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Title: MO-DE-207A-01: Impact of Statistical Weights On Detection of Low-Contrast Details in Model-Based Iterative CT Reconstruction

Abstract

Purpose: Penalized-weighted least-square reconstruction has become an important research topic in CT, to reduce dose without affecting image quality. Two components impact image quality in this reconstruction: the statistical weights and the use of an edge-preserving penalty term. We are interested in assessing the influence of statistical weights on their own, without the edge-preserving feature. Methods: The influence of statistical weights on image quality was assessed in terms of low-contrast detail detection using LROC analysis. The task amounted to detect and localize a 6-mm lesion with random contrast inside the FORBILD head phantom. A two-alternative forced-choice experiment was used with two human observers performing the task. Reconstructions without and with statistical weights were compared, both using the same quadratic penalty term. The beam energy was set to 30keV to amplify spatial differences in attenuation and thereby the role of statistical weights. A fan-beam data acquisition geometry was used. Results: Visual inspection of images clearly showed a difference in noise between the two reconstructions methods. As expected, the reconstruction without statistical weights exhibited noise streaks. The other reconstruction appeared better in this aspect, but presented other disturbing noise patterns and artifacts induced by the weights. The LROC analysis yield the followingmore » 95-percent confidence interval for the difference in reader-averaged AUC (reconstruction without weights minus reconstruction with weights): [0.0026,0.0599]. The mean AUC value was 0.9094. Conclusion: We have investigated the impact of statistical weights without the use of edge-preserving penalty in penalized weighted least-square reconstruction. A decrease rather than increase in image quality was observed when using statistical weights. Thus, the observers were better able to cope with the noise streaks than the noise patterns and artifacts induced by the statistical weights. It may be that different results would be obtained if the penalty term was used with a pixel-dependent weight. F Noo receives research support from Siemens Healthcare GmbH.« less

Authors:
;  [1]
  1. University of Utah, Salt Lake City, UT (United States)
Publication Date:
OSTI Identifier:
22649545
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; BIOMEDICAL RADIOGRAPHY; DATA ACQUISITION; IMAGES; ITERATIVE METHODS; LEAST SQUARE FIT; NOISE; PRODUCTIVITY

Citation Formats

Noo, F, and Guo, Z. MO-DE-207A-01: Impact of Statistical Weights On Detection of Low-Contrast Details in Model-Based Iterative CT Reconstruction. United States: N. p., 2016. Web. doi:10.1118/1.4957229.
Noo, F, & Guo, Z. MO-DE-207A-01: Impact of Statistical Weights On Detection of Low-Contrast Details in Model-Based Iterative CT Reconstruction. United States. doi:10.1118/1.4957229.
Noo, F, and Guo, Z. Wed . "MO-DE-207A-01: Impact of Statistical Weights On Detection of Low-Contrast Details in Model-Based Iterative CT Reconstruction". United States. doi:10.1118/1.4957229.
@article{osti_22649545,
title = {MO-DE-207A-01: Impact of Statistical Weights On Detection of Low-Contrast Details in Model-Based Iterative CT Reconstruction},
author = {Noo, F and Guo, Z},
abstractNote = {Purpose: Penalized-weighted least-square reconstruction has become an important research topic in CT, to reduce dose without affecting image quality. Two components impact image quality in this reconstruction: the statistical weights and the use of an edge-preserving penalty term. We are interested in assessing the influence of statistical weights on their own, without the edge-preserving feature. Methods: The influence of statistical weights on image quality was assessed in terms of low-contrast detail detection using LROC analysis. The task amounted to detect and localize a 6-mm lesion with random contrast inside the FORBILD head phantom. A two-alternative forced-choice experiment was used with two human observers performing the task. Reconstructions without and with statistical weights were compared, both using the same quadratic penalty term. The beam energy was set to 30keV to amplify spatial differences in attenuation and thereby the role of statistical weights. A fan-beam data acquisition geometry was used. Results: Visual inspection of images clearly showed a difference in noise between the two reconstructions methods. As expected, the reconstruction without statistical weights exhibited noise streaks. The other reconstruction appeared better in this aspect, but presented other disturbing noise patterns and artifacts induced by the weights. The LROC analysis yield the following 95-percent confidence interval for the difference in reader-averaged AUC (reconstruction without weights minus reconstruction with weights): [0.0026,0.0599]. The mean AUC value was 0.9094. Conclusion: We have investigated the impact of statistical weights without the use of edge-preserving penalty in penalized weighted least-square reconstruction. A decrease rather than increase in image quality was observed when using statistical weights. Thus, the observers were better able to cope with the noise streaks than the noise patterns and artifacts induced by the statistical weights. It may be that different results would be obtained if the penalty term was used with a pixel-dependent weight. F Noo receives research support from Siemens Healthcare GmbH.},
doi = {10.1118/1.4957229},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
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