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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

A practical acceleration algorithm for real-time imaging.

Uygar Sümbül1, Juan M Santos, John M Pauly

  • 1Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. uygar@mit.edu

IEEE Transactions on Medical Imaging
|August 28, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a fast, real-time magnetic resonance imaging (MRI) algorithm using a Kalman filter. It enables rapid image reconstruction without training data, improving dynamic MRI acquisition efficiency.

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Last Updated: Jun 20, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

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Published on: February 12, 2014

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Signal Processing

Background:

  • Real-time magnetic resonance imaging (MRI) is crucial for dynamic physiological processes.
  • Current acceleration techniques often require extensive training data or embedded samples, limiting their applicability.
  • Dynamic MRI acquisitions with arbitrary trajectories pose reconstruction challenges.

Purpose of the Study:

  • To present a practical acceleration algorithm for real-time MRI.
  • To enable fast and causal image reconstruction without prior training data.
  • To demonstrate the algorithm's robustness in dynamic cardiac MRI.

Main Methods:

  • Development of a Kalman filter-based acceleration algorithm.
  • Implementation for dynamic MRI acquisitions with arbitrary readout trajectories.
  • Testing against abrupt changes in imaging conditions and in vivo cardiac MRI data.

Main Results:

  • The algorithm achieves fast and causal reconstruction of dynamic MRI data.
  • It operates effectively without requiring separate training scans or embedded training samples.
  • Successful offline reconstructions of in vivo cardiac MRI experiments were demonstrated.

Conclusions:

  • The presented Kalman filter-based algorithm offers a practical solution for real-time MRI acceleration.
  • It provides efficient image reconstruction for dynamic acquisitions, even with complex trajectories.
  • The method shows promise for improving the speed and applicability of cardiac MRI.