Population-based Input Function Research Articles

Optimization and impact of sensitivity mode on abbreviated scan protocols with population-based input function for parametric imaging of [18F]-FDG for a long axial FOV PET scanner.

The long axial field of view, combined with the high sensitivity of the Biograph Vision Quadra PET/CT scanner enables the precise deviation of an image derived input function (IDIF) required for parametric imaging. Traditionally, this requires an hour-long dynamic PET scan for [18F]-FDG, which can be significantly reduced by using a population-based input function (PBIF). In this study, we expand these examinations and include the scanner's ultra-high sensitivity (UHS) mode in comparison to the high sensitivity (HS) mode and evaluate the potential for further shortening of the scan time. Patlak Ki and DV estimates were determined by the indirect and direct Patlak methods using dynamic [18F]-FDG data of 6 oncological patients with 26 lesions (0-65min p.i.). Both sensitivity modes for different number/duration of PET data frames were compared, together with the potential of using abbreviated scan durations of 20, 15 and 10min by using a PBIF. The differences in parametric images and tumour-to-background ratio (TBR) due to the shorter scans using the PBIF method and between the sensitivity modes were assessed. A difference of 3.4 ± 7.0% (Ki) and 1.2 ± 2.6% (DV) was found between both sensitivity modes using indirect Patlak and the full IDIF (0-65min). For the abbreviated protocols and indirect Patlak, the UHS mode resulted in a lower bias and higher precision, e.g., 45-65min p.i. 3.8 ± 4.4% (UHS) and 6.4 ± 8.9% (HS), allowing shorter scan protocols, e.g. 50-65min p.i. 4.4 ± 11.2% (UHS) instead of 7.3 ± 20.0% (HS). The variation of Ki and DV estimates for both Patlak methods was comparable, e.g., UHS mode 3.8 ± 4.4% and 2.7 ± 3.4% (Ki) and 14.4 ± 2.7% and 18.1 ± 7.5% (DV) for indirect and direct Patlak, respectively. Only a minor impact of the number of Patlak frames was observed for both sensitivity modes and Patlak methods. The TBR obtained with direct Patlak and PBIF was not affected by the sensitivity mode, was higher than that derived from the SUV image (6.2 ± 3.1) and degraded from 20.2 ± 12.0 (20min) to 10.6 ± 5.4 (15min). Ki and DV estimate images showed good agreement (UHS mode, RC: 6.9 ± 2.3% (Ki), 0.1 ± 3.1% (DV), peak signal-to-noise ratio (PSNR): 64.5 ± 3.3 dB (Ki), 61.2 ± 10.6 dB (DV)) even for abbreviated scan protocols of 50-65min p.i. Both sensitivity modes provide comparable results for the full 65min dynamic scans and abbreviated scans using the direct Patlak reconstruction method, with good Ki and DV estimates for 15min short scans. For the indirect Patlak approach the UHS mode improved the Ki estimates for the abbreviated scans.

Estimation of kinetic parameters in dynamic FDG PET imaging based on shortened protocols: a virtual clinical study.

This study investigated the estimation of kinetic parameters and production of related parametric Ki images in FDG PET imaging using the proposed shortened protocol (three 3-min/bed routine static images) by means of the simulated annealing (SA) algorithm.Six realistic heterogeneous tumors and various levels of [18F] FDG uptake were simulated by the XCAT phantom. An irreversible two-tissue compartment model (2TCM) using population-based input function was employed. By keeping two routine clinical scans fixed (60-min and 90-min post injection), the effect of the early scan time on optimizing the estimation of the pharmacokinetic parameters was investigated. The SA optimization algorithm was applied to estimate micro- and macro-parameters (K1, k2, k3, Ki).The minimum bias for most parameters was observed at a scan time of 20-min, which was < 10%. A highly significant correlation (> 0.9) as well as limited bias (< 10%) were observed between kinetic parameters generated from twomethods [two-tissue compartment full dynamic scan (2TCM-full) and two-tissue compartment by SA algorithm (2TCM-SA)]. The analysis showed a strong correlation (> 0.8) between (2TCM-SA) Ki and SUV images. In addition, the tumor-to-background ratio (TBR) metric in the parametric (2TCM-SA) Ki images was significantly higher than SUV, although the SUV images provide better Contrast-to-noise ratio relative to parametric (2TCM-SA) Ki images.The proposed shortened protocol by the SA algorithm can estimate the kinetic parameters in FDG PET scan with high accuracy and robustness. It was also concluded that the parametric Ki images obtained from the 2TCM-SA as a complementary image of the SUV possess more quantification information than SUV images and can be used by the nuclear medicine specialist. This method has the potential to be an alternative to a full dynamic PET scan.

Clinical application of a population-based input function (PBIF) for a shortened dynamic whole-body FDG-PET/CT protocol in patients with metastatic melanoma treated by immunotherapy

BackgroundThe aim was to investigate the feasibility of a shortened dynamic whole-body (dWB) FDG-PET/CT protocol and Patlak imaging using a population-based input function (PBIF), instead of an image-derived input function (IDIF) across the 60-min post-injection period, and study its effect on the FDG influx rate (Ki) quantification in patients with metastatic melanoma (MM) undergoing immunotherapy.MethodsThirty-seven patients were enrolled, including a PBIF modeling group (n = 17) and an independent validation cohort (n = 20) of MM from the ongoing prospective IMMUNOPET2 trial. All dWB-PET data were acquired on Vision 600 PET/CT systems. The PBIF was fitted using a Feng’s 4-compartments model and scaled to the individual IDIF tail’s section within the shortened acquisition time. The area under the curve (AUC) of PBIFs was compared to respective IDIFs AUC within 9 shortened time windows (TW) in terms of linear correlation (R2) and Bland–Altman tests. Ki metrics calculated with PBIF vs IDIF on 8 organs with physiological tracer uptake, 44 tumoral lesions of MM and 11 immune-induced inflammatory sites of pseudo-progression disease were also compared (Mann–Whitney test).ResultsThe mean ± SD relative AUC bias was calculated at 0.5 ± 3.8% (R2 = 0.961, AUCPBIF = 1.007 × AUCIDIF). In terms of optimal use in routine practice and statistical results, the 5th–7th pass (R2 = 0.999 for both Ki mean and Ki max) and 5th–8th pass (mean ± SD bias = − 4.9 ± 6.5% for Ki mean and − 4.8% ± 5.6% for Ki max) windows were selected. There was no significant difference in Ki values from PBIF5_7 vs IDIF5_7 for physiological uptakes (p > 0.05) as well as for tumor lesions (mean ± SD Ki IDIF5_7 3.07 ± 3.27 vs Ki PBIF5_7 2.86 ± 2.96 100ml/ml/min, p = 0.586) and for inflammatory sites (mean ± SD Ki IDIF5_7 1.13 ± 0.59 vs Ki PBIF5_7 1.13 ± 0.55 100ml/ml/min, p = 0.98).ConclusionOur study showed the feasibility of a shortened dWB-PET imaging protocol with a PBIF approach, allowing to reduce acquisition duration from 70 to 20 min with reasonable bias. These findings open perspectives for its clinical use in routine practice such as treatment response assessment in oncology.

18F]DPA-714: Effect of co-medications, age, sex, BMI and TSPO polymorphism on the human plasma input function.

We aimed to assess the effect of concomitant medication, age, sex, body mass index and 18-kDa translocator protein (TSPO) binding affinity status on the metabolism and plasma pharmacokinetics of [18F]DPA-714 and their influence on the plasma input function in a large cohort of 201 subjects who underwent brain and whole-body PET imaging to investigate the role of neuroinflammation in neurological diseases. The non-metabolized fraction of [18F]DPA-714 was estimated in venous plasma of 138 patients and 63 healthy controls (HCs; including additional arterial sampling in 16 subjects) during the 90min brain PET acquisition using a direct solid-phase extraction method. The mean fraction between 70 and 90min post-injection ([18F]DPA-71470-90) and corresponding normalized plasma concentration (SUV70-90) were correlated with all factors using a multiple linear regression model. Differences between groups (arterial vs venous measurements; HCs vs patients; high- (HAB), mixed- (MAB) and low-affinity binders (LAB); subjects with vs without co-medications, females vs males were also assessed using the non-parametric Mann-Whitney or Kruskal-Wallis ANOVA tests. Finally, the impact of co-medications on the brain uptake of [18F]DPA-714 at equilibrium was investigated. As no significant differences were observed between arterial and venous [18F]DPA-71470-90 and SUV70-90, venous plasma was used for correlations. [18F]DPA-71470-90 was not significantly different between patients and HCS (59.7 ± 12.3% vs 60.2 ± 12.9%) despite high interindividual variability. However, 47 subjects exhibiting a huge increase or decrease of [18F]DPA-71470-90 (up to 88% or down to 23%) and SUV70-90 values (2-threefold) were found to receive co-medications identified as inhibitors or inducers of CYP3A4, known to catalyse [18F]DPA-714 metabolism. Comparison between cortex-to-plasma ratios using individual input function (VTIND) or population-based input function derived from untreated HCs (VTPBIF) indicated that non-considering the individual metabolism rate led to a bias of about 30% in VT values. Multiple linear regression model analysis of subjects free of these co-medications suggested significant correlations between [18F]DPA-71470-90 and age, BMI and sex while TSPO polymorphism did not influence the metabolism of the radiotracer. [18F]DPA-714 metabolism fell with age and BMI and was significantly faster in females than in males. Whole-body PET/CT exhibited a high uptake of the tracer in TSPO-rich organs (heart wall, spleen, kidneys…) and those involved in metabolism and excretion pathways (liver, gallbladder) in HAB and MAB with a strong decrease in LAB (-89% and -85%) resulting in tracer accumulation in plasma (4.5 and 3.3-fold increase). Any co-medication that inhibits or induces CYP3A4 as well as TSPO genetic status, age, BMI and sex mostly contribute to interindividual variations of the radiotracer metabolism and/or concentration that may affect the input function of [18F]DPA-714 and consequently its human brain and peripheral uptake. INFLAPARK, NCT02319382, registered December 18, 2014, retrospectively registered; IMABIO 3, NCT01775696, registered January 25, 2013, retrospectively registered; INFLASEP, NCT02305264, registered December 2, 2014, retrospectively registered; EPI-TEP, EudraCT 2017-003381-27, registered September 24, 2018.

Measurement of Protein Synthesis Rate in Rat by [11C]Leucine PET Imaging: Application to the TgF344-AD Model of Alzheimer’s Disease

Long-term memory requires stable protein synthesis and is altered in Alzheimer’s disease (AD). This study aimed to implement a method to measure the cerebral protein synthesis rate (PSR) with [11C]leucine PET in vivo in rats and evaluate potential PSR alterations longitudinally (6, 12 and 18 months old) in the TgF344-AD rat model of AD. Wistar, wild-type (WT) and TgF344-AD rats (TG) were scanned for 60 min with [11C]leucine. Arterial blood activity was monitored online and with discrete whole blood and plasma samples by γ-counting in Wistar rats, WT (n = 4) and TG (n = 5). Unlabelled amino acids were measured in plasma. The sensitivity of [11C]leucine PET to measure alterations in PSR was assessed in Wistar rats by injection of PSR inhibitor anisomycin before PET acquisition. Anisomycin administration significantly reduced the net uptake rate constant (Kcplx) of [11C]leucine and PSR, proving the suitability of the method. For the longitudinal study, averaged population-based input functions were used to calculate PSR. We found a significant genotype effect on PSR (decrease in TG vs WT) only in the globus pallidus. This study suggests that [11C]leucine PET is sensitive enough to measure brain PSR in rat but that cross-sectional design with individual input function should be preferred.

Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [18F]-FDG datasets from a long axial FOV PET scanner

BackgroundAccurate kinetic modeling of 18F-fluorodeoxyglucose ([18F]-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples, but the introduction of long axial field of view (LAFOV) PET systems enables the use of non-invasive image-derived input functions (IDIFs) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition, which is impractical in a busy clinical setting. Population-based input functions (PBIFs) have previously shown potential in accurate Patlak analysis of [18F]-FDG datasets and can enable the use of shortened dynamic imaging protocols. Here, we exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore the use of PBIF with abbreviated protocols in [18F]-FDG total body kinetic modeling.MethodsDynamic PET data were acquired in 24 oncological subjects for 65 min following the administration of [18F]-FDG. IDIFs were extracted from the descending thoracic aorta, and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIFs) were generated by scaling the PBIF with the AUC of IDIF curve tails using various portions of image data (35–65, 40–65, 45–65, 50–65, and 55–65 min post-injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumor lesions and cerebral gray matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on the accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t* = 35 min were used as reference, and mean bias and precision (standard deviation of bias) were calculated to assess the relative performance of different sPBIFs. A comparison of parametric images generated using IDIF and sPBIFs was also performed.ResultsThere was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P > 0.05). Excellent agreement was shown between Patlak Ki estimates obtained using sPBIF and IDIF. Using the sPBIF55–65 with the Patlak model, 20 min of PET data (i.e., 45 to 65 min post-injection) achieved < 15% precision error in Ki estimates in tumor lesions compared to the estimates with the IDIF. Parametric images reconstructed using the IDIF and sPBIFs with and without an abbreviated protocol were visually comparable. Using Patlak Ki generated with an IDIF and 30 min of PET data as reference, Patlak Ki images generated using sPBIF55–65 with 20 min of PET data (t* = 45 min) provided excellent image quality with structural similarity index measure > 0.99 and peak signal-to-noise ratio > 55 dB.ConclusionWe demonstrate the feasibility of performing accurate [18F]-FDG Patlak analysis using sPBIFs with only 20 min of PET data from a LAFOV PET scanner.

Population-based input function (PBIF) applied to dynamic whole-body 68Ga-DOTATOC-PET/CT acquisition

RationalTo validate a population-based input function (PBIF) model that alleviates the need for scanning since injection time in dynamic whole-body (WBdyn) PET.MethodsThirty-seven patients with suspected/known well-differentiated neuroendocrine tumors were included (GAPETNET trial NTC03576040). All WBdyn 68Ga-DOTATOC-PET/CT acquisitions were performed on a digital PET system (one heart-centered 6 min-step followed by nine WB-passes). The PBIF model was built from 20 image-derived input functions (IDIFs) obtained from a respective number of patients’ WBdyn exams using an automated left-ventricle segmentation tool. All IDIF peaks were aligned to the median time-to-peak, normalized to patient weight and administrated activity, and then fitted to an exponential model function. PBIF was then applied to 17 independent patient studies by scaling it to match the respective IDIF section at 20–55 min post-injection time windows corresponding to WB-passes 3–7. The ratio of area under the curves (AUCs) of IDIFs and PBIF3–7 were compared using a Bland–Altman analysis (mean bias ± SD). The Patlak-estimated mean Ki for physiological uptake (Ki-liver and Ki-spleen) and tumor lesions (Ki-tumor) using either IDIF or PBIF were also compared.ResultsThe mean AUC ratio (PBIF/IDIF) was 0.98 ± 0.06. The mean Ki bias between PBIF3–7 and IDIF was −2.6 ± 6.2% (confidence interval, CI: −5.8; 0.6). For Ki-spleen and Ki-tumor, low relative bias with low SD were found [4.65 ± 7.59% (CI: 0.26; 9.03) and 3.70 ± 8.29% (CI: −1.09; 8.49) respectively]. For Ki-liver analysis, relative bias and SD were slightly higher [7.43 ± 13.13% (CI: −0.15; 15.01)].ConclusionOur study showed that the PBIF approach allows for reduction in WBdyn DOTATOC-PET/CT acquisition times with a minimum gain of 20 min.

Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study

BackgroundTo explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic 18F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10–60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40–60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon’s rank sum test, McNemar’s test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables.ResultsBoth readers similarly rated 51 scans as positive (scores = 1–3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001).ConclusionsShort-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF.

Clinical validation of a population-based input function for 20-min dynamic whole-body 18F-FDG multiparametric PET imaging

PurposeContemporary PET/CT scanners can use 70-min dynamic whole-body (D-WB) PET to generate more quantitative information about FDG uptake than just the SUV by generating parametric images of FDG metabolic rate (MRFDG). The analysis requires the late (50–70 min) D-WB tissue data combined with the full (0–70 min) arterial input function (AIF). Our aim was to assess whether the use of a scaled population-based input function (sPBIF) obviates the need for the early D-WB PET acquisition and allows for a clinically feasible 20-min D-WB PET examination.MethodsA PBIF was calculated based on AIFs from 20 patients that were D-WB PET scanned for 120 min with simultaneous arterial blood sampling. MRFDG imaging using PBIF requires that the area under the curve (AUC) of the sPBIF is equal to the AUC of the individual patient’s input function because sPBIF AUC bias translates into MRFDG bias. Special patient characteristics could affect the shape of their AIF. Thus, we validated the use of PBIF in 171 patients that were divided into 12 subgroups according to the following characteristics: diabetes, cardiac ejection fraction, blood pressure, weight, eGFR and age. For each patient, the PBIF was scaled to the aorta image-derived input function (IDIF) to calculate a sPBIF, and the AUC bias was calculated.ResultsWe found excellent agreement between the AIF and IDIF at all times. For the clinical validation, the use of sPBIF led to an acceptable AUC bias of 1–5% in most subgroups except for patients with diabetes or patients with low eGFR, where the biases were marginally higher at 7%. Multiparametric MRFDG images based on a short 20-min D-WB PET and sPBIF were visually indistinguishable from images produced by the full 70-min D-WB PET and individual IDIF.ConclusionsA short 20-min D-WB PET examination using PBIF can be used for multiparametric imaging without compromising the image quality or precision of MRFDG. The D-WB PET examination may therefore be used in clinical routine for a wide range of patients, potentially allowing for more precise quantification in e.g. treatment response imaging.

Total-body parametric imaging using the Patlak model: Feasibility of reduced scan time.

This study explored the feasibility of reducing the scan time of Patlak parametric imaging on the uEXPLORER. A total of 65 patients (27 females and 38 males, age 56.1 ± 10.4) were recruited in this study. 18F fluorodeoxyglucose was injected, and its dose was adjusted by body weight (4.07 MBq/kg). Total-body dynamic scanning was performed on the uEXPLORER total-body Positron emission tomography/computed tomography (CT) scanner with a total scan time of 60 min from the injection. The image derived input function (IDIF) was obtained from the aortic arch. The voxelwise Patlak analysis was applied to generate the Ki images designated as GIDIF with different acquisition times (20-60, 30-60, 40-60, and 44-60min). The population-based input function (PBIF) was constructed from the mean value of the IDIF from the population, and Ki images designated as GPBIF were generated using the PBIF. Nonlocalmeans (NLM) denoising was applied to the generated images to get two extra groups of (NLM-designated) images: GIDIF+NLM and GPBIF+NLM . Two radiologists evaluated the overall image quality, noise, and lesion detectability of the Ki images from different groups. The 20-60min scans in GIDIF were selected as the gold standard for each patient. We determined that image quality is at sufficient level if all the lesions can be recognized and meet the clinical criteria. Ki values in muscle and lesion were compared across different groups to evaluate the quantitative accuracy. The overall image quality, image noise, and lesion conspicuity were significantly better in long time series than short time series in all four groups (all p<0.001). The Ki images in the GIDIF and GPBIF groups generated from 30-min scans showed diagnostic value equivalent to the 40-min scans of GIDIF . While the image quality of the 16-min scans was poor, all lesions could still be detected. No significant difference was found between Ki values estimated with GIDIF and GPBIF in muscle and lesion regions (all p>0.5). After applying the NLM filter, the coefficient of variation could be reduced on the order of (1%, 15%, 19%, and 37%) and (110%, 125%, 94%, and 69%) with four acquisition time schemes for lesion and muscle. The reduction percentage did not have a substantial difference in IDIF and PBIF group. The Ki images in the GIDIF+NLM and GPBIF+NLM groups generated from the 20-min acquisitions showed acceptable quality. All lesions could be found on the NLM processed images of the 16-min scans. No significant difference was found between Ki values produced with GIDIF+NLM and GPBIF+NLM in muscle and lesion regions(all p>0.7). The Ki images generated by the PBIF-based Patlak model using a 20-min dynamic scan with the NLM filter achieved a similar diagnostic efficiency to images with GIDIF from 40-min dynamic data, and there is no significant difference between Ki images generated using IDIF or PBIF (p>0.5).

Short 2-[18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer.

Purpose: To test a short 2-[18F]Fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET dynamic acquisition protocol to calculate Ki using regional Patlak graphical analysis in patients with non-small-cell lung cancer (NSCLC).Methods: 24 patients with NSCLC who underwent standard dynamic 2-[18F]FDG acquisitions (60 min) were randomly divided into two groups. In group 1 (n = 10), a population-based image-derived input function (pIDIF) was built using a monoexponential trend (10–60 min), and a leave-one-out cross-validation (LOOCV) method was performed to validate the pIDIF model. In group 2 (n = 14), Ki was obtained by standard regional Patlak plot analysis using IDIF (0–60 min) and tissue response (10–60 min) curves from the volume of interests (VOIs) placed on descending thoracic aorta and tumor tissue, respectively. Moreover, with our method, the Patlak analysis was performed to obtain Ki,s using IDIFFitted curve obtained from PET counts (0–10 min) followed by monoexponential coefficients of pIDIF (10–60 min) and tissue response curve obtained from PET counts at 10 min and between 40 and 60 min, simulating two short dynamic acquisitions. Both IDIF and IDIFFitted curves were modeled to assume the value of 2-[18F]FDG plasma activity measured in the venous blood sampling performed at 45 min in each patient. Spearman's rank correlation, coefficient of determination, and Passing–Bablok regression were used for the comparison between Ki and Ki,s. Finally, Ki,s was obtained with our method in a separate group of patients (group 3, n = 8) that perform two short dynamic acquisitions.Results: Population-based image-derived input function (10–60 min) was modeled with a monoexponential curve with the following fitted parameters obtained in group 1: a = 9.684, b = 16.410, and c = 0.068 min−1. The LOOCV error was 0.4%. In patients of group 2, the mean values of Ki and Ki,s were 0.0442 ± 0.0302 and 0.33 ± 0.0298, respectively (R2 = 0.9970). The Passing–Bablok regression for comparison between Ki and Ki,s showed a slope of 0.992 (95% CI: 0.94–1.06) and intercept value of −0.0003 (95% CI: −0.0033–0.0011).Conclusions: Despite several practical limitations, like the need to position the patient twice and to perform two CT scans, our method contemplates two short 2-[18F]FDG dynamic acquisitions, a population-based input function model, and a late venous blood sample to obtain robust and personalized input function and tissue response curves and to provide reliable regional Ki estimation.

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

To identify a reliable alternative to the full blood [11C]PBR28 quantification method that would be easily replicated in multiple research and clinical settings. Ten [11C]PBR28 scans were acquired from 7 healthy non-human primates (NHP). Arterial input functions (AIFs) were averaged to create a population template input function (TIF). Population-based input functions were created by scaling the TIF with injected activity per body weight (PBIF) or unmetabolized tracer activity in blood at 15-,30-, and 60-min post-injection (PBIF15, PBIF30, and PBIF60). Two additional input functions were used: the native unmetabolized total plasma activity (Totals) and the Totals curve metabolite corrected by a scaled template parent fraction from a 30-min sample (TPF30-IF). Total distribution volumes (VTs) were calculated using PBIF, PBIF30, PBIF15, PBIF60, Totals, TPF30-IF, and the individual AIF (VTAIF). Distribution volume ratios (DVR) were computed using the cerebellum and the centrum semiovale (CSO), as pseudo-reference regions (DVRCereb, DVRCSO). Results obtained with each method were compared to VTAIF. Applicability of these alternative methods was tested on an independent pharmacological challenge dataset of microglial activation and depletion. Evaluation was carried at baseline, immediately after intervention (acute), and weeks post-intervention (post-recovery). VTs computed using PBIF15 and PBIF30 showed the best correlation to VTAIF (r > 0.90), while VT derived from the blood-free-scaled PBIF showed poor correlation (r = 0.46) and DVRCSO correlated the least (r = 0.26). In the pharmacological challenge study, most population-derived VT values were comparable to VTAIF at baseline and showed varied sensitivity to challenges at acute and post-recovery evaluation. DVR values did not detect relevant changes. Population-based input functions scaled with a single blood sample might be a useful alternative to using AIF to compute [11C]PBR28 binding in healthy NHPs or animals with comparable metabolism and overall perform better than pseudo-reference regions approaches.

Generation of parametric Ki images for FDG PET using two 5‐min scans

AbstractPurposeThe net uptake rate constant (Ki) derived from dynamic imaging is considered the gold standard quantification index for FDG PET. In this study, we investigated the feasibility and assessed the clinical usefulness of generating Ki images for FDG PET using only two 5‐min scans with population‐based input function (PBIF).MethodsUsing a Siemens Biograph mCT, 10 subjects with solid lung nodules underwent a single‐bed dynamic FDG PET scan and 13 subjects (five healthy and eight cancer patients) underwent a whole‐body dynamic FDG PET scan in continuous‐bed‐motion mode. For each subject, a standard Ki image was generated using the complete 0–90 min dynamic data with Patlak analysis (t* = 20 min) and individual patient's input function, while a dual‐time‐point Ki image was generated from two 5‐min scans based on the Patlak equations at early and late scans with the PBIF. Different start times for the early (ranging from 20 to 55 min with an increment of 5 min) and late (ranging from 50 to 85 min with an increment of 5 min) scans were investigated with the interval between scans being at least 30 min (36 protocols in total). The optimal dual‐time‐point protocols were then identified. Regions of interest (ROI) were drawn on nodules for the lung nodule subjects, and on tumors, cerebellum, and bone marrow for the whole‐body‐imaging subjects. Quantification accuracy was compared using the mean value of each ROI between standard Ki (gold standard) and dual‐time‐point Ki, as well as between standard Ki and relative standardized uptake value (SUV) change that is currently used in clinical practice. Correlation coefficients and least squares fits were calculated for each dual‐time‐point protocol and for each ROI. Then, the predefined criteria for identifying a reliable dual‐time‐point Ki estimation for each ROI were empirically determined as: (1) the squared correlation coefficient (R2) between standard Ki and dual‐time‐point Ki is larger than 0.9; (2) the absolute difference between the slope of the equality line (1.0) and that of the fitted line when plotting standard Ki versus dual‐time‐point Ki is smaller than 0.1; (3) the absolute value of the intercept of the fitted line when plotting standard Ki versus dual‐time‐point Ki normalized by the mean of the standard Ki across all subjects for each ROI is smaller than 10%. Using Williams’ one‐tailed t test, the correlation coefficient (R) between standard Ki and dual‐time‐point Ki was further compared with that between standard Ki and relative SUV change, for each dual‐time‐point protocol and for each ROI.ResultsReliable dual‐time‐point Ki images were obtained for all the subjects using our proposed method. The percentage error introduced by the PBIF on the dual‐time‐point Ki estimation was smaller than 1% for all 36 protocols. Using the predefined criteria, reliable dual‐time‐point Ki estimation could be obtained in 25 of 36 protocols for nodules and in 34 of 36 protocols for tumors. A longer time interval between scans provided a more accurate Ki estimation in general. Using the protocol of 20–25 min plus 80–85 or 85–90 min, very high correlations were obtained between standard Ki and dual‐time‐point Ki (R2 = 0.994, 0.980, 0.971 and 0.925 for nodule, tumor, cerebellum, and bone marrow), with all the slope values with differences ≤0.033 from 1 and all the intercept values with differences ≤0.0006 mL/min/cm3 from 0. The corresponding correlations were much lower between standard Ki and relative SUV change (R2 = 0.673, 0.684, 0.065, 0.246). Dual‐time‐point Ki showed a significantly higher quantification accuracy with respect to standard Ki than relative SUV change for all the 36 protocols (p &lt; 0.05 using Williams’ one‐tailed t test).ConclusionsOur proposed approach can obtain reliable Ki images and accurate Ki quantification from dual‐time‐point scans (5‐min per scan), and provide significantly higher quantification accuracy than relative SUV change that is currently used in clinical practice.

Whole-Body Parametric Imaging of 18F-FDG PET Using uEXPLORER with Reduced Scanning Time.

Parametric imaging of the net influx rate (Ki ) in 18F-FDG PET has been shown to provide improved quantification and specificity for cancer detection compared with SUV imaging. Current methods of generating parametric images usually require a long dynamic scanning time. With the recently developed uEXPLORER scanner, a dramatic increase in sensitivity has reduced the noise in dynamic imaging, making it more robust to use a nonlinear estimation method and flexible protocols. In this work, we explored 2 new possible protocols besides the standard 60-min one for the possibility of reducing scanning time for Ki imaging. Methods: The gold standard protocol (protocol 1) was conventional dynamic scanning with a 60-min scanning time. The first proposed protocol (protocol 2) included 2 scanning periods: 0-4 min and 54-60 min after injection. The second proposed protocol (protocol 3) consisted of a single scanning period from 50 to 60 min after injection, with a second injection applied at 56 min. The 2 new protocols were simulated from the 60-min standard scans. A hybrid input function combining the population-based input function and the image-derived input function (IDIF) was used. The results were also compared with the IDIF acquired from protocol 1. A previously developed maximum-likelihood approach was used to estimate the Ki images. In total, 7 cancer patients imaged using the uEXPLORER scanner were enrolled in this study. Lesions were identified from the patient data, and the lesion Ki values were compared among the different protocols. Results: The acquired hybrid input function was comparable in shape to the IDIF for each patient. The average difference in area under the curve was about 3%, suggesting good quantitative accuracy. The visual difference between the Ki images generated using IDIF and those generated using the hybrid input function was also minimal. The acquired Ki images using different protocols were visually comparable. The average Ki difference in the lesions was 2.8% ± 2.1% for protocol 2 and 1% ± 2.2% for protocol 3. Conclusion: The results suggest that it is possible to acquire Ki images using the nonlinear estimation approach with a much-reduced scanning time. Between the 2 new protocols, the protocol with dual injection shows the greatest promise in terms of practicality.

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

IntroductionQuantitative 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 methodsEighteen 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.ResultsBland-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%).ConclusionsPSAIF 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.

Simplified protocol for whole-body Patlak parametric imaging with 18 F-FDG PET/CT: Feasibility and error analysis.

Parametric imaging using the Patlak model has been shown to provide improved lesion detectability and specificity. The Patlak model requires both tissue time-activity curves (TACs) after equilibrium and knowledge of the input function from the start of injection. Therefore, the conventional dynamic scanning protocol typically starts from the radiotracer injection all the way to equilibrium. In this paper, we propose the use of hybrid population-based and model-based input function estimation and evaluate its use for whole-body Patlak analysis, in order to reduce the total scan time and simplify clinical Patlak parametric imaging protocols. Possible quantitative errors caused by the simplified scanning protocol were also analyzed both theoretically and with the use of clinical data. Clinical data from 24 patients referred for tumor staging were included in this study. The patients underwent a whole-body dynamic PET study, 20min after FDG injection (0.13mCi/kg). The proposed whole-body scanning protocol includes 6 passes with 4-5 bed positions, depending on the size of the patient, with 2min for each bed position. An input function from the literature was selected as the shape of the population-based input function. The descending aorta from the corresponding CT image was segmented and applied on the reconstructed dynamic PET images to acquire an image-based input function, which was later fitted using an exponential model. Due to the late scan time, only the later portion of the input function was available, which was used to scale the population-based input function. The hybrid input function was used to derive the whole-body Patlak images. Assuming a given error in the population-based input function, its influence on the final Patlak images were also derived theoretically and verified using the clinical data sets. Finally, the image quality of the reconstructed Patlak slope image was evaluated by an experienced radiologist in four different aspects: image artifacts, image noise, lesion sharpness, and lesion detectability. It was found that errors in the population-based input function only affect the absolute scale of the Patlak slope image. The induced error is proportional to the percentage area-under-curve (AUC) error in the input function. These findings were also confirmed by numerical analysis. The predicted global scale was in good agreement with results from both image-based Patlak and direct Patlak approach. The fractions of the AUC from the early portion population-based input function were also found to be around 18% of the total AUC of the input function, further limiting the propagation of quantitation error from population-based input function to the final Patlak slope image. The reconstructed Patlak images were also found by the radiologist to provide excellent confidence in lesion detection tasks. We have proposed a simplified whole-body scanning protocol that utilizes both population-based input function and model-based input function. The error from the population-based function was found to only affect the global scale and the overall quantitative impact can be predicted using our proposed formulas.

Effect of Shortening the Scan Duration on Quantitative Accuracy of [18F]Flortaucipir Studies

PurposeDynamic positron emission tomography (PET) protocols allow for accurate quantification of [18F]flortaucipir-specific binding. However, dynamic acquisitions can be challenging given the long required scan duration of 130 min. The current study assessed the effect of shorter scan protocols for [18F]flortaucipir on its quantitative accuracy.ProceduresTwo study cohorts with Alzheimer’s disease (AD) patients and healthy controls (HC) were included. All subjects underwent a 130-min dynamic [18F]flortaucipir PET scan consisting of two parts (0–60/80–130 min) post-injection. Arterial sampling was acquired during scanning of the first cohort only. For the second cohort, a second PET scan was acquired within 1–4 weeks of the first PET scan to assess test-retest repeatability (TRT). Three alternative time intervals were explored for the second part of the scan: 80–120, 80–110 and 80–100 min. Furthermore, the first part of the scan was also varied: 0–50, 0–40 and 0–30 min time intervals were assessed. The gap in the reference TACs was interpolated using four different interpolation methods: population-based input function 2T4k_VB (POP-IP_2T4k_VB), cubic, linear and exponential. Regional binding potential (BPND) and relative tracer delivery (R1) values estimated using simplified reference tissue model (SRTM) and/or receptor parametric mapping (RPM). The different scan protocols were compared to the respective values estimated using the original scan acquisition. In addition, TRT of the RPM BPND and R1 values estimated using the optimal shortest scan duration was also assessed.ResultsRPM BPND and R1 obtained using 0–30/80–100 min scan and POP-IP_2T4k_VB reference region interpolation had an excellent correlation with the respective parametric values estimated using the original scan duration (r2 > 0.95). The TRT of RPM BPND and R1 using the shortest scan duration was − 1 ± 5 % and − 1 ± 6 % respectively.ConclusionsThis study demonstrated that [18F]flortaucipir PET scan can be acquired with sufficient quantitative accuracy using only 50 min of dual-time-window scanning time.

Assessment of population-based input functions for Patlak imaging of whole body dynamic 18F-FDG PET

BackgroundArterial blood sampling is the gold standard method to obtain the arterial input function (AIF) for quantification of whole body (WB) dynamic 18F-FDG PET imaging. However, this procedure is invasive and not typically available in clinical environments. As an alternative, we compared AIFs to population-based input functions (PBIFs) using two normalization methods: area under the curve (AUC) and extrapolated initial plasma concentration (CP*(0)). To scale the PBIFs, we tested two methods: (1) the AUC of the image-derived input function (IDIF) and (2) the estimated CP*(0). The aim of this study was to validate IDIF and PBIF for FDG oncological WB PET studies by comparing to the gold standard arterial blood sampling.MethodsThe Feng 18F-FDG plasma concentration model was applied to estimate AIF parameters (n = 23). AIF normalization used either AUC(0–60 min) or CP*(0), estimated from an exponential fit. CP*(0) is also described as the ratio of the injected dose (ID) to initial distribution volume (iDV). iDV was modeled using the subject height and weight, with coefficients that were estimated in 23 subjects. In 12 oncological patients, we computed IDIF (from the aorta) and PBIFs with scaling by the AUC of the IDIF from 4 time windows (15–45, 30–60, 45–75, 60–90 min) (PBIFAUC) and estimated CP*(0) (PBIFiDV). The IDIF and PBIFs were compared with the gold standard AIF, using AUC values and Patlak Ki values.ResultsThe IDIF underestimated the AIF at early times and overestimated it at later times. Thus, based on the AUC and Ki comparison, 30–60 min was the most accurate time window for PBIFAUC; later time windows for scaling underestimated Ki (− 6 ± 8 to − 13 ± 9%). Correlations of AUC between AIF and IDIF, PBIFAUC(30–60), and PBIFiDV were 0.91, 0.94, and 0.90, respectively. The bias of Ki was − 9 ± 10%, − 1 ± 8%, and 3 ± 9%, respectively.ConclusionsBoth PBIF scaling methods provided good mean performance with moderate variation. Improved performance can be obtained by refining IDIF methods and by evaluating PBIFs with test-retest data.

Reliable quantification of 18F-GE-180 PET neuroinflammation studies using an individually scaled population-based input function or late tissue-to-blood ratio

PurposeTracer kinetic modeling of tissue time activity curves and the individual input function based on arterial blood sampling and metabolite correction is the gold standard for quantitative characterization of microglia activation by PET with the translocator protein (TSPO) ligand 18F-GE-180. This study tested simplified methods for quantification of 18F-GE-180 PET.MethodsDynamic 18F-GE-180 PET with arterial blood sampling and metabolite correction was performed in five healthy volunteers and 20 liver-transplanted patients. Population-based input function templates were generated by averaging individual input functions normalized to the total area under the input function using a leave-one-out approach. Individual population-based input functions were obtained by scaling the input function template with the individual parent activity concentration of 18F-GE-180 in arterial plasma in a blood sample drawn at 27.5 min or by the individual administered tracer activity, respectively. The total 18F-GE-180 distribution volume (VT) was estimated in 12 regions-of-interest (ROIs) by the invasive Logan plot using the measured or the population-based input functions. Late ROI-to-whole-blood and ROI-to-cerebellum ratio were also computed.ResultsCorrelation with the reference VT (with individually measured input function) was very high for VT with the population-based input function scaled with the blood sample and for the ROI-to-whole-blood ratio (Pearson correlation coefficient = 0.989 ± 0.006 and 0.970 ± 0.005). The correlation was only moderate for VT with the population-based input function scaled with tracer activity dose and for the ROI-to-cerebellum ratio (0.653 ± 0.074 and 0.384 ± 0.177). Reference VT, population-based VT with scaling by the blood sample, and ROI-to-whole-blood ratio were sensitive to the TSPO gene polymorphism. Population-based VT with scaling to the administered tracer activity and the ROI-to-cerebellum ratio failed to detect a polymorphism effect.ConclusionThese results support the use of a population-based input function scaled with a single blood sample or the ROI-to-whole-blood ratio at a late time point for simplified quantitative analysis of 18F-GE-180 PET.

Plasma pharmacokinetic and metabolism of [18F]THK-5317 are dependent on sex

IntroductionTau deposition is one of the hallmarks of Alzheimer's disease (AD) and can be visualized and quantified using [18F]THK-5317 together with kinetic modeling. To determine the feasibility of this approach, we measured blood/plasma pharmacokinetics and radiotracer metabolism in female and male rats. MethodsFemale and male rats (n = 11–12) were cannulated via the femoral artery for continuous blood sampling. Blood sampling was performed at regular intervals after intravenous injection of [18F]THK-5317. After collection of the last blood sample, animals were sacrificed, and organs were excised. Blood from minute 5, 20 and 60 was centrifuged to obtain plasma. Radiolabeled metabolites in plasma, brain, liver and urine were analyzed by radio-thin-layer chromatography (radio-TLC). ResultsPlasma pharmacokinetics and metabolism were significantly different between female and male rats. [18F]THK-5317 plasma clearance was faster in female (0.66 ± 0.08 mL/h/kg BW) than in male (0.52 ± 0.11 mL/h/kg BW) rats (p = .005). The percentage of unmetabolized parent was significantly different between both sexes at 20 min and 60 min p.i. In the liver, a 1.6-fold higher radioactivity concentration was found in male versus female animals and in addition also the percentage of unmetabolized parent was different. ConclusionOur results show pronounced sex differences in blood/plasma pharmacokinetics and metabolism of [18F]THK-5317 in rats. Female animals showed a faster plasma clearance compared to males. These results underline the importance of investigating both sexes and also support the notion that individual input functions or sex-specific population-based input functions are needed for kinetic modeling analyses. Advances in knowledgeFirst preclinical study in rats showing pronounced sex differences in blood/plasma pharmacokinetics and metabolism of [18F]THK-5317. Implications for patient careSex-specific differences might also be present in humans and thus clinical trials should have adequate sample size to account for effects in men and women separately.