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13N-ammonia and 18F-flurpiridaz require longer delays between rest and stress studies to allow for decay, lowering clinical throughput. In this study, we investigated the impact of residual subtraction on MBF and MFR estimates, as well as its effects on diagnostic accuracy. We retrospectively analyzed 63 patients who underwent a dynamic ammonia rest/stress study and 231 patients from the flurpiridaz 301 trial. Residual subtraction was performed by subtracting the mean pre-injection activity in each sampled region from that region’s time activity curve. Corrected and uncorrected MBF and MFR were analyzed. Diagnostic accuracy was compared to quantitative coronary angiograms (QCA) for the flurpiridaz population. With delays between injections above 3 half-lives, and a doubled stress dose, residual activity did not meaningfully increase ammonia MBF (

Introduction: Thorium-227 is an alpha-emitting radioisotope with potential therapeutic applications in targeted alpha therapy. Thorium-227 decays to Radium-223, which may have an independent biodistribution to that of the parent Thorium-227 radiopharmaceutical. Quantitative in vivo imaging with sodium iodide (NaI) detectors is challenging due to cross-talk between neighboring γ-photopeaks as well as scattered γ-photons. The aim of this work was to validate the use of a spectral analysis technique to estimate the activity of each isotope within a region of interest applied to a pair of conjugate view planar acquisitions, acquired at multiple energy windows. Methods: Energy spectra per unit activity arising from unscattered Thorium-227 photons and Radium-223 photons as well as from scattered photons were modeled. These spectra were scaled until the combination of these component spectra resulted in the closest match to the measured data in four energy windows. Results: Measured estimates of activity followed the known decay curves in phantoms representative of a human torso. The mean errors in estimating Thorium-227 and Radium-223 were 5.1% (range -8.0% to 40.0%) and 3.4% (range -50.0% to 48.7%), respectively. The differences between the integrals of the theoretical and estimated time activity curve were

Purpose A 2-m axial field-of-view, total-body PET/CT scanner (uEXPLORER) has been recently developed to provide total-body coverage and ultra-high sensitivity, which together, enables opportunities for in vivo time-activity curve (TAC) measurement of all investigated organs simultaneously with high temporal resolution. This study aims at quantifying the cumulated activity and patient dose of 2-[F-18]fluoro-2-deoxy-D-glucose (F-18 FDG ) imaging by using delayed time-activity curves (TACs), measured out to 8-h post-injection, for different organs so that the comparison between quantifying approaches using short-time method (up to 75 min post-injection) or long-time method (up to 8 h post-injection) could be performed. Methods Organ TACs of 10 healthy volunteers were collected using total-body PET/CT in 4 periods after the intravenous injection of F-18 FDG. The 8-h post-injection TACs of 6 source organs were fitted using a spline method (based on Origin (version 8.1)). To compare with cumulated activity estimated from spline-fitted curves, the cumulated activity estimated from multi-exponential curve was also calculated. Exponential curve was fitted with shorter series of data consistent with clinical procedure and previous dosimetry works. An 8-h dynamic bladder wall dose model considering 2 voiding were employed to illustrate the differences in bladder wall dose caused by the different measurement durations. Organ absorbed doses were further estimated using Medical Internal Radiation Dose (MIRD) method and voxel phantoms. Results A short-time measurement could lead to significant bias in estimated cumulated activity for liver compared with long-time-measured spline fitted method, and the differences of cumulated activity were 18.38% on average. For the myocardium, the estimated cumulated activity difference was not statistically significant due to large variation in metabolism among individuals. The average residence time differences of brain, heart, kidney, liver, and lungs were 8.38%, 15.13%, 25.02%, 23.94%, and 16.50% between short-time and long-time methods. Regarding effective dose, the maximum differences of residence time between long-time-measured spline fitted curve and short-time-measured multi-exponential fitted curve was 9.93%. When using spline method, the bladder revealed the most difference in the effective dose among all the investigated organs with a bias up to 21.18%. The bladder wall dose calculated using a long-time dynamic model was 13.79% larger than the two-voiding dynamic model, and at least 50.17% lower than previous studies based on fixed bladder content volume. Conclusions Long-time measurement of multi-organ TACs with high temporal resolution enabled by a total-body PET/CT demonstrated that the clinical procedure with 20 min PET scan at 1 h after injection could be used for retrospective dosimetry analysis in most organs. As the bladder content contributed the most to the effective dose, a long-time dynamic model was recommended for the bladder wall dose estimation.

1406 Purpose: FDG uptake rate constant Ki is a main physiology parameter measured by dynamic PET study. A model-independent graphical analysis using Patlak plot with plasma input function is a standard approach used to estimate Ki. The plasma input function is the FDG time activity curve in plasma obtained by arterial blood sampling. The purpose of the study is to evaluate aPatlak plot-based optimization approach for noninvasive quantification of dynamic FDG PET. Methods: Eight 60-min monkey dynamic FDG PET studies with arterial blood samples were collected. The measured plasma input function (mPIF) was determined by arterial blood samples. Time activity curves of seven cerebral regions of interest were generated from each study. With a given number of blood samples, the estimated PIF (ePIF) was determined by either interpolation or extrapolation method using scale calibrated population mean of normalized PIF. The optimal time points for those blood samples to estimate PIF (ePIF) was determined by maximizing the correlations between the Ki estimated ePIF and ones estimated mPIF. A leave-two-out cross-validation method was used for evaluation. Results: The linear correlations between the Ki estimates from ePIF with optimal sampling schemes and those from measured PIF were: Ki (ePIF 1 sample) = 1.09 Ki (mPIF) - 0.00, R2 =0.95±0.08; Ki (ePIF 2 samples) = 1.09 Ki (mPIF) - 0.00, R2 =0.95±0.07; Ki (ePIF 3 samples) = 1.04 Ki (mPIF) - 0.00, R2 =0.96±0.05; and Ki (ePIF 4 samples) = 1.02 Ki (mPIF)-0.00, R2 =0.97±0.04. As sample size became greater or equal 4, the Ki estimates from ePIF with optimal protocol were almost identical to those from mPIF. Conclusions: The Patlak plot-based optimization approach is a robust method to estimate plasma input function for noninvasive quantification of non-human primate dynamic FDG PET. KEYWORDSFDG PET; Patlak plot; plasma input function; quantification

Background Arterial sampling in PET studies for the purposes of kinetic modeling remains an invasive, time-intensive, and expensive procedure. Alternatives to derive the blood time-activity curve (BTAC) non-invasively are either reliant on large vessels in the field of view or are laborious to implement and analyze as well as being prone to many processing errors. An alternative method is proposed in this work by the simulation of a non-invasive coincidence detection unit. Results We utilized GATE simulations of a human forearm phantom with a blood flow model, as well as a model for dynamic radioactive bolus activity concentration based on clinical measurements. A fixed configuration of 14 and, also separately, 8 detectors were employed around the phantom, and simulations were performed to investigate signal detection parameters. Bismuth germanate (BGO) crystals proved to show the highest count rate capability and sensitivity to a simulated BTAC with a maximum coincidence rate of 575 cps. Repeatable location of the blood vessels in the forearm allowed a half-ring design with only 8 detectors. Using this configuration, maximum coincident rates of 250 cps and 42 cps were achieved with simulation of activity concentration determined from 15O and 18F arterial blood sampling. NECR simulated in a water phantom at 3 different vertical positions inside the 8-detector system (Y = − 1 cm, Y = − 2 cm, and Y = −3 cm) was 8360 cps, 13,041 cps, and 20,476 cps at an activity of 3.5 MBq. Addition of extra axial detection rings to the half-ring configuration provided increases in system sensitivity by a factor of approximately 10. Conclusions Initial simulations demonstrated that the configuration of a single half-ring 8 detector of monolithic BGO crystals could describe the simulated BTAC in a clinically relevant forearm phantom with good signal properties, and an increased number of axial detection rings can provide increased sensitivity of the system. The system would find use in the derivation of the BTAC for use in the application of kinetic models without physical arterial sampling or reliance on image-based techniques.

A reference tissue model (RTM) is a compartmental approach to the estimation of the kinetic parameters of the tracer flow in a given two-compartment target tissue (TT) without explicit knowledge of the time activity curve (TAC) of tracer concentration in the arterial blood. An “indirect” measure of arterial concentration is provided by the TAC of a suitably chosen one-compartment reference tissue (RT). The RTM is formed by the RT and the TT. In this paper, it is shown that the RTM is identifiable, i.e., the rate constants are uniquely retrievable, provided that a selection criterion for one of the coefficients, which is based on the Logan plot of the RT, is introduced. The exchange coefficients are then evaluated by the application of a Gauss–Newton method, with a regularizing term, accounting for the ill-posedness of the problem. The reliability of the method is validated against synthetic data generated according to realistic conditions, and compared with the full two-compartment model for the TT, here used as “gold standard.” Finally, the RTM is applied to the estimate of the rate constants in the case of animal models with murine cancer cell lines CT26 inoculated.

The aim of this study was to assess whether dynamic PET with 11C-methionine (MET) (MET-PET) is useful in the diagnosis of brain tumors. One hundred sixty patients with brain tumors (139 gliomas, 9 meningiomas, 4 hemangioblastomas and 8 primary central nervous system lymphomas [PCNSL]) underwent dynamic MET-PET with a 3-dimensional acquisition mode, and the maximum tumor MET-standardized uptake value (MET-SUV) was measured consecutively to construct a time-activity curve (TAC). Furthermore, receiver operating characteristic (ROC) curves were generated from the time-to-peak (TTP) and the slope of the curve in the late phase (SLOPE). The TAC patterns of MET-SUVs (MET-TACs) could be divided into four characteristic types when MET dynamics were analyzed by dividing the MET-TAC into three phases. MET-SUVs were significantly higher in early and late phases in glioblastoma compared to anaplastic astrocytoma, diffuse astrocytoma and the normal frontal cortex (P

Hintergrund: Auf Basis der Kinetischen Modellierung wurde ein Algorithmus entwickelt, welcher die Quantifizierung physiologischer Vorg��nge im K��rper erleichtert. Daf��r werden mit Positron-Emissions-Tomographie (PET) aufgenommene Zeitaktivit��tskurven (TAC) eingelesen und auf verschiedene mathematischen Charakteristika untersucht. Hierdurch bekommt man eine erste Einsch��tzung der Zeitkonstanten, welche f��r einen weiteren Kurvenfit verwendet werden k��nnen. Methoden: Um Korrelationen zwischen den Zeitkonstanten und den mathematischen Eigenschaften der TACs zu finden, wurden mit dem Programm Excel etwa 1200 TACs simuliert. Ein Python Skript diente dabei der mathematischen Analyse der TACs. Um verschiedene Modelltypen miteinander vergleichen zu k��nnen, wurde ein t-Test durchgef��hrt. Ergebnisse: Signifikante Korrelationen konnten nur f��r die ersten beiden Zeitkonstanten gefunden werden (r1 > 0.954, r2 > 0.799). Die restlichen Zeitkonstanten wurden daher ��ber Konfidenzintervalle zum Niveau 95% gesch��tzt. Schlussfolgerungen: Durch eine erste Absch��tzung der Zeitkonstanten wird einerseits die Zuverl��ssigkeit von Programmen zum Fitten von Kurven erh��ht, andererseits k��nnen dadurch auch neue Tracer in der Nuklearmedizin einfacher studiert und ihr Verhalten im K��rper analysiert werden., Background: An algorithm was programed to facilitate the quantification of physiological processes in kinetic modeling. In order to do so, it reads in a measured time activity curve (TAC) acquired via positron emission tomography (PET), analyzes it for various mathematical characteristics and outputs values for the rate constants, which can be used for further curve fitting tools. Methods: Around 1200 TACs with different rate constants and model types have been simulated via Excel. These TACs were used to find correlations between their rate constants and mathematical properties, which were calculated by a Python script. Plus, a t-test was performed to see if there is a connection between different compartment model types. The obtained information can then be used for the analysis of real measured TACs, which yields good initial values for the rate constants. Results: Useful correlations were found for the first two rate constants only (r1 > 0.954, r2 > 0.799). The assessment of the remaining rate constants was done by providing two-sided 95% confidence intervals. Conclusion: By assessing the rate constants, both the reliability of curve fitting programs can be increased and new tracers in nuclear medicine can be studied more precisely.

18F-FDG uptake rate constant Ki is the main physiology parameter measured in dynamic PET studies. A model-independent graphical analysis using Patlak plot with plasma input function (PIF) is a standard approach used to estimate Ki . The PIF is the 18F-FDG time activity curve (TAC) in plasma that is obtained by serial arterial blood sampling. The purpose of the study is to evaluate a Patlak plot-based optimization approach with reduced blood samples for noninvasive quantification of dynamic 18F-FDG PET imaging. Eight 60 min rhesus monkey brain dynamic 18F-FDG PET scans with arterial blood samples were collected. The measured PIF (mPIF) was determined by arterial blood samples. TACs of seven cerebral regions of interest were generated from each study. With a given number of blood samples, the population-based PIF (pPIF) was determined by either interpolation or extrapolation method using scale calibrated population mean of normalized PIF. The optimal sampling scheme with given blood sample size was determined by maximizing the correlations between the Ki estimated from pPIF and those obtained by mPIF. A leave-two-out cross-validation method was used for evaluation. The linear correlations between the Ki estimates from pPIF with optimal sampling schemes and those from mPIF were: Ki (pPIF 1 sample at 40 min) = 1.015 Ki (mPIF) − 0.000, R 2 = 0.974; Ki (pPIF 2 samples at 35 and 50 min) = 1.052 Ki (mPIF) − 0.001, R 2 = 0.976; Ki (pPIF 3 samples at 12, 40, and 50 min) = 1.030 Ki (mPIF) − 0.000, R 2 = 0.985; and Ki (pPIF 4 samples at 10, 20, 40, and 50 min) = 1.016 Ki (mPIF)- 0.000, R 2 = 0.993. As the sample size became greater or equal to 4, the Ki estimates from pPIF with the optimal protocol were almost identical to those from mPIF. The Patlak plot-based optimization approach is a reliable method to estimate PIF for noninvasive quantification of non-human primate dynamic 18F-FDG PET imaging and is potentially extendable to further translational human studies.

PURPOSE This study aimed to evaluate the novel index of hepatic receptor (IHR) on the regression analysis derived from time activity curve of the liver for hepatic functional reserve. METHODS Sixty patients had undergone (99m)Tc-galactosyl serum albumin ((99m)Tc-GSA) scintigraphy in the retrospective clinical study. Time activity curves for liver were obtained by region of interest (ROI) on the whole liver. A novel hepatic functional predictor was calculated with multiple regression analysis of time activity curves. In the multiple regression function, the objective variables were the indocyanine green (ICG) retention rate at 15 min, and the explanatory variables were the liver counts in 3-min intervals until end from beginning. Then, this result was defined by IHR, and we analyzed the correlation between IHR and ICG, uptake ratio of the heart at 15 minutes to that at 3 minutes (HH15), uptake ratio of the liver to the liver plus heart at 15 minutes (LHL15), and index of convexity (IOC). RESULTS Regression function of IHR was derived as follows: IHR=0.025×L(6)-0.052×L(12)+0.027×L(27). The multiple regression analysis indicated that liver counts at 6 min, 12 min, and 27 min were significantly related to objective variables. The correlation coefficient between IHR and ICG was 0.774, and the correlation coefficient between ICG and conventional indices (HH15, LHL15, and IOC) were 0.837, 0.773, and 0.793, respectively. IHR had good correlation with HH15, LHL15, and IOC. CONCLUSIONS The finding results suggested that IHR would provide clinical benefit for hepatic functional assessment in the (99m)Tc-GSA scintigraphy.

Dynamic Positron Emission Tomography (PET) allows quantification of underlying physiological processes in a tissue or organ of interest by modeling the radioactivity concentration measured in time (time-activity curve, i.e. TAC). Although the [11C]-(R)-PK11195 PET radiotracer binds to activated microglia and therefore images neuroinflammation, its quantification is still challenging. The aim of this study was to investigate a novel method for analyzing [11C]-(R)-PK11195 TAC behavior from dynamic PET. Seven healthy subjects underwent dynamic 60 min [11C]-(R)-PK11195 PET scans, and TACs were generated for 30 brain cortical regions using the AAL-Merged atlas. The proposed method supposes healthy subjects have similar TACs, which allows the construction of a “healthy template”. Then, TACs of patients can be compared to the healthy template to determine the likelihood that their behavior is abnormal. To evaluate the differences between a healthy region and an inflamed region, a cut-off value for abnormality was created. The method was then tested on a multiple sclerosis (MS) patient. The proposed method was able to identify a number of brain cortical regions with distinct behavior in one MS patient as compared to the healthy template. Further studies are required to evaluate the applicability of the proposed method with additional MS patients, and with data acquired in different scanners and reconstructed with other algorithms.

Major management decisions in patients with solid tumors and lymphomas are often based on 18F-fluorodeoxyglucose (18F-FDG) PET/CT. The misadministration of 18F-FDG outside the systemic circulation can have an adverse impact on this test's sensitivity (1) and is not uncommon (2–7). This report describes how an 18F-FDG misadministration led to a repeat PET/CT study, resulting in the visualization of distant metastases that changed the original treatment plan. The findings suggest that routine injection monitoring is indicated whenever sensitivity is critical, and support claims that infiltrations can confound interpretation of semi-quantitative PET outcome measures in patients who are followed longitudinally (2).

F-fluoroethyltyrosine (FET) is a well-established PET tracer for the imaging of cerebral gliomas. Recent studies reported interest in meningiomas. A study published by Cornelius et al concludes that FET PET may provide additional information for noninvasive grading of meningiomas. Indeed, the combination of tumor background ratio with a cutoff value of 2.3 associated with time activity curve pattern slightly improved the differentiation of high-grade from low-grade meningiomas (accuracy, 92%; P = 0.001). We present the case of a 75-year-old man that underlined the need to confirm the performance of these tools (curve pattern, tumor background ratios) to characterize meningiomas.

International audience; Purpose: Estimating the biodistribution and the pharmacokinetics from time-sequence SPECT images on a per-voxel basis is useful for studying activity nonuniformity or computing absorbed dose distributions by convolution of voxel kernels or Monte-Carlo radiation transport. Current approaches are either region-based, thus assuming uniform activity within the region, or voxel-based but using the same fitting model for all voxels. Methods: We propose a voxel-based multimodel fitting method (VoMM) that estimates a fitting function for each voxel by automatically selecting the most appropriate model among a predetermined set with Akaike criteria. This approach can be used to compute the time integrated activity (TIA) for all voxels in the image. To control fitting optimization that may fail due to excessive image noise, an approximated version based on trapezoid integration, named restricted method, is also studied. From this comparison, the number of failed fittings within images was estimated and analyzed. Numerical experiments were used to quantify uncertainties and feasibility was demonstrated with real patient data. Results: Regarding numerical experiments, root mean square errors of TIA obtained with VoMM were similar to those obtained with bi-exponential fitting functions, and were lower (< 5% vs. > 10%) than with single model approaches that consider the same fitting function for all voxels. Failure rates were lower with VoMM and restricted approaches than with single-model methods. On real clinical data, VoMM was able to fit 90% of the voxels and led to less failed fits than single-model approaches. On regions of interest (ROI) analysis, the difference between ROI-based and voxel-based TIA estimations was low, less than 4%. However, the computation of the mean residence time exhibited larger differences, up to 25%. Conclusions: The proposed voxel-based multimodel fitting method, VoMM, is feasible on patient data. VoMM leads organ-based TIA estimations similar to conventional ROI-based method. However , for pharmacokinetics analysis, studies of spatial heterogeneity or voxel-based absorbed dose assessment, VoMM could be used preferentially as it prevents model overfitting.

177Lu-DOTATATE therapy has been shown to produce encouraging results in treatment of neuroendocrine tumours (NETs). Unfortunately, since dosimetry for radionuclide therapy is considered to be challenging, typically similar amount of radiopharmaceutical is administered to every patient. There is growing evidence that the efficacy of this therapy can be significantly improved by employing personalized protocols, based on the organ-at-risk dosimetry. The aim of this study is to propose a practical and accurate dosimetry protocol based on the simplified acquisition schedules. Data from fifty-three therapy cycles in thirty-nine NET patients were analyzed. Three SPECT/CT scans, acquired at 4 h (D0), 23 h (D1) and 70 h (D3) after injection, were performed. The kidney volume was determined using CT and the activity was determined from quantitative SPECT using an iterative thresholding method. For each dataset, four methods were used to model the time-activity-curves (TAC): M1-two trapezoid segments (0 to D0 and D0 to D1), followed by monoexponential fit to D1 + D3 data; M2-monoexponential fit to D0 + D1 + D3 data; M3 and M4-monoexponential fit to D0 + D3 and D1 + D3 data, respectively. Additionally, kidney doses obtained from single time point method using a monoexponential curve with the population mean effective half-life, normalized to activities at D0 or D1 or D3 points, were calculated. The accuracy of simplified dosimetry methods was assessed as the percentage difference relative to doses calculated from M1. The major contribution to the absorbed dose estimate comes from the area under the late time portion of the TAC (D1 to infinity). Therefore, information from the late scan (D3) is crucial for the determination of kidney absorbed doses. Single time point method using monoexponential TAC, with the population mean effective half-life normalized to the late data point (48-72 h for kidneys) produces