Performance analysis for nonlinear tomographic data processing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Image quality analysis of nonlinear algorithms is challenging due to numerous dependencies on the imaging system, algorithmic parameters, object, and stimulus. In particular, traditional notions of linearity and local linearity are of limited utility when the system response is dependent on the stimulus itself. In this work, we analyze the performance of nonlinear systems using perturbation response - the difference between the mean output with and without a stimulus, and introduce a new metric to examine variation of the responses in individual images. We applied the analysis to four algorithms with different degrees of nonlinearity for a spherical stimulus of varying contrast. For model-based reconstruction methods [penalized-likelihood (PL) reconstruction with a quadratic penalty and a Huber penalty], perturbation response analysis reaffirmed known trends in terms of object- and location-dependence. For a CNN denoising network, the response exhibits highly nonlinear behavior as the contrast increases - from the stimulus completely disappearing, to appearing at the right contrast but smaller in size, to being fully admitted by the algorithm. Furthermore, the variation metric for PL reconstruction with a Huber penalty and the CNN network reveals high variation at the edge of the stimulus, i.e., perturbation response computed from the mean images is a smoothed version of individual responses due to "jitter" in edges. This behavior suggests that the mean response alone may not be representative of performance in individual images and image quality metrics traditionally defined based on the mean response may be inappropriate for certain nonlinear algorithms. This work demonstrates the potential utility of perturbation response and response variation in the analysis and optimization of nonlinear imaging algorithms.

Original languageEnglish (US)
Title of host publication15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
EditorsSamuel Matej, Scott D. Metzler
PublisherSPIE
ISBN (Electronic)9781510628373
DOIs
StatePublished - Jan 1 2019
Event15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019 - Philadelphia, United States
Duration: Jun 2 2019Jun 6 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11072
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019
CountryUnited States
CityPhiladelphia
Period6/2/196/6/19

Fingerprint

Performance Analysis
stimuli
Image quality
penalties
Perturbation
Penalty
perturbation
Penalized Likelihood
hubs
Jitter
Imaging systems
Linearity
Metric
Nonlinear systems
Image Quality
linearity
Imaging techniques
Denoising
nonlinear systems
Imaging System

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Gang, G. J., Guo, X., & Stayman, J. W. (2019). Performance analysis for nonlinear tomographic data processing. In S. Matej, & S. D. Metzler (Eds.), 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine [110720W] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11072). SPIE. https://doi.org/10.1117/12.2534983

Performance analysis for nonlinear tomographic data processing. / Gang, Grace J.; Guo, Xueqi; Stayman, J. W.

15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. ed. / Samuel Matej; Scott D. Metzler. SPIE, 2019. 110720W (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11072).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gang, GJ, Guo, X & Stayman, JW 2019, Performance analysis for nonlinear tomographic data processing. in S Matej & SD Metzler (eds), 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine., 110720W, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11072, SPIE, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019, Philadelphia, United States, 6/2/19. https://doi.org/10.1117/12.2534983
Gang GJ, Guo X, Stayman JW. Performance analysis for nonlinear tomographic data processing. In Matej S, Metzler SD, editors, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. SPIE. 2019. 110720W. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2534983
Gang, Grace J. ; Guo, Xueqi ; Stayman, J. W. / Performance analysis for nonlinear tomographic data processing. 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. editor / Samuel Matej ; Scott D. Metzler. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).
@inproceedings{4ec04d141cac49cda939984bd03c76a8,
title = "Performance analysis for nonlinear tomographic data processing",
abstract = "Image quality analysis of nonlinear algorithms is challenging due to numerous dependencies on the imaging system, algorithmic parameters, object, and stimulus. In particular, traditional notions of linearity and local linearity are of limited utility when the system response is dependent on the stimulus itself. In this work, we analyze the performance of nonlinear systems using perturbation response - the difference between the mean output with and without a stimulus, and introduce a new metric to examine variation of the responses in individual images. We applied the analysis to four algorithms with different degrees of nonlinearity for a spherical stimulus of varying contrast. For model-based reconstruction methods [penalized-likelihood (PL) reconstruction with a quadratic penalty and a Huber penalty], perturbation response analysis reaffirmed known trends in terms of object- and location-dependence. For a CNN denoising network, the response exhibits highly nonlinear behavior as the contrast increases - from the stimulus completely disappearing, to appearing at the right contrast but smaller in size, to being fully admitted by the algorithm. Furthermore, the variation metric for PL reconstruction with a Huber penalty and the CNN network reveals high variation at the edge of the stimulus, i.e., perturbation response computed from the mean images is a smoothed version of individual responses due to {"}jitter{"} in edges. This behavior suggests that the mean response alone may not be representative of performance in individual images and image quality metrics traditionally defined based on the mean response may be inappropriate for certain nonlinear algorithms. This work demonstrates the potential utility of perturbation response and response variation in the analysis and optimization of nonlinear imaging algorithms.",
author = "Gang, {Grace J.} and Xueqi Guo and Stayman, {J. W.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1117/12.2534983",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Samuel Matej and Metzler, {Scott D.}",
booktitle = "15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine",

}

TY - GEN

T1 - Performance analysis for nonlinear tomographic data processing

AU - Gang, Grace J.

AU - Guo, Xueqi

AU - Stayman, J. W.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Image quality analysis of nonlinear algorithms is challenging due to numerous dependencies on the imaging system, algorithmic parameters, object, and stimulus. In particular, traditional notions of linearity and local linearity are of limited utility when the system response is dependent on the stimulus itself. In this work, we analyze the performance of nonlinear systems using perturbation response - the difference between the mean output with and without a stimulus, and introduce a new metric to examine variation of the responses in individual images. We applied the analysis to four algorithms with different degrees of nonlinearity for a spherical stimulus of varying contrast. For model-based reconstruction methods [penalized-likelihood (PL) reconstruction with a quadratic penalty and a Huber penalty], perturbation response analysis reaffirmed known trends in terms of object- and location-dependence. For a CNN denoising network, the response exhibits highly nonlinear behavior as the contrast increases - from the stimulus completely disappearing, to appearing at the right contrast but smaller in size, to being fully admitted by the algorithm. Furthermore, the variation metric for PL reconstruction with a Huber penalty and the CNN network reveals high variation at the edge of the stimulus, i.e., perturbation response computed from the mean images is a smoothed version of individual responses due to "jitter" in edges. This behavior suggests that the mean response alone may not be representative of performance in individual images and image quality metrics traditionally defined based on the mean response may be inappropriate for certain nonlinear algorithms. This work demonstrates the potential utility of perturbation response and response variation in the analysis and optimization of nonlinear imaging algorithms.

AB - Image quality analysis of nonlinear algorithms is challenging due to numerous dependencies on the imaging system, algorithmic parameters, object, and stimulus. In particular, traditional notions of linearity and local linearity are of limited utility when the system response is dependent on the stimulus itself. In this work, we analyze the performance of nonlinear systems using perturbation response - the difference between the mean output with and without a stimulus, and introduce a new metric to examine variation of the responses in individual images. We applied the analysis to four algorithms with different degrees of nonlinearity for a spherical stimulus of varying contrast. For model-based reconstruction methods [penalized-likelihood (PL) reconstruction with a quadratic penalty and a Huber penalty], perturbation response analysis reaffirmed known trends in terms of object- and location-dependence. For a CNN denoising network, the response exhibits highly nonlinear behavior as the contrast increases - from the stimulus completely disappearing, to appearing at the right contrast but smaller in size, to being fully admitted by the algorithm. Furthermore, the variation metric for PL reconstruction with a Huber penalty and the CNN network reveals high variation at the edge of the stimulus, i.e., perturbation response computed from the mean images is a smoothed version of individual responses due to "jitter" in edges. This behavior suggests that the mean response alone may not be representative of performance in individual images and image quality metrics traditionally defined based on the mean response may be inappropriate for certain nonlinear algorithms. This work demonstrates the potential utility of perturbation response and response variation in the analysis and optimization of nonlinear imaging algorithms.

UR - http://www.scopus.com/inward/record.url?scp=85074284993&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074284993&partnerID=8YFLogxK

U2 - 10.1117/12.2534983

DO - 10.1117/12.2534983

M3 - Conference contribution

AN - SCOPUS:85074284993

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine

A2 - Matej, Samuel

A2 - Metzler, Scott D.

PB - SPIE

ER -