Emission Tomography Quantification Research Articles

Reliable quantification of myocardial sympathetic innervation and regional denervation using [11C]meta-hydroxyephedrine PET.

Cardiac sympathetic nervous system (SNS) dysfunction is associated with poor prognosis in chronic heart failure patients. This study characterized the reproducibility and repeatability of [11C]meta-hydroxyephedrine (HED) positron emission tomography (PET) quantification of cardiac SNS innervation, regional denervation, and myocardial blood flow (MBF). Dynamic HED PET-CT scans were performed 47 ± 22days apart in 20 patients with stable heart failure and reduced ejection fraction. Three observers, blinded to clinical data, used FlowQuant® to evaluate the test-retest repeatability and inter- and intra-observer reproducibility of HED tracer uptake and clearance rates to measure global (LV-mean) retention index (RI), volume of distribution (VT), and MBF. Values were also compared with and without regional partial-volume correction. Regional denervation was quantified as %LV defect size of values < 75% of the LV-maximum. Test-retest repeatability and observer reproducibility were evaluated using intra-class-correlation (ICC) and Bland-Altman coefficient of repeatability (NPC). Intra- and inter-observer correlations of both VT and RI were excellent (ICC = 0.93-0.99). Observer reproducibility (NPC = 3-13%) was lower than test-retest repeatability (NPC = 12-61%). Both regional (%LV defect size) and global (LV-mean) measures of sympathetic innervation were more repeatable using the simple RI model compared to VT (NPC = 12% vs. 19% and 30% vs. 54%). Using either model, quantification of regional denervation (defect size) was consistently more reliable than the global LV-mean values of RI or VT. Regional partial-volume correction degraded repeatability of both the global and regional VT measures by 2-12%. Test-retest repeatability of MBF estimation was relatively poor (NPC = 30-61%) compared with the RI. Quantitative measures of global and regional SNS innervation were most repeatable using the simple RI method of analysis compared with the more complex VT. Observer variability was significantly lower than the test-retest repeatability using a highly automated analysis program. These results support the use of the simple RI method for reliable analysis of HED PET images in clinical research studies for future evaluation of new therapies and for risk stratification in patients with heart failure.

Effects of ketamine/xylazine and isoflurane on rat brain glucose metabolism measured by 18F‐fluorodeoxyglucose‐positron emission tomography

The aim of the present study was to investigate changes in glucose metabolism in male Wistar rats induced by the anesthetics isoflurane and ketamine combined with xylazine via 18 F-fluorodeoxyglucose-positron emission tomography. We analyzed the differential effects of the anesthetics on 18 F-fluorodeoxyglucose uptake and pharmacokinetics in 33 rats using quantification methods: (a) the standardized uptake value, (b) voxel-based analyses, and (c) kinetic analysis. Both anesthetics reduced glucose uptake in the entire brain. The voxel-based analyses detected smaller uptake reductions in the bilateral primary somatosensory system cortex and part of the limbic system in the ketamine-xylazine (KX) group and in the vestibular nucleus in the isoflurane group. Through kinetic analysis, we found that the volume of distribution and the membrane transport rate K1 were reduced in the KX group. Through various methods of 18 F-fluorodeoxyglucose-positron emission tomography quantification, the present study found that anesthesia with the ketamine-xylazine combination induced a global reduction of glucose metabolism compared with isoflurane; this reduction of metabolism was relatively lower in the primary somatosensory cortex and part of the limbic system. The volume of distribution of 18 F-fluorodeoxyglucose and its Glut1-mediated transport across the brain membranes (K1 ) were decreased in the KX group.

A Curve Fitting Approach Using ANN for Converting CT Number to Linear Attenuation Coefficient for CT-based PET Attenuation Correction

Energy-mapping, the conversion of linear attenuation coefficients ( $\mu $ ) calculated at the effective computed tomography (CT) energy to those corresponding to 511 keV, is an important step in CT-based attenuation correction (CTAC) for positron emission tomography (PET) quantification. The aim of this study was to implement energy-mapping step by using curve fitting ability of artificial neural network (ANN). Eleven digital phantoms simulated by Geant4 application for tomographic emission (GATE) and 12 physical phantoms composed of various volume concentrations of iodine contrast were used in this study to generate energy-mapping curves by acquiring average CT values and linear attenuation coefficients at 511 keV of these phantoms. The curves were built with ANN toolbox in MATLAB. To evaluate the effectiveness of the proposed method, another two digital phantoms (liver and spine-bone) and three physical phantoms (volume concentrations of 3%, 10% and 20%) were used to compare the energy-mapping curves built by ANN and bilinear transformation, and a semi-quantitative analysis was proceeded by injecting 0.5 mCi FDG into a SD rat for micro-PET scanning. The results showed that the percentage relative difference (PRD) values of digital liver and spine-bone phantom are 5.46% and 1.28% based on ANN, and 19.21% and 1.87% based on bilinear transformation. For 3%, 10% and 20% physical phantoms, the PRD values of ANN curve are 0.91%, 0.70% and 3.70%, and the PRD values of bilinear transformation are 3.80%, 1.44% and 4.30%, respectively. Both digital and physical phantoms indicated that the ANN curve can achieve better performance than bilinear transformation. The semi-quantitative analysis of rat PET images showed that the ANN curve can reduce the inaccuracy caused by attenuation effect from 13.75% to 4.43% in brain tissue, and 23.26% to 9.41% in heart tissue. On the other hand, the inaccuracy remained 6.47% and 11.51% in brain and heart tissue when the bilinear transformation was used. Overall, it can be concluded that the bilinear transformation method resulted in considerable bias and the newly proposed calibration curve built by ANN could achieve better results with acceptable accuracy.

Quantification in emission tomography: Challenges, solutions, and performance

Emission tomography (single photon emission tomography—SPECT—and positron emission tomography—PET) offers a great potential for the quantitative characterization of functional and molecular processes in vivo. Indeed, voxel values in the reconstructed SPECT and PET images can theoretically be translated into a well-understood physical quantity, namely radiotracer concentration, from which all kinds of parameters characterizing molecular processes can be derived using appropriate modeling. Such quantitative interpretation of SPECT and PET images is often referred to as “quantification”. The objective of this short review is to examine how far we are in this quest for quantification by presenting the different problems that have to be addressed, the methods that have been developed to solve these problems, and the current performance of these methods.

Non-invasive methods to study drug disposition: Positron Emission Tomography Detection and quantification of brain receptors in man

Positron emission tomography (PET) has been used mostly for the study of brain blood flow and metabolism in normal controls and in a variety of neurological and psychiatric conditions. With the appropriate radiotracers, PET also allows non-invasive imaging and quantification of a growing list of neuroreceptors, the target of most psychotropic drugs. For example, 11C-carfentanil and 11C-diprenorphine, two potent opiate ligands, have been used to label opiate receptors in vivo in man. Methods have been developed to quantify receptor studies with PET in terms of receptor density and affinity. PET is a unique tool that now allows measurement of receptor occupancy in vivo and could be used by the pharmacologist to optimize drug treatment.