Analysis of Lesion Detectability in Bayesian Emission Reconstruction With Nonstationary Object Variability

Jinyi Qi

Research output: Contribution to journalArticle

40 Citations (Scopus)

Abstract

Bayesian methods based on the maximum a posteriori principle (also called penalized maximum-likelihood methods) have been developed to improve image quality in emission tomography. To explore the full potential of Bayesian reconstruction for lesion detection, we derive simplified theoretical expressions that allow fast evaluation of the detectability of a lesion in Bayesian reconstruction. This work is builded on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers. We explicitly model the nonstationary variation of the lesion and background without assuming that they are locally stationary. The results can be used to choose the optimum prior parameters for the maximum lesion detectability. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predictions and the Monte Carlo results. We also demonstrate that the lesion detectability can be reliably estimated using one noisy data set.

Original languageEnglish (US)
Pages (from-to)321-329
Number of pages9
JournalIEEE Transactions on Medical Imaging
Volume23
Issue number3
DOIs
StatePublished - Mar 2004
Externally publishedYes

Fingerprint

Bayes Theorem
Image quality
Maximum likelihood
Tomography
Monte Carlo simulation
Datasets

Keywords

  • Bayesian reconstruction
  • Emission tomography
  • Lesion detection
  • Maximum a posteriori
  • Penalized maximum-likelihood

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Analysis of Lesion Detectability in Bayesian Emission Reconstruction With Nonstationary Object Variability. / Qi, Jinyi.

In: IEEE Transactions on Medical Imaging, Vol. 23, No. 3, 03.2004, p. 321-329.

Research output: Contribution to journalArticle

@article{6848fd729f0d4606998a1771ed77c507,
title = "Analysis of Lesion Detectability in Bayesian Emission Reconstruction With Nonstationary Object Variability",
abstract = "Bayesian methods based on the maximum a posteriori principle (also called penalized maximum-likelihood methods) have been developed to improve image quality in emission tomography. To explore the full potential of Bayesian reconstruction for lesion detection, we derive simplified theoretical expressions that allow fast evaluation of the detectability of a lesion in Bayesian reconstruction. This work is builded on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers. We explicitly model the nonstationary variation of the lesion and background without assuming that they are locally stationary. The results can be used to choose the optimum prior parameters for the maximum lesion detectability. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predictions and the Monte Carlo results. We also demonstrate that the lesion detectability can be reliably estimated using one noisy data set.",
keywords = "Bayesian reconstruction, Emission tomography, Lesion detection, Maximum a posteriori, Penalized maximum-likelihood",
author = "Jinyi Qi",
year = "2004",
month = "3",
doi = "10.1109/TMI.2004.824239",
language = "English (US)",
volume = "23",
pages = "321--329",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

TY - JOUR

T1 - Analysis of Lesion Detectability in Bayesian Emission Reconstruction With Nonstationary Object Variability

AU - Qi, Jinyi

PY - 2004/3

Y1 - 2004/3

N2 - Bayesian methods based on the maximum a posteriori principle (also called penalized maximum-likelihood methods) have been developed to improve image quality in emission tomography. To explore the full potential of Bayesian reconstruction for lesion detection, we derive simplified theoretical expressions that allow fast evaluation of the detectability of a lesion in Bayesian reconstruction. This work is builded on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers. We explicitly model the nonstationary variation of the lesion and background without assuming that they are locally stationary. The results can be used to choose the optimum prior parameters for the maximum lesion detectability. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predictions and the Monte Carlo results. We also demonstrate that the lesion detectability can be reliably estimated using one noisy data set.

AB - Bayesian methods based on the maximum a posteriori principle (also called penalized maximum-likelihood methods) have been developed to improve image quality in emission tomography. To explore the full potential of Bayesian reconstruction for lesion detection, we derive simplified theoretical expressions that allow fast evaluation of the detectability of a lesion in Bayesian reconstruction. This work is builded on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers. We explicitly model the nonstationary variation of the lesion and background without assuming that they are locally stationary. The results can be used to choose the optimum prior parameters for the maximum lesion detectability. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predictions and the Monte Carlo results. We also demonstrate that the lesion detectability can be reliably estimated using one noisy data set.

KW - Bayesian reconstruction

KW - Emission tomography

KW - Lesion detection

KW - Maximum a posteriori

KW - Penalized maximum-likelihood

UR - http://www.scopus.com/inward/record.url?scp=1642402943&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1642402943&partnerID=8YFLogxK

U2 - 10.1109/TMI.2004.824239

DO - 10.1109/TMI.2004.824239

M3 - Article

VL - 23

SP - 321

EP - 329

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

SN - 0278-0062

IS - 3

ER -