Noninvasive quantification of the cerebral metabolic rate for glucose using positron emission tomography, 18F-fluoro-2-deoxyglucose, the Patlak method, and an image-derived input function

Extraction of an input function from dynamic micro-PET images using wavelet packet based sub-band decomposition independent component analysis

Positron emission tomography (PET) can be used to quantify physiological parameters. However to perform quantification requires that an input function is measured, namely a plasma time activity curve (TAC). Image-derived input functions (IDIFs) are attractive because they are noninvasive and nearly no blood loss is involved. However, the spatial resolution and the signal to noise ratio (SNR) of PET images are low, which degrades the accuracy of IDIFs. The objective of this study was to extract accurate input functions from microPET images with zero or one plasma sample using wavelet packet based sub-band decomposition independent component analysis (WP SDICA). Two approaches were used in this study. The first was the use of simulated dynamic rat images with different spatial resolutions and SNRs, and the second was the use of dynamic images of eight Sprague-Dawley rats. We also used a population-based input function and a fuzzy c-means clustering approach and compared their results with those obtained by our method using normalized root mean square errors, area under curve errors, and correlation coefficients. Our results showed that the accuracy of the one-sample WP SDICA approach was better than the other approaches using both simulated and realistic comparisons. The errors in the metabolic rate, as estimated by one-sample WP SDICA, were also the smallest using our approach.

A method for generating image-derived input function in quantitative 18F-FDG PET study based on the monotonicity of the input and output function curve

OBJECTIVE

A method of defining the image-derived input function (IDIF) was introduced and evaluated for the quantification of the regional cerebral metabolic rate of glucose in PET studies.

METHODS

The voxels in the brain vasculature are extracted on the basis of the different monotonicities between the input and the output function curves. Time activity curves (TACs) of such voxels are averaged to obtain the uncorrected TAC of the brain vasculature. The IDIF was obtained from the raw TAC after correcting for the partial volume and spillover effects by an empirical formula in conjunction with a single blood sample and the TAC of the brain tissue. Data from 16 patients were used to test the proposed method. The Patlak approach is used to calculate the net fluoro-2-deoxyglucose clearance with plasma-derived input function and our generated IDIF, respectively.

RESULTS

The net fluoro-2-deoxyglucose clearances calculated with the IDIF generated by our approach are not only highly correlated (correlation coefficients close to 1) to, but also highly comparable (regression slopes close to 1 and intercepts close to 0) with those calculated with plasma-derived input function.

CONCLUSION

The method used in the present work is feasible and accurate.

Extraction of time activity curves from gated FDG-PET images for small animals' heart studies

We introduce a new approach to extract the input function and the tissue time activity curve from dynamic ECG-gated (18)F-FDG PET images. These curves are mandatory to model the myocardium metabolic rate of glucose for heart studies. The proposed method utilizes coupled active contours to track the myocardium and the blood pool deformations. Furthermore, a statistical approach is developed to model the blood and tissue activities and to correct for spillovers. The developed algorithm offers a reliable alternative to serial blood sampling for small animal cardiac PET studies. Indeed, the calculated MMRG value differs by 1.54% only from the reference value.

Quantitative PET Imaging Using (18)F Sodium Fluoride in the Assessment of Metabolic Bone Diseases and the Monitoring of Their Response to Therapy

Studies of bone remodeling using bone biopsy and biochemical markers of bone turnover measured in serum and urine are important for investigating how new treatments for osteoporosis affect bone metabolism. Positron emission tomography with (18)F sodium fluoride ((18)F NaF PET) for studying bone metabolism complements these conventional methods. Unlike biochemical markers, which measure the integrated response to treatment across the whole skeleton, (18)F NaF PET can distinguish changes occurring at sites of clinically important osteoporotic fractures. Future studies using (18)F NaF PET may illuminate current clinical problems, such as the possible association between long-term treatment with bisphosphonates and atypical fractures of the femur.

Combining image-derived and venous input functions enables quantification of serotonin-1A receptors with [carbonyl-11C]WAY-100635 independent of arterial sampling

image- derived input functions (IDIFs) represent a promising technique for a simpler and less invasive quantification of PET studies as compared to arterial cannulation. However, a number of limitations complicate the routine use of IDIFs in clinical research protocols and the full substitution of manual arterial samples by venous ones has hardly been evaluated. This study aims for a direct validation of IDIFs and venous data for the quantification of serotonin-1A receptor binding (5-HT(1A)) with [carbonyl-(11)C]WAY-100635 before and after hormone treatment.

METHODS

Fifteen PET measurements with arterial and venous blood sampling were obtained from 10 healthy women, 8 scans before and 7 after eight weeks of hormone replacement therapy. Image-derived input functions were derived automatically from cerebral blood vessels, corrected for partial volume effects and combined with venous manual samples from 10 min onward (IDIF+VIF). Corrections for plasma/whole-blood ratio and metabolites were done separately with arterial and venous samples. 5-HT(1A) receptor quantification was achieved with arterial input functions (AIF) and IDIF+VIF using a two-tissue compartment model.

RESULTS

Comparison between arterial and venous manual blood samples yielded excellent reproducibility. Variability (VAR) was less than 10% for whole-blood activity (p>0.4) and below 2% for plasma to whole-blood ratios (p>0.4). Variability was slightly higher for parent fractions (VARmax=24% at 5 min, p<0.05 and VAR<13% after 20 min, p>0.1) but still within previously reported values. IDIFs after partial volume correction had peak values comparable to AIFs (mean difference Δ=-7.6 ± 16.9 kBq/ml, p>0.1), whereas AIFs exhibited a delay (Δ=4 ± 6.4s, p<0.05) and higher peak width (Δ=15.9 ± 5.2s, p<0.001). Linear regression analysis showed strong agreement for 5-HT(1A) binding as obtained with AIF and IDIF+VIF at baseline (R(2)=0.95), after treatment (R(2)=0.93) and when pooling all scans (R(2)=0.93), with slopes and intercepts in the range of 0.97 to 1.07 and -0.05 to 0.16, respectively. In addition to the region of interest analysis, the approach yielded virtually identical results for voxel-wise quantification as compared to the AIF.

CONCLUSIONS

Despite the fast metabolism of the radioligand, manual arterial blood samples can be substituted by venous ones for parent fractions and plasma to whole-blood ratios. Moreover, the combination of image-derived and venous input functions provides a reliable quantification of 5-HT(1A) receptors. This holds true for 5-HT(1A) binding estimates before and after treatment for both regions of interest-based and voxel-wise modeling. Taken together, the approach provides less invasive receptor quantification by full independence of arterial cannulation. This offers great potential for the routine use in clinical research protocols and encourages further investigation for other radioligands with different kinetic characteristics.

Dynamic PET-CT studies for characterizing nasopharyngeal carcinoma metabolism: comparison of analytical methods

OBJECTIVES

To investigate the optimal PET protocol and analytical method to characterize the glucose metabolism in nasopharyngeal carcinoma (NPC).

METHODS

Newly diagnosed NPC patients were recruited and a dynamic PET-CT scan was performed. The optimized threshold to derive the arterial input function (AIF) was studied. Two-tissue compartmental kinetic modeling using three, four, and five parameters, Patlak graphical analysis, and time sensitivity (S-factor) analysis were performed. The best compartmental model was determined in terms of goodness of fit, and correlated with Ki from Patlak graphical analysis and the S-factor. The methods with R>0.9 and P<0.05 were considered acceptable. The protocols using two static scans with its retention index (RI=(SUV(2)/SUV(1)-1)×100%, where SUV is the standardized uptake value) were also studied and compared with S-factor analysis.

RESULTS

The best threshold of 0.6 was determined and used to derive AIF. The kinetic model with five parameters yields the best statistical results, but the model with k4=0 was used as the gold standard. All Ki values and some S-factors from data between various intervals (10-30, 10-45, 15-30, 15-45, 20-30, and 20-45 min) fulfilled the criteria. The RIs calculated from the S-factor were highly correlated to RI derived from simple two-point static scans at 10 and 30 min (R=0.9, P<0.0001).

CONCLUSION

The Patlak graphical analyses and even a 20-min-interval S-factor analysis or simple two-point static scans were shown to be sufficient to characterize NPC metabolism, confirming the clinical feasibility of applying a short dynamic with image-derived AIF or simple two-point static PET scans for studying NPC.

Minimally invasive input function for 2-18F-fluoro-A-85380 brain PET studies

PURPOSE

Quantitative neuroreceptor positron emission tomography (PET) studies often require arterial cannulation to measure input function. While population-based input function (PBIF) would be a less invasive alternative, it has only rarely been used in conjunction with neuroreceptor PET tracers. The aims of this study were (1) to validate the use of PBIF for 2-(18)F-fluoro-A-85380, a tracer for nicotinic receptors; (2) to compare the accuracy of measures obtained via PBIF to those obtained via blood-scaled image-derived input function (IDIF) from carotid arteries; and (3) to explore the possibility of using venous instead of arterial samples for both PBIF and IDIF.

METHODS

Ten healthy volunteers underwent a dynamic 2-(18)F-fluoro-A-85380 brain PET scan with arterial and, in seven subjects, concurrent venous serial blood sampling. PBIF was obtained by averaging the normalized metabolite-corrected arterial input function and subsequently scaling each curve with individual blood samples. IDIF was obtained from the carotid arteries using a blood-scaling method. Estimated Logan distribution volume (V(T)) values were compared to the reference values obtained from arterial cannulation.

RESULTS

For all subjects, PBIF curves scaled with arterial samples were similar in shape and magnitude to the reference arterial input function. The Logan V(T) ratio was 1.00 ± 0.05; all subjects had an estimation error <10%. IDIF gave slightly less accurate results (V(T) ratio 1.03 ± 0.07; eight of ten subjects had an error <10%). PBIF scaled with venous samples yielded inaccurate results (V(T) ratio 1.13 ± 0.13; only three of seven subjects had an error <10%). Due to arteriovenous differences at early time points, IDIF could not be calculated using venous samples.

CONCLUSION

PBIF scaled with arterial samples accurately estimates Logan V(T) for 2-(18)F-fluoro-A-85380. Results obtained with PBIF were slightly better than those obtained with IDIF. Due to arteriovenous concentration differences, venous samples cannot be substituted for arterial samples.

Early evaluation of cerebral metabolic rate of glucose (CMRglu) with 18F-FDG PET/CT and clinical assessment in idiopathic normal pressure hydrocephalus (INPH) patients before and after ventricular shunt placement: preliminary experience

PURPOSE

We evaluated the relationships between the cerebral metabolic rate of glucose (CMRglu) measured by dynamic (18)F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and the clinical and neuropsychological assessment before and after the surgical procedure in idiopathic normal pressure hydrocephalus (INPH) patients.

METHODS

Eleven selected INPH patients underwent clinical assessment (modified Rankin scale, Krauss scale, Larsson categorization system and Stein-Langfitt scale), cognitive evaluation (Mini-Mental State Examination, MMSE) and dynamic (18)F-FDG PET/CT scan 3 days before and 1 week after ventricular shunt placement.

RESULTS

After shunting, the global CMRglu significantly increased (2.95 ± 0.44 vs 4.38 ± 0.68, p = 10(-7)) in all INPH patients with a mean percentage value of 48.7%. After shunting, no significant change was found in the Evans ratio whereas a significant decrease in all clinical scale scores was observed. Only a slight reduction in the MMSE was found. After shunting, a significant correlation between the global CMRglu value and clinical assessment was found (R (2) = 0.75, p = 0.024); indeed all clinical scale scores varied (decreasing) and the CMRglu value also varied (increasing) in all INPH patients.

CONCLUSION

Our preliminary data show that changes in the CMRglu are promptly reversible after surgery and that there is a relationship between the early metabolic changes and clinical symptoms, independently from the simultaneous changes in the ventricular size. The remarkable and prompt improvement in the global CMRglu and in symptoms may also have important implications for the current concept of "neuronal plasticity" and for the cells' reactivity in order to recover their metabolic function.

Image-derived input function for brain PET studies: many challenges and few opportunities

Quantitative positron emission tomography (PET) brain studies often require that the input function be measured, typically via arterial cannulation. Image-derived input function (IDIF) is an elegant and attractive noninvasive alternative to arterial sampling. However, IDIF is also a very challenging technique associated with several problems that must be overcome before it can be successfully implemented in clinical practice. As a result, IDIF is rarely used as a tool to reduce invasiveness in patients. The aim of the present review was to identify the methodological problems that hinder widespread use of IDIF in PET brain studies. We conclude that IDIF can be successfully implemented only with a minority of PET tracers. Even in those cases, it only rarely translates into a less-invasive procedure for the patient. Finally, we discuss some possible alternative methods for obtaining less-invasive input function.

Image-derived input function for human brain using high resolution PET imaging with [C](R)-rolipram and [C]PBR28

BACKGROUND

The aim of this study was to test seven previously published image-input methods in state-of-the-art high resolution PET brain images. Images were obtained with a High Resolution Research Tomograph plus a resolution-recovery reconstruction algorithm using two different radioligands with different radiometabolite fractions. Three of the methods required arterial blood samples to scale the image-input, and four were blood-free methods.

METHODS

All seven methods were tested on twelve scans with [(11)C](R)-rolipram, which has a low radiometabolite fraction, and on nineteen scans with [(11)C]PBR28 (high radiometabolite fraction). Logan V(T) values for both blood and image inputs were calculated using the metabolite-corrected input functions. The agreement of image-derived Logan V(T) values with the reference blood-derived Logan V(T) values was quantified using a scoring system. Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model.

RESULTS

For both radioligands the highest scores were obtained with two blood-based methods, while the blood-free methods generally performed poorly. All methods gave higher scores with [(11)C](R)-rolipram, which has a lower metabolite fraction. Compartment modeling gave less reliable results, especially for the estimation of individual rate constants.

CONCLUSION

OUR STUDY SHOWS THAT: 1) Image input methods that are validated for a specific tracer and a specific machine may not perform equally well in a different setting; 2) despite the use of high resolution PET images, blood samples are still necessary to obtain a reliable image input function; 3) the accuracy of image input may also vary between radioligands depending on the magnitude of the radiometabolite fraction: the higher the metabolite fraction of a given tracer (e.g., [(11)C]PBR28), the more difficult it is to obtain a reliable image-derived input function; and 4) in association with image inputs, graphical analyses should be preferred over compartmental modelling.

Kinetic analysis in human brain of [11C](R)-rolipram, a positron emission tomographic radioligand to image phosphodiesterase 4: a retest study and use of an image-derived input function

[(11)C](R)-rolipram provides a measure of the density of phosphodiesterase 4 (PDE4) in brain, an enzyme that metabolizes cAMP. The aims of this study were to perform kinetic modeling of [(11)C](R)-rolipram in healthy humans using an arterial input function and to replace this arterial input in humans with an image-derived input function.

METHODS

Twelve humans had two injections of [(11)C](R)-rolipram. An image-derived input function was obtained from the carotid arteries and four blood samples. The samples were used for partial volume correction and for estimating the parent concentration using HPLC analysis.

RESULTS

An unconstrained two-compartment model and Logan analysis measured distribution volume V(T), with good identifiability but with moderately high retest variability (15%). Similar results were obtained using the image input (ratio image/arterial V(T)=1.00±0.06).

CONCLUSIONS

Binding of [(11)C](R)-rolipram to PDE4 can be quantified in human brain using kinetic modeling and an arterial input function. Image input function from carotid arteries provides an equally accurate and reproducible method to quantify PDE4.

Image-derived input function in dynamic human PET/CT: methodology and validation with 11C-acetate and 18F-fluorothioheptadecanoic acid in muscle and 18F-fluorodeoxyglucose in brain

Purpose

Despite current advances in PET/CT systems, blood sampling still remains the standard method to obtain the radiotracer input function for tracer kinetic modelling. The purpose of this study was to validate the use of image-derived input functions (IDIF) of the carotid and femoral arteries to measure the arterial input function (AIF) in PET imaging. The data were obtained from two different research studies, one using ¹⁸F-FDG for brain imaging and the other using ¹¹C-acetate and ¹⁸F-fluoro-6-thioheptadecanoic acid (¹⁸F-FTHA) in femoral muscles.

Methods

The method was validated with two phantom systems. First, a static phantom consisting of syringes of different diameters containing radioactivity was used to determine the recovery coefficient (RC) and spill-in factors. Second, a dynamic phantom built to model bolus injection and clearance of tracers was used to establish the correlation between blood sampling, AIF and IDIF. The RC was then applied to the femoral artery data from PET imaging studies with ¹¹C-acetate and ¹⁸F-FTHA and to carotid artery data from brain imaging with ¹⁸F-FDG. These IDIF data were then compared to actual AIFs from patients.

Results

With ¹¹C-acetate, the perfusion index in the femoral muscle was 0.34±0.18 min⁻¹ when estimated from the actual time–activity blood curve, 0.29±0.15 min⁻¹ when estimated from the corrected IDIF, and 0.66±0.41 min⁻¹ when the IDIF data were not corrected for RC. A one-way repeated measures (ANOVA) and Tukey’s test showed a statistically significant difference for the IDIF not corrected for RC (p<0.0001). With ¹⁸F-FTHA there was a strong correlation between Patlak slopes, the plasma to tissue transfer rate calculated using the true plasma radioactivity content and the corrected IDIF for the femoral muscles (vastus lateralis r=0.86, p=0.027; biceps femoris r=0.90, p=0.017). On the other hand, there was no correlation between the values derived using the AIF and those derived using the uncorrected IDIF. Finally, in the brain imaging study with ¹⁸F-FDG, the cerebral metabolic rate of glucose (CMRglc) measured using the uncorrected IDIF was consistently overestimated. The CMRglc obtained using blood sampling was 13.1±3.9 mg/100 g per minute and 14.0±5.7 mg/100 g per minute using the corrected IDIF (r²=0.90).

Conclusion

Correctly obtained, carotid and femoral artery IDIFs can be used as a substitute for AIFs to perform tracer kinetic modelling in skeletal femoral muscles and brain analyses.

Simultaneous estimation of input functions: an empirical study

In neuroreceptor mapping, methods for the estimation of distribution volume require determination of a metabolite-corrected arterial input function. In application, this may be accomplished by collecting arterial blood samples during scanning, adjusting these measurements according to a separate metabolite analysis, and then modeling the resulting concentration data. Although many groups do this routinely, it is invasive and requires considerable effort. Furthermore, both the plasma and the metabolite data are noisy, and thus estimation of kinetic parameters can be affected by this variability. One promising alternative to full-input function modeling is the simultaneous estimation (SIME) approach, in which kinetic parameters and common input function parameters are estimated using results obtained from several regions at once. We investigate the performance of this approach on data from four different radioligands, using various kinetic models, comparing the results with those obtained by estimation using full-input function modeling. Results indicate that SIME provides a promising alternative for all the radioligands considered.

Kinetic quantitation of cerebral PET-FDG studies without concurrent blood sampling: statistical recovery of the arterial input function

Kinetic quantitation of dynamic positron emission tomography (PET) studies via compartmental modeling usually requires the time-course of the radio-tracer concentration in the arterial blood as an arterial input function (AIF). For human and animal imaging applications, significant practical difficulties are associated with direct arterial sampling and as a result there is substantial interest in alternative methods that require no blood sampling at the time of the study. A fixed population template input function derived from prior experience with directly sampled arterial curves is one possibility. Image-based extraction, including requisite adjustment for spillover and recovery, is another approach. The present work considers a hybrid statistical approach based on a penalty formulation in which the information derived from a priori studies is combined in a Bayesian manner with information contained in the sampled image data in order to obtain an input function estimate. The absolute scaling of the input is achieved by an empirical calibration equation involving the injected dose together with the subject's weight, height and gender. The technique is illustrated in the context of (18)F -Fluorodeoxyglucose (FDG) PET studies in humans. A collection of 79 arterially sampled FDG blood curves are used as a basis for a priori characterization of input function variability, including scaling characteristics. Data from a series of 12 dynamic cerebral FDG PET studies in normal subjects are used to evaluate the performance of the penalty-based AIF estimation technique. The focus of evaluations is on quantitation of FDG kinetics over a set of 10 regional brain structures. As well as the new method, a fixed population template AIF and a direct AIF estimate based on segmentation are also considered. Kinetics analyses resulting from these three AIFs are compared with those resulting from radially sampled AIFs. The proposed penalty-based AIF extraction method is found to achieve significant improvements over the fixed template and the segmentation methods. As well as achieving acceptable kinetic parameter accuracy, the quality of fit of the region of interest (ROI) time-course data based on the extracted AIF, matches results based on arterially sampled AIFs. In comparison, significant deviation in the estimation of FDG flux and degradation in ROI data fit are found with the template and segmentation methods. The proposed AIF extraction method is recommended for practical use.

Comparison of eight methods for the estimation of the image-derived input function in dynamic [(18)F]-FDG PET human brain studies

The aim of this study was to compare eight methods for the estimation of the image-derived input function (IDIF) in [(18)F]-FDG positron emission tomography (PET) dynamic brain studies. The methods were tested on two digital phantoms and on four healthy volunteers. Image-derived input functions obtained with each method were compared with the reference input functions, that is, the activity in the carotid labels of the phantoms and arterial blood samples for the volunteers, in terms of visual inspection, areas under the curve, cerebral metabolic rates of glucose (CMRglc), and individual rate constants. Blood-sample-free methods provided less reliable results as compared with those obtained using the methods that require the use of blood samples. For some of the blood-sample-free methods, CMRglc estimations considerably improved when the IDIF was calibrated with a single blood sample. Only one of the methods tested in this study, and only in phantom studies, allowed a reliable calculation of the individual rate constants. For the estimation of CMRglc values using an IDIF in [(18)F]-FDG PET brain studies, a reliable absolute blood-sample-free procedure is not available yet.

An activity-subspace approach for estimating the integrated input function and relative distribution volume in PET parametric imaging

Dynamic positron emission tomography (PET) imaging technique enables the measurement of neuroreceptor distributions corresponding to anatomic structures, and thus, allows image-wide quantification of physiological and biochemical parameters. Accurate quantification of the concentration of neuroreceptor has been the objective of many research efforts. Compartment modeling is the most widely used approach for receptor binding studies. However, current compartment-model-based methods often either require intrusive collection of accurate arterial blood measurements as the input function, or assume the existence of a reference region. To obviate the need for the input function or a reference region, in this paper, we propose to estimate the input function. We propose a novel concept of activity subspace, and estimate the input function by the analysis of the intersection of the activity subspaces. Then, the input function and the distribution volume (DV) parameter are refined and estimated iteratively. Thus, the underlying parametric image of the total DV is obtained. The proposed method is compared with a blind estimation method, iterative quadratic maximum-likelihood (IQML) via simulation, and the proposed method outperforms IQML. The proposed method is also evaluated in a brain PET dataset.

A novel blood-cell-two-compartment model for transferring a whole blood time activity curve to plasma in rodents

The term input function usually refers to the tracer plasma time activity curve (pTAC), which is necessary for quantitative positron emission tomography (PET) studies. The purpose of this study was to acquire the pTAC by independent component analysis (ICA) estimation from the whole blood time activity curve (wTAC) using a novel method, namely the FDG blood-cell-two-compartment model (BCM). This approach was compared to a number of published models, including linear haematocrit (HCT) correction, non-linear HCT correction and two-exponential correction. The results of this study show that the normalized root mean square error (NRMSE) and the error of the area under curve (EAUC) for the BCM estimate of the pTAC were the smallest. Compartmental and graphic analyses were used to estimate the metabolic rate of the FDG (MR(FDG)). The percentage error for the MR(FDG) (PE(MRFDG)) was estimated from the BCM corrected pTAC and this was also the smallest. It is concluded that the BCM is a better choice when transferring wTAC into pTAC for quantification.

Image-derived input functions for PET brain studies

PURPOSE

To assess the robustness of a previously introduced method to obtain accurate image-derived input functions (IDIF) for three other tracers.

METHODS

Dynamic PET and online blood data of five repeat [(11)C]PIB (Pittsburgh Compound-B) ([(11)C]PIB), six repeat (R)-[(11)C]verapamil, and ten single (R)-[(11)C]PK11195 studies were used. IDIFs were extracted from partial volume corrected scans using the four hottest pixels per plane method. Results obtained with IDIFs were compared with those using standard online measured arterial input functions (BSIF). IDIFs were used both with and without calibration based on manual blood samples.

RESULTS

For (R)-[(11)C]verapamil, accurate IDIFs were obtained using noncalibrated IDIFs (slope 0.96+/-0.17; R (2) 0.92+/-0.07). However, calibration was necessary to obtain IDIFs comparable to the BSIF for both [(11)C]PIB (slope 1.04+/-0.05; R (2) 1.00+/-0.01) and (R)-[(11)C]PK11195 (slope 0.96+/-0.05; R (2) 0.99+/-0.01). The need for calibration may be explained by the sticking property of both tracers, indicating that BSIF may be affected by sticking and therefore may be unreliable.

CONCLUSION

The present study shows that a previously proposed method to extract IDIFs is suitable for analysing [(11)C]PIB, (R)-[(11)C]verapamil and (R)-[(11)C]PK11195 studies, thereby obviating the need for online arterial sampling.

Model-Based receptor quantization analysis for PET parametric imaging

Dynamic PET (positron emission tomography) imaging technique allows image-wide quantification of physiologic and biochemical parameters. Compartment modeling is the most popular approach for receptor binding studies. However, current compartment-model based methods often either require the accurate arterial blood measurements as the input function or assume the existence of a reference region. To obviate the need for the input function or a reference region, in this paper, we propose to estimate the input function and the kinetic parameters simultaneously. The initial estimate of the input functions is obtained by the analysis of space intersections. Then both the input function and the receptor parameters, thus the underlying distribution volume (DV) parametric image, are estimated iteratively. The performance of the proposed scheme is examined by both simulations and real brain PET data in obtaining the underlying parametric images.

A Factor-Image Framework to Quantification of Brain Receptor Dynamic PET Studies

The positron emission tomography (PET) imaging technique enables the measurement of receptor distribution or neurotransmitter release in the living brain and the changes of the distribution with time and thus allows quantification of binding sites as well as the affinity of a radioligand. However, quantification of receptor binding studies obtained with PET is complicated by tissue heterogeneity in the sampling image elements (i.e., voxels, pixels). This effect is caused by a limited spatial resolution of the PET scanner. Spatial heterogeneity is often essential in understanding the underlying receptor binding process. Tracer kinetic modeling also often requires an intrusive collection of arterial blood samples. In this paper, we propose a likelihood-based framework in the voxel domain for quantitative imaging with or without the blood sampling of the input function. Radioligand kinetic parameters are estimated together with the input function. The parameters are initialized by a subspace-based algorithm and further refined by an iterative likelihood-based estimation procedure. The performance of the proposed scheme is examined by simulations. The results show that the proposed scheme provides reliable estimation of factor time-activity curves (TACs) and the underlying parametric images. A good match is noted between the result of the proposed approach and that of the Logan plot. Real brain PET data are also examined, and good performance is observed in determining the TACs and the underlying factor images.

Spillover and partial-volume correction for image-derived input functions for small-animal 18F-FDG PET studies

We present and validate a method to obtain an input function from dynamic image data and 0 or 1 blood sample for small-animal 18F-FDG PET studies. The method accounts for spillover and partial-volume effects via a physiologic model to yield a model-corrected input function (MCIF).

METHODS

Image-derived input functions (IDIFs) from heart ventricles and myocardial time-activity curves were obtained from 14 Sprague-Dawley rats and 17 C57BL/6 mice. Each MCIF was expressed as a mathematic equation with 7 parameters, which were estimated simultaneously with the myocardial model parameters by fitting the IDIFs and myocardium curves to a dual-output compartment model. Zero or 1 late blood sample was used in the simultaneous estimation. MCIF was validated by comparison with input measured from blood samples. Validation included computing errors in the areas under the curves (AUCs) and in the 18F-FDG influx constant Ki in 3 types of tissue.

RESULTS

For the rat data, the AUC error was 5.3% +/- 19.0% in the 0-sample MCIF and -2.3% +/- 14.8% in the 1-sample MCIF. When the MCIF was used to calculate the Ki of the myocardium, brain, and muscle, the overall errors were -6.3% +/- 27.0% in the 0-sample method (correlation coefficient r = 0.967) and 3.1% +/- 20.6% in the 1-sample method (r = 0.970). The t test failed to detect a significant difference (P > 0.05) in the Ki estimates from both the 0-sample and the 1-sample MCIF. For the mouse data, AUC errors were 4.3% +/- 25.5% in the 0-sample MCIF and -1.7% +/- 20.9% in the 1-sample MCIF. Ki errors averaged -8.0% +/- 27.6% for the 0-sample method (r = 0.955) and -2.8% +/- 22.7% for the 1-sample method (r = 0.971). The t test detected significant differences in the brain and muscle in the Ki for the 0-sample method but no significant differences with the 1-sample method. In both rat and mouse, 0-sample and 1-sample MCIFs both showed at least a 10-fold reduction in AUC and Ki errors compared with uncorrected IDIFs.

CONCLUSION

MCIF provides a reliable, noninvasive estimate of the input function that can be used to accurately quantify the glucose metabolic rate in small-animal 18F-FDG PET studies.

Noninvasive quantification of local cerebral metabolic rate of glucose for clinical application using positron emission tomography and 18F-fluoro-2-deoxy-D-glucose

Until now, input function is still required in quantification of local cerebral metabolic rate of glucose (LCMRGlc) using positron emission tomography (PET) and (18)F-fluoro-2-deoxy-D-glucose (FDG). Some image-derived methods were developed to get input function noninvasively instead of the arterial blood sampling method, but they needed to make complicated corrections manually, so they cannot always be applied in clinic directly. Here, we propose a simple method based on the Patlak approach by using a reference tissue region and without using any information of input function. This simulation study revealed that the present method was in good agreement with Patlak method; the difference between two methods was less than 5%. The statistical errors with two methods were also obtained, and the results showed the accuracy of LCMRGlc estimated with present method was better than that with Patlak method slightly. The simulation results indicated that the calculation of LCMRGlc with present method was quite stable and independent of the choosing of reference tissue region. All of these show that the present method is a good approximation to Patlak method. The calculation with this method is very simple and easy to perform voxel by voxel; therefore, it can be widely used not only in laboratory studies but also in clinical applications although it only provides the relative rates.

[18F]galacto-RGD positron emission tomography for imaging of alphavbeta3 expression on the neovasculature in patients with squamous cell carcinoma of the head and neck

PURPOSE

[(18)F]Galacto-RGD has been developed for positron emission tomography (PET)-imaging of alphavbeta3 expression, a receptor involved in angiogenesis and metastasis. Our aim was to study the feasibility of PET imaging with [(18)F]Galacto-RGD in patients with squamous cell carcinoma of the head and neck (SCCHN).

EXPERIMENTAL DESIGN

Eleven patients with primary diagnosis of SCCHN were examined. After injection of 140 to 200 MBq [(18)F]Galacto-RGD, static emission scans 60 min post injection from the head to the abdomen (n = 11) and dynamic scans >60 min covering the tumor region (n = 6) for kinetic modeling were acquired. Standardized uptake values (SUV) were measured in tumors, muscle and oral mucosa. Immunohistochemistry was done using an alphavbeta3-specific antibody (n = 7). Image fusion with magnetic resonance imaging and/or computed tomography (CT) scans (n = 8) and calculation of tumor subvolumes based on SUVs was done using the iPlan software (BrainLAB).

RESULTS

[(18)F]Galacto-RGD PET identified 10 of 12 tumors, with SUVs ranging from 2.2 to 5.8 (mean, 3.4 +/- 1.2). Two tumors <5 mm were missed. Tumor/blood and tumor/muscle ratios were 2.8 +/- 1.1 and 5.5 +/- 1.6, respectively. Tumor kinetics was consistent with a two-tissue compartmental model with reversible specific binding. Immunohistochemistry confirmed alphavbeta3 expression in all tumors with alphavbeta3 being located on the microvessels in all specimens and additionally on tumor cells in one specimen. Image fusion of [(18)F]Galacto-RGD PET with magnetic resonance imaging/multislice CT and definition of tumor subvolumes was feasible in all cases.

CONCLUSIONS

[(18)F]Galacto-RGD PET allows for specific imaging of alphavbeta3 expression in SCCHN with good contrast. Image fusion and definition of tumor subvolumes is feasible. This technique might be used for the assessment of angiogenesis and for planning and response evaluation of alphavbeta3-targeted therapies.

Partial volume corrected image derived input functions for dynamic PET brain studies: methodology and validation for [11C]flumazenil

Extraction of arterial input functions from dynamic brain scans may obviate the need for arterial sampling and would increase the clinical applicability of quantitative PET studies. The aim of the present study was to evaluate applicability and accuracy of image derived input functions (IDIFs) following reconstruction based partial volume correction (PVC). Settings for the PVC ordered subset expectation maximization (PVC-OSEM) reconstruction algorithm were varied. In addition, different methods for defining arterial regions of interest (ROI) in order to extract IDIFs were evaluated. [(11)C]flumazenil data of 10 subjects were used in the present study. Results obtained with IDIFs were compared with those using standard on-line measured arterial input functions. These included areas under the curve (AUC) for peak (1-2 min) and tail (2-60 min), volume of distribution (V(T)) obtained using Logan analysis, and V(T) and K(1) obtained with a basis function implementation of a single tissue compartment model. Best results were obtained with PVC-OSEM using 4 iterations and 16 subsets. Based on (11)C point source measurements, a 4.5 mm FWHM (full width at half maximum) resolution kernel was used to correct for partial volume effects. A ROI consisting of the four hottest pixels per plane (over the carotid arteries) was the best method to extract IDIFs. Excellent peak AUC ratios (0.99+/-0.09) between IDIF and blood sampler input function (BSIF) were found. Furthermore, extracted IDIFs provided V(T) and K(1) values that were very similar to those obtained using BSIFs. The proposed method appears to be suitable for analysing [(11)C]flumazenil data without the need for online arterial sampling.

An input function estimation method for FDG-PET human brain studies

BACKGROUND

A new model of an input function for human [(18)F]-2-Deoxy-2-fluoro-d-glucose fluoro (FDG) positron emission tomography (PET) brain studies with bolus injection is presented.

METHODS

Input data for early time, roughly up to 0.6 min, were obtained noninvasively from the time-activity curve (TAC) measured from a carotid artery region of interest. Representative tissue TACs were obtained by clustering the output curves to a limited number of dominant clusters. Three venous plasma samples at a later time were used to fit the functional form of the input function in conjunction with obtaining kinetic rate parameters of the dominant clusters, K(1), k(2) and k(3), using the compartmental model for FDG-PET. Experiments to test the approach used data from 18 healthy subjects.

RESULTS

The model provides an effective means to recover the input function in FDG-PET studies. Weighted nonlinear least squares parameter estimation using the recovered input function, as contrasted with use of plasma samples, yielded highly correlated values of K=K(1)k(3)/(k(2)+k(3)) for simulated data, a correlation coefficient of 0.99780, a slope of 1.019 and an intercept of almost zero. The estimates of K for real data by graphical Patlak analysis using the recovered input function were almost identical to those obtained using arterial plasma samples, with correlation coefficients greater than 0.9976, regression slopes between 0.958 and 1.091 and intercepts that are virtually zero.

CONCLUSIONS

A reliable semiautomated alternative for input function estimation that uses image-derived data augmented with three plasma samples is presented and evaluated for FDG-PET human brain studies.

Image-derived input function for assessment of 18F-FDG uptake by the inflamed lung

Pulmonary uptake of (18)F-FDG assessed with PET has been used to quantify the metabolic activity of inflammatory cells in the lung. This assessment involves modeling of tracer kinetics and knowledge of a time-activity curve in pulmonary artery plasma as an input function, usually acquired by manual blood sampling. This paper presents and validates a method to accurately derive an input function from a blood-pool region of interest (ROI) defined in dynamic PET images.

METHODS

The method is based on a 2-parameter model describing the activity of blood and that from spillover into the time-activity curve for the ROI. The model parameters are determined using an iterative algorithm, with 2 blood samples used to calibrate the raw PET-derived activity data. We validated both the 2-parameter model and the method to derive a quantitative input function from ROIs defined for the cavities of the right and left heart and for the descending aorta by comparing them against the time-activity curve obtained by manual blood sampling from the pulmonary artery in lungs with acute inflammation.

RESULTS

The model accurately described the time-activity curve from sampled blood. The 2-sample calibration method provided an efficient algorithm to derive input functions that were virtually identical to those sampled manually, including the fast kinetics of the early phase. The (18)F-FDG uptake rates in acutely injured lungs obtained using this method correlated well with those obtained exclusively using manual blood sampling (R(2) > 0.993). Within some bounds, the model was found quite insensitive to the timing of calibration blood samples or the exact definition of the blood-pool ROIs.

CONCLUSION

Using 2 mixed venous blood samples, the method accurately assesses the entire time course of the pulmonary (18)F-FDG input function and does not require the precise geometry of a specific blood-pool ROI or a population-based input function. This method may substantially facilitate studies involving modeling of pulmonary (18)F-FDG in patients with viral or bacterial infections, pulmonary fibrosis, and chronic obstructive pulmonary disease.

Attenuation of insulin-evoked responses in brain networks controlling appetite and reward in insulin resistance: the cerebral basis for impaired control of food intake in metabolic syndrome?

The rising prevalence of obesity and type 2 diabetes is a global challenge. A possible mechanism linking insulin resistance and weight gain would be attenuation of insulin-evoked responses in brain areas relevant to eating in systemic insulin resistance. We measured brain glucose metabolism, using [(18)F]fluorodeoxyglucose positron emission tomography, in seven insulin-sensitive (homeostasis model assessment of insulin resistance [HOMA-IR] = 1.3) and seven insulin-resistant (HOMA-IR = 6.3) men, during suppression of endogenous insulin by somatostatin, with and without an insulin infusion that elevated insulin to 24.6 +/- 5.2 and 23.2 +/- 5.8 mU/l (P = 0.76), concentrations similar to fasting levels of the resistant subjects and approximately threefold above those of the insulin-sensitive subjects. Insulin-evoked change in global cerebral metabolic rate for glucose was reduced in insulin resistance (+7 vs. +17.4%, P = 0.033). Insulin was associated with increased metabolism in ventral striatum and prefrontal cortex and with decreased metabolism in right amygdala/hippocampus and cerebellar vermis (P < 0.001), relative to global brain. Insulin's effect was less in ventral striatum and prefrontal cortex in the insulin-resistant subjects (mean +/- SD for right ventral striatum 3.2 +/- 3.9 vs. 7.7 +/- 1.7, P = 0.017). We conclude that brain insulin resistance exists in peripheral insulin resistance, especially in regions subserving appetite and reward. Diminishing the link be-tween control of food intake and energy balance may contribute to development of obesity in insulin resistance.

Quantitative measurement of oxygen metabolic rate in the rat brain using microPET imaging of briefly inhaled 15O-labelled oxygen gas

OBJECTIVE

The quantitative measurement of cerebral metabolic rate of oxygen (CMRO(2)) for rats using positron emission tomography (PET) has been technically difficult. The present study was performed to provide a technique to measure CMRO(2) for rats using a dedicated animal PET technique.

METHODS

CMRO(2) in the rat brain was quantitatively measured under alpha-chloralose anaesthesia (30 mg . kg(-1) . h(-1), intravenous infusion) using a PET imaging technique. In our experiment, the (15)O-labelled gas tracer (O(15)O) was administered by a bolus insufflation into the lung through a surgically placed cannula in the trachea. The tracer distribution was then dynamically imaged using the microPET. Unlike other conventional PET methods in which a series of arterial blood samples need to be withdrawn for the measurement of an arterial input function, no arterial blood sampling was employed. Instead, the heart was scanned in dynamic mode at the same time of imaging the brain, and the region of interest drawn over the heart was analysed to obtain an arterial input function.

RESULTS

The CMRO(2) value (micromol . 100 g(-1) . min(-1)) from 10 rats was 208 +/- 15 (mean +/- SD).

CONCLUSIONS

Our results suggest that the microPET-based CMRO(2) measurement in the rat brain combined with a non-invasive measurement of arterial input function is promising, especially for many applications involving small animals in which repeated measurements of absolute CMRO(2) need to be performed.

Corrections of arterial input function for dynamic H215O PET to assess perfusion of pelvic tumours: arterial blood sampling versus image extraction

Assessment of perfusion with 15O-labelled water (H215O) requires measurement of the arterial input function (AIF). The arterial time activity curve (TAC) measured using the peripheral sampling scheme requires corrections for delay and dispersion. In this study, parametrizations with and without arterial spillover correction for fitting of the tissue curve are evaluated. Additionally, a completely noninvasive method for generation of the AIF from a dynamic positron emission tomography (PET) acquisition is applied to assess perfusion of pelvic tumours. This method uses a volume of interest (VOI) to extract the TAC from the femoral artery. The VOI TAC is corrected for spillover using a separate tissue TAC and for recovery by determining the recovery coefficient on a coregistered CT data set. The techniques were applied in five patients with pelvic tumours who underwent a total of 11 examinations. Delay and dispersion correction of the blood TAC without arterial spillover correction yielded in seven examinations solutions inconsistent with physiology. Correction of arterial spillover increased the fitting accuracy and yielded consistent results in all patients. Generation of an AIF from PET image data was investigated as an alternative to arterial blood sampling and was shown to have an intrinsic potential to determine the AIF noninvasively and reproducibly. The AIF extracted from a VOI in a dynamic PET scan was similar in shape to the blood AIF but yielded significantly higher tissue perfusion values (mean of 104.0 +/- 52.0%) and lower partition coefficients (-31.6 +/- 24.2%). The perfusion values and partition coefficients determined with the VOI technique have to be corrected in order to compare the results with those of studies using a blood AIF.

Quantification method in [18F]fluorodeoxyglucose brain positron emission tomography using independent component analysis

OBJECTIVE

To extract accurate image-derived input functions from dynamic brain positron emission tomography images (DBPIs) using independent component analysis (ICA).

METHODS

A modified linear model with haematocrit correction was used to improve the accuracy of input functions estimated by independent component analysis and to reduce the error of quantitative analysis. Two types of material were examined: (1) a simulated dynamic phantom with a three-compartment, four-parameter model; (2) clinical 2-h DBPIs with a standard plasma sampling procedure. The input function was extracted from DBPIs using independent component analysis. The modified linear model with haematocrit correction was used to obtain the independent component analysis-estimated input function (Iica). For comparison, the input function derived from the last three blood samples (Iest) was used. The image-derived input functions (Iica and Iest) were compared with the input function from blood sampling (Itp). The mean percentage error of the metabolic rate of [F]-2-fluoro-2-deoxy-D-glucose (MRFDG) was calculated for both Iica and Iest against that of Itp.

RESULTS

In simulated studies, the mean percentage errors of MRFDG between true simulated and estimated values of Iest and Iica were 8.2% and 4.2%, respectively. In clinical studies, six clinical cases were collected. The mean percentage errors and standard deviations of MRFDG with Iest and Iica were 12.6+/-7.5% and 7.7+/-3.3%, respectively.

CONCLUSIONS

We have proposed a technique for estimating image-derived input functions using independent component analysis without blood sampling. The results of our method were highly correlated with those from standard blood sampling, and more accurate than those of other methods proposed previously.

Correlations between apolipoprotein E epsilon4 gene dose and brain-imaging measurements of regional hypometabolism

Patients with Alzheimer's disease (AD) have abnormally low positron emission tomography (PET) measurements of the cerebral metabolic rate for glucose (CMRgl) in regions of the precuneus and the posterior cingulate, parietotemporal, and frontal cortex. Apolipoprotein E (APOE) epsilon4 gene dose (i.e., the number of epsilon4 alleles in a person's APOE genotype) is associated with a higher risk of AD and a younger age at dementia onset. We previously found that cognitively normal late-middle-aged APOE epsilon4 carriers have abnormally low CMRgl in the same brain regions as patients with probable Alzheimer's dementia. In a PET study of 160 cognitively normal subjects 47-68 years of age, including 36 epsilon4 homozygotes, 46 heterozygotes, and 78 epsilon4 noncarriers who were individually matched for their gender, age, and educational level, we now find that epsilon4 gene dose is correlated with lower CMRgl in each of these brain regions. This study raises the possibility of using PET as a quantitative presymptomatic endophenotype to help evaluate the individual and aggregate effects of putative genetic and nongenetic modifiers of AD risk.

Direct reconstruction of kinetic parameter images from dynamic PET data

Our goal in this paper is the estimation of kinetic model parameters for each voxel corresponding to a dense three-dimensional (3-D) positron emission tomography (PET) image. Typically, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated by fitting a model to the reconstructed time-activity response of each voxel. However, this "indirect" approach to kinetic parameter estimation tends to reduce signal-to-noise ratio (SNR) because of the requirement that the sinogram data be divided into individual time frames. In 1985, Carson and Lange proposed, but did not implement, a method based on the expectation-maximization (EM) algorithm for direct parametric reconstruction. The approach is "direct" because it estimates the optimal kinetic parameters directly from the sinogram data, without an intermediate reconstruction step. However, direct voxel-wise parametric reconstruction remained a challenge due to the unsolved complexities of inversion and spatial regularization. In this paper, we demonstrate and evaluate a new and efficient method for direct voxel-wise reconstruction of kinetic parameter images using all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem. The PICD algorithm is computationally efficient and is implemented with spatial regularization in the domain of the physiologically relevant parameters. Our experimental simulations of a rat head imaged in a working small animal scanner indicate that direct parametric reconstruction can substantially reduce root-mean-squared error (RMSE) in the estimation of kinetic parameters, as compared to indirect methods, without appreciably increasing computation.

Quantification in positron emission tomography for research in pharmacology and drug development

Positron emission tomography (PET) is a quantitative in vivo tracer technique, enabling images of the distribution of biochemical, physiological and pharmacological functions in living tissue, at a resolution of a few millimetres. Applications include the imaging of blood flow rate, metabolic rate and neuroreceptor distribution and function. These applications are playing an increasing role in drug development. This brief article seeks to emphasize how these applications of PET need to rest on a solid quantitative foundation.

Tracer kinetic modeling of 11C-acetate applied in the liver with positron emission tomography

It is well known that 40%-50% of hepatocellular carcinoma (HCC) do not show increased 18F-fluorodeoxyglucose (FDG) uptake. Recent research studies have demonstrated that 11C-acetate may be a complementary tracer to FDG in positron emission tomography (PET) imaging of HCC in the liver. Quantitative dynamic modeling is, therefore, conducted to evaluate the kinetic characteristics of this tracer in HCC and nontumor liver tissue. A three-compartment model consisting of four parameters with dual inputs is proposed and compared with that of five parameters. Twelve regions of dynamic datasets of the liver extracted from six patients are used to test the models. Estimation of the adequacy of these models is based on Akaike Information Criteria (AIC) and Schwarz Criteria (SC) by statistical study. The forward clearance K = K1 * k3/(k2 + k3) is estimated and defined as a new parameter called the local hepatic metabolic rate-constant of acetate (LHMRAct) using both the weighted nonlinear least squares (NLS) and the linear Patlak methods. Preliminary results show that the LHMRAct of the HCC is significantly higher than that of the nontumor liver tissue. These model parameters provide quantitative evidence and understanding on the kinetic basis of C-acetate for its potential role in the imaging of HCC using PET.

Parametrically defined cerebral blood vessels as non-invasive blood input functions for brain PET studies

A non-invasive alternative to arterial blood sampling for the generation of a blood input function for brain positron emission tomography (PET) studies is presented. The method aims to extract the dimensions of the blood vessel directly from PET images and to simultaneously correct the radioactivity concentration for partial volume and spillover. This involves simulation of the tomographic imaging process to generate images of different blood vessel and background geometries and selecting the one that best fits, in a least-squares sense, the acquired PET image. A phantom experiment was conducted to validate the method which was then applied to eight subjects injected with 6-[18F]fluoro-L-DOPA and one subject injected with [11C]CO-labelled red blood cells. In the phantom study, the diameter of syringes filled with an 11C solution and inserted into a water-filled cylinder were estimated with an accuracy of half a pixel (1 mm). The radioactivity concentration was recovered to 100 +/- 4% in the 8.7 mm diameter syringe, the one that most closely approximated the superior sagittal sinus. In the human studies, the method systematically overestimated the calibre of the superior sagittal sinus by 2-3 mm compared to measurements made in magnetic resonance venograms on the same subjects. Sources of discrepancies related to the anatomy of the blood vessel were found not to be fundamental limitations to the applicability of the method to human subjects. This method has the potential to provide accurate quantification of blood radioactivity concentration from PET images without the need for blood samples, corrections for delay and dispersion, co-registered anatomical images, or manually defined regions of interest.

Blind identification of the kinetic parameters in three-compartment models

Quantified knowledge of tissue kinetic parameters in the regions of the brain and other organs can offer information useful in clinical and research applications. Dynamic medical imaging with injection of radioactive or paramagnetic tracer can be used for this measurement. The kinetics of some widely used tracers such as [18F]2-fluoro-2-deoxy-D-glucose can be described by a three-compartment physiological model. The kinetic parameters of the tissue can be estimated from dynamically acquired images. Feasibility of estimation by blind identification, which does not require knowledge of the blood input, is considered analytically and numerically in this work for the three-compartment type of tissue response. The non-uniqueness of the two-region case for blind identification of kinetic parameters in three-compartment model is shown; at least three regions are needed for the blind identification to be unique. Numerical results for the accuracy of these blind identification methods in different conditions were considered. Both a separable variables least-squares (SLS) approach and an eigenvector-based algorithm for multichannel blind deconvolution approach were used. The latter showed poor accuracy. Modifications for non-uniform time sampling were also developed. Also, another method which uses a model for the blood input was compared. Results for the macroparameter K, which reflects the metabolic rate of glucose usage, using three regions with noise showed comparable accuracy for the separable variables least squares method and for the input model-based method, and slightly worse accuracy for SLS with the non-uniform sampling modification.

Cluster analysis in kinetic modelling of the brain: a noninvasive alternative to arterial sampling

In emission tomography, quantification of brain tracer uptake, metabolism or binding requires knowledge of the cerebral input function. Traditionally, this is achieved with arterial blood sampling. We propose a noninvasive alternative via the use of a blood vessel time-activity curve (TAC) extracted directly from dynamic positron emission tomography (PET) scans by cluster analysis. Five healthy subjects were injected with the 5HT(2A)-receptor ligand [(18)F]-altanserin and blood samples were subsequently taken from the radial artery and cubital vein. Eight regions-of-interest (ROI) TACs were extracted from the PET data set. Hierarchical K-means cluster analysis was performed on the PET time series to extract a cerebral vasculature ROI. The number of clusters was varied from K = 1 to 10 for the second of the two-stage method. Determination of the correct number of clusters was performed by the 'within-variance' measure and by 3D visual inspection of the homogeneity of the determined clusters. The cluster-determined input curve was then used in Logan plot analysis and compared with the arterial and venous blood samples, and additionally with one of the currently used alternatives to arterial blood sampling, the Simplified Reference Tissue Model (SRTM) and Logan analysis with cerebellar TAC as an input. There was a good agreement (P < 0.05) between the values of Distribution Volume (DV) obtained from the K-means-clustered input function and those from the arterial blood samples. This work acts as a proof-of-principle that the use of cluster analysis on a PET data set could obviate the requirement for arterial cannulation when determining the input function for kinetic modelling of ligand binding, and that this may be a superior approach as compared to the other noninvasive alternatives.

Estimating the input function non-invasively for FDG-PET quantification with multiple linear regression analysis: simulation and verification with in vivo data

A novel statistical method, namely Regression-Estimated Input Function (REIF), is proposed in this study for the purpose of non-invasive estimation of the input function for fluorine-18 2-fluoro-2-deoxy- d-glucose positron emission tomography (FDG-PET) quantitative analysis. We collected 44 patients who had undergone a blood sampling procedure during their FDG-PET scans. First, we generated tissue time-activity curves of the grey matter and the whole brain with a segmentation technique for every subject. Summations of different intervals of these two curves were used as a feature vector, which also included the net injection dose. Multiple linear regression analysis was then applied to find the correlation between the input function and the feature vector. After a simulation study with in vivo data, the data of 29 patients were applied to calculate the regression coefficients, which were then used to estimate the input functions of the other 15 subjects. Comparing the estimated input functions with the corresponding real input functions, the averaged error percentages of the area under the curve and the cerebral metabolic rate of glucose (CMRGlc) were 12.13+/-8.85 and 16.60+/-9.61, respectively. Regression analysis of the CMRGlc values derived from the real and estimated input functions revealed a high correlation (r=0.91). No significant difference was found between the real CMRGlc and that derived from our regression-estimated input function (Student's t test, P>0.05). The proposed REIF method demonstrated good abilities for input function and CMRGlc estimation, and represents a reliable replacement for the blood sampling procedures in FDG-PET quantification.

Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer's dementia

Fluorodeoxyglucose positron emission tomography (PET) studies have found that patients with Alzheimer's dementia (AD) have abnormally low rates of cerebral glucose metabolism in posterior cingulate, parietal, temporal, and prefrontal cortex. We previously found that cognitively normal, late-middle-aged carriers of the apolipoprotein E epsilon4 allele, a common susceptibility gene for late-onset Alzheimer's dementia, have abnormally low rates of glucose metabolism in the same brain regions as patients with probable AD. We now consider whether epsilon4 carriers have these regional brain abnormalities as relatively young adults. Apolipoprotein E genotypes were established in normal volunteers 20-39 years of age. Clinical ratings, neuropsychological tests, magnetic resonance imaging, and PET were performed in 12 epsilon4 heterozygotes, all with the epsilon3/epsilon4 genotype, and 15 noncarriers of the epsilon4 allele, 12 of whom were individually matched for sex, age, and educational level. An automated algorithm was used to generate an aggregate surface-projection map that compared regional PET measurements in the two groups. The young adult epsilon4 carriers and noncarriers did not differ significantly in their sex, age, educational level, clinical ratings, or neuropsychological test scores. Like previously studied patients with probable AD and late-middle-aged epsilon4 carriers, the young epsilon4 carriers had abnormally low rates of glucose metabolism bilaterally in the posterior cingulate, parietal, temporal, and prefrontal cortex. Carriers of a common Alzheimer's susceptibility gene have functional brain abnormalities in young adulthood, several decades before the possible onset of dementia.

Clustering huge data sets for parametric PET imaging

A new preprocessing clustering technique for quantification of kinetic PET data is presented. A two-stage clustering process, which combines a precluster and a classic hierarchical cluster analysis, provides data which are clustered according to a distance measure between time activity curves (TACs). The resulting clustered mean TACs can be used directly for estimation of kinetic parameters at the cluster level, or to span a vector space that is used for subsequent estimation of voxel level kinetics. The introduction of preclustering significantly reduces the overall time for clustering of multiframe kinetic data. The efficiency and superiority of the preclustering scheme combined with thresholding is validated by comparison of the results for clustering both with and without preclustering for FDG-PET brain data of 13 healthy subjects.

Estimation of kinetic parameters without input functions: analysis of three methods for multichannel blind identification

Compartment modeling of dynamic medical image data implies that the concentration of the tracer over time in a particular region of the organ of interest is well modeled as a convolution of the tissue response with the tracer concentration in the blood stream. The tissue response is different for different tissues while the blood input is assumed to be the same for different tissues. The kinetic parameters characterizing the tissue responses can be estimated by multichannel blind identification methods. These algorithms use the simultaneous measurements of concentration in separate regions of the organ; if the regions have different responses, the measurement of the blood input function may not be required. Three blind identification algorithms are analyzed here to assess their utility in medical imaging: eigenvector-based algorithm for multichannel blind deconvolution; cross relations; and iterative quadratic maximum-likelihood (IQML). Comparisons of accuracy with conventional (not blind) identification techniques where the blood input is known are made as well. Tissue responses corresponding to a physiological two-compartment model are primarily considered. The statistical accuracies of estimation for the three methods are evaluated and compared for multiple parameter sets. The results show that IQML gives more accurate estimates than the other two blind identification methods.