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Improving quantitative CT perfusion parameter measurements using principal component analysis.

Timothy Pok Chi Yeung1, Mark Dekaban2, Nathan De Haan3

  • 1London Regional Cancer Program, London Health Sciences Centre, London, ON, Canada; Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Robarts Research Institute, Western University, 1151 Richmond St. N., London, Ontario, N6A 5B7, Canada; Department of Medical Biophysics, Western University, London, ON, Canada.

Academic Radiology
|April 8, 2014
PubMed
Summary
This summary is machine-generated.

Principal component analysis (PCA) filtering significantly enhances computed tomography (CT) perfusion imaging quality. This method improves measurements of blood flow (BF), blood volume (BV), and permeability-surface area product (PS), reducing errors in both simulations and in vivo experiments.

Keywords:
CT perfusionblood flowbrain tumorimage noiseprincipal component analysis

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Area of Science:

  • Medical Imaging
  • Radiology
  • Image Processing

Background:

  • Computed tomography (CT) perfusion imaging provides crucial data on blood flow (BF), blood volume (BV), and permeability-surface area product (PS).
  • Image quality, particularly contrast-to-noise ratio (CNR), can be suboptimal in CT perfusion, affecting measurement accuracy.
  • Principal component analysis (PCA) is a dimensionality reduction technique with potential applications in improving image processing.

Purpose of the Study:

  • To assess the impact of principal component analysis (PCA) filtering on the accuracy of blood flow (BF), blood volume (BV), and permeability-surface area product (PS) measurements derived from CT perfusion images.
  • To evaluate the enhancement of CT perfusion image quality, specifically contrast-to-noise ratio (CNR), using PCA filtering in in vivo studies.

Main Methods:

  • A digital phantom simulating CT perfusion images with known BF, BV, and PS values was created and processed with PCA.
  • Reliability and error reduction were assessed using intraclass correlation coefficients and Bland-Altman analysis.
  • In vivo experiments involved CT perfusion imaging of rats with C6 gliomas, followed by PCA filtering of the images. Differences in CNR, BF, BV, and PS were analyzed before and after filtering.

Main Results:

  • PCA filtering significantly reduced mean errors in simulated BF, BV, and PS measurements, with map noise also decreasing substantially.
  • In vivo experiments demonstrated a significant improvement in contrast-to-noise ratio (CNR) for both normal brain tissue and tumors after PCA filtering.
  • Post-PCA filtering, significant differences in tumor and brain blood flow (BF) were detectable when using four principal components.

Conclusions:

  • Principal component analysis (PCA) effectively improves contrast-to-noise ratio (CNR) in in vivo CT perfusion imaging.
  • PCA filtering reduces measurement errors for blood flow (BF), blood volume (BV), and permeability-surface area product (PS) in simulations.
  • A minimum of four principal components is recommended for optimal results in CT perfusion image analysis.