Hsiao-Ming Wu

Metabolic crisis without brain ischemia is common after traumatic brain injury: a combined microdialysis and positron emission tomography study

Brain trauma is accompanied by regional alterations of brain metabolism, reduction in metabolic rates and possible energy crisis. We hypothesize that microdialysis markers of energy crisis are present during the critical period of intensive care despite the absence of brain ischemia. In all, 19 brain injury patients (mean GCS 6) underwent combined positron emission tomography (PET) for metabolism of glucose (CMRglu) and oxygen (CMRO2) and cerebral microdialysis (MD) at a mean time of 36 h after injury. Microdialysis values were compared with the regional mean PET values adjacent to the probe. Longitudinal MD data revealed a 25% incidence rate of metabolic crisis (elevated lactate/pyruvate ratio (LPR)>40) but only a 2.4% incidence rate of ischemia. Positron emission tomography imaging revealed a 1% incidence of ischemia across all voxels as measured by oxygen extraction fraction (OEF) and cerebral venous oxygen content (CvO2). In the region of the MD probe, PET imaging revealed ischemia in a single patient despite increased LPR in other patients. Lactate/pyruvate ratio correlated negatively with CMRO2 (P<0.001), but not with OEF or CvO2. Traumatic brain injury leads to a state of persistent metabolic crisis as reflected by abnormal cerebral microdialysis LPR that is not related to ischemia.

Cerebral metabolism in major depression and obsessive-compulsive disorder occurring separately and concurrently

BACKGROUND The frequent comorbidity of major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) suggests a fundamental relationship between them. We sought to determine whether MDD and OCD have unique cerebral metabolic patterns that remain the same when they coexist as when they occur independently. METHODS [18F]-fluorodeoxyglucose positron emission tomography (PET) brain scans were obtained on 27 subjects with OCD alone, 27 with MDD alone, 17 with concurrent OCD+MDD, and 17 normal control subjects, all in the untreated state. Regional cerebral glucose metabolism was compared between groups. RESULTS Left hippocampal metabolism was significantly lower in subjects with MDD alone and in subjects with concurrent OCD+MDD than in control subjects or subjects with OCD alone. Hippocampal metabolism was negatively correlated with depression severity across all subjects. Thalamic metabolism was significantly elevated in OCD alone and in MDD alone. Subjects with concurrent OCD+MDD had significantly lower metabolism in thalamus, caudate, and hippocampus than subjects with OCD alone. CONCLUSIONS Left hippocampal dysfunction was associated with major depressive episodes, regardless of primary diagnosis. Other cerebral metabolic abnormalities found in OCD and MDD occurring separately were not seen when the disorders coexisted. Depressive episodes occurring in OCD patients may be mediated by different basal ganglia-thalamic abnormalities than in primary MDD patients.

In Vivo Quantitation of Glucose Metabolism in Mice Using Small-Animal PET and a Microfluidic Device

The challenge of sampling blood from small animals has hampered the realization of quantitative small-animal PET. Difficulties associated with the conventional blood-sampling procedure need to be overcome to facilitate the full use of this technique in mice. Methods: We developed an automated blood-sampling device on an integrated microfluidic platform to withdraw small blood samples from mice. We demonstrate the feasibility of performing quantitative small-animal PET studies using 18F-FDG and input functions derived from the blood samples taken by the new device. 18F-FDG kinetics in the mouse brain and myocardial tissues were analyzed. Results: The studies showed that small (∼220 nL) blood samples can be taken accurately in volume and precisely in time from the mouse without direct user intervention. The total blood loss in the animal was <0.5% of the body weight, and radiation exposure to the investigators was minimized. Good model fittings to the brain and the myocardial tissue time–activity curves were obtained when the input functions were derived from the 18 serial blood samples. The R2 values of the curve fittings are >0.90 using a 18F-FDG 3-compartment model and >0.99 for Patlak analysis. The 18F-FDG rate constants \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(K_{1}^{{\ast}}\) \end{document}, \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(k_{2}^{{\ast}}\) \end{document}, \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(k_{3}^{{\ast}}\) \end{document}, and \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(k_{4}^{{\ast}}\) \end{document}, obtained for the 4 mouse brains, were comparable. The cerebral glucose metabolic rates obtained from 4 normoglycemic mice were 21.5 ± 4.3 μmol/min/100 g (mean ± SD) under the influence of 1.5% isoflurane. By generating the whole-body parametric images of \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(K_{FDG}^{{\ast}}\) \end{document} (mL/min/g), the uptake constant of 18F-FDG, we obtained similar pixel values as those obtained from the conventional regional analysis using tissue time–activity curves. Conclusion: With an automated microfluidic blood-sampling device, our studies showed that quantitative small-animal PET can be performed in mice routinely, reliably, and safely in a small-animal PET facility.

Selective Metabolic Reduction in Gray Matter Acutely following Human Traumatic Brain Injury

The aim of this study was to determine whether the apparent loss of overall gray-white matter contrast (GM/WM) seen on FDG-PET imaging reflects the differential changes of glucose metabolic rate (CMRglc) in cortical gray mater (GM) and subcortical white mater (WM) following TBI. The clinical significance of the CMRglc GM-to-WM ratio was also evaluated. Nineteen normal volunteers and 14 TBI patients were studied. Each subject had a quantitative FDG-PET, a quantitative H215O-PET and a MR scan acutely following TBI. Stabilities of the global and regional FDG lumped constants (LC) were studied. Parametric images (pixel unit: mg/min/100g) of FDG uptake rate (CURFDG) and CMRglc were generated. The changes of CMR(glc) in whole brain, GM and WM were studied separately by using a MRI-segmentation-based technique. The GM-to-WM ratios of both CURFDG and CMRglc images were significantly (p < 0.001) decreased (>31%) in TBI patients. The global LC value reduced significantly (p < 0.01) in TBI patients. The CMRglc decreased significantly (p < 0.001) in GM but not in WM (p > 0.1). Kinetic analysis revealed significant (p < 0.001) decrease of GM hexokinase activity in TBI patients. The GM-to-WM ratios of CMRglc correlated (r = 0.64) with the initial Glasgow Coma Score (GCS) of TBI patients. The patients with higher CMRglc GM-to-WM ratios (>1.54) showed good recovery 12 months after TBI. There was a selective CMRglc reduction in cortical GM following TBI. The pathophysiological basis for the reduction in GM-to-WM CMRglc ratio seen on FDG-PET imaging following TBI remains to be determined.

Estimation of the 18F-FDG Input Function in Mice by Use of Dynamic Small-Animal PET and Minimal Blood Sample Data

Derivation of the plasma time–activity curve in murine small-animal PET studies is a challenging task when tracers that are sequestered by the myocardium are used, because plasma time–activity curve estimation usually involves drawing a region of interest within the area of the reconstructed image that corresponds to the left ventricle (LV) of the heart. The small size of the LV relative to the resolution of the small-animal PET system, coupled with spillover effects from adjacent myocardial pixels, makes this method reliable only for the earliest frames of the scan. We sought to develop a method for plasma time–activity curve estimation based on a model of tracer kinetics in blood, muscle, and liver. Methods: Sixteen C57BL/6 mice were injected with 18F-FDG, and approximately 15 serial blood samples were taken from the femoral artery via a surgically inserted catheter during 60-min small-animal PET scans. Image data were reconstructed by use of filtered backprojection with CT-based attenuation correction. We constructed a 5-compartment model designed to predict the plasma time–activity curve of 18F-FDG by use of data from a minimum of 2 blood samples and the dynamic small-animal PET scan. The plasma time–activity curve (TACp) was assumed to have 4 exponential components \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \((TAC_{P}{=}A_{1}e^{\mathrm{{\lambda}}_{1}t}{+}A_{2}e^{\mathrm{{\lambda}}_{2}t}{+}A_{3}e^{\mathrm{{\lambda}}_{3}t}{-}(A_{1}{+}A_{2}{+}A_{3})e^{\mathrm{{\lambda}}_{4}t})\) \end{document} based on the serial blood samples. Using Bayesian constraints, we fitted 2-compartment submodels of muscle and liver to small-animal PET data for these organs and simultaneously fitted the input (forcing) function to early small-animal PET LV data and 2 blood samples (∼10 min and ∼1 h). Results: The area under the estimated plasma time–activity curve had an overall Spearman correlation of 0.99 when compared with the area under the gold standard plasma time–activity curve calculated from multiple blood samples. Calculated organ uptake rates (Patlak Ki) based on the predicted plasma time–activity curve had a correlation of approximately 0.99 for liver, muscle, myocardium, and brain when compared with those based on the gold standard plasma time–activity curve. The model was also able to accurately predict the plasma time–activity curve under experimental conditions that resulted in different rates of clearance of the tracer from blood. Conclusion: We have developed a robust method for accurately estimating the plasma time–activity curve of 18F-FDG by use of dynamic small-animal PET data and 2 blood samples.

Subcortical white matter metabolic changes remote from focal hemorrhagic lesions suggest diffuse injury after human traumatic brain injury.

OBJECTIVE:We used positron emission tomographic studies to prospectively examine the relationship between glucose and oxidative metabolism in the subcortical white matter (WM) acutely after traumatic brain injury (TBI). The objective was to determine the nature, extent, and degree of metabolic abnormalities in subcortical brain regions remote from hemorrhagic lesions. METHODS:Sixteen normal volunteers and 10 TBI patients (Glasgow Coma Scale score, 4–10; age, 17–64 yr; 6 with focal and 4 with diffuse injury) were studied. Each subject underwent dynamic positron emission tomographic studies using [15O]CO, 15O2, [15O]H2O, and fluorodeoxyglucose plus a magnetic resonance imaging scan acutely after TBI. Parametric images of the metabolic rate of oxygen and metabolic rate of glucose were generated, and a molar oxygen-to-glucose utilization ratio was calculated. Data from gray matter and WM remote from hemorrhagic lesions, plus whole brain, were analyzed. RESULTS:There was a significant reduction in the subcortical WM oxygen-to-glucose utilization ratio after TBI compared with normal values (3.99 ± 0.77 versus 5.37 ± 1.00; P < 0.01), whereas the mean cortical gray matter and whole-brain values remained unchanged. WM metabolic changes, which were diffuse throughout the hemispheres, were characterized by a reduction in the metabolic rate of oxygen without a concomitant drop in the metabolic rate of glucose. CONCLUSION:The extent and degree of subcortical WM metabolic abnormalities after moderate and severe TBI suggest that diffuse WM injury is a general phenomenon after such injuries. This pervasive finding may indicate that the concept of focal traumatic injury, although valid from a computed tomographic imaging standpoint, may be misleading when considering metabolic derangements associated with TBI.

An Internet-Based “Kinetic Imaging System” (KIS) for MicroPET

Many considerations, involving understanding and selection of multiple experimental parameters, are required to perform MicroPET studies properly. The large number of these parameters/variables and their complicated interdependence make their optimal choice nontrivial. We have a developed kinetic imaging system (KIS), an integrated software system, to assist the planning, design, and data analysis of MicroPET studies. The system serves multiple functions—education, virtual experimentation, experimental design, and image analysis of simulated/experimental data—and consists of four main functional modules—“Dictionary,” “Virtual Experimentation,” “Image Analysis,” and “Model Fitting.” The “Dictionary” module provides didactic information on tracer kinetics, pharmacokinetic, MicroPET imaging, and relevant biological/pharmacological information. The “Virtual Experimentation” module allows users to examine via computer simulations the effect of biochemical/pharmacokinetic parameters on tissue tracer kinetics. It generates dynamic MicroPET images based on the users assignment of kinetics or kinetic parameters to different tissue organs in a 3-D digital mouse phantom. Experimental parameters can be adjusted to investigate the design options of a MicroPET experiment. The “Image Analysis” module is a full-fledged image display/manipulation program. The “Model Fitting” module provides model-fitting capability for measured/simulated tissue kinetics. The system can be run either through the Web or as a stand-alone process. With KIS, radiotracer characteristics, administration method, dose level, imaging sequence, and image resolution-to-noise tradeoff can be evaluated using virtual experimentation. KIS is designed for biology/pharmaceutical scientists to make learning and applying tracer kinetics fun and easy.

Measurement of the global lumped constant for 2-deoxy-2-[18F]fluoro-D-glucose in normal human brain using [15O]water and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography imaging. A method with validation based on multiple methodologies.

PURPOSE This study aims to determine a lumped constant (LC) value that can be applied to the 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography (FDG-PET) study to yield a physiological value of cerebral metabolic rate of glucose (CMR(glc)) in normal brain. PROCEDURES We developed a more robust method for determining the global FDG LC. Dynamic FDG and H(2)(15)O PET studied were acquired in 18 normal subjects. Arterial-venous difference of blood glucose level was measured. RESULTS A global LC of 0.65 +/- 0.15 was obtained if a 3-microparameter FDG model (k*(4)=0)was assumed. Assumption of a 4-microparameter FDG model (k*(4) not equal 0) in analyzing the FDG data resulted in a higher LC value of 0.81 +/- 0.18. CONCLUSION The value of LC used for quantitating CMR(glc) should match the assumption inherent to the method of data analysis. The LC results in this study agree well with recent findings in the literature.

Quantification of Cerebral Glucose Metabolic Rate in Mice Using 18F-FDG and Small-Animal PET

The aim of this study was to evaluate various methods for estimating the metabolic rate of glucose utilization in the mouse brain (cMRglc) using small-animal PET and reliable blood curves derived by a microfluidic blood sampler. Typical values of 18F-FDG rate constants of normal mouse cerebral cortex were estimated and used for cMRglc calculations. The feasibility of using the image-derived liver time–activity curve as a surrogate input function in various quantification methods was also evaluated. Methods: Thirteen normoglycemic C57BL/6 mice were studied. Eighteen blood samples were taken from the femoral artery by the microfluidic blood sampler. Tissue time–activity curves were derived from PET images. cMRglc values were calculated using 2 different input functions (one derived from the blood samples [IFblood] and the other from the liver time–activity curve [IFliver]) in various quantification methods, which included the 3-compartment 18F-FDG model (from which the 18F-FDG rate constants were derived), the Patlak analysis, and operational equations. The estimated cMRglc value based on IFblood and the 3-compartment model served as a standard for comparisons with the cMRglc values calculated by the other methods. Results: The values of \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{K}_{1}^{{\ast}}\) \end{document}, \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(k_{2}^{{\ast}}\) \end{document}, \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(k_{3}^{{\ast}}\) \end{document}, \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(k_{4}^{{\ast}}\) \end{document}, and \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{K}_{\mathrm{FDG}}^{{\ast}}\) \end{document} estimated by IFblood and the 3-compartment model were 0.22 ± 0.05 mL/min/g, 0.48 ± 0.09 min−1, 0.06 ± 0.02 min−1, 0.025 ± 0.010 min−1, and 0.024 ± 0.007 mL/min/g, respectively. The standard cMRglc value was, therefore, 40.6 ± 13.3 μmol/100 g/min (lumped constant = 0.6). No significant difference between the standard cMRglc and the cMRglc estimated by the operational equation that includes \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(k_{4}^{{\ast}}\) \end{document} was observed. The standard cMRglc was also found to have strong correlations (r > 0.8) with the cMRglc value estimated by the use of IFliver in the 3-compartment model and with those estimated by the Patlak analysis (using either IFblood or IFliver). Conclusion: The 18F-FDG rate constants of normal mouse cerebral cortex were determined. These values can be used in the \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(k_{4}^{{\ast}}\) \end{document}-included operational equation to calculate cMRglc. IFliver can be used to estimate cMRglc in most methods included in this study, with proper linear corrections applied. The validity of using the Patlak analysis for estimating cMRglc in mouse PET studies was also confirmed.

Factor analysis in prostate cancer: delineation of organ structures and automatic generation of in- and output functions

Factor analysis (FA) is used for extracting the properties of dynamic datasets. Objective: FA was applied to dynamic studies using positron emission topography (PET) to create factor images and factor curves from which input and output functions could be derived for kinetic modeling. This noninvasive automated and image-based analysis should permit routine application of quantitative PET in cancer patients. Methods: In nine men with prostate cancer, dynamic PET studies were performed on an ECAT HR+ system. After administration of 13.5 mCi of /sup 11/C-labeled acetate, data were acquired for 20 min. Images were reconstructed with iterative algorithms, a maximum a posteriori (MAP) for transmission scans, and ordered subset expectation maximization (OSEM) for emission scans. The body contour was determined with thresholding of the transmission images. All voxels included in the body contour were used for processing. FA extracted the shape of the pure time activity curves (TACs). The factors were used to create functional images, from which a region-of-interest (ROI) could be generated with thresholding techniques. These ROIs were used to create image-based TACs. Results: The automated procedure was successful in eight out of nine patients. Minimal intervention generated reliable factors in the remaining patient. Factors are normalized; their magnitude was adjusted by a scale factor using: (1) reversed normalization and (2) image-based parameters. In principle, the input factor generated by FA has no spillover and produces a pure vascular image and curve. Factor images provided diagnostic information on tumors. The method is operator independent and reproducible. Conclusion: The automated procedure generated factors from dynamic PET data corresponding to vessels and tumor. FA can noninvasively generate input and output functions. This processing tool facilitates PET as a reproducible quantification method in routine oncological applications.