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6 December 2002 Human vision model for the objective evaluation of perceived image quality applied to MRI and image restoration
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We are developing a method to objectively quantify image quality and applying it to the optimization of interventional magnetic resonance imaging (iMRI). In iMRI, images are used for live-time guidance of interventional procedures such as the minimally invasive treatment of cancer. Hence, not only does one desire high quality images, but they must also be acquired quickly. In iMRI, images are acquired in the Fourier domain, or k-space, and this allows many creative ways to image quickly such as keyhole imaging where k-space is preferentially subsampled, yielding suboptimal images at very high frame rates. Other techniques include spiral, radial, and the combined acquisition technique. We have built a perceptual difference model (PDM) that incorporates various components of the human visual system. The PDM was validated using subjective image quality ratings by naive observers and task-based measures defined by interventional radiologists. Using the PDM, we investigated the effects of various imaging parameters on image quality and quantified the degradation due to novel imaging techniques. Results have provided significant information about imaging time versus quality tradeoffs aiding the MR sequence engineer. The PDM has also been used to evaluate other applications such as Dixon fat suppressed MRI and image restoration. In image restoration, the PDM has been used to evaluate the Generalized Minimal Residual (GMRES) image restoration method and to examine the ability to appropriately determine a stopping condition for such iterative methods. The PDM has been shown to be an objective tool for measuring image quality and can be used to determine the optimal methodology for various imaging applications.
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Kyle A. Salem and David L. Wilson "Human vision model for the objective evaluation of perceived image quality applied to MRI and image restoration", Proc. SPIE 4791, Advanced Signal Processing Algorithms, Architectures, and Implementations XII, (6 December 2002);

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