title: Use of local noise power spectrum and wavelet analysis in quantitative image quality assurance for EPIDs.
abstract: OBJECTIVE
  To investigate the use of local noise power spectrum (NPS) to characterize image noise and wavelet analysis to isolate defective pixels and inter-subpanel flat-fielding artifacts for quantitative quality assurance (QA) of electronic portal imaging devices (EPIDs).
  
  
  METHODS
  A total of 93 image sets including custom-made bar-pattern images and open exposure images were collected from four iViewGT a-Si EPID systems over three years. Global quantitative metrics such as modulation transform function (MTF), NPS, and detective quantum efficiency (DQE) were computed for each image set. Local NPS was also calculated for individual subpanels by sampling region of interests within each subpanel of the EPID. The 1D NPS, obtained by radially averaging the 2D NPS, was fitted to a power-law function. The r-square value of the linear regression analysis was used as a singular metric to characterize the noise properties of individual subpanels of the EPID. The sensitivity of the local NPS was first compared with the global quantitative metrics using historical image sets. It was then compared with two commonly used commercial QA systems with images collected after applying two different EPID calibration methods (single-level gain and multilevel gain). To detect isolated defective pixels and inter-subpanel flat-fielding artifacts, Haar wavelet transform was applied on the images.
  
  
  RESULTS
  Global quantitative metrics including MTF, NPS, and DQE showed little change over the period of data collection. On the contrary, a strong correlation between the local NPS (r-square values) and the variation of the EPID noise condition was observed. The local NPS analysis indicated image quality improvement with the r-square values increased from 0.80 ± 0.03 (before calibration) to 0.85 ± 0.03 (after single-level gain calibration) and to 0.96 ± 0.03 (after multilevel gain calibration), while the commercial QA systems failed to distinguish the image quality improvement between the two calibration methods. With wavelet analysis, defective pixels and inter-subpanel flat-fielding artifacts were clearly identified as spikes after thresholding the inversely transformed images.
  
  
  CONCLUSIONS
  The proposed local NPS (r-square values) showed superior sensitivity to the noise level variations of individual subpanels compared with global quantitative metrics such as MTF, NPS, and DQE. Wavelet analysis was effective in detecting isolated defective pixels and inter-subpanel flat-fielding artifacts. The proposed methods are promising for the early detection of imaging artifacts of EPIDs.
authors: Department of Radiation Oncology, University Hospitals Case and Medical Center, Cleveland, Ohio 44106.
authors: Soyoung
authors: S
authors: Lee
authors: Department of Radiation Oncology, University of Florida College of Medicine, Gainesville, Florida 32608.
authors: Guanghua
authors: G
authors: Yan
authors: Department of Radiation Oncology, University of Florida College of Medicine, Gainesville, Florida 32608.
authors: Philip
authors: P
authors: Bassett
authors: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201.
authors: Arun
authors: A
authors: Gopal
authors: Department of Radiation Oncology, University of Florida College of Medicine, Gainesville, Florida 32608.
authors: Sanjiv
authors: S
authors: Samant
mesh_terms: D016477:Artifacts; D055615:Electrical Equipment and Supplies / Q000592:standards*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D011786:Quality Control; D059629:Signal-To-Noise Ratio; D058067:Wavelet Analysis