Optimal Design of RF Pulses With Arbitrary Profiles in Magnetic Resonance Imaging

Abstract

The proper design of RF pulses in magnetic resonance imaging has a direct impact on the quality of acquired images. Several techniques have been proposed to obtain the RF pulse envelope given the desired slice profile. Unfortunately, these techniques do not take into account the limitations of practical implementation such as limited amplitude resolution. Moreover, implementing constraints for special RF pulses on most techniques is not possible. In this work, we propose an approach for designing optimal RF under theoretically any constraints. The new technique poses the RF pulse design problem as a combinatorial optimization problem and uses efficient techniques from this area to solve this problem. In particular, an objective function is proposed as the norm of the difference between the desired profile and the one obtained from solving the Bloch equations for the current RF pulse design values. Two global optimization techniques were implemented using genetic algorithms and simulated annealing. The results show a significant improvement over conventional design techniques and suggest the practicality of using of the new technique for clinical use.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA411968

Entities

People

  • Abou-bakr M. Youssef
  • Ayman M. Khalifa
  • Yasser M. Kadah

Organizations

  • Cairo University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Amplitude
  • Annealing
  • Biomedical Engineering
  • Chromosomes
  • Demographic Cohorts
  • Engineering
  • Filters
  • Genetic Algorithms
  • Ground State
  • Magnetic Resonance
  • Magnetic Resonance Imaging
  • Mutations
  • Optimization
  • Probability
  • Radio Frequency Generators
  • Radio Frequency Pulses

Fields of Study

  • Physics

Readers

  • Medical Imaging.
  • Operations Research
  • Phased Array Antenna Design.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology