method of X - Ray Flat Panel Detector Based on Pre - Offset

“…In 10 minutes, collecting the original bright field plots of the three sets as follows : TABLE I. THREE SETS OF TESTING SCHEME TABLE Dose 70Kv5mAs Exposure time 2s 3s 6s 10s 20s 30s Dose 65Kv5mAs Exposure time 2s 6s 10s 20s 30s Dose 60Kv5mAs Exposure time 2s 6s 10s 20s 30s  The gain_map template is generated form 70Kv5mAs bright-field image in 2s/3s exposure time window, 0.5s and 5s pre-offset template are used to do linear fitting and 3 suitable time interval pre-offset templates are used to do quadratic function [5]. This two type of functions are used to calibrate the original bright-field image.…”