Paper
16 March 2011 Predictive models for observer performance in CT: applications in protocol optimization
Author Affiliations +
Abstract
The relationship between theoretical descriptions of imaging performance (Fourier-based) and the performance of real human observers was investigated for detection tasks in multi-slice CT. The detectability index for the Fisher-Hotelling model observer and non-prewhitening model observer (with and without internal noise and eye filter) was computed using: 1) the measured modulation transfer function (MTF) and noise-power spectrum (NPS) for CT; and 2) a Fourier description of imaging task. Based upon CT images of human patients with added simulated lesions, human observer performance was assessed via an observer study in terms of the area under the ROC curve (Az). The degree to which the detectability index correlated with human observer performance was investigated and results for the non-prewhitening model observer with internal noise and eye filter (NPWE) were found to agree best with human performance over a broad range of imaging conditions. Results provided initial validation that CT image acquisition and reconstruction parameters can be optimized for observer performance rather than system performance (i.e., contrast-to-noise ratio, MTF, and NPS). The NPWE model was further applied for the comparison of FBP with a novel modelbased iterative reconstruction algorithm to assess its potential for dose reduction.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Richard, X. Li, G. Yadava, and E. Samei "Predictive models for observer performance in CT: applications in protocol optimization", Proc. SPIE 7961, Medical Imaging 2011: Physics of Medical Imaging, 79610H (16 March 2011); https://doi.org/10.1117/12.877069
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Cited by 27 scholarly publications.
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KEYWORDS
Modulation transfer functions

Performance modeling

Eye models

Reconstruction algorithms

Computed tomography

Eye

Optimization (mathematics)

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