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March 15, 2026Radiation Protection Dosimetry0 citations

Clinical experience of automated QC for homogeneous image analysis

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TVTanny VisanuyanontTMTomas MobergEHEmanuel Hillberg

Key Points

  • To evaluate the effectiveness of an automated quality control system for image analysis in X-ray imaging.
  • Implemented DOSESTAT-QC® for quality assurance in mammography and interventional systems.
  • Analyzed standard deviations in mean pixel values and signal-to-noise ratio in homogenous images.
  • Compared values from 1 cm2 regions of interest with entire image values.
  • Mean pixel values remained stable, while fluctuations in SNR revealed limitations in image quality.
  • Enhanced method allows precise SNR baseline settings and improved visualization of image deviations.
  • Clinical implementation of DOSESTAT-QC® demonstrated adaptability to conventional X-ray systems.

Abstract

DOSESTAT-QC® is a stand-alone automated quality control (QC) system used for daily quality assurance of X-ray equipment in Jönköping Region, Sweden. The software has been implemented for all mammography systems and interventional systems in the region. One of the image analysis included in the DOSESTAT-QC® software is performed in homogenous images and focuses on the standard deviations in mean pixel value (MV) and signal-to-noise ratio (SNR) in the images. Initially, the analyses were performed in 1 cm2 regions of interest (ROIs) and the obtained values in each ROI were compared to the corresponding values for the entire image. While MV remained relatively stable over time, fluctuations in SNR together with imprecise localization of pixel errors, especially in the automatic exposure control (AEC) area, highlighted limitations. In this paper, an improved method for image evaluation is presented, which enables precise SNR baseline settings and clear visualization of deviations and dead pixels. Additionally, the adaption and clinical implementation of DOSESTAT-QC® to conventional X-ray systems in the region are described.

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Cite This Study

Visanuyanont et al. (2025) studied this question.

synapsesocial.com/papers/69b6069b83145bc643d1c96ehttps://doi.org/10.1093/rpd/ncaf194
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