Bayesian image decomposition applied to relaxographic imaging

Truman R. Brown, Michael F. Ochs, William D. Rooney, Radka Stoyanova, Xin Li, Charles S. Springer

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

T 1 relaxographic imaging is a precise and accurate way to characterize tissue. A number of fast MRI acquisition techniques allow both spatial and magnetization recoveries to be well sampled in reasonable imaging times. However, two limitations common to the analysis of relaxographic imaging data are (1) the assumption of single exponential behavior for each image voxel and (2) the treatment of each pixel as an independent entity. The first assumption disregards tissue heterogeneity known to be present and reduces the information content that can be extracted. The latter assumption reduces both the modeling stability and the accuracy of extracted parameters. A new method that overcomes these limitations is presented here. The method, Bayesian Image Decomposition, recovers individual tissue type magnetization recovery curves and their corresponding tissuespecific relaxographic images (i.e. segmented images) from a series of inversion recovery images. The general form of the decomposition is given together with its specific implementation to longitudinal relaxographic imaging. The method is validated by comparison of the results with those of the standard method and by comparison across data sets. A specific advantage of the new method is the ability to determine fractional contributions of tissue subtypes to each image voxel.

Original languageEnglish
Pages (from-to)2-9
Number of pages8
JournalInternational Journal of Imaging Systems and Technology
Volume15
Issue number1
DOIs
StatePublished - Oct 5 2005
Externally publishedYes

Fingerprint

Tissue
Decomposition
Imaging techniques
decomposition
Recovery
Magnetization
recovery
Magnetic resonance imaging
magnetization
Pixels
acquisition
pixels
inversions
curves

Keywords

  • Brain
  • Intravoxel segmentation
  • Inversion recovery
  • Matrix decomposition
  • MRI
  • Multiple sclerosis
  • Super-resolution

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition

Cite this

Bayesian image decomposition applied to relaxographic imaging. / Brown, Truman R.; Ochs, Michael F.; Rooney, William D.; Stoyanova, Radka; Li, Xin; Springer, Charles S.

In: International Journal of Imaging Systems and Technology, Vol. 15, No. 1, 05.10.2005, p. 2-9.

Research output: Contribution to journalArticle

Brown, Truman R. ; Ochs, Michael F. ; Rooney, William D. ; Stoyanova, Radka ; Li, Xin ; Springer, Charles S. / Bayesian image decomposition applied to relaxographic imaging. In: International Journal of Imaging Systems and Technology. 2005 ; Vol. 15, No. 1. pp. 2-9.
@article{e828d4b0d330495d866e9954c8d5f0a5,
title = "Bayesian image decomposition applied to relaxographic imaging",
abstract = "T 1 relaxographic imaging is a precise and accurate way to characterize tissue. A number of fast MRI acquisition techniques allow both spatial and magnetization recoveries to be well sampled in reasonable imaging times. However, two limitations common to the analysis of relaxographic imaging data are (1) the assumption of single exponential behavior for each image voxel and (2) the treatment of each pixel as an independent entity. The first assumption disregards tissue heterogeneity known to be present and reduces the information content that can be extracted. The latter assumption reduces both the modeling stability and the accuracy of extracted parameters. A new method that overcomes these limitations is presented here. The method, Bayesian Image Decomposition, recovers individual tissue type magnetization recovery curves and their corresponding tissuespecific relaxographic images (i.e. segmented images) from a series of inversion recovery images. The general form of the decomposition is given together with its specific implementation to longitudinal relaxographic imaging. The method is validated by comparison of the results with those of the standard method and by comparison across data sets. A specific advantage of the new method is the ability to determine fractional contributions of tissue subtypes to each image voxel.",
keywords = "Brain, Intravoxel segmentation, Inversion recovery, Matrix decomposition, MRI, Multiple sclerosis, Super-resolution",
author = "Brown, {Truman R.} and Ochs, {Michael F.} and Rooney, {William D.} and Radka Stoyanova and Xin Li and Springer, {Charles S.}",
year = "2005",
month = "10",
day = "5",
doi = "10.1002/ima.20033",
language = "English",
volume = "15",
pages = "2--9",
journal = "International Journal of Imaging Systems and Technology",
issn = "0899-9457",
publisher = "John Wiley and Sons Inc.",
number = "1",

}

TY - JOUR

T1 - Bayesian image decomposition applied to relaxographic imaging

AU - Brown, Truman R.

AU - Ochs, Michael F.

AU - Rooney, William D.

AU - Stoyanova, Radka

AU - Li, Xin

AU - Springer, Charles S.

PY - 2005/10/5

Y1 - 2005/10/5

N2 - T 1 relaxographic imaging is a precise and accurate way to characterize tissue. A number of fast MRI acquisition techniques allow both spatial and magnetization recoveries to be well sampled in reasonable imaging times. However, two limitations common to the analysis of relaxographic imaging data are (1) the assumption of single exponential behavior for each image voxel and (2) the treatment of each pixel as an independent entity. The first assumption disregards tissue heterogeneity known to be present and reduces the information content that can be extracted. The latter assumption reduces both the modeling stability and the accuracy of extracted parameters. A new method that overcomes these limitations is presented here. The method, Bayesian Image Decomposition, recovers individual tissue type magnetization recovery curves and their corresponding tissuespecific relaxographic images (i.e. segmented images) from a series of inversion recovery images. The general form of the decomposition is given together with its specific implementation to longitudinal relaxographic imaging. The method is validated by comparison of the results with those of the standard method and by comparison across data sets. A specific advantage of the new method is the ability to determine fractional contributions of tissue subtypes to each image voxel.

AB - T 1 relaxographic imaging is a precise and accurate way to characterize tissue. A number of fast MRI acquisition techniques allow both spatial and magnetization recoveries to be well sampled in reasonable imaging times. However, two limitations common to the analysis of relaxographic imaging data are (1) the assumption of single exponential behavior for each image voxel and (2) the treatment of each pixel as an independent entity. The first assumption disregards tissue heterogeneity known to be present and reduces the information content that can be extracted. The latter assumption reduces both the modeling stability and the accuracy of extracted parameters. A new method that overcomes these limitations is presented here. The method, Bayesian Image Decomposition, recovers individual tissue type magnetization recovery curves and their corresponding tissuespecific relaxographic images (i.e. segmented images) from a series of inversion recovery images. The general form of the decomposition is given together with its specific implementation to longitudinal relaxographic imaging. The method is validated by comparison of the results with those of the standard method and by comparison across data sets. A specific advantage of the new method is the ability to determine fractional contributions of tissue subtypes to each image voxel.

KW - Brain

KW - Intravoxel segmentation

KW - Inversion recovery

KW - Matrix decomposition

KW - MRI

KW - Multiple sclerosis

KW - Super-resolution

UR - http://www.scopus.com/inward/record.url?scp=25444522277&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=25444522277&partnerID=8YFLogxK

U2 - 10.1002/ima.20033

DO - 10.1002/ima.20033

M3 - Article

AN - SCOPUS:25444522277

VL - 15

SP - 2

EP - 9

JO - International Journal of Imaging Systems and Technology

JF - International Journal of Imaging Systems and Technology

SN - 0899-9457

IS - 1

ER -