Statistical Modeling and Reconstruction of Rebinned PET Measurements

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Abstract

Positron emission tomography (PET) provides insight into the physiology of living subjects and is an invaluable tool for the detection and staging of cancer. A PET scanner detects photons emitted from targeted regions inside the body and reconstructs them into images. These emission measurements are inherently noisy due to the limitations of the detection system and the physics of the decay. Consequently, this noise causes poor representations of the region of interest and ultimately complicates diagnoses. One primary method for reducing the influence of the noise is to improve the models used in the image reconstruction. This dissertation defends that the statistics of the PET measurements provide valuable information for improving the system and data model.In particular, this work develops a successful method for estimating geometric system parameters directly from data measurements. These parameters are then used to accurately describe the system model. Along with refining the system model, this research improves the data model for rebinned PET measurements. Rebinning represents an approach for simplifying high sensitivity 3D PET data into a form that can be quickly reconstructed into a meaningful image. The proposed improved data models are incorporated into direct and statistical reconstruction algorithms, leading to techniques tailored for the statistics of rebinned measurements. Results prove that some of the new tailored methods significantly outperform conventional 2D reconstruction algorithms.

Attributes

Attribute NameValues
URN
  • etd-08042003-113439

Author Adam Michael Alessio
Advisor Ken Sauer
Contributor Ken Sauer, Committee Member
Contributor Nicholas Laneman, Committee Member
Contributor Thomas Fuja, Committee Member
Contributor Robert Stevenson, Committee Member
Contributor Yih-Fang Huang, Committee Member
Degree Level 2
Degree Discipline Electrical Engineering
Degree Name Doctor of Philosophy
Defense Date
  • 2003-07-30

Submission Date 2003-08-04
Country
  • United States of America

Subject
  • FORE Rebinning

  • Positron Emission Tomography

  • FORE

  • Correlated Estimation

  • Diagnostic Imaging

  • Image Reconstruction

  • Parameter Estimation

  • Tomography

  • PET

Publisher
  • University of Notre Dame

Language
  • English

Access Rights Open Access
Content License
  • All rights reserved

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