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As a first step toward developing a methodology suitable for optimizing the many parameters in keyhole and other fast imaging techniques, we applied an accepted human visual system (HVS) perceptual difference model to simulated keyhole images. A series of 'gold-standard' full k-space images were acquired during the insertion of a needle into ex vivo bovine liver. Keyhole imaging, a method by which image frame rate is increased due to sub-sampling k-space, was simulated from this image data. A perceptual difference HVS model was used to create a map of the likelihood of visible differences between a simulated keyhole image and the corresponding full k-space acquisition. Visible difference degradation was compared with a mean squared error (MSE) metric for both entire images and regions of interest around the needle tip. The output of the HVS model was a spatial map of perceptual differences. This map proved useful since it provided an accurate tool for finding the location of image differences. According to the perceptual model, the quality of the entire image is preserved most favorably with a stripe parallel to the direction of insertion. For a region of interest surrounding the needle, a perpendicular stripe resulted in the lowest level of image error. The HVS model agreed favorably with anecdotal human inspection. For example, while high frequency noise in the image produces effective changes in the MSE metric, the visual model and inspection show no true perceivable image difference. Additionally, inspection verified the importance of the direction of the k-space sub-sampling. Examination of rotated stripes of k-space show that a step of 45 degrees is preferred. Larger steps caused high initial error, while smaller steps took too long to traverse k-space. Experience indicates that the HVS model is an objective, promising tool for the automated evaluation and optimization of keyhole imaging sequences. Hopefully, it will provide a rational method for optimizing the large number of potential techniques and infinite number of parameters in fast MR imaging.
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