Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation

Julian C. Matthews; Georgios I. Angelis; Fotis A. Kotasidis; Pawel J. Markiewicz; Andrew J. Reader

doi:10.1109/NSSMIC.2010.5874225

Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation

Julian C. Matthews, Georgios I. Angelis, Fotis A. Kotasidis, Pawel J. Markiewicz, Andrew J. Reader

Research output: Chapter in Book/Conference proceeding › Conference contribution

Abstract

Direct application of the expectation maximisation (EM) algorithm to the spatiotemporal maximum likelihood problem results in a convenient separation of the image based problem from the projection based problem. This enables any spatiotemporal 4D image model to be incorporated into MLEM image reconstruction with relative ease, only requiring tailored calculation of the fitting weights. As a preliminary example, assessment using direct estimation of spectral analysis coefficients is presented, exploiting an image based non-negative least squares algorithm, where a specially-weighted least squares update is equivalent to the required update towards the maximum likelihood estimate. The proposed approach demonstrates a reduced root mean square error (RMSE) in the estimates of volume of distribution. Future work will include the exploration of alternative spatiotemporal models. © 2010 IEEE.

Original language	English
Title of host publication	IEEE Nuclear Science Symposium Conference Record\|IEEE Nucl. Sci. Symp. Conf. Rec.
Pages	2435-2441
Number of pages	6
DOIs	https://doi.org/10.1109/NSSMIC.2010.5874225
Publication status	Published - 2010
Event	2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010 - Knoxville, TN Duration: 1 Jul 2010 → …

Conference

Conference	2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010
City	Knoxville, TN
Period	1/07/10 → …

Keywords

expectation-maximisation algorithm
image reconstruction
least mean squares methods
medical image processing
positron emission tomography
spectral analysis
MLEM image reconstruction
PET reconstruction problem
image based non-negative least square algorithm
maximum likelihood expectation maximisation
parametric image direct reconstruction
root mean square error
spatiotemporal 4D image based model
specially-weighted least squares
spectral analysis coefficients

Access to Document

10.1109/NSSMIC.2010.5874225

Cite this

Matthews, J. C., Angelis, G. I., Kotasidis, F. A., Markiewicz, P. J., & Reader, A. J. (2010). Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation. In IEEE Nuclear Science Symposium Conference Record|IEEE Nucl. Sci. Symp. Conf. Rec. (pp. 2435-2441) https://doi.org/10.1109/NSSMIC.2010.5874225

@inproceedings{f0b5f1515d19460a81956a53c427eb69,

title = "Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation",

abstract = "Direct application of the expectation maximisation (EM) algorithm to the spatiotemporal maximum likelihood problem results in a convenient separation of the image based problem from the projection based problem. This enables any spatiotemporal 4D image model to be incorporated into MLEM image reconstruction with relative ease, only requiring tailored calculation of the fitting weights. As a preliminary example, assessment using direct estimation of spectral analysis coefficients is presented, exploiting an image based non-negative least squares algorithm, where a specially-weighted least squares update is equivalent to the required update towards the maximum likelihood estimate. The proposed approach demonstrates a reduced root mean square error (RMSE) in the estimates of volume of distribution. Future work will include the exploration of alternative spatiotemporal models. {\textcopyright} 2010 IEEE.",

keywords = "expectation-maximisation algorithm, image reconstruction, least mean squares methods, medical image processing, positron emission tomography, spectral analysis, MLEM image reconstruction, PET reconstruction problem, image based non-negative least square algorithm, maximum likelihood expectation maximisation, parametric image direct reconstruction, root mean square error, spatiotemporal 4D image based model, specially-weighted least squares, spectral analysis coefficients",

author = "Matthews, {Julian C.} and Angelis, {Georgios I.} and Kotasidis, {Fotis A.} and Markiewicz, {Pawel J.} and Reader, {Andrew J.}",

year = "2010",

doi = "10.1109/NSSMIC.2010.5874225",

language = "English",

isbn = "9781424491063",

pages = "2435--2441",

booktitle = "IEEE Nuclear Science Symposium Conference Record|IEEE Nucl. Sci. Symp. Conf. Rec.",

note = "2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010 ; Conference date: 01-07-2010",

}

Matthews, JC, Angelis, GI, Kotasidis, FA, Markiewicz, PJ & Reader, AJ 2010, Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation. in IEEE Nuclear Science Symposium Conference Record|IEEE Nucl. Sci. Symp. Conf. Rec.. pp. 2435-2441, 2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010, Knoxville, TN, 1/07/10. https://doi.org/10.1109/NSSMIC.2010.5874225

Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation. / Matthews, Julian C.; Angelis, Georgios I.; Kotasidis, Fotis A. et al.
IEEE Nuclear Science Symposium Conference Record|IEEE Nucl. Sci. Symp. Conf. Rec.. 2010. p. 2435-2441.

Research output: Chapter in Book/Conference proceeding › Conference contribution

TY - GEN

T1 - Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation

AU - Matthews, Julian C.

AU - Angelis, Georgios I.

AU - Kotasidis, Fotis A.

AU - Markiewicz, Pawel J.

AU - Reader, Andrew J.

PY - 2010

Y1 - 2010

N2 - Direct application of the expectation maximisation (EM) algorithm to the spatiotemporal maximum likelihood problem results in a convenient separation of the image based problem from the projection based problem. This enables any spatiotemporal 4D image model to be incorporated into MLEM image reconstruction with relative ease, only requiring tailored calculation of the fitting weights. As a preliminary example, assessment using direct estimation of spectral analysis coefficients is presented, exploiting an image based non-negative least squares algorithm, where a specially-weighted least squares update is equivalent to the required update towards the maximum likelihood estimate. The proposed approach demonstrates a reduced root mean square error (RMSE) in the estimates of volume of distribution. Future work will include the exploration of alternative spatiotemporal models. © 2010 IEEE.

AB - Direct application of the expectation maximisation (EM) algorithm to the spatiotemporal maximum likelihood problem results in a convenient separation of the image based problem from the projection based problem. This enables any spatiotemporal 4D image model to be incorporated into MLEM image reconstruction with relative ease, only requiring tailored calculation of the fitting weights. As a preliminary example, assessment using direct estimation of spectral analysis coefficients is presented, exploiting an image based non-negative least squares algorithm, where a specially-weighted least squares update is equivalent to the required update towards the maximum likelihood estimate. The proposed approach demonstrates a reduced root mean square error (RMSE) in the estimates of volume of distribution. Future work will include the exploration of alternative spatiotemporal models. © 2010 IEEE.

KW - expectation-maximisation algorithm

KW - image reconstruction

KW - least mean squares methods

KW - medical image processing

KW - positron emission tomography

KW - spectral analysis

KW - MLEM image reconstruction

KW - PET reconstruction problem

KW - image based non-negative least square algorithm

KW - maximum likelihood expectation maximisation

KW - parametric image direct reconstruction

KW - root mean square error

KW - spatiotemporal 4D image based model

KW - specially-weighted least squares

KW - spectral analysis coefficients

U2 - 10.1109/NSSMIC.2010.5874225

DO - 10.1109/NSSMIC.2010.5874225

M3 - Conference contribution

SN - 9781424491063

SP - 2435

EP - 2441

BT - IEEE Nuclear Science Symposium Conference Record|IEEE Nucl. Sci. Symp. Conf. Rec.

T2 - 2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010

Y2 - 1 July 2010

ER -

Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this