scholarly journals Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors

2006 ◽  
Vol 2006 ◽  
pp. 1-10 ◽  
Author(s):  
Jinyi Qi
Keyword(s):  
Region Of Interest ◽  
Lesion Detection ◽  
Emission Tomography ◽  
Reconstruction Methods ◽  
Positron Emission ◽  
Hotelling Observer ◽  
Sharp Edges ◽  
Gaussian Prior ◽  
Non Gaussian ◽  
Detection And Quantification

Statistical image reconstruction methods based on maximum a posteriori (MAP) principle have been developed for emission tomography. The prior distribution of the unknown image plays an important role in MAP reconstruction. The most commonly used prior are Gaussian priors, whose logarithm has a quadratic form. Gaussian priors are relatively easy to analyze. It has been shown that the effect of a Gaussian prior can be approximated by linear filtering a maximum likelihood (ML) reconstruction. As a result, sharp edges in reconstructed images are not preserved. To preserve sharp transitions, non-Gaussian priors have been proposed. However, their effect on clinical tasks is less obvious. In this paper, we compare MAP reconstruction with Gaussian and non-Gaussian priors for lesion detection and region of interest quantification using computer simulation. We evaluate three representative priors: Gaussian prior, Huber prior, and Geman-McClure prior. We simulate imaging a prostate tumor using positron emission tomography (PET). The detectability of a known tumor in either a fixed background or a random background is measured using a channelized Hotelling observer. The bias-variance tradeoff curves are calculated for quantification of the total tumor activity. The results show that for the detection and quantification tasks, the Gaussian prior is as effective as non-Gaussian priors.

scholarly journals Preclinical dosimetry models and the prediction of clinical doses of novel positron emission tomography radiotracers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Adam A. Garrow ◽  
Jack P. M. Andrews ◽  
Zaniah N. Gonzalez ◽  
Carlos A. Corral ◽  
Christophe Portal ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Human Studies ◽  
Emission Tomography ◽  
Whole Body ◽  
Preclinical Model ◽  
Reconstruction Method ◽  
Preclinical Research ◽  
Reconstruction Methods ◽  
Positron Emission ◽  
Pet Radiotracers

Abstract Dosimetry models using preclinical positron emission tomography (PET) data are commonly employed to predict the clinical radiological safety of novel radiotracers. However, unbiased clinical safety profiling remains difficult during the translational exercise from preclinical research to first-in-human studies for novel PET radiotracers. In this study, we assessed PET dosimetry data of six 18F-labelled radiotracers using preclinical dosimetry models, different reconstruction methods and quantified the biases of these predictions relative to measured clinical doses to ease translation of new PET radiotracers to first-in-human studies. Whole-body PET images were taken from rats over 240 min after intravenous radiotracer bolus injection. Four existing and two novel PET radiotracers were investigated: [18F]FDG, [18F]AlF-NOTA-RGDfK, [18F]AlF-NOTA-octreotide ([18F]AlF-NOTA-OC), [18F]AlF-NOTA-NOC, [18F]ENC2015 and [18F]ENC2018. Filtered-back projection (FBP) and iterative methods were used for reconstruction of PET data. Predicted and true clinical absorbed doses for [18F]FDG and [18F]AlF-NOTA-OC were then used to quantify bias of preclinical model predictions versus clinical measurements. Our results show that most dosimetry models were biased in their predicted clinical dosimetry compared to empirical values. Therefore, normalization of rat:human organ sizes and correction for reconstruction method biases are required to achieve higher precision of dosimetry estimates.

scholarly journals 11C-Diprenorphine Binding in Huntington's Disease: A Comparison of Region of Interest Analysis with Statistical Parametric Mapping

1997 ◽  
Vol 17 (9) ◽  
pp. 943-949 ◽  
Author(s):  
Robert A. Weeks ◽  
Vincent J. Cunningham ◽  
Paola Piccini ◽  
Simon Waters ◽  
Anita E. Harding ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Statistical Parametric Mapping ◽  
Region Of Interest ◽  
Emission Tomography ◽  
Z Score ◽  
Parametric Images ◽  
Positron Emission ◽  
Opioid Binding ◽  
Static Images ◽  
Analytical Approaches

We compare region of interest (ROI) analytical approaches with statistical parametric mapping (SPM) of 11C-diprenorphine positron emission tomography findings in five patients with Huntington's disease (HD) and nine age-matched controls. The ROI were placed on caudate, putamen, and an occipital reference area. Ratios of striatal–occipital uptake from averaged static images centered at 60 minutes showed a mean 20% reduction in caudate ( P = 0.034) and 15% reduction in putamen ( P = 0.095) receptor binding in the HD patients. Dynamic data from caudate and putamen ROI, together with a plasma tracer input function, were analyzed using spectral analysis to give regional impulse response functions. Regional data at 60 minutes after impulse showed a mean 29% decrease in caudate ( P = 0.006) and 23% decrease in putamen ( P = 0.029) opioid binding in the HD cohort. Parametric images of tracer binding also were produced with spectral analysis on a voxel basis. The images of the unit impulse response function at 60 minutes showed a mean 31 % decrease in caudate ( P = 0.005) and a 26% decrease in putamen binding ( P = 0.011) in HD. The voxel-based parametric images were transformed into standard stereotactic space, and a between-group comparison (patient versus controls) was performed with SPM. This approach revealed symmetrical decreases in caudate (peak 40% decrease, z score = 4.38) and putamen opioid binding (peak 24% decrease, z score = 4.686) with additional nonhypothesized changes in cingulate, prefrontal, and thalamic areas. The significance and precision of changes measured with spectral analysis applied to dynamic data sets were superior to ROI-based ratio analysis on static images. The SPM replicated the striatal reductions in opioid binding in HD and detected additional nonpredicted changes. This study suggests that SPM is a valid alternative to conventional ROI analytical approaches for determining binding changes with positron emission tomography and may have advantages over region-based analyses in exploratory studies.

Tracer kinetic model of regional pulmonary function using positron emission tomography

2002 ◽  
Vol 93 (3) ◽  
pp. 1104-1114 ◽  
Author(s):  
Gaetano G. Galletti ◽  
José G. Venegas
Keyword(s):  
Positron Emission Tomography ◽  
Pulmonary Function ◽  
Goodness Of Fit ◽  
Molecular Nitrogen ◽  
Region Of Interest ◽  
Emission Tomography ◽  
Gas Content ◽  
Pulmonary Vasculature ◽  
Model Parameters ◽  
Positron Emission

To determine the spatial distributions of pulmonary perfusion, shunt, and ventilation, we developed a compartmental model of regional 13N-labeled molecular nitrogen (13NN) kinetics measured from positron emission tomography (PET) images. The model features a compartment for right heart and pulmonary vasculature and two compartments for each region of interest: 1) aerated alveolar units and 2) alveolar units with no gas content (shunting). The model was tested on PET data from normal animals (dogs and sheep) and from animals with experimentally injured lungs simulating acute respiratory distress syndrome. The analysis yielded estimates of regional perfusion, shunt fraction, and specific ventilation with excellent goodness-of-fit to the data ( R 2 > 0.99). Model parameters were estimated to within 10% accuracy in the presence of exaggerated levels of experimental noise by using a Monte Carlo sensitivity analysis. Main advantages of the present model are that 1) it separates intraregional blood flow to aerated alveolar units from that shunting across nonaerated units and 2) it accounts and corrects for intraregional tracer removal by shunting blood when estimating ventilation from subsequent washout of tracer. The model was thus found to provide estimates of regional parameters of pulmonary function in sizes of lung regions that could potentially approach the intrinsic resolution for PET images of 13NN in lung (∼7.0 mm for a multiring PET camera).

scholarly journals CROSS-REFERENCE MAXIMUM LIKELIHOOD ESTIMATE RECONSTRUCTION FOR POSITRON EMISSION TOMOGRAPHY

2001 ◽  
Vol 13 (04) ◽  
pp. 190-198 ◽  
Author(s):  
CHUNG-MING CHEN ◽  
HENRY HORNG-SHING LU ◽  
YUN-PAI HSU
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimate ◽  
Region Of Interest ◽  
Likelihood Estimate ◽  
Emission Tomography ◽  
Estimation Errors ◽  
Structure Information ◽  
Positron Emission ◽  
Monte Carlo Studies ◽  
Cross Reference

Maximum likelihood estimate (MLE) is a widely used approach for PET image reconstruction. However, it has been shown that reconstructing emission tomography based on MLE without regularization would result in noise and edge artifacts. In the attempt to regularize the maximum likelihood estimate, we propose a new and efficient method in this paper to incorporate the correlated but possibly incomplete structure information which may be derived from expertise, PET systems or other imaging modalities. A mean estimate smoothing the MLE locally within each region of interest is derived according to the boundaries provided by the structure information. Since the boundaries may not be correct, a penalized MLE using the mean estimate is sought. The resulting reconstruction is called a cross-reference maximum likelihood estimate (CRMLE). The CRMLE can be obtained through a modified EM algorithm, which is computation and storage efficient. By borrowing the strength from the correct portion of boundary information, the CRMLE is able to extract the useful information to improve reconstruction for different kinds of incomplete and incorrect boundaries in Monte Carlo studies. The proposed CRMLE algorithm not only reduces the estimation errors, but also preserves the correct boundaries. The penalty parameters can be selected through human interactions or automatically data-driven methods, such as the generalized cross validation method.

scholarly journals Interpolation-Based Reconstruction Methods for Tomographic Imaging in 3D Positron Emission Tomography

2008 ◽  
Vol 18 (1) ◽  
pp. 63-73 ◽  
Author(s):  
Yingbo Li ◽  
Anton Kummert ◽  
Fritz Boschen ◽  
Hans Herzog
Keyword(s):  
Positron Emission Tomography ◽  
Imaging Techniques ◽  
Tomographic Imaging ◽  
Emission Tomography ◽  
Projection Data ◽  
Valuable Insight ◽  
Two Dimensional ◽  
Reconstruction Procedure ◽  
Reconstruction Methods ◽  
Positron Emission

Interpolation-Based Reconstruction Methods for Tomographic Imaging in 3D Positron Emission TomographyPositron Emission Tomography (PET) is considered a key diagnostic tool in neuroscience, by means of which valuable insight into the metabolism functionin vivomay be gained. Due to the underlying physical nature of PET, 3D imaging techniques in terms of a 3D measuring mode are intrinsically demanded to assure satisfying resolutions of the reconstructed images. However, incorporating additional cross-plane measurements, which are specific for the 3D measuring mode, usually imposes an excessive amount of projection data and significantly complicates the reconstruction procedure. For this reason, interpolation-based reconstruction methods deserve a thorough investigation, whose crucial parts are the interpolating processes in the 3D frequency domain. The benefit of such approaches is apparently short reconstruction duration, which can, however, only be achieved at the expense of accepting the inaccuracies associated with the interpolating process. In the present paper, two distinct approaches to the realization of the interpolating procedure are proposed and analyzed. The first one refers to a direct approach based on linear averaging (inverse distance weighting), and the second one refers to an indirect approach based on two-dimensional convolution (gridding method). In particular, attention is paid to two aspects of the gridding method. The first aspect is the choice of the two-dimensional convolution function applied, and the second one is the correct discretization of the underlying continuous convolution. In this respect, the geometrical structure named the Voronoi diagram and its computational construction are considered. At the end, results of performed simulation studies are presented and discussed.

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