Perceptual difference paradigm for analyzing image quality of fast MRI techniques

David L. Wilson; Kyle A. Salem; Donglai Huo; Jeffrey L. Duerk

doi:10.1117/12.480335

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22 May 2003 Perceptual difference paradigm for analyzing image quality of fast MRI techniques

David L. Wilson, Kyle A. Salem, Donglai Huo, Jeffrey L. Duerk

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Proceedings Volume 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment; (2003) https://doi.org/10.1117/12.480335
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

We are developing a method to objectively quantify image quality and applying it to the optimization of fast magnetic resonance imaging methods. In MRI, to capture the details of a dynamic process, it is critical to have both high temporal and spatial resolution. However, there is typically a trade-off between the two, making the sequence engineer choose to optimize imaging speed or spatial resolution. In response to this problem, a number of different fast MRI techniques have been proposed. To evaluate different fast MRI techniques quantitatively, we use 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 as defined by radiologists. Using the PDM, we investigated the effects of various imaging parameters on image quality and quantified the degradation due to novel imaging techniques including keyhole, keyhole Dixon fat suppression, and spiral imaging. Results have provided significant information about imaging time versus quality tradeoffs aiding the MR sequence engineer. 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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David L. Wilson, Kyle A. Salem, Donglai Huo, and Jeffrey L. Duerk "Perceptual difference paradigm for analyzing image quality of fast MRI techniques", Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); https://doi.org/10.1117/12.480335

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