The simplified reference tissue model for SPECT/PET brain receptor studies

2008 ◽  
Vol 47 (04) ◽  
pp. 167-174 ◽  
Author(s):  
F. Thiele ◽  
R. Buchert
Keyword(s):  
Region Of Interest ◽  
Compartment Model ◽  
Receptor Imaging ◽  
Reference Tissue ◽  
Time Activity ◽  
Tissue Model ◽  
Activity Curve ◽  
Reference Tissue Model ◽  
Simplified Reference Tissue Model ◽  
Brain Receptor

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.

scholarly journals Noise Reduction in the Simplified Reference Tissue Model for Neuroreceptor Functional Imaging

2002 ◽  
Vol 22 (12) ◽  
pp. 1440-1452 ◽  
Author(s):  
Yanjun Wu ◽  
Richard E. Carson
Keyword(s):  
Noise Reduction ◽  
Compartment Model ◽  
Binding Potential ◽  
Reference Region ◽  
Reference Tissue ◽  
Tissue Model ◽  
Tissue Compartment ◽  
Functional Images ◽  
Reference Tissue Model ◽  
Simplified Reference Tissue Model

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.

scholarly journals Comparison of Plasma Input and Reference Tissue Models for Analysing [11C]flumazenil Studies

2007 ◽  
Vol 28 (3) ◽  
pp. 579-587 ◽  
Author(s):  
Ursula MH Klumpers ◽  
Dick J Veltman ◽  
Ronald Boellaard ◽  
Emile F Comans ◽  
Cassandra Zuketto ◽  
...  
Keyword(s):  
White Matter ◽  
Rejection Rate ◽  
Compartment Model ◽  
Reference Tissue ◽  
Tissue Model ◽  
Arterial Blood ◽  
Positron Emission ◽  
Reference Tissue Model ◽  
Simplified Reference Tissue Model ◽  
Tissue Models

A single-tissue compartment model with plasma input is the established method for analysing [11C]flumazenil ([11C]FMZ) studies. However, arterial cannulation and measurement of metabolites are time-consuming. Therefore, a reference tissue approach is appealing, but this approach has not been fully validated for [11C]FMZ. Dynamic [11C]FMZ positron emission tomography scans with arterial blood sampling were performed in nine drug-free depressive patients and eight healthy subjects. Regions of interest were defined on co-registered magnetic resonance imaging scans and projected onto dynamic [11C]FMZ images. Using a Hill-type metabolite function, single (1T) and reversible two-tissue (2T) compartmental models were compared. Simplified reference tissue model (SRTM) and full reference tissue model (FRTM) were investigated using both pons and (centrum semiovale) white matter as reference tissue. The 2T model provided the best fit in 59% of cases. Two-tissue VT values were on average 1.6% higher than 1T VT values. Owing to the higher rejection rate of 2T fits (7.3%), the 1T model was selected as plasma input method of choice. SRTM was superior to FRTM, irrespective whether pons or white matter was used as reference tissue. BPND values obtained with SRTM correlated strongly with 1T VT ( r = 0.998 and 0.995 for pons and white matter, respectively). Use of white matter as reference tissue resulted in 5.5% rejected fits, primarily in areas with intermediate receptor density. No fits were rejected using pons as reference tissue. Pons produced 23% higher BPND values than white matter. In conclusion, for most clinical studies, SRTM with pons as reference tissue can be used for quantifying [11C]FMZ binding.

An extended simplified reference tissue model for the quantification of dynamic PET with amphetamine challenge

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

A reference tissue forward model for improved PET accuracy using within-scan displacement studies

2021 ◽  
pp. 0271678X2110652
Author(s):  
Joseph B Mandeville ◽  
Michael A Levine ◽  
John T Arsenault ◽  
Wim Vanduffel ◽  
Bruce R Rosen ◽  
...  
Keyword(s):  
Radioactive Decay ◽  
Forward Model ◽  
Binding Potential ◽  
Reference Tissue ◽  
Primate Model ◽  
Tissue Model ◽  
Pet Tracer ◽  
Reference Tissue Model ◽  
Simplified Reference Tissue Model ◽  
Improved Accuracy

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.

scholarly journals Optimization of Supervised Cluster Analysis for Extracting Reference Tissue Input Curves in (R)-[11C]PK11195 Brain PET Studies

2012 ◽  
Vol 32 (8) ◽  
pp. 1600-1608 ◽  
Author(s):  
Maqsood Yaqub ◽  
Bart NM van Berckel ◽  
Alie Schuitemaker ◽  
Rainer Hinz ◽  
Federico E Turkheimer ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Binding Potential ◽  
Reference Tissue ◽  
Time Activity ◽  
Positron Emission ◽  
Lower Blood ◽  
Activity Curve ◽  
Simplified Reference Tissue Model ◽  
Significant Concentration ◽  
Brain Positron Emission Tomography

Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[11C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimer's disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPM V b). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions ( V b) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18 kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of V b in RPM improved both parametric images and binding potential contrast between groups. Incorporation of V b within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R)-[11C]PK11195 neurodegeneration studies.

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