Perception difference paradigm for analyzing image quality of fast mri techniques

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

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

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 naïve 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.

Original languageEnglish (US)
Pages (from-to)297-308
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5034
DOIs
StatePublished - 2003
Externally publishedYes
EventMedical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment - San Diego, CA, United States
Duration: Feb 18 2003Feb 20 2003

Keywords

  • Image perception
  • Image quality
  • Magnetic resonance imaging
  • Vision modeling

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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