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

Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging

Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation.

METHODS

The conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored.

RESULTS

The estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method.

CONCLUSION

The proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.

Validation of cardiac image-derived input functions for functional PET quantification

PURPOSE

Functional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations.

METHODS

Twenty healthy individuals underwent fPET/MR scans using [18F]FDG or 6-[18F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard.

RESULTS

For both radiotracer cohorts, moderate to high agreement (r: 0.60-0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87-0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975-0.998) with AIF-derived measurements.

CONCLUSION

Our proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.

Microglial activation persists beyond clinical recovery following sport concussion in collegiate athletes

Introduction

In concussion, clinical and physiological recovery are increasingly recognized as diverging definitions. This study investigated whether central microglial activation persisted in participants with concussion after receiving an unrestricted return-to-play (uRTP) designation using [18F]DPA-714 PET, an in vivo marker of microglia activation.

Methods

Eight (5 M, 3 F) current athletes with concussion (Group 1) and 10 (5 M, 5 F) healthy collegiate students (Group 2) were enrolled. Group 1 completed a pre-injury (Visit1) screen, follow-up Visit2 within 24 h of a concussion diagnosis, and Visit3 at the time of uRTP. Healthy participants only completed assessments at Visit2 and Visit3. At Visit2, all participants completed a multidimensional battery of tests followed by a blood draw to determine genotype and study inclusion. At Visit3, participants completed a clinical battery of tests, brain MRI, and brain PET; no imaging tests were performed outside of Visit3.

Results

For Group 1, significant differences were observed between Visits 1 and 2 (p < 0.05) in ImPACT, SCAT5 and SOT performance, but not between Visit1 and Visit3 for standard clinical measures (all p > 0.05), reflecting clinical recovery. Despite achieving clinical recovery, PET imaging at Visit3 revealed consistently higher [18F]DPA-714 tracer distribution volume (VT) of Group 1 compared to Group 2 in 10 brain regions (p < 0.001) analyzed from 164 regions of the whole brain, most notably within the limbic system, dorsal striatum, and medial temporal lobe. No notable differences were observed between clinical measures and VT between Group 1 and Group 2 at Visit3.

Discussion

Our study is the first to demonstrate persisting microglial activation in active collegiate athletes who were diagnosed with a sport concussion and cleared for uRTP based on a clinical recovery.

Image Quantification for TSPO PET with a Novel Image-Derived Input Function Method

There is a growing interest in using 18F-DPA-714 PET to study neuroinflammation and microglial activation through imaging the 18-kDa translocator protein (TSPO). Although quantification of 18F-DPA-714 binding can be achieved through kinetic modeling analysis with an arterial input function (AIF) measured with blood sampling procedures, the invasiveness of such procedures has been an obstacle for wide application. To address these challenges, we developed an image-derived input function (IDIF) that noninvasively estimates the arterial input function from the images acquired for 18F-DPA-714 quantification. Methods: The method entails three fully automatic steps to extract the IDIF, including a segmentation of voxels with highest likelihood of being the arterial blood over the carotid artery, a model-based matrix factorization to extract the arterial blood signal, and a scaling optimization procedure to scale the extracted arterial blood signal into the activity concentration unit. Two cohorts of human subjects were used to evaluate the extracted IDIF. In the first cohort of five subjects, arterial blood sampling was performed, and the calculated IDIF was validated against the measured AIF through the comparison of distribution volumes from AIF (VT,AIF) and IDIF (VT,IDIF). In the second cohort, PET studies from twenty-eight healthy controls without arterial blood sampling were used to compare VT,IDIF with VT,REF measured using a reference region-based analysis to evaluate whether it can distinguish high-affinity (HAB) and mixed-affinity (MAB) binders. Results: In the arterial blood-sampling cohort, VT derived from IDIF was found to be an accurate surrogate of the VT from AIF. The bias of VT, IDIF was −5.8 ± 7.8% when compared to VT,AIF, and the linear mixed effect model showed a high correlation between VT,AIF and VT, IDIF (p < 0.001). In the nonblood-sampling cohort, VT, IDIF showed a significance difference between the HAB and MAB healthy controls. VT, IDIF and standard uptake values (SUV) showed superior results in distinguishing HAB from MAB subjects than VT,REF. Conclusions: A novel IDIF method for 18F-DPA-714 PET quantification was developed and evaluated in this study. This IDIF provides a noninvasive alternative measurement of VT to quantify the TSPO binding of 18F-DPA-714 in the human brain through dynamic PET scans.

Comparison of Invasive and Non-invasive Estimation of [11C]PBR28 Binding in Non-human Primates

PURPOSE

To identify a reliable alternative to the full blood [¹¹C]PBR28 quantification method that would be easily replicated in multiple research and clinical settings.

PROCEDURES

Ten [¹¹C]PBR28 scans were acquired from 7 healthy non-human primates (NHP). Arterial input functions (AIFs) were averaged to create a population template input function (TIF). Population-based input functions were created by scaling the TIF with injected activity per body weight (PBIF) or unmetabolized tracer activity in blood at 15-,30-, and 60-min post-injection (PBIF15, PBIF30, and PBIF60). Two additional input functions were used: the native unmetabolized total plasma activity (Totals) and the Totals curve metabolite corrected by a scaled template parent fraction from a 30-min sample (TPF30-IF). Total distribution volumes (V_Ts) were calculated using PBIF, PBIF30, PBIF15, PBIF60, Totals, TPF30-IF, and the individual AIF (V_T^AIF). Distribution volume ratios (DVR) were computed using the cerebellum and the centrum semiovale (CSO), as pseudo-reference regions (DVR^Cereb, DVR^CSO). Results obtained with each method were compared to V_T^AIF. Applicability of these alternative methods was tested on an independent pharmacological challenge dataset of microglial activation and depletion. Evaluation was carried at baseline, immediately after intervention (acute), and weeks post-intervention (post-recovery).

RESULTS

V_Ts computed using PBIF15 and PBIF30 showed the best correlation to V_T^AIF (r > 0.90), while V_T derived from the blood-free-scaled PBIF showed poor correlation (r = 0.46) and DVR^CSO correlated the least (r = 0.26). In the pharmacological challenge study, most population-derived V_T values were comparable to V_T^AIF at baseline and showed varied sensitivity to challenges at acute and post-recovery evaluation. DVR values did not detect relevant changes.

CONCLUSIONS

Population-based input functions scaled with a single blood sample might be a useful alternative to using AIF to compute [¹¹C]PBR28 binding in healthy NHPs or animals with comparable metabolism and overall perform better than pseudo-reference regions approaches.

Validation of a combined image derived input function and venous sampling approach for the quantification of [18F]GE-179 PET binding in the brain

Blood-based kinetic analysis of PET data relies on an accurate estimate of the arterial plasma input function (PIF). An alternative to invasive measurements from arterial sampling is an image-derived input function (IDIF). However, an IDIF provides the whole blood radioactivity concentration, rather than the required free tracer radioactivity concentration in plasma. To estimate the tracer PIF, we corrected an IDIF from the carotid artery with estimates of plasma parent fraction (PF) and plasma-to-whole blood (PWB) ratio obtained from five venous samples. We compared the combined IDIF+venous approach to gold standard data from arterial sampling in 10 healthy volunteers undergoing [¹⁸F]GE-179 brain PET imaging of the NMDA receptor. Arterial and venous PF and PWB ratio estimates determined from 7 patients with traumatic brain injury (TBI) were also compared to assess the potential effect of medication. There was high agreement between areas under the curves of the estimates of PF (r = 0.99, p<0.001), PWB ratio (r = 0.93, p<0.001), and the PIF (r = 0.92, p<0.001) as well as total distribution volume (V_T) in 11 regions across the brain (r = 0.95, p<0.001). IDIF+venous V_T had a mean bias of -1.7% and a comparable regional coefficient of variation (arterial: 21.3 ± 2.5%, IDIF+venous: 21.5 ± 2.0%). Simplification of the IDIF+venous method to use only one venous sample provided less accurate V_T estimates (mean bias 9.9%; r = 0.71, p<0.001). A version of the method that avoids the need for blood sampling by combining the IDIF with population-based PF and PWB ratio estimates systematically underestimated V_T (mean bias -20.9%), and produced V_T estimates with a poor correlation to those obtained using arterial data (r = 0.45, p<0.001). Arterial and venous blood data from 7 TBI patients showed high correlations for PF (r = 0.92, p = 0.003) and PWB ratio (r = 0.93, p = 0.003). In conclusion, the IDIF+venous method with five venous samples provides a viable alternative to arterial sampling for quantification of [¹⁸F]GE-179 V_T.

A Nonhuman Primate PET Study: Measurement of Brain PDE4 Occupancy by Roflumilast Using (R)-[11C]Rolipram

PURPOSE

Phosphodiesterase 4 (PDE4) inhibition in the brain has been reported to improve cognitive function in animal models. Therefore, PDE4 inhibitors are one of key targets potential for drug development. Investigation of brain PDE4 occupancy would help to understand the effects of PDE4 inhibition to cognitive functions. Roflumilast is a selective phosphodiesterase type 4 (PDE4) inhibitor used clinically for severe chronic obstructive pulmonary disease, but the effects to the brain have not been well investigated. In this study, we aimed to investigate whether roflumilast entered the brain and occupied PDE4 in nonhuman primates.

PROCEDURES

Positron emission tomography (PET) measurements with (R)-[¹¹C]rolipram were performed at baseline and after intravenous (i.v.) administration of roflumilast (3.6 to 200 μg/kg) in three female rhesus monkeys. Arterial blood samples were taken to obtain the input function. Protein binding was measured to obtain the free fraction (fp) of the radioligand. Total distribution volume (V_T) and V_T/fp were calculated as outcome measures from two tissue compartment model. Lassen plot approach was taken to estimate the target occupancy.

RESULTS

The brain uptake of (R)-[¹¹C]rolipram decreased after roflumilast administration. PDE 4 occupancy by roflumilast showed dose- and plasma concentration-dependent increase, although PDE4 occupancy did not reach 50 % even after the administration of up to 200 μg/kg of roflumilast, regardless of outcome measures, V_T or V_T/fp.

CONCLUSIONS

This PET study showed that the brain PDE4 binding was blocked to a certain extent after i.v. administration of clinical relevant doses of roflumilast in nonhuman primates. Further clinical PET evaluation is needed to understand the relationship between PDE4 inhibition and potential improvement of cognitive function in human subjects.

Hybrid PET/MR Kernelised Expectation Maximisation Reconstruction for Improved Image-Derived Estimation of the Input Function from the Aorta of Rabbits

Positron emission tomography (PET) provides simple noninvasive imaging biomarkers for multiple human diseases which can be used to produce quantitative information from single static images or to monitor dynamic processes. Such kinetic studies often require the tracer input function (IF) to be measured but, in contrast to direct blood sampling, the image-derived input function (IDIF) provides a noninvasive alternative technique to estimate the IF. Accurate estimation can, in general, be challenging due to the partial volume effect (PVE), which is particularly important in preclinical work on small animals. The recently proposed hybrid kernelised ordered subsets expectation maximisation (HKEM) method has been shown to improve accuracy and contrast across a range of different datasets and count levels and can be used on PET/MR or PET/CT data. In this work, we apply the method with the purpose of providing accurate estimates of the aorta IDIF for rabbit PET studies. In addition, we proposed a method for the extraction of the aorta region of interest (ROI) using the MR and the HKEM image, to minimise the PVE within the rabbit aortic region-a method which can be directly transferred to the clinical setting. A realistic simulation study was performed with ten independent noise realisations while two, real data, rabbit datasets, acquired with the Biograph Siemens mMR PET/MR scanner, were also considered. For reference and comparison, the data were reconstructed using OSEM, OSEM with Gaussian postfilter and KEM, as well as HKEM. The results across the simulated datasets and different time frames show reduced PVE and accurate IDIF values for the proposed method, with 5% average bias (0.8% minimum and 16% maximum bias). Consistent results were obtained with the real datasets. The results of this study demonstrate that HKEM can be used to accurately estimate the IDIF in preclinical PET/MR studies, such as rabbit mMR data, as well as in clinical human studies. The proposed algorithm is made available as part of an open software library, and it can be used equally successfully on human or animal data acquired from a variety of PET/MR or PET/CT scanners.

The Evaluation of Dynamic FDG-PET for Detecting Epileptic Foci and Analyzing Reduced Glucose Phosphorylation in Refractory Epilepsy

Aims: Static fluorodeoxyglucose (FDG)-positron emission tomographic (PET) imaging plays an important role in the localization of epileptic foci. Dynamic FDG PET allows calculation of kinetic parameters. The aim of this study was to investigate whether kinetic parameters have potential for identifying epileptic foci, and to assess the correlation of parameters asymmetry indexes (ASYM) between dynamic and static FDG PET for understanding the pathophysiology of hypometabolism within intractable epilepsy.

Methods: Seventeen patients who had refractory epilepsy correctly localized by static FDG PET with good outcome after foci resection were included. Eight controls were also studied. We performed dynamic and static FDG PET scan before operation. Images of both scans were coregistered to the montreal neurological institute space, regional time activity curves and activity concentration (AC) were obtained by applying the automated anatomical labeling template to the two spatially normalized images, respectively. Kinetic parameters were obtained using a two-tissue non-reversible compartmental model with an image-derived input function. AC from the static scan was used. Side-to-side ASYM of both static AC and kinetic parameters were calculated and analyzed in the hypometabolic epileptogenic regions and non-epileptogenic regions.

Results: Higher values of ASYM from both kinetic parameters and static AC were found in the patients compared to the controls from epileptogenic regions. In the non-epileptogenic regions, no ASYM differences were seen between patients and controls for all parameters. In patients, static AC showed larger ASYM than influx (K₁) and efflux (k₂) of capillaries, but there were no statistical differences of ASYM between net metabolic flux (K_i) or the phosphorylation (k₃) and static AC. ASYM of static AC positively correlated with ASYM of k₃.

Conclusion: Dynamic FDG PET can provide equally effective in detecting the epileptic foci compared to static FDG PET in this small cohort. In addition, compared to capillary influx, the hypometabolism of epileptic foci may be related to reduced glucose phosphorylation.

Parametric mapping using spectral analysis for 11C-PBR28 PET reveals neuroinflammation in mild cognitive impairment subjects

PURPOSE

Neuroinflammation and microglial activation play an important role in amnestic mild cognitive impairment (MCI) and Alzheimer's disease. In this study, we investigated the spatial distribution of neuroinflammation in MCI subjects, using spectral analysis (SA) to generate parametric maps and quantify ¹¹C-PBR28 PET, and compared these with compartmental and other kinetic models of quantification.

METHODS

Thirteen MCI and nine healthy controls were enrolled in this study. Subjects underwent ¹¹C-PBR28 PET scans with arterial cannulation. Spectral analysis with an arterial plasma input function was used to generate ¹¹C-PBR28 parametric maps. These maps were then compared with regional ¹¹C-PBR28 V_T (volume of distribution) using a two-tissue compartment model and Logan graphic analysis. Amyloid load was also assessed with ¹⁸F-Flutemetamol PET.

RESULTS

With SA, three component peaks were identified in addition to blood volume. The ¹¹C-PBR28 impulse response function (IRF) at 90 min produced the lowest coefficient of variation. Single-subject analysis using this IRF demonstrated microglial activation in five out of seven amyloid-positive MCI subjects. IRF parametric maps of ¹¹C-PBR28 uptake revealed a group-wise significant increase in neuroinflammation in amyloid-positive MCI subjects versus HC in multiple cortical association areas, and particularly in the temporal lobe. Interestingly, compartmental analysis detected group-wise increase in ¹¹C-PBR28 binding in the thalamus of amyloid-positive MCI subjects, while Logan parametric maps did not perform well.

CONCLUSIONS

This study demonstrates for the first time that spectral analysis can be used to generate parametric maps of ¹¹C-PBR28 uptake, and is able to detect microglial activation in amyloid-positive MCI subjects. IRF parametric maps of ¹¹C-PBR28 uptake allow voxel-wise single-subject analysis and could be used to evaluate microglial activation in individual subjects.

Standardized Input Function for 18F-FDG PET Studies in Mice: A Cautionary Study

Aim of the Study

The aim of this study was to assess the accuracy of a standardized arterial input function (SAIF) for positron emission tomography ¹⁸F-FDG studies in mice. In particular, we tested whether the same SAIF could be applied to populations of mice whose fasting conditions differed.

Methods

The SAIF was first created from a population of fasting mice (n = 11) and validated within this group using a correlation analysis and a leave-one-out procedure. Then, the SAIF was prospectively applied to a population of non-fasting mice (n = 16). The SAIFs were scaled using a single individual blood sample taken 25 min after injection. The metabolic rates of glucose (CMRglc) calculated with the SAIFs were compared with the reference values obtained by full arterial sampling (AIF).

Results

In both populations of mice, CMRglc values showed a very small bias but an important variability. The SAIF/AIF CMRglc ratio in the fasting mice was 0.97 ± 0.22 (after excluding a major outlier). The SAIF/AIF CMRglc ratio in the non-fasting mice was 1.04 ± 0.22. This variability was due to the presence of cases in which the SAIF poorly estimated the shape of the input function based on full arterial sampling.

Conclusion

Although SAIF allows the estimation of the ¹⁸F-FDG mice input function with negligible bias and independently from the fasting state, errors in individual mice (as high as 30–50%) cause an important variability. Alternative techniques, such as image-derived input function, might be a better option for mice PET studies.

Determination of the Input Function at the Entry of the Tissue of Interest and Its Impact on PET Kinetic Modeling Parameters

Quantitative positron emission tomography (PET) imaging is employed with several measurement protocols all relying on the a priori determination of the input function (IF). The standard technique to determine IF is by blood sampling. However, a unique IF determined in a subject for a given PET study, either defined by sampling or in the images, and commonly utilized for all analyzed tissues in that study equally at rest and during interventions, is expected to provoke biases in the rate constants and in tissue blood volume. The determination of a specific IF at the site of the tissue to be analyzed enhances PET accuracy and renders PET imaging less invasive.

Image-derived input function with factor analysis and a-priori information

BACKGROUND

Quantitative PET studies often require the cumbersome and invasive procedure of arterial cannulation to measure the input function. This study sought to minimize the number of necessary blood samples by developing a factor-analysis-based image-derived input function (IDIF) methodology for dynamic PET brain studies.

MATERIALS AND METHODS

IDIF estimation was performed as follows: (a) carotid and background regions were segmented manually on an early PET time frame; (b) blood-weighted and tissue-weighted time-activity curves (TACs) were extracted with factor analysis; (c) factor analysis results were denoised and scaled using the voxels with the highest blood signal; (d) using population data and one blood sample at 40 min, whole-blood TAC was estimated from postprocessed factor analysis results; and (e) the parent concentration was finally estimated by correcting the whole-blood curve with measured radiometabolite concentrations. The methodology was tested using data from 10 healthy individuals imaged with [(11)C](R)-rolipram. The accuracy of IDIFs was assessed against full arterial sampling by comparing the area under the curve of the input functions and by calculating the total distribution volume (VT).

RESULTS

The shape of the image-derived whole-blood TAC matched the reference arterial curves well, and the whole-blood area under the curves were accurately estimated (mean error 1.0±4.3%). The relative Logan-V(T) error was -4.1±6.4%. Compartmental modeling and spectral analysis gave less accurate V(T) results compared with Logan.

CONCLUSION

A factor-analysis-based IDIF for [(11)C](R)-rolipram brain PET studies that relies on a single blood sample and population data can be used for accurate quantification of Logan-V(T) values.

Comparison of standardized uptake values with volume of distribution for quantitation of [(11)C]PBR28 brain uptake

INTRODUCTION

[(11)C]PBR28 is a high-affinity ligand for the Translocator Protein 18 kDa (TSPO), which is considered to be a marker for microglial activation. Volume of distribution (VT) estimated with an arterial plasma input function is the gold standard for quantitation of [(11)C]PBR28 binding. However, arterial sampling is impractical at many PET sites for multiple reasons. Reference region modeling approaches are not ideal for TSPO tracers, as the existence of a true reference region cannot be assumed. Given that it would be desirable to have a non-invasive index of [(11)C]PBR28 binding, we elected to study the utility of the semi-quantitative metric, standardized uptake value (SUV) for use in brain [(11)C]PBR PET studies. The primary goal of this study was to determine the relationship between SUV and VT.

METHODS

We performed a retrospective analysis of data from sixteen [(11)C]PBR28 PET scans acquired in baboons at baseline and at multiple time points after IV injection of lipopolysaccharide, an endotoxin that transiently induces neuroinflammation. For each scan, data from 14 brain regions of interest were studied. VT was estimated with the Logan plot, using metabolite-corrected input functions. SUV was calculated with data from 30 to 60 minutes after [(11)C]PBR28 injection.

RESULTS

Within individual PET studies, SUV tended to correlate well with VT. Across studies, the relationship between SUV and VT was variable.

CONCLUSIONS

From study to study, there was variability in the degree of correlation between [(11)C]PBR28 VT and SUV. There are multiple physiological factors that may contribute to this variance.

ADVANCES IN KNOWLEDGE

As currently applied, the non-invasive measurement of SUV does not appear to be a reliable outcome variable for [(11)C]PBR28. Additional work is needed to discover the source of the discrepancy in SUV between [(11)C]PBR28 scans.

IMPLICATIONS FOR PATIENT CARE

There is a need to develop alternatives to arterial plasma input functions for TSPO ligands in order to facilitate multi-center trials.

Kinetic modeling without accounting for the vascular component impairs the quantification of [(11)C]PBR28 brain PET data

The positron emission tomography radioligand [(11)C]PBR28 targets translocator protein (18 kDa) (TSPO) and is a potential marker of neuroinflammation. [(11)C]PBR28 binding is commonly quantified using a two-tissue compartment model and an arterial input function. Previous studies with [(11)C]-(R)-PK11195 demonstrated a slow irreversible binding component to the TSPO proteins localized in the endothelium of brain vessels, such as venous sinuses and arteries. However, the impact of this component on the quantification of [(11)C]PBR28 data has never been investigated. In this work we propose a novel kinetic model for [(11)C]PBR28. This model hypothesizes the existence of an additional irreversible component from the blood to the endothelium. The model was tested on a data set of 19 healthy subjects. A simulation was also performed to quantify the error generated by the standard two-tissue compartmental model when the presence of the irreversible component is not taken into account. Our results show that when the vascular component is included in the model the estimates that include the vascular component (2TCM-1K) are more than three-fold smaller, have a higher time stability and are better correlated to brain mRNA TSPO expression than those that do not include the model (2TCM).

18F-FLT uptake kinetics in head and neck squamous cell carcinoma: a PET imaging study

PURPOSE

To analyze the kinetics of 3(')-deoxy-3(')-[F-18]-fluorothymidine (18F-FLT) uptake by head and neck squamous cell carcinomas and involved nodes imaged using positron emission tomography (PET).

METHODS

Two- and three-tissue compartment models were fitted to 12 tumor time-activity-curves (TACs) obtained for 6 structures (tumors or involved nodes) imaged in ten dynamic PET studies of 1 h duration, carried out for five patients. The ability of the models to describe the data was assessed using a runs test, the Akaike information criterion (AIC) and leave-one-out cross-validation. To generate parametric maps the models were also fitted to TACs of individual voxels. Correlations between maps of different parameters were characterized using Pearson'sr coefficient; in particular the phosphorylation rate-constants k3-2tiss and k5 of the two- and three-tissue models were studied alongside the flux parameters KFLT- 2tiss and KFLT of these models, and standardized uptake values (SUV). A methodology based on expectation-maximization clustering and the Bayesian information criterion ("EM-BIC clustering") was used to distil the information from noisy parametric images.

RESULTS

Fits of two-tissue models 2C3K and 2C4K and three-tissue models 3C5K and 3C6K comprising three, four, five, and six rate-constants, respectively, pass the runs test for 4, 8, 10, and 11 of 12 tumor TACs. The three-tissue models have lower AIC and cross-validation scores for nine of the 12 tumors. Overall the 3C6K model has the lowest AIC and cross-validation scores and its fitted parameter values are of the same orders of magnitude as literature estimates. Maps of KFLT and KFLT- 2tiss are strongly correlated (r = 0.85) and also correlate closely with SUV maps (r = 0.72 for KFLT- 2tiss, 0.64 for KFLT). Phosphorylation rate-constant maps are moderately correlated with flux maps (r = 0.48 for k3-2tiss vs KFLT- 2tiss and r = 0.68 for k5 vs KFLT); however, neither phosphorylation rate-constant correlates significantly with SUV. EM-BIC clustering reduces the parametric maps to a small number of levels--on average 5.8, 3.5, 3.4, and 1.4 for KFLT- 2tiss, KFLT, k3-2tiss, and k5. This large simplification is potentially useful for radiotherapy dose-painting, but demonstrates the high noise in some maps. Statistical simulations show that voxel level noise degrades TACs generated from the 3C6K model sufficiently that the average AIC score, parameter bias, and total uncertainty of 2C4K model fits are similar to those of 3C6K fits, whereas at the whole tumor level the scores are lower for 3C6K fits.

CONCLUSIONS

For the patients studied here, whole tumor FLT uptake time-courses are represented better overall by a three-tissue than by a two-tissue model. EM-BIC clustering simplifies noisy parametric maps, providing the best description of the underlying information they contain and is potentially useful for radiotherapy dose-painting. However, the clustering highlights the large degree of noise present in maps of the phosphorylation rate-constantsk5 and k3-2tiss, which are conceptually tightly linked to cellular proliferation. Methods must be found to make these maps more robust-either by constraining other model parameters or modifying dynamic imaging protocols.

Image-derived input function derived from a supervised clustering algorithm: methodology and validation in a clinical protocol using [11C](R)-rolipram

Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [¹¹C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter- and intra-operator variability. To overcome this limitation, we developed a fully automated technique for deriving IDIF with a supervised clustering algorithm (SVCA). To validate this technique, 25 healthy controls and 26 patients with moderate to severe major depressive disorder (MDD) underwent T1-weighted brain magnetic resonance imaging (MRI) and a 90-minute [¹¹C](R)-rolipram PET scan. For each subject, metabolite-corrected input function was measured from the radial artery. SVCA templates were obtained from 10 additional healthy subjects who underwent the same MRI and PET procedures. Cluster-IDIF was obtained as follows: 1) template mask images were created for carotid and surrounding tissue; 2) parametric image of weights for blood were created using SVCA; 3) mask images to the individual PET image were inversely normalized; 4) carotid and surrounding tissue time activity curves (TACs) were obtained from weighted and unweighted averages of each voxel activity in each mask, respectively; 5) partial volume effects and radiometabolites were corrected using individual arterial data at four points. Logan-distribution volume (V_T/f_P) values obtained by cluster-IDIF were similar to reference results obtained using arterial data, as well as those obtained using manual-IDIF; 39 of 51 subjects had a V_T/f_P error of <5%, and only one had error >10%. With automatic voxel selection, cluster-IDIF curves were less noisy than manual-IDIF and free of operator-related variability. Cluster-IDIF showed widespread decrease of about 20% [¹¹C](R)-rolipram binding in the MDD group. Taken together, the results suggest that cluster-IDIF is a good alternative to full arterial input function for estimating Logan-V_T/f_P in [¹¹C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.

Estimation of input functions from dynamic [18F]FLT PET studies of the head and neck with correction for partial volume effects

BACKGROUND

We present a method for extracting arterial input functions from dynamic [18F]FLT PET images of the head and neck, directly accounting for the partial volume effect. The method uses two blood samples, for which the optimum collection times are assessed.

METHODS

Six datasets comprising dynamic PET images, co-registered computed tomography (CT) scans and blood-sampled input functions were collected from four patients with head and neck tumours. In each PET image set, a region was identified that comprised the carotid artery (outlined on CT images) and surrounding tissue within the voxels containing the artery. The time course of activity in the region was modelled as the sum of the blood-sampled input function and a compartmental model of tracer uptake in the surrounding tissue.The time course of arterial activity was described by a mathematical function with seven parameters. The parameters of the function and the compartmental model were simultaneously estimated, aiming to achieve the best match between the modelled and imaged time course of regional activity and the best match of the estimated blood activity to between 0 and 3 samples. The normalised root-mean-square (RMSnorm) differences and errors in areas under the curves (AUCs) between the measured and estimated input functions were assessed.

RESULTS

A one-compartment model of tracer movement to and from the artery best described uptake in the tissue surrounding the artery, so the final model of the input function and tissue kinetics has nine parameters to be estimated. The estimated and blood-sampled input functions agreed well when two blood samples, obtained at times between 2 and 8 min and between 8 and 60 min, were used in the estimation process (RMSnorm values of 1.1 ± 0.5 and AUC errors for the peak and tail region of the curves of 15% ± 9% and 10% ± 8%, respectively). A third blood sample did not significantly improve the accuracy of the estimated input functions.

CONCLUSIONS

Input functions for FLT-PET studies of the head and neck can be estimated well using a one-compartment model of tracer movement and TWO blood samples obtained after the peak in arterial activity.

Calibrated image-derived input functions for the determination of the metabolic uptake rate of glucose with [18F]-FDG PET

Purpose

We investigated the use of a simple calibration method to remove bias in previously proposed approaches to image-derived input functions (IDIFs) when used to calculate the metabolic uptake rate of glucose (K_m) from dynamic [¹⁸F]-FDG PET scans of the thigh. Our objective was to obtain nonbiased, low-variance K_m values without blood sampling.

Materials and methods

We evaluated eight previously proposed IDIF methods. K_m values derived from these IDIFs were compared with K_m values calculated from the arterial blood samples (gold standard). We used linear regression to extract calibration parameters to remove bias. Following calibration, cross-validation and bootstrapping were used to estimate the mean square error and variance.

Results

Three of the previously proposed methods failed mainly because of zero-crossings of the IDIF. The remaining five methods were improved by calibration, yielding unbiased K_m values. The method with the lowest SD yielded an SD of 0.0017/min – that is, below 10% of the muscle K_m value in this study.

Conclusion

Previously proposed IDIF methods can be improved by using a simple calibration procedure. The calibration procedure may be used in other studies, thus obviating the need for arterial blood sampling, once the calibration parameters have been established in a subgroup of participants. The method has potential for use in other parts of the body as it is robust with regard to partial volume effects.

Brain tissue selection procedures for image derived input functions derived using independent components analysis

Absolute quantification of positron emission tomography (PET) data requires invasive blood sampling in order to obtain the arterial input function (AIF). This procedure involves considerable costs and risks. A less invasive approach is to estimate the AIF directly from images, known as an image derived input function (IDIF). One promising method, EPICA, extracts IDIF by applying independent components analysis (ICA) on dynamic PET data from the entire brain. EPICA requires exclusion of non-brain voxels from the PET images, which is achieved by using a brain mask prior to ICA. Including the entire brain in the mask may degrade the performance of ICA due to noise, artifacts and confounding information. We applied EPICA to 3 [(18)F]FDG and 3 [(11)C]WAY data sets and investigated if altering the brain mask by including or excluding tissue structures improves EPICA performance. EPICA applied to whole brain data yields poor performance but with the appropriate brain mask IDIF curves approximate the AIF well. Different tissue structures are important for different radiotracers suggesting that the kinetics of the radiotracer and its diffusion characteristics in the brain influence IDIF estimation with ICA.

Arterial input function derived from pairwise correlations between PET-image voxels

A metabolite corrected arterial input function is a prerequisite for quantification of positron emission tomography (PET) data by compartmental analysis. This quantitative approach is also necessary for radioligands without suitable reference regions in brain. The measurement is laborious and requires cannulation of a peripheral artery, a procedure that can be associated with patient discomfort and potential adverse events. A non invasive procedure for obtaining the arterial input function is thus preferable. In this study, we present a novel method to obtain image-derived input functions (IDIFs). The method is based on calculation of the Pearson correlation coefficient between the time-activity curves of voxel pairs in the PET image to localize voxels displaying blood-like behavior. The method was evaluated using data obtained in human studies with the radioligands [(11)C]flumazenil and [(11)C]AZ10419369, and its performance was compared with three previously published methods. The distribution volumes (VT) obtained using IDIFs were compared with those obtained using traditional arterial measurements. Overall, the agreement in VT was good (∼3% difference) for input functions obtained using the pairwise correlation approach. This approach performed similarly or even better than the other methods, and could be considered in applied clinical studies. Applications to other radioligands are needed for further verification.

Population-based input function modeling for [(18)F]FMPEP-d 2, an inverse agonist radioligand for cannabinoid CB1 receptors: validation in clinical studies

Background

Population-based input function (PBIF) may be a valid alternative to full blood sampling for quantitative PET imaging. PBIF is typically validated by comparing its quantification results with those obtained via arterial sampling. However, for PBIF to be employed in actual clinical research studies, its ability to faithfully capture the whole spectrum of results must be assessed. The present study validated a PBIF for [¹⁸F]FMPEP-d₂, a cannabinoid CB₁ receptor radioligand, in healthy volunteers, and also attempted to utilize PBIF to replicate three previously published clinical studies in which the input function was acquired with arterial sampling.

Methods

The PBIF was first created and validated with data from 42 healthy volunteers. This PBIF was used to assess the retest variability of [¹⁸F]FMPEP-d₂, and then to quantify CB₁ receptors in alcoholic patients (n = 18) and chronic daily cannabis smokers (n = 29). Both groups were scanned at baseline and after 2–4 weeks of monitored drug abstinence.

Results

PBIF yielded accurate results in the 42 healthy subjects (average Logan-distribution volume (V_T) was 13.3±3.8 mL/cm³ for full sampling and 13.2±3.8 mL/cm³ for PBIF; R² = 0.8765, p<0.0001) and test-retest results were comparable to those obtained with full sampling (variability: 16%; intraclass correlation coefficient: 0.89). PBIF accurately replicated the alcoholism study, showing a widespread ∼20% reduction of CB₁ receptors in alcoholic subjects, without significant change after abstinence. However, a small PBIF-V_T bias of −9% was unexpectedly observed in cannabis smokers. This bias led to substantial errors, including a V_T decrease in regions that had shown no downregulation in the full input function. Simulated data showed that the original findings could only have been replicated with a PBIF bias between −6% and +4%.

Conclusions

Despite being initially well validated in healthy subjects, PBIF may misrepresent clinical protocol results and be a source of variability between different studies and institutions.

Cerebral blood flow with [15O]water PET studies using an image-derived input function and MR-defined carotid centerlines

Full quantitative analysis of brain PET data requires knowledge of the arterial input function into the brain. Such data are normally acquired by arterial sampling with corrections for delay and dispersion to account for the distant sampling site. Several attempts have been made to extract an image-derived input function (IDIF) directly from the internal carotid arteries that supply the brain and are often visible in brain PET images. We have devised a method of delineating the internal carotids in co-registered magnetic resonance (MR) images using the level-set method and applying the segmentations to PET images using a novel centerline approach. Centerlines of the segmented carotids were modeled as cubic splines and re-registered in PET images summed over the early portion of the scan. Using information from the anatomical center of the vessel should minimize partial volume and spillover effects. Centerline time-activity curves were taken as the mean of the values for points along the centerline interpolated from neighboring voxels. A scale factor correction was derived from calculation of cerebral blood flow (CBF) using gold standard arterial blood measurements. We have applied the method to human subject data from multiple injections of [(15)O]water on the HRRT. The method was assessed by calculating the area under the curve (AUC) of the IDIF and the CBF, and comparing these to values computed using the gold standard arterial input curve. The average ratio of IDIF to arterial AUC (apparent recovery coefficient: aRC) across 9 subjects with multiple (n = 69) injections was 0.49 ± 0.09 at 0-30 s post tracer arrival, 0.45 ± 0.09 at 30-60 s, and 0.46 ± 0.09 at 60-90 s. Gray and white matter CBF values were 61.4 ± 11.0 and 15.6 ± 3.0 mL/min/100 g tissue using sampled blood data. Using IDIF centerlines scaled by the average aRC over each subjects' injections, gray and white matter CBF values were 61.3 ± 13.5 and 15.5 ± 3.4 mL/min/100 g tissue. Using global average aRC values, the means were unchanged, and intersubject variability was noticeably reduced. This MR-based centerline method with local re-registration to [(15)O]water PET yields a consistent IDIF over multiple injections in the same subject, thus permitting the absolute quantification of CBF without arterial input function measurements.

Image-derived input function in PET brain studies: blood-based methods are resistant to motion artifacts

BACKGROUND

Image-derived input function (IDIF) from carotid arteries is an elegant alternative to full arterial blood sampling for brain PET studies. However, a recent study using blood-free IDIFs found that this method is particularly vulnerable to patient motion. The present study used both simulated and clinical [11C](R)-rolipram data to assess the robustness of a blood-based IDIF method (a method that is ultimately normalized with blood samples) with regard to motion artifacts.

METHODS

The impact of motion on the accuracy of IDIF was first assessed with an analytical simulation of a high-resolution research tomograph using a numerical phantom of the human brain, equipped with internal carotids. Different degrees of translational (from 1 to 20 mm) and rotational (from 1 to 15°) motions were tested. The impact of motion was then tested on the high-resolution research tomograph dynamic scans of three healthy volunteers, reconstructed with and without an online motion correction system. IDIFs and Logan-distribution volume (VT) values derived from simulated and clinical scans with motion were compared with those obtained from the scans with motion correction.

RESULTS

In the phantom scans, the difference in the area under the curve (AUC) for the carotid time-activity curves was up to 19% for rotations and up to 66% for translations compared with the motionless simulation. However, for the final IDIFs, which were fitted to blood samples, the AUC difference was 11% for rotations and 8% for translations. Logan-VT errors were always less than 10%, except for the maximum translation of 20 mm, in which the error was 18%. Errors in the clinical scans without motion correction appeared to be minor, with differences in AUC and Logan-VT always less than 10% compared with scans with motion correction.

CONCLUSION

When a blood-based IDIF method is used for neurological PET studies, the motion of the patient affects IDIF estimation and kinetic modeling only minimally.

Population-based input function and image-derived input function for [¹¹C](R)-rolipram PET imaging: methodology, validation and application to the study of major depressive disorder

Quantitative PET studies of neuroreceptor tracers typically require that arterial input function be measured. The aim of this study was to explore the use of a population-based input function (PBIF) and an image-derived input function (IDIF) for [(11)C](R)-rolipram kinetic analysis, with the goal of reducing - and possibly eliminating - the number of arterial blood samples needed to measure parent radioligand concentrations.

METHODS

A PBIF was first generated using [(11)C](R)-rolipram parent time-activity curves from 12 healthy volunteers (Group 1). Both invasive (blood samples) and non-invasive (body weight, body surface area, and lean body mass) scaling methods for PBIF were tested. The scaling method that gave the best estimate of the Logan-V(T) values was then used to determine the test-retest variability of PBIF in Group 1 and then prospectively applied to another population of 25 healthy subjects (Group 2), as well as to a population of 26 patients with major depressive disorder (Group 3). Results were also compared to those obtained with an image-derived input function (IDIF) from the internal carotid artery. In some subjects, we measured arteriovenous differences in [(11)C](R)-rolipram concentration to see whether venous samples could be used instead of arterial samples. Finally, we assessed the ability of IDIF and PBIF to discriminate depressed patients (MDD) and healthy subjects.

RESULTS

Arterial blood-scaled PBIF gave better results than any non-invasive scaling technique. Excellent results were obtained when the blood-scaled PBIF was prospectively applied to the subjects in Group 2 (V(T) ratio 1.02±0.05; mean±SD) and Group 3 (V(T) ratio 1.03±0.04). Equally accurate results were obtained for two subpopulations of subjects drawn from Groups 2 and 3 who had very differently shaped (i.e. "flatter" or "steeper") input functions compared to PBIF (V(T) ratio 1.07±0.04 and 0.99±0.04, respectively). Results obtained via PBIF were equivalent to those obtained via IDIF (V(T) ratio 0.99±0.05 and 1.00±0.04 for healthy subjects and MDD patients, respectively). Retest variability of PBIF was equivalent to that obtained with full input function and IDIF (14.5%, 15.2%, and 14.1%, respectively). Due to [(11)C](R)-rolipram arteriovenous differences, venous samples could not be substituted for arterial samples. With both IDIF and PBIF, depressed patients had a 20% reduction in [(11)C](R)-rolipram binding as compared to control (two-way ANOVA: p=0.008 and 0.005, respectively). These results were almost equivalent to those obtained using 23 arterial samples.

CONCLUSION

Although some arterial samples are still necessary, both PBIF and IDIF are accurate and precise alternatives to full arterial input function for [(11)C](R)-rolipram PET studies. Both techniques give accurate results with low variability, even for clinically different groups of subjects and those with very differently shaped input functions.

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.

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.

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.

Advances in biomathematical modeling for PET neuroreceptor imaging

The quantitative application of PET neuroreceptor imaging to study pathophysiology, diagnostics and drug development has continued to benefit from associated advances in biomathematical imaging methodology. We review some of these advances with particular focus on multi-modal image processing, tracer kinetic modeling, occupancy studies and discovery and development of novel radioligands.: