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Related Concept Videos

Orthogonal Trajectories01:26

Orthogonal Trajectories

Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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Related Experiment Video

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An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging
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Published on: September 24, 2017

Smoothed random-like trajectory for compressed sensing MRI.

Haifeng Wang1, Xiaoyan Wang, Yihang Zhou

  • 1Department of Electrical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA. haifeng@uwm.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

This study introduces a novel random-like trajectory for rapid magnetic resonance imaging (MRI) using compressed sensing (CS). The method reduces artifacts compared to conventional spiral trajectories, enhancing image quality in accelerated MRI acquisition.

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Compressed Sensing (CS)

Background:

  • Compressed sensing (CS) requires sampling trajectories to satisfy the Restricted Isometry Property (RIP).
  • Conventional CS methods focus on random sampling on standard trajectories, potentially leading to artifacts.

Purpose of the Study:

  • To develop a rapid imaging method using a novel random-like trajectory for compressed sensing (CS).
  • To improve image quality by reducing artifacts in accelerated MRI acquisition.

Main Methods:

  • A random-like trajectory generated using High Order Chirp (HOC) sequences.
  • Optimization using a Traveling Salesman Problem (TSP) solver for trajectory length.
  • Design of time-optimal gradient waveforms respecting hardware limitations.
  • Verification of physical feasibility using Bloch simulations.

Main Results:

  • The proposed random-like trajectory satisfies the RIP condition.
  • Bloch simulations confirmed the physical feasibility of the method.
  • The novel trajectory demonstrated reduced artifacts compared to conventional Spiral trajectories within the CS framework.

Conclusions:

  • The proposed random-like trajectory offers a promising approach for accelerated MRI with compressed sensing.
  • This method enhances image quality by minimizing artifacts, outperforming traditional Spiral trajectories.