1 April 2002 Validation of a human vision model for image quality evaluation of fast interventional magnetic resonance imaging
Author Affiliations +
J. of Electronic Imaging, 11(2), (2002). doi:10.1117/1.1453412
Perceptual difference models (PDMs) have become popular for evaluating the perceived degradation of an image by a process such as compression. We used a PDM to evaluate interventional magnetic resonance imaging (iMRI) methods that rapidly acquire an image at the expense of some anticipated image degradation compared to a conventional slower diagnostic technique. In particular, we examined MR keyhole techniques whereby only a portion of the spatial frequency domain, or k-space, was acquired, thereby reducing the time for the creation of image updates. We used a PDM based on the architecture of another visual differencemodel and validated it for noise and blur, degrading processes present in fast iMRI. The PDM showed superior correlation with human observer ratings of noise and blur compared to the mean squared error (MSE). In an example application, we simulated four keyhole techniques and compared them to a slower, full k-space diagnostic acquisition. For keyhole images, the MSE gave erratic results compared to the ratings by interventional radiologists. The PDM performed much better and gave an Az value >0.9 in a receiver operating characteristic analysis. Keyhole simulations showed that a single, central stripe acquisition, which sampled 25% of k-space, provided stable image quality within a clinically acceptable range, unlike three other keyhole schemes described in the literature. Our early experience shows the PDM to be an objective, promising tool for the evaluation of fast iMRI methods. It allows one to quantitatively make engineering decisions in the design of iMRI pulse sequences.
Kyle A. Salem, Jonathan S. Lewin, Andrik J. Aschoff, Jeffrey L. Duerk, David L. Wilson, "Validation of a human vision model for image quality evaluation of fast interventional magnetic resonance imaging," Journal of Electronic Imaging 11(2), (1 April 2002). http://dx.doi.org/10.1117/1.1453412

Image quality

Visual process modeling

Image processing

Magnetic resonance imaging

Image quality standards

Signal to noise ratio

Spatial frequencies

Back to Top