Image derived input functions for dynamic High Resolution Research Tomograph PET brain studies

Non-invasive kinetic modelling approaches for quantitative analysis of brain PET studies

Pharmacokinetic modelling with arterial sampling is the gold standard for analysing dynamic PET data of the brain. However, the invasive character of arterial sampling prevents its widespread clinical application. Several methods have been developed to avoid arterial sampling, in particular reference region methods. Unfortunately, for some tracers or diseases, no suitable reference region can be defined. For these cases, other potentially non-invasive approaches have been proposed: (1) a population based input function (PBIF), (2) an image derived input function (IDIF), or (3) simultaneous estimation of the input function (SIME). This systematic review aims to assess the correspondence of these non-invasive methods with the gold standard. Studies comparing non-invasive pharmacokinetic modelling methods with the current gold standard methods using an input function derived from arterial blood samples were retrieved from PubMed/MEDLINE (until December 2021). Correlation measurements were extracted from the studies. The search yielded 30 studies that correlated outcome parameters (V_T, DVR, or BP_ND for reversible tracers; K_i or CMR_glu for irreversible tracers) from a potentially non-invasive method with those obtained from modelling using an arterial input function. Some studies provided similar results for PBIF, IDIF, and SIME-based methods as for modelling with an arterial input function (R² = 0.59-1.00, R² = 0.71-1.00, R² = 0.56-0.96, respectively), if the non-invasive input curve was calibrated with arterial blood samples. Even when the non-invasive input curve was calibrated with venous blood samples or when no calibration was applied, moderate to good correlations were reported, especially for the IDIF and SIME (R² = 0.71-1.00 and R² = 0.36-0.96, respectively). Overall, this systematic review illustrates that non-invasive methods to generate an input function are still in their infancy. Yet, IDIF and SIME performed well, not only with arterial blood calibration, but also with venous or no blood calibration, especially for some tracers without plasma metabolites, which would potentially make these methods better suited for clinical application. However, these methods should still be properly validated for each individual tracer and application before implementation.

Total-Body PET Multiparametric Imaging of Cancer Using a Voxelwise Strategy of Compartmental Modeling

Quantitative dynamic PET with compartmental modeling has the potential to enable multiparametric imaging and more accurate quantification than static PET imaging. Conventional methods for parametric imaging commonly use a single kinetic model for all image voxels and neglect the heterogeneity of physiologic models, which can work well for single-organ parametric imaging but may significantly compromise total-body parametric imaging on a scanner with a long axial field of view. In this paper, we evaluate the necessity of voxelwise compartmental modeling strategies, including time delay correction (TDC) and model selection, for total-body multiparametric imaging. Methods: Ten subjects (5 patients with metastatic cancer and 5 healthy volunteers) were scanned on a total-body PET/CT system after injection of 370 MBq of 18F-FDG. Dynamic data were acquired for 60 min. Total-body parametric imaging was performed using 2 approaches. One was the conventional method that uses a single irreversible 2-tissue-compartment model with and without TDC. The second approach selects the best kinetic model from 3 candidate models for individual voxels. The differences between the 2 approaches were evaluated for parametric imaging of microkinetic parameters and the 18F-FDG net influx rate, KiResults: TDC had a nonnegligible effect on kinetic quantification of various organs and lesions. The effect was larger in lesions with a higher blood volume. Parametric imaging of Ki with the standard 2-tissue-compartment model introduced vascular-region artifacts, which were overcome by the voxelwise model selection strategy. Conclusion: The time delay and appropriate kinetic model vary in different organs and lesions. Modeling of the time delay of the blood input function and model selection improved total-body multiparametric imaging.

PET Imaging of Leg Arteries for Determining the Input Function in PET/MRI Brain Studies Using a Compact, MRI-Compatible PET System

In this study, we used a compact, high-resolution, and MRI-compatible PET camera (VersaPET) to assess the feasibility of measuring the image-derived input function (IDIF) from arteries in the leg with the ultimate goal of enabling fully quantitative PET brain imaging without blood sampling. We used this approach in five ¹⁸F-FDG PET/MRI brain studies in which the input function was also acquired using the gold standard of serial arterial blood sampling. After accounting for partial volume, dispersion, and calibration effects, we compared the metabolic rates of glucose (MRglu) quantified from VersaPET IDIFs in 80 brain regions to those using the gold standard and achieved a bias and variability of <5% which is within the range of reported test-retest values for this type of study. We also achieved a strong linear relationship (R² >0.97) against the gold standard across regions. The results of this preliminary study are promising and support further studies to optimize methods, validate in a larger cohort, and extend to the modeling of other radiotracers.

Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of 15O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects.Twenty-five subjects underwent two 10 min dynamic 15O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-based AIF prediction model, considering both baseline and acetazolamide scans as training data. The MLIF approach was evaluated by comparing AIF and MLIF curves, as well as whole-brain grey matter CBF values estimated by kinetic modelling derived with either AIF or MLIF.The results showed that AIF and MLIF curves were similar and that corresponding CBF values were highly correlated and successfully differentiated before and after acetazolamide medication. In conclusion, our non-invasive MLIF method shows potential to replace the AIF obtained from blood sampling for CBF measurements using 15O-water PET and kinetic modelling.

Population-based input function for TSPO quantification and kinetic modeling with [11C]-DPA-713

INTRODUCTION

Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF.

MATERIALS AND METHODS

Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected, (b) area under AIF curve (AUC), and (c) Weightsubject×AUC. The variability in the VT measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed.

RESULTS

Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is -10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ± 38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%).

CONCLUSIONS

PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.

Simulation Study of a Frame-Based Motion Correction Algorithm for Positron Emission Imaging

Positron emission tomography (PET) is a functional non-invasive imaging modality that uses radioactive substances (radiotracers) to measure changes in metabolic processes. Advances in scanner technology and data acquisition in the last decade have led to the development of more sophisticated PET devices with good spatial resolution (1–3 mm of full width at half maximum (FWHM)). However, there are involuntary motions produced by the patient inside the scanner that lead to image degradation and potentially to a misdiagnosis. The adverse effect of the motion in the reconstructed image increases as the spatial resolution of the current scanners continues improving. In order to correct this effect, motion correction techniques are becoming increasingly popular and further studied. This work presents a simulation study of an image motion correction using a frame-based algorithm. The method is able to cut the acquired data from the scanner in frames, taking into account the size of the object of study. This approach allows working with low statistical information without losing image quality. The frames are later registered using spatio-temporal registration developed in a multi-level way. To validate these results, several performance tests are applied to a set of simulated moving phantoms. The results obtained show that the method minimizes the intra-frame motion, improves the signal intensity over the background in comparison with other literature methods, produces excellent values of similarity with the ground-truth (static) image and is able to find a limit in the patient-injected dose when some prior knowledge of the lesion is present.

Towards quantitative [18F]FDG-PET/MRI of the brain: Automated MR-driven calculation of an image-derived input function for the non-invasive determination of cerebral glucose metabolic rates

Absolute quantification of PET brain imaging requires the measurement of an arterial input function (AIF), typically obtained invasively via an arterial cannulation. We present an approach to automatically calculate an image-derived input function (IDIF) and cerebral metabolic rates of glucose (CMRGlc) from the [18F]FDG PET data using an integrated PET/MRI system. Ten healthy controls underwent test-retest dynamic [18F]FDG-PET/MRI examinations. The imaging protocol consisted of a 60-min PET list-mode acquisition together with a time-of-flight MR angiography scan for segmenting the carotid arteries and intermittent MR navigators to monitor subject movement. AIFs were collected as the reference standard. Attenuation correction was performed using a separate low-dose CT scan. Assessment of the percentage difference between area-under-the-curve of IDIF and AIF yielded values within ±5%. Similar test-retest variability was seen between AIFs (9 ± 8) % and the IDIFs (9 ± 7) %. Absolute percentage difference between CMRGlc values obtained from AIF and IDIF across all examinations and selected brain regions was 3.2% (interquartile range: (2.4-4.3) %, maximum < 10%). High test-retest intravariability was observed between CMRGlc values obtained from AIF (14%) and IDIF (17%). The proposed approach provides an IDIF, which can be effectively used in lieu of AIF.

Hybrid PET/MRI imaging in healthy unsedated newborn infants with quantitative rCBF measurements using 15O-water PET

In this study, a new hybrid PET/MRI method for quantitative regional cerebral blood flow (rCBF) measurements in healthy newborn infants was assessed and the low values of rCBF in white matter previously obtained by arterial spin labeling (ASL) were tested. Four healthy full-term newborn subjects were scanned in a PET/MRI scanner during natural sleep after median intravenous injection of 14 MBq ¹⁵O-water. Regional CBF was quantified using a one-tissue-compartment model employing an image-derived input function (IDIF) from the left ventricle. PET rCBF showed the highest values in the thalami, mesencephalon and brain stem and the lowest in cortex and unmyelinated white matter. The average global CBF was 17.8 ml/100 g/min. The average frontal and occipital unmyelinated white matter CBF was 10.3 ml/100 g/min and average thalamic CBF 31.3 ml/100 g/min. The average white matter/thalamic ratio CBF was 0.36, significantly higher than previous ASL data. The rCBF ASL measurements were all unsuccessful primarily owing to subject movement. In this study, we demonstrated for the first time, a minimally invasive PET/MRI method using low activity ¹⁵O-water PET for quantitative rCBF assessment in unsedated healthy newborn infants and found a white/grey matter CBF ratio similar to that of the adult human brain.

High resolution BrainPET combined with simultaneous MRI

After the successful clinical introduction of PET/CT, a novel hybrid imaging technology combining PET with the versatile attributes of MRI is emerging. At the Forschungszentrum Jülich, one of four prototypes available worldwide combining a commercial 3T MRI with a newly developed BrainPET insert has been installed, allowing simultaneous data acquisition with PET and MRI. The BrainPET is equipped with LSO crystals of 2.5 mm width and Avalanche photodiodes (APD) as readout electronics. Here we report on some performance characteristics obtained by phantom studies and also on the initial BrainPET studies on various patients as compared with a conventional HR+ PET-only scanner. Material, methods: The radiotracers [18F]-fluoroethyl- tyrosine (FET), [11C]-flumazenil and [18F]-FP-CIT were applied. Results: Comparing komthe PET data obtained with the BrainPET to those of the HR+ scanner demonstrated the high image quality and the superior resolution capability of the BrainPET. Furthermore, it is shown that various MR images of excellent quality could be acquired simultaneously with BrainPET scans without any relevant artefacts. Discussion, conclusion: Initial experiences with the hybrid MRI/BrainPET indicate a promising basis for further developments of this unique technique allowing simultaneous PET imaging combined with both anatomical and functional MRI.

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.

Test-retest repeatability of myocardial blood flow and infarct size using ¹¹C-acetate micro-PET imaging in mice

PURPOSE

Global and regional responses of absolute myocardial blood flow index (iMBF) are used as surrogate markers to assess response to therapies in coronary artery disease. In this study, we assessed the test-retest repeatability of iMBF imaging, and the accuracy of infarct sizing in mice using (11)C-acetate PET.

METHODS

(11)C-Acetate cardiac PET images were acquired in healthy controls, endothelial nitric oxide synthase (eNOS) knockout transgenic mice, and mice after myocardial infarction (MI) to estimate global and regional iMBF, and myocardial infarct size compared to (18)F-FDG PET and ex-vivo histology results.

RESULTS

Global test-retest iMBF values had good coefficients of repeatability (CR) in healthy mice, eNOS knockout mice and normally perfused regions in MI mice (CR = 1.6, 2.0 and 1.5 mL/min/g, respectively). Infarct size measured on (11)C-acetate iMBF images was also repeatable (CR = 17 %) and showed a good correlation with the infarct sizes found on (18)F-FDG PET and histopathology (r (2) > 0.77; p < 0.05).

CONCLUSION

(11)C-Acetate micro-PET assessment of iMBF and infarct size is repeatable and suitable for serial investigation of coronary artery disease progression and therapy.

Methodological considerations in quantification of 3'-deoxy-3'-[18F]fluorothymidine uptake measured with positron emission tomography in patients with non-small cell lung cancer

PURPOSE

To investigate the effect of image-derived input functions (IDIF), input function corrections and volume of interest (VOI) size in quantification of [(18)F]FLT uptake in non-small cell lung cancer (NSCLC) patients.

PROCEDURES

Twenty-three NSCLC patients were scanned on a HR+ scanner. IDIFs were defined over the aorta and left ventricle. Activity concentration and metabolite fraction were measured in venous blood samples. Venous blood samples at 30, 40 and 60 min after injection were used to calibrate the IDIF time-activity curves. Adaptive thresholds were used for VOI definition. Full kinetic analysis and simplified measures were performed.

RESULTS

Non-linear regression analysis showed better fits for the irreversible model compared to the reversible model in the majority. Calibrated and metabolite corrected plus plasma-to-blood ratio corrected input function resulted in high correlations between SUV and Patlak K i (Pearson correlation coefficients 0.86-0.96, p value < 0.001). No significant differences in correlation between SUV and Patlak K i were observed with variation of IDIF structure or VOI size.

CONCLUSIONS

Plasma-to-blood ratio correction, metabolite correction and calibration improved the correlation between SUV and Patlak K i significantly, indicating the need for these corrections when K i is used to validate semi-quantitative measures, such as SUV.

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.

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.

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.

Cerebral blood flow and glucose metabolism in healthy volunteers measured using a high-resolution PET scanner

Background

Positron emission tomography (PET) allows for the measurement of cerebral blood flow (CBF; based on [¹⁵O]H₂O) and cerebral metabolic rate of glucose utilization (CMR_glu; based on [¹⁸ F]-2-fluoro-2-deoxy-d-glucose ([¹⁸ F]FDG)). By using kinetic modeling, quantitative CBF and CMR_glu values can be obtained. However, hardware limitations led to the development of semiquantitive calculation schemes which are still widely used. In this paper, the analysis of CMR_glu and CBF scans, acquired on a current state-of-the-art PET brain scanner, is presented. In particular, the correspondence between nonlinear as well as linearized methods for the determination of CBF and CMR_glu is investigated. As a further step towards widespread clinical applicability, the use of an image-derived input function (IDIF) is investigated.

Methods

Thirteen healthy male volunteers were included in this study. Each subject had one scanning session in the fasting state, consisting of a dynamic [¹⁵O]H₂O scan and a dynamic [¹⁸ F]FDG PET scan, acquired at a high-resolution research tomograph. Time-activity curves (TACs) were generated for automatically delineated and for manually drawn gray matter (GM) and white matter regions. Input functions were derived using on-line arterial blood sampling (blood sampler derived input function (BSIF)). Additionally, the possibility of using carotid artery IDIFs was investigated. Data were analyzed using nonlinear regression (NLR) of regional TACs and parametric methods.

Results

After quality control, 9 CMR_glu and 11 CBF scans were available for analysis. Average GM CMR_glu values were 0.33 ± 0.04 μmol/cm³ per minute, and average CBF values were 0.43 ± 0.09 mL/cm³ per minute. Good correlation between NLR and parametric CMR_glu measurements was obtained as well as between NLR and parametric CBF values. For CMR_glu Patlak linearization, BSIF and IDIF derived results were similar. The use of an IDIF, however, did not provide reliable CBF estimates.

Conclusion

Nonlinear regression analysis, allowing for the derivation of regional CBF and CMR_glu values, can be applied to data acquired with high-spatial resolution current state-of-the-art PET brain scanners. Linearized models, applied to the voxel level, resulted in comparable values. CMR_glu measurements do not require invasive arterial sampling to define the input function.

Trial registration

ClinicalTrials.gov NCT00626080

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.

Evaluation of limited blood sampling population input approaches for kinetic quantification of [18F]fluorothymidine PET data

BACKGROUND

Quantification of kinetic parameters of positron emission tomography (PET) imaging agents normally requires collecting arterial blood samples which is inconvenient for patients and difficult to implement in routine clinical practice. The aim of this study was to investigate whether a population-based input function (POP-IF) reliant on only a few individual discrete samples allows accurate estimates of tumour proliferation using [18F]fluorothymidine (FLT).

METHODS

Thirty-six historical FLT-PET data with concurrent arterial sampling were available for this study. A population average of baseline scans blood data was constructed using leave-one-out cross-validation for each scan and used in conjunction with individual blood samples. Three limited sampling protocols were investigated including, respectively, only seven (POP-IF7), five (POP-IF5) and three (POP-IF3) discrete samples of the historical dataset. Additionally, using the three-point protocol, we derived a POP-IF3M, the only input function which was not corrected for the fraction of radiolabelled metabolites present in blood. The kinetic parameter for net FLT retention at steady state, Ki, was derived using the modified Patlak plot and compared with the original full arterial set for validation.

RESULTS

Small percentage differences in the area under the curve between all the POP-IFs and full arterial sampling IF was found over 60 min (4.2%-5.7%), while there were, as expected, larger differences in the peak position and peak height.A high correlation between Ki values calculated using the original arterial input function and all the population-derived IFs was observed (R2 = 0.85-0.98). The population-based input showed good intra-subject reproducibility of Ki values (R2 = 0.81-0.94) and good correlation (R2 = 0.60-0.85) with Ki-67.

CONCLUSIONS

Input functions generated using these simplified protocols over scan duration of 60 min estimate net PET-FLT retention with reasonable accuracy.

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 functions: effects of motion on tracer kinetic analyses

Purpose

To quantify the effects of motion affected image-derived input functions (IDIF) on the outcome of tracer kinetic analyses.

Procedures

Two simulation studies, one based on high and the other on low cortical uptake, were performed. Different degrees of rotational and axial translational motion were added to the final frames of simulated dynamic positron emission tomography scans. Extracted IDIFs from motion affected simulated scans were compared to original IDIFs and to outcome of tracer kinetic analysis (volume of distribution, V_T).

Results

Differences in IDIF values of up to 239% were found for the last frames. Patient motion of more than 6° or 5 mm resulted in at least 10% higher or lower V_T values for the high cortical tracer.

Conclusion

The degrees of motion studied are commonly observed in clinical studies and hamper the extraction of accurate IDIFs. Therefore, it is essential to ensure that patient motion is minimal and corrected for.

Off-line motion correction methods for multi-frame PET data

Purpose

Patient motion during PET acquisition may affect measured time-activity curves, thereby reducing accuracy of tracer kinetic analyses. The aim of the present study was to evaluate different off-line frame-by-frame methods to correct patient motion, which is of particular interest when no optical motion tracking system is available or when older data sets have to be reanalysed.

Methods

Four different motion correction methods were evaluated. In the first method attenuation-corrected frames were realigned with the summed image of the first 3 min. The second method was identical, except that non-attenuation-corrected images were used. In the third and fourth methods non-attenuation-corrected images were realigned with standard and cupped transmission images, respectively. Two simulation studies were performed, based on [¹¹C]flumazenil and (R)-[¹¹C]PK11195 data sets, respectively. For both simulation studies different types (rotational, translational) and degrees of motion were added. Simulated PET scans were corrected for motion using all correction methods. The optimal method derived from these simulation studies was used to evaluate two (one with and one without visible movement) clinical data sets of [¹¹C]flumazenil, (R)-[¹¹C]PK11195 and [¹¹C]PIB. For these clinical data sets, the volume of distribution (V_T) was derived using Logan analysis and values were compared before and after motion correction.

Results

For both [¹¹C]flumazenil and (R)-[¹¹C]PK11195 simulation studies, optimal results were obtained when realignment was based on non-attenuation-corrected images. For the clinical data sets motion disappeared visually after motion correction. Regional differences of up to 433% in V_T before and after motion correction were found for scans with visible movement. On the other hand, when no visual motion was present in the original data set, overall differences in V_T before and after motion correction were <1.5 ± 1.3%.

Conclusion

Frame-by-frame motion correction using non-attenuation-corrected images improves the accuracy of tracer kinetic analysis compared to non-motion-corrected data.

Electronic supplementary material

The online version of this article (doi:10.1007/s00259-009-1193-y) contains supplementary material, which is available to authorised users.

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.

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.

In vivo validation of reconstruction-based resolution recovery for human brain studies

The aim of this study was to validate in vivo the accuracy of a reconstruction-based partial volume correction (PVC), which takes into account the point spread function of the imaging system. The NEMA NU2 Image Quality phantom and five healthy volunteers (using [(11)C]flumazenil) were scanned on both HR+ and high-resolution research tomograph (HRRT) scanners. HR+ data were reconstructed using normalization and attenuation-weighted ordered subsets expectation maximization (NAW-OSEM) and a PVC algorithm (PVC-NAW-OSEM). HRRT data were reconstructed using 3D ordinary Poisson OSEM (OP-OSEM) and a PVC algorithm (PVC-OP-OSEM). For clinical studies, parametric volume of distribution (V(T)) images were generated. For phantom data, good recovery was found for both OP-OSEM (0.84 to 0.97) and PVC-OP-OSEM (0.91 to 0.98) HRRT reconstructions. In addition, for the HR+, good recovery was found for PVC-NAW-OSEM (0.84 to 0.94), corresponding well with OP-OSEM. Finally, for clinical data, good correspondence was found between PVC-NAW-OSEM and OP-OSEM-derived V(T) values (slope: 1.02+/-0.08). This study showed that HR+ image resolution using PVC-NAW-OSEM was comparable to that of the HRRT scanner. As the HRRT has a higher intrinsic resolution, this agreement validates reconstruction-based PVC as a means of improving the spatial resolution of the HR+ scanner and thereby improving the quantitative accuracy of positron emission tomography.

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.

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.