scholarly journals Parametric Mapping of [18F]Fluoromisonidazole Positron Emission Tomography using Basis Functions

2010 ◽  
Vol 31 (2) ◽  
pp. 648-657 ◽  
Author(s):  
Young T Hong ◽  
John S Beech ◽  
Rob Smith ◽  
Jean-Claude Baron ◽  
Tim D Fryer
Keyword(s):  
Positron Emission Tomography ◽  
Kinetic Parameters ◽  
Simulated Data ◽  
Nonlinear Least Squares ◽  
Real Data ◽  
Compartment Model ◽  
Emission Tomography ◽  
Positron Emission ◽  
Lower Variability ◽  
Parametric Maps

In this study, we show a basis function method (BAFPIC) for voxelwise calculation of kinetic parameters ( K1, k2, k3, Ki) and blood volume using an irreversible two-tissue compartment model. BAFPIC was applied to rat ischaemic stroke micro-positron emission tomography data acquired with the hypoxia tracer [18F]fluoromisonidazole because irreversible two-tissue compartmental modelling provided good fits to data from both hypoxic and normoxic tissues. Simulated data show that BAFPIC produces kinetic parameters with significantly lower variability and bias than nonlinear least squares (NLLS) modelling in hypoxic tissue. The advantage of BAFPIC over NLLS is less pronounced in normoxic tissue. Ki determined from BAFPIC has lower variability than that from the Patlak–Gjedde graphical analysis (PGA) by up to 40% and lower bias, except for normoxic tissue at mid-high noise levels. Consistent with the simulation results, BAFPIC parametric maps of real data suffer less noise-induced variability than do NLLS and PGA. Delineation of hypoxia on BAFPIC k3 maps is aided by low variability in normoxic tissue, which matches that in Ki maps. BAFPIC produces Ki values that correlate well with those from PGA ( r2 = 0.93 to 0.97; slope 0.99 to 1.05, absolute intercept < 0.00002 mL/g per min). BAFPIC is a computationally efficient method of determining parametric maps with low bias and variance.

A New Approach to Image Reconstruction in Positron Emission Tomography Using Artificial Neural Networks

1998 ◽  
Vol 09 (01) ◽  
pp. 71-85 ◽  
Author(s):  
A. Bevilacqua ◽  
D. Bollini ◽  
R. Campanini ◽  
N. Lanconelli ◽  
M. Galli
Keyword(s):  
Positron Emission Tomography ◽  
Simulated Data ◽  
Emission Tomography ◽  
Source Distribution ◽  
New Approach ◽  
High Data ◽  
Positron Emission ◽  
Physical Effects ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study investigates the possibility of using an Artificial Neural Network (ANN) for reconstructing Positron Emission Tomography (PET) images. The network is trained with simulated data which include physical effects such as attenuation and scattering. Once the training ends, the weights of the network are held constant. The network is able to reconstruct every type of source distribution contained inside the area mapped during the learning. The reconstruction of a simulated brain phantom in a noiseless case shows an improvement if compared with Filtered Back-Projection reconstruction (FBP). In noisy cases there is still an improvement, even if we do not compensate for noise fluctuations. These results show that it is possible to reconstruct PET images using ANNs. Initially we used a Dec Alpha; then, due to the high data parallelism of this reconstruction problem, we ported the learning on a Quadrics (SIMD) machine, suited for the realization of a small medical dedicated system. These results encourage us to continue in further studies that will make possible reconstruction of images of bigger dimension than those used in the present work (32 × 32 pixels).

Robustness of Anatomically Guided Pixel-by-Pixel Algorithms for Partial Volume Effect Correction in Positron Emission Tomography

1999 ◽  
Vol 19 (5) ◽  
pp. 547-559 ◽  
Author(s):  
Daniel Strul ◽  
Bernard Bendriem
Keyword(s):  
Positron Emission Tomography ◽  
Real Data ◽  
Volume Effect ◽  
Performance Criterion ◽  
Three Dimensions ◽  
Emission Tomography ◽  
Tissue Segmentation ◽  
Positron Emission ◽  
Current Article ◽  
Effective Use

Several algorithms have been proposed to improve positron emission tomography quantification by combining anatomical and functional information in a pixel-by-pixel correction scheme. The precision of these methods when applied to real data depends on the precision of the manifold correction steps, such as full-width half-maximum modeling, magnetic resonance imaging-positron emission tomography registration, tissue segmentation, or background activity estimation. A good understanding of the influence of these parameters thus is critical to the effective use of the algorithms. In the current article, the authors present a monodimensional model that allows a simple theoretical and experimental evaluation of correction imprecision. The authors then assess correction robustness in three dimensions with computer simulations, and evaluate the validity of regional SD as a correction performance criterion.

scholarly journals Quantitation of Translocator Protein Binding in Human Brain with the Novel Radioligand [18F]-FEPPA and Positron Emission Tomography

2011 ◽  
Vol 31 (8) ◽  
pp. 1807-1816 ◽  
Author(s):  
Pablo M Rusjan ◽  
Alan A Wilson ◽  
Peter M Bloomfield ◽  
Irina Vitcu ◽  
Jeffrey H Meyer ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Human Brain ◽  
Specific Binding ◽  
Compartment Model ◽  
Distribution Volume ◽  
Translocator Protein ◽  
Emission Tomography ◽  
Binding Potential ◽  
Specific Distribution ◽  
Positron Emission

This article describes the kinetic modeling of [18F]-FEPPA binding to translocator protein 18 kDa in the human brain using high-resolution research tomograph (HRRT) positron emission tomography. Positron emission tomography scans were performed in 12 healthy volunteers for 180 minutes. A two-tissue compartment model (2-CM) provided, with no exception, better fits to the data than a one-tissue model. Estimates of total distribution volume ( VT), specific distribution volume ( VS), and binding potential ( BPND) demonstrated very good identifiability (based on coefficient of variation ( COV)) for all the regions of interest (ROIs) in the gray matter ( COV VT < 7%, COV VS < 8%, COV BPND < 11%). Reduction of the length of the scan to 2 hours is feasible as VS and VT showed only a small bias (6% and 7.5%, respectively). Monte Carlo simulations showed that, even under conditions of a 500% increase in specific binding, the identifiability of VT and VS was still very good with COV<10%, across high-uptake ROIs. The excellent identifiability of VT values obtained from an unconstrained 2-CM with data from a 2-hour scan support the use of VT as an appropriate and feasible outcome measure for [18F]-FEPPA.

scholarly journals Measurement of Regional Cerebral pH in Human Subjects Using Continuous Inhalation of 11CO2 and Positron Emission Tomography

1984 ◽  
Vol 4 (3) ◽  
pp. 458-465 ◽  
Author(s):  
David J. Brooks ◽  
Adriaan A. Lammertsma ◽  
Ronald P. Beaney ◽  
Klaus L. Leenders ◽  
Peter D. Buckingham ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Human Subjects ◽  
Compartment Model ◽  
Emission Tomography ◽  
Inhalation Technique ◽  
Positron Emission ◽  
Brain Ph ◽  
Normal Human ◽  
Uptake Data ◽  
Arterial Plasma

The cerebral pH of four normal human subjects has been measured using continuous inhalation of 11CO2 and positron emission tomography (PET). 11CO2 was administered to each subject at a constant rate for 15 min, during which time serial arterial plasma 11C levels were determined and serial 11C cerebral uptake PET scans were performed at a fixed axial tomographic level. 11C uptake kinetics were analysed using a three-compartment model. Rate constants have been estimated for the free exchange of 11CO2 between plasma and cerebral compartments for each subject, and their cerebral pH calculated. Whole brain pH values ranged from 6.96 to 7.05, and no significant pH difference between regions containing predominantly grey or white matter was noted. Best fits to 11C uptake data were achieved by effectively neglecting the metabolic fixation of 11C by cerebral tissue. The purpose of this study was to test the feasibility of pH measurement using the 11CO2 continuous inhalation technique. It is concluded from the results and the error analysis that continuous 11CO2 inhalation combined with PET is potentially a simple and useful method for determining regional cerebral pH.

scholarly journals Measurement of Benzodiazepine Receptor Number and Affinity in Humans Using Tracer Kinetic Modeling, Positron Emission Tomography, and [11C]Flumazenil

1993 ◽  
Vol 13 (4) ◽  
pp. 656-667 ◽  
Author(s):  
Julie C. Price ◽  
Helen S. Mayberg ◽  
Robert F. Dannals ◽  
Alan A. Wilson ◽  
Hayden T. Ravert ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Specific Activity ◽  
Free Fraction ◽  
Benzodiazepine Receptor ◽  
Occipital Cortex ◽  
Compartment Model ◽  
Brain Regions ◽  
Emission Tomography ◽  
Positron Emission ◽  
Convergence Problems

Kinetic methods were used to obtain regional estimates of benzodiazepine receptor concentration ( Bmax) and equilibrium dissociation constant ( Kd) from high and low specific activity (SA) [11C]flumazenil ([11C] Ro 15-1788) positron emission tomography studies of five normal volunteers. The high and low SA data were simultaneously fit to linear and nonlinear three-compartment models, respectively. An additional inhibition study (pretreatment with 0.15 mg/kg of flumazenil) was performed on one of the volunteers, which resulted in an average gray matter K1/ k2 estimate of 0.68 ± 0.08 ml/ml (linear three-compartment model, nine brain regions). The free fraction of flumazenil in plasma ( f1) was determined for each study (high SA f1: 0.50 ± 0.03; low SA f1: 0.48 ± 0.05). The free fraction in brain ( f2) was calculated using the inhibition K1/ k2 ratio and each volunteer's mean f1 value ( f2 across volunteers = 0.72 ± 0.03 ml/ml). Three methods (Methods I–III) were examined. Method I determined five kinetic parameters simultaneously [ K1, k2, k3 (= kon f2 Bmax), k4, and kon f2/SA] with no a priori constraints. An average kon value of 0.030 ± 0.003 n M−1 min−1 was estimated for receptor-rich regions using Method I. In Methods II and III, the kon f2/SA parameter was specifically constrained using the Method I value of kon and the volunteer's values of f2 and low SA (Ci/μmol). Four parameters were determined simultaneously using Method II. In Method III, K1/ k2 was fixed to the inhibition value and only three parameters were estimated. Method I provided the most variable results and convergence problems for regions with low receptor binding. Method II provided results that were less variable but very similar to the Method I results, without convergence problems. However, the K1/ k2 ratios obtained by Method II ranged from 1.07 in the occipital cortex to 0.61 in the thalamus. Fixing the K1/ k2 ratio in Method III provided a method that was physiologically consistent with the fixed value of f2 and resulted in parameters with considerably lower variability. The average Bmax values obtained using Method III were 100 ± 25 n M in the occipital cortex, 64 ±18 n M in the cerebellum, and 38 ± 5.5 n M in the thalamus; the average Kd was 8.9 ± 1.0 n M (five brain regions).

scholarly journals Erratum to: Quantitative 18F-fluorocholine positron emission tomography for prostate cancer: correlation between kinetic parameters and Gleason scoring

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Joshua D. Schaefferkoetter ◽  
Ziting Wang ◽  
Mary C. Stephenson ◽  
Sharmili Roy ◽  
Maurizio Conti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Positron Emission Tomography ◽  
Kinetic Parameters ◽  
Emission Tomography ◽  
Positron Emission

scholarly journals Evaluation of Regional Differences of Tracer Appearance Time in Cerebral Tissues Using [15O]Water and Dynamic Positron Emission Tomography

1988 ◽  
Vol 8 (2) ◽  
pp. 285-288 ◽  
Author(s):  
H. Iida ◽  
S. Higano ◽  
N. Tomura ◽  
F Shishido ◽  
I. Kanno ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Human Subjects ◽  
Sampling Site ◽  
Nonlinear Least Squares ◽  
Correction Method ◽  
Emission Tomography ◽  
Appearance Time ◽  
Dynamic Positron Emission Tomography ◽  
Fitting Procedure ◽  
Positron Emission

The tracer appearance time relative to the radial artery–sampling site has been evaluated in six brain locations in five human subjects using dynamic positron emission tomography (PET) following the bolus injection of H215O. There was a maximum difference of ± 2 s from the average in each location. T o globally adjust the timing difference between the measured arterial curve and the PET scan, a correction method was developed based on a nonlinear least-squares fitting procedure. This new technique determined the global time delay with an accuracy of ± 0.5 s. On the other hand, the linear backward extrapolation method resulted in a systematic error of 4 s.

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