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Title: Fast computation of statistical uncertainty for spatiotemporal distributions estimated directly from dynamic cone beam SPECT projections

Abstract

The estimation of time-activity curves and kinetic model parameters directly from projection data is potentially useful for clinical dynamic single photon emission computed tomography (SPECT) studies, particularly in those clinics that have only single-detector systems and thus are not able to perform rapid tomographic acquisitions. Because the radiopharmaceutical distribution changes while the SPECT gantry rotates, projections at different angles come from different tracer distributions. A dynamic image sequence reconstructed from the inconsistent projections acquired by a slowly rotating gantry can contain artifacts that lead to biases in kinetic parameters estimated from time-activity curves generated by overlaying regions of interest on the images. If cone beam collimators are used and the focal point of the collimators always remains in a particular transaxial plane, additional artifacts can arise in other planes reconstructed using insufficient projection samples [1]. If the projection samples truncate the patient's body, this can result in additional image artifacts. To overcome these sources of bias in conventional image based dynamic data analysis, we and others have been investigating the estimation of time-activity curves and kinetic model parameters directly from dynamic SPECT projection data by modeling the spatial and temporal distribution of the radiopharmaceutical throughout the projected field of viewmore » [2-8]. In our previous work we developed a computationally efficient method for fully four-dimensional (4-D) direct estimation of spatiotemporal distributions from dynamic SPECT projection data [5], which extended Formiconi's least squares algorithm for reconstructing temporally static distributions [9]. In addition, we studied the biases that result from modeling various orders temporal continuity and using various time samplings [5]. the present work, we address computational issues associated with evaluating the statistical uncertainty of spatiotemporal model parameter estimates, and use Monte Carlo simulations to a fast algorithm for computing the covariance matrix for the parameters. Given this covariance matrix, the covariance between the time-activity curve models for the blood input function and tissue volumes of interest can be calculated and used to estimate compartmental model kinetic parameters more precisely, using nonlinear weighted least squares [10,11].« less

Authors:
; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab., CA (US)
Sponsoring Org.:
USDOE Director, Office of Science. Office of Biological and Environmental Research. Medical Sciences Division; National Institutes of Health (US)
OSTI Identifier:
785286
Report Number(s):
LBNL-47761
R&D Project: 802317; TRN: US0108421
DOE Contract Number:  
AC03-76SF00098
Resource Type:
Conference
Resource Relation:
Conference: The Sixth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pacific Grove, CA (US), 10/30/2001--11/02/2001; Other Information: PBD: 9 Apr 2001
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ALGORITHMS; BLOOD; COLLIMATORS; CONES; DATA ANALYSIS; DISTRIBUTION; KINETICS; NUCLEAR MEDICINE; RADIOLOGY; RADIOPHARMACEUTICALS; SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY

Citation Formats

Reutter, Bryan W., Gullberg, Grant T., and Huesman, Ronald H. Fast computation of statistical uncertainty for spatiotemporal distributions estimated directly from dynamic cone beam SPECT projections. United States: N. p., 2001. Web.
Reutter, Bryan W., Gullberg, Grant T., & Huesman, Ronald H. Fast computation of statistical uncertainty for spatiotemporal distributions estimated directly from dynamic cone beam SPECT projections. United States.
Reutter, Bryan W., Gullberg, Grant T., and Huesman, Ronald H. Mon . "Fast computation of statistical uncertainty for spatiotemporal distributions estimated directly from dynamic cone beam SPECT projections". United States. https://www.osti.gov/servlets/purl/785286.
@article{osti_785286,
title = {Fast computation of statistical uncertainty for spatiotemporal distributions estimated directly from dynamic cone beam SPECT projections},
author = {Reutter, Bryan W. and Gullberg, Grant T. and Huesman, Ronald H.},
abstractNote = {The estimation of time-activity curves and kinetic model parameters directly from projection data is potentially useful for clinical dynamic single photon emission computed tomography (SPECT) studies, particularly in those clinics that have only single-detector systems and thus are not able to perform rapid tomographic acquisitions. Because the radiopharmaceutical distribution changes while the SPECT gantry rotates, projections at different angles come from different tracer distributions. A dynamic image sequence reconstructed from the inconsistent projections acquired by a slowly rotating gantry can contain artifacts that lead to biases in kinetic parameters estimated from time-activity curves generated by overlaying regions of interest on the images. If cone beam collimators are used and the focal point of the collimators always remains in a particular transaxial plane, additional artifacts can arise in other planes reconstructed using insufficient projection samples [1]. If the projection samples truncate the patient's body, this can result in additional image artifacts. To overcome these sources of bias in conventional image based dynamic data analysis, we and others have been investigating the estimation of time-activity curves and kinetic model parameters directly from dynamic SPECT projection data by modeling the spatial and temporal distribution of the radiopharmaceutical throughout the projected field of view [2-8]. In our previous work we developed a computationally efficient method for fully four-dimensional (4-D) direct estimation of spatiotemporal distributions from dynamic SPECT projection data [5], which extended Formiconi's least squares algorithm for reconstructing temporally static distributions [9]. In addition, we studied the biases that result from modeling various orders temporal continuity and using various time samplings [5]. the present work, we address computational issues associated with evaluating the statistical uncertainty of spatiotemporal model parameter estimates, and use Monte Carlo simulations to a fast algorithm for computing the covariance matrix for the parameters. Given this covariance matrix, the covariance between the time-activity curve models for the blood input function and tissue volumes of interest can be calculated and used to estimate compartmental model kinetic parameters more precisely, using nonlinear weighted least squares [10,11].},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2001},
month = {4}
}

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