Characterization of the image-derived carotid artery input function using independent component analysis for the quantitation of [18F] fluorodeoxyglucose positron emission tomography images

Automated extraction of the arterial input function from brain images for parametric PET studies

BACKGROUND

Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images.

RESULTS

Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07.

CONCLUSIONS

Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.

An update on the use of image-derived input functions for human PET studies: new hopes or old illusions?

BACKGROUND

The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome.

MAIN BODY

This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the field's opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF.

CONCLUSION

Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.

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.

Investigation of image-derived input functions for non-invasive quantification of myelin density using [11C]MeDAS PET

Multiple sclerosis (MS) is an inflammatory demyelinating disease. Current treatments are focussed on immune suppression to modulate pathogenic activity that causes myelin damage. New treatment strategies are needed to prevent demyelination and promote remyelination. Development of such myelin repair therapies require a sensitive and specific biomarker for efficacy evaluation. Recently, it has been shown that quantification of myelin density is possible using [¹¹C]MeDAS PET. This method, however, requires arterial blood sampling to generate an arterial input function (AIF). As the invasive nature of arterial sampling will reduce clinical applicability, the purpose of this study was to assess whether an image-derived input function (IDIF) can be used as an alternative way to facilitate its routine clinical use. Six healthy controls and 11 MS patients underwent MRI and [¹¹C]MeDAS PET with arterial blood sampling. The application of both population-based whole blood-to-plasma conversion and metabolite corrections were assessed for the AIF. Next, summed images of the early time frames (0-70 s) and the frame with the highest blood-brain contrast were used to generate IDIFs. IDIFs were created using either the hottest 2, 4, 6 or 12 voxels, or an isocontour of the hottest 10% voxels of the carotid artery. This was followed by blood-to-plasma conversion and metabolite correction of the IDIF. The application of a population-based metabolite correction of the AIF resulted in high correlations of tracer binding (K_i) within subjects, but variable bias across subjects. All IDIFs had a sharper and higher peak in the blood curves than the AIF, most likely due to dispersion during blood sampling. All IDIF methods resulted in similar high correlations within subjects (r = 0.95-0.98), but highly variable bias across subjects (mean slope=0.90-1.09). Therefore, both the use of population based blood-plasma and metabolite corrections and the generation of the image-derived whole-blood curve resulted in substantial bias in [¹¹C]MeDAS PET quantification, due to high inter-subject variability. Consequently, when unbiased quantification of [¹¹C]MeDAS PET data is required, individual AIF needs to be used.

Source-to-Target Automatic Rotating Estimation (STARE) - a publicly-available, blood-free quantification approach for PET tracers with irreversible kinetics: Theoretical framework and validation for [18F]FDG

INTRODUCTION

Full quantification of positron emission tomography (PET) data requires an input function. This generally means arterial blood sampling, which is invasive, labor-intensive and burdensome. There is no current, standardized method to fully quantify PET radiotracers with irreversible kinetics in the absence of blood data. Here, we present Source-to-Target Automatic Rotating Estimation (STARE), a novel, data-driven approach to quantify the net influx rate (K_i) of irreversible PET radiotracers, that requires only individual-level PET data and no blood data. We validate STARE with human [¹⁸F]FDG PET scans and assess its performance using simulations.

METHODS

STARE builds upon a source-to-target tissue model, where the tracer time activity curves (TACs) in multiple "target" regions are expressed at once as a function of a "source" region, based on the two-tissue irreversible compartment model, and separates target region K_i from source K_i by fitting the source-to-target model across all target regions simultaneously. To ensure identifiability, data-driven, subject-specific anchoring is used in the STARE minimization, which takes advantage of the PET signal in a vasculature cluster in the field of view (FOV) that is automatically extracted and partial volume-corrected. To avoid the need for any a priori determination of a single source region, each of the considered regions acts in turn as the source, and a final K_i is estimated in each region by averaging the estimates obtained in each source rotation.

RESULTS

In a large dataset of human [¹⁸F]FDG scans (N=69), STARE K_i estimates were correlated with corresponding arterial blood-based K_i estimates (r=0.80), with an overall regression slope of 0.88, and were precisely estimated, as assessed by comparing STARE K_i estimates across several runs of the algorithm (coefficient of variation across runs=6.74 ± 2.48%). In simulations, STARE K_i estimates were largely robust to factors that influence the individualized anchoring used within its algorithm.

CONCLUSION

Through simulations and application to [¹⁸F]FDG PET data, feasibility is demonstrated for STARE blood-free, data-driven quantification of K_i. Future work will include applying STARE to PET data obtained with a portable PET camera and to other irreversible radiotracers.

Blood-brain barrier permeability in Parkinson's disease patients with and without dyskinesia

OBJECTIVE

Recent studies on a rodent model of Parkinson's disease (PD) have raised the possibility of increased blood-brain barrier (BBB) permeability, demonstrated by histology, autoradiography, and positron emission tomography (PET). However, in human PD patients, in vivo evidence of increased BBB permeability is lacking. We examined the hypothesis that levodopa treatment increases BBB permeability in human subjects with PD, particularly in those with levodopa-induced dyskinesia (LID).

METHODS

We used rubidium-82 (82Rb) and PET to quantify BBB influx in vivo in 19 PD patients, including eight with LID, and 12 age- and sex-matched healthy subjects. All subjects underwent baseline 82Rb scans. Seventeen chronically levodopa-treated patients were additionally rescanned during intravenous levodopa infusion. Influx rate constant, K1, by compartmental modeling or net influx transport, Ki, by graphical approach could not be estimated reliably. However, Vd, the "apparent volume of distribution" based on the 82Rb concentration in brain tissue and blood, was estimated with good stability as a local measure of the volume of distribution.

RESULTS

Rubidium influx into brain tissue was undetectable in PD patients with or without LID, scanned on and off drug. No significant differences in regional Vd were observed for PD patients with or without LID relative to healthy subjects, except in left thalamus. Moreover, changes in Vd measured off- and on-levodopa infusion were also not significant for dyskinetic and non-dyskinetic subjects.

CONCLUSION

82Rb PET did not reveal significant changes in BBB permeability in PD patients.

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.

Image-Derived Input Functions for Quantification of A1 Adenosine Receptors Availability in Mice Brains Using PET and [18F]CPFPX

Purpose

In vivo imaging for the A₁ adenosine receptors (A₁ARs) with positron emission tomography (PET) using 8-cyclopentyl-3-(3-[¹⁸F]fluoropropyl)-1-propylxan- thine ([¹⁸F]CPFPX) has become an important tool for studying physiological processes quantitatively in mice. However, the measurement of arterial input functions (AIFs) on mice is a method with restricted applicability because of the small total blood volume and the related difficulties in withdrawing blood. Therefore, the aim of this study was to extract an appropriate [¹⁸F]CPFPX image-derived input function (IDIF) from dynamic PET images of mice.

Procedures

In this study, five mice were scanned with [¹⁸F]CPFPX for 60 min. Arterial blood samples (n = 7 per animal) were collected from the femoral artery and corrected for metabolites. To generate IDIFs, three different approaches were selected: (A) volume of interest (VOI) placed over the heart (cube, 10 mm); (B) VOI set over abdominal vena cava/aorta region with a cuboid (5 × 5 × 15 mm); and (C) with 1 × 1 × 1 mm voxels on five consecutive slices. A calculated scaling factor (α) was used to correct for partial volume effect; the method of obtaining the total metabolite correction of [¹⁸F]CPFPX for IDIFs was developed. Three IDIFs were validated by comparison with AIF. Validation included the following: visual performance; computing area under the curve (AUC) ratios (IDIF/AIF) of whole-blood curves and parent curves; and the mean distribution volume (V_T) ratios (IDIF/AIF) of A₁ARs calculated by Logan plot and two-tissue compartment model.

Results

Compared with the AIF, the IDIF with VOI over heart showed the best performance among the three IDIFs after scaling by 1.77 (α) in terms of visual analysis, AUC ratios (IDIF/AIF; whole-blood AUC ratio, 1.03 ± 0.06; parent curve AUC ratio, 1.01 ± 0.10) and V_T ratios (IDIF/AIF; Logan V_T ratio, 1.00 ± 0.17; two-tissue compartment model V_T ratio, 1.00 ± 0.13) evaluation. The A₁ARs distribution of average parametric images was in good accordance to autoradiography of the mouse brain.

Conclusion

The proposed study provides evidence that IDIF with VOI over heart can replace AIF effectively for quantification of A₁ARs using PET and [¹⁸F]CPFPX in mice brains.

Comparative assessment of linear least-squares, nonlinear least-squares, and Patlak graphical method for regional and local quantitative tracer kinetic modeling in cerebral dynamic 18 F-FDG PET

PURPOSE

Dynamic ¹⁸ F-FDG PET allows quantitative estimation of cerebral glucose metabolism both at the regional and local (voxel) level. Although sensitive to noise and highly computationally expensive, nonlinear least-squares (NLS) optimization stands as the reference approach for the estimation of the kinetic model parameters. Nevertheless, faster techniques, including linear least-squares (LLS) and Patlak graphical method, have been proposed to deal with high resolution noisy data, representing a more adaptable solution for routine clinical implementation. Former research investigating the relative performance of the available algorithms lack precise evaluation of kinetic parameter estimates under realistic acquisition conditions.

METHODS

The present study aims at the systematic comparison of the feasibility and pertinence of kinetic modeling of dynamic cerebral ¹⁸ F-FDG PET using NLS, LLS, and Patlak method, based on numerical simulations and patient data. Numerical simulations were used to study the bias and variance of K₁ and K_i parameters estimation under representative noise levels. Patient data allowed to assess the concordance between the three methods at the regional and voxel scale, and to evaluate the robustness of the estimations with respect to patient head motion.

RESULTS AND CONCLUSIONS

Our findings indicate that at the regional level NLS and LLS provide kinetic parameter estimates (K₁ and K_i ) with similar bias and variance characteristics (K₁ bias ± relative standard deviation [RSD] 0.0 ± 5.1% and 0.1% ± 4.9% for NLS and LLS respectively, K_i bias ± RSD 0.1% ± 4.5% and -0.7% ± 4.4% for NLS and LLS respectively). NLS estimates appear, however, to be slightly less sensitive to patient motion. At the voxel level, provided that patient motion is negligible or corrected, LLS offers an appealing alternative solution for local K₁ mapping. It yields K1 estimates that are highly correlated, with high correlation with NLS values (Pearson's r = 0.95 on actual data) within computations times less than two orders of magnitude lower. Last, Patlak method appears as the most robust and accurate technique for the estimation of K_i values at the regional and voxel scale, with or without head motion. It provides low bias/low variance K_i quantification (bias ± RSD -1.5 ± 9.5% and -4.1 ± 19.7% for Patlak and NLS respectively) as well as smooth parametric images suitable for visual assessment.

Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging

In dynamic cardiac PET FDG studies the assessment of myocardial metabolic rate of glucose (MMRG) requires the knowledge of the blood input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. The IF is contaminated by spillover of radioactivity from the adjacent myocardium and this could cause important error in the estimated MMRG. In this study, we show that the IF can be extracted from the images in a rat heart study with 18F-fluorodeoxyglucose (18F-FDG) by means of Independent Component Analysis (ICA) based on Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling method (BICA). Images of the heart from rats were acquired with the Sherbrooke small animal PET scanner. A region of interest (ROI) was drawn around the rat image and decomposed into blood and tissue using BICA. The Statistical study showed that there is a significant difference (p < 0.05) between MMRG obtained with IF extracted by BICA with respect to IF extracted from measured images corrupted with spillover.

Toward noninvasive quantification of brain radioligand binding by combining electronic health records and dynamic PET imaging data

Quantitative analysis of positron emission tomography (PET) brain imaging data requires a metabolite-corrected arterial input function (AIF) for estimation of distribution volume and related outcome measures. Collecting arterial blood samples adds risk, cost, measurement error, and patient discomfort to PET studies. Minimally invasive AIF estimation is possible with simultaneous estimation (SIME), but at least one arterial blood sample is necessary. In this study, we describe a noninvasive SIME (nSIME) approach that utilizes a pharmacokinetic input function model and constraints derived from machine learning applied to an electronic health record database consisting of "long tail" data (digital records, paper charts, and handwritten notes) that were collected ancillary to the PET studies. We evaluated the performance of nSIME on 95 [(11)C]DASB PET scans that had measured AIFs. The results indicate that nSIME is a promising alternative to invasive AIF measurement. The general framework presented here may be expanded to other metabolized radioligands, potentially enabling quantitative analysis of PET studies without blood sampling. A glossary of technical abbreviations is provided at the end of this paper.

Potential Clinical Applications of PET/MR Imaging in Neurodegenerative Diseases

Neurodegenerative disorders such as Alzheimer disease are among today's most alarming health problems in our aging society. The clinical assessment of neurodegenerative disorders benefits from recent innovations in the field of imaging technology. These innovations include emerging tracers for molecular imaging of neurodegenerative pathology and the introduction of novel integrated PET/MR imaging instruments. Because both PET and MR imaging procedures have shown critical value in the diagnostic work-up of neurodegenerative disorders, the combination of both imaging modalities in the form of an integrated PET/MR imaging system may be of value. This combination includes practical methodologic advantages and an improved workflow facilitated by the combined acquisition of dual-modality data. It offers clinical advantages because of the systematic combination of complementary information, potentially allowing the creation of novel integrated imaging biomarkers. The effectiveness of new disease-modifying treatments may depend on the timely initiation of therapy before irreversible neuronal damage in slowly progressive neurodegenerative disorders. Integrated PET/MR imaging may be able to improve such early diagnosis through both structural and functional information.

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.

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.

Assessment of brain glucose metabolism with input function determined from brain PET images by means of Bayesian ICA and MCMC methods

Positron emission tomography (PET) imaging has the capability to produce regional or parametric images of physiological aspects in a tissue of interest. Apart from the acquired PET data, the concentration of the radiotracer supplied to the tissue through the vascularization has to be known as the input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. In the present work, we determine IF from internal artery in fluorodeoxyglucose (18F-FDG) brain images by means of Independent Component Analysis (ICA) based on Bayesian theory and Monte Carlo Markov Chain (MCMC) sampling method (BICA, Bayesian ICA). Dynamic brain images were decomposed with BICA into image sequences of blood and tissue components. A region of interest was drawn around the internal artery in the blood image to determine BICA-IF. BICA-IF was therefore corrected for spillover of radioactivity emission from tissue, then it was normalized with three plasma samples to correct for partial volume and blood to plasma radioactivity ratio. BICA-IF was found comparable to IF determined by blood sampling, and rCMRG values in several brain structures obtained with BICA-IF and sampled IF showed a bias of 6.4% which was attributed to the difference in the time sampling of 4s for sampled IF versus 15 s for BICA-MC-IF at early times. In conclusion, BICA is a strong approach in image decomposition to extract blood curves in a noninvasive way.

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

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

Quantitative assessment of dynamic PET imaging data in cancer imaging

Clinical imaging in positron emission tomography (PET) is often performed using single-time-point estimates of tracer uptake or static imaging that provides a spatial map of regional tracer concentration. However, dynamic tracer imaging can provide considerably more information about in vivo biology by delineating both the temporal and spatial pattern of tracer uptake. In addition, several potential sources of error that occur in static imaging can be mitigated. This review focuses on the application of dynamic PET imaging to measuring regional cancer biologic features and especially in using dynamic PET imaging for quantitative therapeutic response monitoring for cancer clinical trials. Dynamic PET imaging output parameters, particularly transport (flow) and overall metabolic rate, have provided imaging end points for clinical trials at single-center institutions for years. However, dynamic imaging poses many challenges for multicenter clinical trial implementations from cross-center calibration to the inadequacy of a common informatics infrastructure. Underlying principles and methodology of PET dynamic imaging are first reviewed, followed by an examination of current approaches to dynamic PET image analysis with a specific case example of dynamic fluorothymidine imaging to illustrate the approach.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Iterative filtering decomposition based on local spectral evolution kernel

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms.

Mode decomposition evolution equations

Partial differential equation (PDE) based methods have become some of the most powerful tools for exploring the fundamental problems in signal processing, image processing, computer vision, machine vision and artificial intelligence in the past two decades. The advantages of PDE based approaches are that they can be made fully automatic, robust for the analysis of images, videos and high dimensional data. A fundamental question is whether one can use PDEs to perform all the basic tasks in the image processing. If one can devise PDEs to perform full-scale mode decomposition for signals and images, the modes thus generated would be very useful for secondary processing to meet the needs in various types of signal and image processing. Despite of great progress in PDE based image analysis in the past two decades, the basic roles of PDEs in image/signal analysis are only limited to PDE based low-pass filters, and their applications to noise removal, edge detection, segmentation, etc. At present, it is not clear how to construct PDE based methods for full-scale mode decomposition. The above-mentioned limitation of most current PDE based image/signal processing methods is addressed in the proposed work, in which we introduce a family of mode decomposition evolution equations (MoDEEs) for a vast variety of applications. The MoDEEs are constructed as an extension of a PDE based high-pass filter (Europhys. Lett., 59(6): 814, 2002) by using arbitrarily high order PDE based low-pass filters introduced by Wei (IEEE Signal Process. Lett., 6(7): 165, 1999). The use of arbitrarily high order PDEs is essential to the frequency localization in the mode decomposition. Similar to the wavelet transform, the present MoDEEs have a controllable time-frequency localization and allow a perfect reconstruction of the original function. Therefore, the MoDEE operation is also called a PDE transform. However, modes generated from the present approach are in the spatial or time domain and can be easily used for secondary processing. Various simplifications of the proposed MoDEEs, including a linearized version, and an algebraic version, are discussed for computational convenience. The Fourier pseudospectral method, which is unconditionally stable for linearized the high order MoDEEs, is utilized in our computation. Validation is carried out to mode separation of high frequency adjacent modes. Applications are considered to signal and image denoising, image edge detection, feature extraction, enhancement etc. It is hoped that this work enhances the understanding of high order PDEs and yields robust and useful tools for image and signal analysis.

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.

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.

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

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

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

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

A Multimodal Approach to Image-Derived Input Functions for Brain PET

Many methods have been proposed for generating an image-derived input function (IDIF) exclusively from PET images. The purpose of this study was to assess the viability of a multimodality approach utilizing registered MR images. 3T-MR and HRRT-PET data were acquired from human subjects. Segmentation of both the left and right carotid arteries was performed in MR images using a 3D level sets method. Vessel centerlines were extracted by parameterization of the segmented voxel coordinates with either a single polynomial curve or a B-spline curve fitted to the segmented data. These centerlines were subsequently re-registered to static PET data to maximize the accurate classification of PET voxels in the ROI. The accuracy of this approach was assessed by comparison of the area under the curve (AUC) of the IDIF to that measured from conventional automated arterial blood sampling.Our method produces curves similar in shape to that of blood sampling. The mean AUC ratio of the centerline region was 0.40±0.19 before re-registration and 0.69±0.26 after re-registration. Increasing the diameter of the carotid ROI produced a smooth reduction in AUC. Thus, even with the high resolution of the HRRT, partial volume correction is still necessary. This study suggests that the combination of PET information with MR segmented regions will demonstrate an improvement over regions based solely on MR or PET alone.