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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
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Modeling human observer detection in undersampled magnetic resonance imaging (MRI).

Alexandra G O'Neill1, Emely L Valdez1, Sajan Goud Lingala2

  • 1Mathematics Department, Manhattan College, Riverdale, NY, 10471, USA.

Proceedings of Spie--The International Society for Optical Engineering
|October 21, 2022
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Summary

Assessing image quality in undersampled MRI is crucial. Model observers accurately tracked human performance in detecting brain metastases using FLAIR images, even with increased undersampling.

Keywords:
Model observersimage quality assessmentmagnetic resonance imaging

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Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction

Background:

  • Task-based assessment of image quality in undersampled MRI is vital for quantifying artifact impact.
  • Fluid-attenuated inversion recovery (FLAIR) images are critical for detecting small brain metastases.

Purpose of the Study:

  • To evaluate human observer performance in detecting small brain metastases using undersampled FLAIR images.
  • To assess the impact of regularization parameters on image quality and task performance.
  • To validate model observers (NPWE and S-DOG) against human performance in undersampled MRI.

Main Methods:

  • Conducted two-alternative forced choice (2-AFC) studies with varying backgrounds for reconstructed FLAIR images from undersampled multi-coil data (4x undersampling).
  • Employed a total variation (TV) constraint and analyzed its effect on human observer detection performance across a range of regularization parameters.
  • Utilized non-prewhitening eye (NPWE) and sparse difference-of-Gaussians (S-DOG) with internal noise model observers to track human performance.

Main Results:

  • Human observer detection performance remained stable for a broad range of TV regularization parameters, decreasing only at high values.
  • The TV constraint did not significantly improve task performance in detecting small metastases.
  • The S-DOG model observer with internal noise accurately tracked human performance across all studied regularization levels, while the NPWE model slightly overestimated performance at high regularization.

Conclusions:

  • Model observers, particularly S-DOG with internal noise, can effectively track human detection performance in undersampled MRI tasks.
  • This study demonstrates the first use of model observers to track human detection in undersampled MRI, offering a valuable tool for image quality assessment.
  • Further research can refine model observers for more accurate prediction of human performance in complex imaging scenarios.