Direct 4D PET MLEM reconstruction of parametric images using the simplified reference tissue model with the basis function method for [11C]raclopride

2015 ◽  
Vol 60 (11) ◽  
pp. 4533-4549 ◽  
Author(s):  
Paul Gravel ◽  
Andrew J Reader
2019 ◽  
Vol 40 (4) ◽  
pp. 895-903 ◽  
Author(s):  
Thomas Koopman ◽  
Niels Verburg ◽  
Petra JW Pouwels ◽  
Pieter Wesseling ◽  
Otto S Hoekstra ◽  
...  

Quantitative parametric images of O-(2-[18F]fluoroethyl)-L-tyrosine kinetics in diffuse gliomas could be used to improve glioma grading, tumour delineation or the assessment of the uptake distribution of this positron emission tomography tracer. In this study, several parametric images and tumour-to-normal maps were compared in terms of accuracy of region averages (when compared to results from nonlinear regression of a reversible two-tissue compartment plasma input model) and image noise using 90 min of dynamic scan data acquired in seven patients with diffuse glioma. We included plasma input methods (the basis function implementation of the single-tissue compartment model, spectral analysis and Logan graphical analysis) and reference tissue methods (basis function implementations of the simplified reference tissue model, variations of the multilinear reference tissue model and non-invasive Logan graphical analysis) as well as tumour-to-normal ratio maps at three intervals. (Non-invasive) Logan graphical analysis provided volume of distribution maps and distribution volume ratio maps with the lowest level of noise, while the basis function implementations provided the best accuracy. Tumour-to-normal ratio maps provided better results if later interval times were used, i.e. 60–90 min instead of 20–40 min, leading to lower bias (2.9% vs. 10.8%, respectively) and less noise (12.8% vs. 14.4%).


NeuroImage ◽  
2006 ◽  
Vol 33 (2) ◽  
pp. 550-563 ◽  
Author(s):  
Yun Zhou ◽  
Ming-Kai Chen ◽  
Christopher J. Endres ◽  
Weiguo Ye ◽  
James R. Brašić ◽  
...  

2021 ◽  
pp. 0271678X2110652
Author(s):  
Joseph B Mandeville ◽  
Michael A Levine ◽  
John T Arsenault ◽  
Wim Vanduffel ◽  
Bruce R Rosen ◽  
...  

We report a novel forward-model implementation of the full reference tissue model (fFTRM) that addresses the fast-exchange approximation employed by the simplified reference tissue model (SRTM) by incorporating a non-zero dissociation time constant from the specifically bound compartment. The forward computational approach avoided errors associated with noisy and nonorthogonal basis functions using an inverse linear model. Compared to analysis by a multilinear single-compartment reference tissue model (MRTM), fFTRM provided improved accuracy for estimation of binding potentials at early times in the scan, with no worse reproducibility across sessions. To test the model’s ability to identify small focal changes in binding potential using a within-scan challenge, we employed a nonhuman primate model of focal dopamine release elicited by deep brain microstimulation remote to ventral striatum (VST) during imaging by simultaneous PET and fMRI. The new model reported an unambiguously lateralized response in VST consistent with fMRI, whereas the MRTM-derived response was not lateralized and was consistent with simulations of model bias. The proposed model enabled better accuracy in PET [11C]raclopride displacement studies and may also facilitate challenges sooner after injection, thereby recovering some sensitivity lost to radioactive decay of the PET tracer.


2008 ◽  
Vol 47 (04) ◽  
pp. 167-174 ◽  
Author(s):  
F. Thiele ◽  
R. Buchert

SummaryAim: The SRTM (simplified reference tissue model) of brain receptor imaging assumes that the time activity curve in the receptor-rich region of interest can be fitted satisfactorily by the 1-tissue compartment model. This assumption has been formulated by a rather restrictive constraint on the rate constants. Empirically, the SRTM might well describe also tracers which do not fulfil this constraint, such as [11C]raclopride, for example. However, this has not been justified rigorously. Methods: The requirements for the SRTM to be applicable are analyzed in detail. Results: The SRTM is applicable under a less restrictive constraint than described previously. The interpretation of the SRTM parameters R1 and K2 in physiological terms depends on the constraint, while the interpretation of BPND does not. Conclusion: Correct interpretation of the results of the SRTM is tracer specific. In particular, the parameter R1, which in case of compliance with the original constraint might be used to detect perfusion and/or extraction effects, might not be appropriate for this purpose in case of raclopride-like tracers.


2002 ◽  
Vol 22 (12) ◽  
pp. 1440-1452 ◽  
Author(s):  
Yanjun Wu ◽  
Richard E. Carson

The Simplified Reference Tissue Model (SRTM) produces functional images of receptor binding parameters using an input function derived from a reference region and assuming a model with one tissue compartment. Three parameters are estimated: binding potential ( BP), relative delivery ( R1), and the reference region clearance constant k′2 Since k′2 should not vary across brain pixels, the authors developed a two-step method (SRTM2) using a global value of k′2. Whole-brain simulations were performed using human input functions and rate constants for [18F]FCWAY, [11C]flumazenil, and [11C]raclopride, and parameter SD and bias were determined for SRTM and SRTM2. The global mean of k′2 was slightly biased (2% to 6%), but the median was unbiased (<1%) and was used as the global value. Binding potential noise reductions with SRTM2 were 4% to 14%, 20% to 53%, and 10% to 30% for [18F]FCWAY, [11C]flumazenil, and [11C]raclopride, respectively, with larger reductions for shorter scans. R1 noise reduction was larger than that of BP. Simulations were also performed to assess bias when the reference and/or tissue regions followed a two-tissue compartment model. Owing to the constrained k′2, SRTM2 showed somewhat larger biases due to violations of the one-compartment model assumption. These studies demonstrate that SRTM2 should be a useful method to improve the quality of neuroreceptor functional images.


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