CALIPER: A software for blood-free parametric Patlak mapping using PET/MRI input function.

Routine clinical use of absolute PET quantification techniques is limited by the need for serial arterial blood sampling for input function and more importantly by the lack of automated pharmacokinetic analysis tools that can be readily implemented in clinic with minimal effort. PET/MRI provides the ability for absolute quantification of PET probes without the need for serial arterial blood sampling using image-derived input functions (IDIFs). Here we introduce CALIPER, a tool for simplified pharmacokinetic modelling of PET probes with irreversible uptake or binding based on and PET/MR IDIFs and Patlak Plot analysis. CALIPER generates regional values or parametric maps of net influx rate (Ki) for tracers using reconstructed dynamic PET images and anatomical MRI for IDIF vessel delineation. We evaluated the performance of CALIPER for blood-free region-based and pixel-wise Patlak analyses of [18F]FDG. IDIFs corrected for partial volume errors including spill-out and spill-in effects were similar to AIF with a general bias of around 6-8%, even for arteries <5 mm. The Ki and cerebral metabolic rate of glucose estimated using IDIF were similar to estimates using blood sampling (<2%) and within limits of whole brain values reported in literature. Overall, CALIPER is a promising tool for irreversible PET tracer quantification and can simplify the ability to perform parametric analysis in clinical settings without the need for blood sampling.

Model Corrected Blood Input Function to Compute Cerebral FDG Uptake Rates From Dynamic Total-Body PET Images of Rats in vivo

Recently, we developed a three-compartment dual-output model that incorporates spillover (SP) and partial volume (PV) corrections to simultaneously estimate the kinetic parameters and model-corrected blood input function (MCIF) from dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) images of mouse heart in vivo. In this study, we further optimized this model and utilized the estimated MCIF to compute cerebral FDG uptake rates, Ki, from dynamic total-body FDG PET images of control Wistar–Kyoto (WKY) rats and compared to those derived from arterial blood sampling in vivo. Dynamic FDG PET scans of WKY rats (n = 5), fasted for 6 h, were performed using the Albira Si Trimodal PET/SPECT/CT imager for 60 min. Arterial blood samples were collected for the entire imaging duration and then fitted to a seven-parameter function. The 60-min list mode PET data, corrected for attenuation, scatter, randoms, and decay, were reconstructed into 23 time bins. A 15-parameter dual-output model with SP and PV corrections was optimized with two cost functions to compute MCIF. A four-parameter compartment model was then used to compute cerebral Ki. The computed area under the curve (AUC) and Ki were compared to that derived from arterial blood samples. Experimental and computed AUCs were 1,893.53 ± 195.39 kBq min/cc and 1,792.65 ± 155.84 kBq min/cc, respectively (p = 0.76). Bland–Altman analysis of experimental vs. computed Ki for 35 cerebral regions in WKY rats revealed a mean difference of 0.0029 min−1 (~13.5%). Direct (AUC) and indirect (Ki) comparisons of model computations with arterial blood sampling were performed in WKY rats. AUC and the downstream cerebral FDG uptake rates compared well with that obtained using arterial blood samples. Experimental vs. computed cerebral Ki for the four super regions including cerebellum, frontal cortex, hippocampus, and striatum indicated no significant differences.

A first-in-human study of 11C-MTP38, a novel PET ligand for phosphodiesterase 7

Purpose Phosphodiesterase 7 (PDE7) is an enzyme that selectively hydrolyses cyclic adenosine monophosphate, and its dysfunction is implicated in neuropsychiatric diseases. However, in vivo visualization of PDE7 in human brains has hitherto not been possible. Using the novel PET ligand 11C-MTP38, which we recently developed, we aimed to image and quantify PDE7 in living human brains. Methods Seven healthy males underwent a 90-min PET scan after injection of 11C-MTP38. We performed arterial blood sampling and metabolite analysis of plasma in six subjects to obtain a metabolite-corrected input function. Regional total distribution volumes (VTs) were estimated using compartment models, and Logan plot and Ichise multilinear analysis (MA1). We further quantified the specific radioligand binding using the original multilinear reference tissue model (MRTMO) and standardized uptake value ratio (SUVR) method with the cerebellar cortex as reference. Results PET images with 11C-MTP38 showed relatively high retentions in several brain regions, including in the striatum, globus pallidus, and thalamus, as well as fast washout from the cerebellar cortex, in agreement with the known distribution of PDE7. VT values were robustly estimated by two-tissue compartment model analysis (mean VT = 4.2 for the pallidum), Logan plot, and MA1, all in excellent agreement with each other, suggesting the reversibility of 11C-MTP38 binding. Furthermore, there were good agreements between binding values estimated by indirect method and those estimated by both MRTMO and SUVR, indicating that these methods could be useful for reliable quantification of PDE7. Because MRTMO and SUVR do not require arterial blood sampling, they are the most practical for the clinical use of 11C-MTP38-PET. Conclusion We have provided the first demonstration of PET visualization of PDE7 in human brains. 11C-MTP38 is a promising novel PET ligand for the quantitative investigation of central PDE7.

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

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

The effect of mother’s voice on pain and physiological parameters during arterial blood sampling in children hospitalized in PICUs

Introduction: Arterial blood sampling is a painful procedure, and is frequently performed in PICUs. Listening to mother's voice may be effective in reducing pain during arterial blood sampling in children. This study was aimed to determine the effect of mother’s recorded voice on pain and physiological parameters during arterial blood sampling in children hospitalized in PICUs. Methodology: This study was a single blind randomized clinical trial with crossover design. Fifty children hospitalized in PICUs participated in this study. The participants were selected through “sequential sampling” and randomly allocated into two groups; e.g., Group AB (n = 25) and Group BA (n = 25). Physiological parameters and pain in each child were measured before and during arterial blood sampling with (B) and without (A) listening to their mothers’ voices with a minimum of 24 hrs interval. To measure the pain, COMFORT pain scale was used and the physiological parameters were measured using the monitoring devices connected to the children. Data were analyzed with SPSS version 22, repeated measure test, paired and non-paired t-test, Wilcoxon and Mann-Whitney test. Results: Means of pain scores and physiologic parameters did not show a significant difference before the intervention on the first and second day between the two groups (AB-BA). The results showed the period effect (f = 0.581 and p = 0.89) and carry over effect (f = 0.055, p-value = 0.881) were not significant. Results of paired t-test showed that the mean pain score during arterial blood sampling with (B: 21.82 ± 5.53) and without (A: 22.40 ± 4.76) listening to their mothers’ voices was significantly different (p = 0.002). That is, with the mother’s voice, the children felt a lower level of pain. The SpO2 decline during the sampling was less with the mother’s voice. Additionally, the heart rate declined less during the sampling compared to the rate before the sampling with the mother’s voice; still, the difference was not significant (p > 0.05). Conclusion: Listening to mothers’ recorded voice was effective to alleviate pain during arterial blood sampling in children hospitalized in PICUs. The use of this method to reduce pain during this painful procedure is effective even in the absence of the mother. Key words: Pain; Mother’s voice; Arterial blood sampling; Physiological indices; PICU Citation: Shoghi M, Ahmadi M, Rasouli M. The effect of mother’s voice on pain and physiological parameters during arterial blood sampling in children hospitalized in PICUs. Anaesth. pain intensive care 2021;25(1):40-47.DOI: 10.35975/apic.v25i1.1440 Received: 20 June 2020, Reviewed: 16 September 2020, Accepted: 25 November 2020

Feasibility and Safety of a Novel Distal Radial Artery Approach for Arterial Blood Gas in COVID-19 Patients

Background: Hypoxemia has been reported in association with the recent COVID-19 pandemic requiring serial arterial blood gas sampling in infected patients. Radial puncture remains the technique of choice for arterial blood sampling but present limitations and complications. Aims: We presents the safety and feasibility of distal radial artery puncture in COVID-19 patients requiring arterial blood sampling. Methods: Twenty COVID-19 patients requiring arterial blood sampling were prospectively enrolled in the emergency room upon presentation. Results: All patients were successfully punctured with no reported complications. Conclusion: Distal radial artery puncture for arterial blood sampling is safe and feasible in COVID-19 patients.

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

Background Arterial 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. Methods The 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. Results The 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. Conclusions Both 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

Purpose Tracer 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. Methods Dynamic 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. Results Correlation 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. Conclusion These 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.