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A fast parametric modelling algorithm with the Powell method

L Lu1, B H Brown, D C Barber

  • 1Department of Medical Physics, Royal Hallamshire Hospital, Sheffield, UK.

Physiological Measurement
|August 1, 1995
PubMed
Summary
This summary is machine-generated.

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This study introduces a two-parameter model for parametric imaging and compares function minimization algorithms. The modified Powell method significantly reduces computation time, enabling rapid image generation.

Area of Science:

  • Medical Imaging
  • Computational Science
  • Data Analysis

Background:

  • Parametric imaging requires efficient model fitting.
  • Minimizing computational time is crucial for real-time applications.

Purpose of the Study:

  • To evaluate and compare three function minimization algorithms (simplex, Powell, modified Powell) for a two-parameter model (R/S, fr) in parametric imaging.
  • To identify the most efficient algorithm for generating parametric images.

Main Methods:

  • A two-parameter model (R/S, fr) was utilized.
  • Three function minimization algorithms were applied and compared: simplex, Powell, and modified Powell.
  • Performance was assessed based on efficiency and reliability.

Main Results:

Related Experiment Videos

  • The modified Powell method demonstrated superior efficiency and reliability compared to simplex and Powell algorithms.
  • Proper initialization allowed approximation of the minimization function as quadratic, reducing iterations.
  • Substantial reduction in computation time was achieved, enabling 16x16 parametric image generation in 1 second.

Conclusions:

  • The modified Powell method is highly effective for accelerating parametric imaging.
  • This advancement facilitates faster and more efficient generation of parametric images.