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

Kinetic modeling of the monoamine oxidase-B radioligand [18F]SMBT-1 in human brain with positron emission tomography

This paper describes pharmacokinetic analyses of the monoamine-oxidase-B (MAO-B) radiotracer [18F](S)-(2-methylpyrid-5-yl)-6-[(3-fluoro-2-hydroxy)propoxy]quinoline ([18F]SMBT-1) for positron emission tomography (PET) brain imaging. Brain MAO-B expression is widespread, predominantly within astrocytes. Reactive astrogliosis in response to neurodegenerative disease pathology is associated with MAO-B overexpression. Fourteen elderly subjects (8 control, 5 mild cognitive impairment, 1 Alzheimer's disease) with amyloid ([11C]PiB) and tau ([18F]flortaucipir) imaging assessments underwent dynamic [18F]SMBT-1 PET imaging with arterial input function determination. [18F]SMBT-1 showed high brain uptake and a retention pattern consistent with the known MAO-B distribution. A two-tissue compartment (2TC) model where the K1/k2 ratio was fixed to a whole brain value best described [18F]SMBT-1 kinetics. The 2TC total volume of distribution (VT) was well identified and highly correlated (r2∼0.8) with post-mortem MAO-B indices. Cerebellar grey matter (CGM) showed the lowest mean VT of any region and is considered the optimal pseudo-reference region. Simplified analysis methods including reference tissue models, non-compartmental models, and standard uptake value ratios (SUVR) agreed with 2TC outcomes (r2 > 0.9) but with varying bias. We found the CGM-normalized 70-90 min SUVR to be highly correlated (r2 = 0.93) with the 2TC distribution volume ratio (DVR) with acceptable bias (∼10%), representing a practical alternative for [18F]SMBT-1 analyses.

Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients

INTRODUCTION

Neuroinflammation evaluation after acute ischemic stroke is a promising option for selecting an appropriate post-stroke treatment strategy. To assess neuroinflammation in vivo, translocator protein PET (TSPO PET) can be used. However, the gold standard TSPO PET quantification method includes a 90 min scan and continuous arterial blood sampling, which is challenging to perform on a routine basis. In this work, we determine what information is required for a simplified quantification approach using a machine learning algorithm.

MATERIALS AND METHODS

We analyzed data from 18 patients with ischemic stroke who received 0-90 min [18F]GE-180 PET as well as T1-weigted (T1w), FLAIR, and arterial spin labeling (ASL) MRI scans. During PET scans, five manual venous blood samples at 5, 15, 30, 60, and 85 min post injection (p.i.) were drawn, and plasma activity concentration was measured. Total distribution volume (VT) was calculated using Logan plot with the full dynamic PET and an image-derived input function (IDIF) from the carotid arteries. IDIF was scaled by a calibration factor derived from all the measured plasma activity concentrations. The calculated VT values were used for training a random forest regressor. As input features for the model, we used three late PET frames (60-70, 70-80, and 80-90 min p.i.), the ASL image reflecting perfusion, the voxel coordinates, the lesion mask, and the five plasma activity concentrations. The algorithm was validated with the leave-one-out approach. To estimate the impact of the individual features on the algorithm's performance, we used Shapley Additive Explanations (SHAP). Having determined that the three late PET frames and the plasma activity concentrations were the most important features, we tested a simplified quantification approach consisting of dividing a late PET frame by a plasma activity concentration. All the combinations of frames/samples were compared by means of concordance correlation coefficient and Bland-Altman plots.

RESULTS

When using all the input features, the algorithm predicted VT values with high accuracy (87.8 ± 8.3%) for both lesion and non-lesion voxels. The SHAP values demonstrated high impact of the late PET frames (60-70, 70-80, and 80-90 min p.i.) and plasma activity concentrations on the VT prediction, while the influence of the ASL-derived perfusion, voxel coordinates, and the lesion mask was low. Among all the combinations of the late PET frames and plasma activity concentrations, the 70-80 min p.i. frame divided by the 30 min p.i. plasma sample produced the closest VT estimate in the ischemic lesion.

CONCLUSION

Reliable TSPO PET quantification is achievable by using a single late PET frame divided by a late blood sample activity concentration.

Implant Imaging: Perspectives of Nuclear Imaging in Implant, Biomaterial, and Stem Cell Research

Until now, very few efforts have been made to specifically trace, monitor, and visualize implantations, artificial organs, and bioengineered scaffolds for tissue engineering in vivo. While mainly X-Ray, CT, and MRI methods have been used for this purpose, the applications of more sensitive, quantitative, specific, radiotracer-based nuclear imaging techniques remain a challenge. As the need for biomaterials increases, so does the need for research tools to evaluate host responses. PET (positron emission tomography) and SPECT (single photon emission computer tomography) techniques are promising tools for the clinical translation of such regenerative medicine and tissue engineering efforts. These tracer-based methods offer unique and inevitable support, providing specific, quantitative, visual, non-invasive feedback on implanted biomaterials, devices, or transplanted cells. PET and SPECT can improve and accelerate these studies through biocompatibility, inertivity, and immune-response evaluations over long investigational periods at high sensitivities with low limits of detection. The wide range of radiopharmaceuticals, the newly developed specific bacteria, and the inflammation of specific or fibrosis-specific tracers as well as labeled individual nanomaterials can represent new, valuable tools for implant research. This review aims to summarize the opportunities of nuclear-imaging-supported implant research, including bone, fibrosis, bacteria, nanoparticle, and cell imaging, as well as the latest cutting-edge pretargeting methods.

Image Quantification for TSPO PET with a Novel Image-Derived Input Function Method

There is a growing interest in using 18F-DPA-714 PET to study neuroinflammation and microglial activation through imaging the 18-kDa translocator protein (TSPO). Although quantification of 18F-DPA-714 binding can be achieved through kinetic modeling analysis with an arterial input function (AIF) measured with blood sampling procedures, the invasiveness of such procedures has been an obstacle for wide application. To address these challenges, we developed an image-derived input function (IDIF) that noninvasively estimates the arterial input function from the images acquired for 18F-DPA-714 quantification. Methods: The method entails three fully automatic steps to extract the IDIF, including a segmentation of voxels with highest likelihood of being the arterial blood over the carotid artery, a model-based matrix factorization to extract the arterial blood signal, and a scaling optimization procedure to scale the extracted arterial blood signal into the activity concentration unit. Two cohorts of human subjects were used to evaluate the extracted IDIF. In the first cohort of five subjects, arterial blood sampling was performed, and the calculated IDIF was validated against the measured AIF through the comparison of distribution volumes from AIF (VT,AIF) and IDIF (VT,IDIF). In the second cohort, PET studies from twenty-eight healthy controls without arterial blood sampling were used to compare VT,IDIF with VT,REF measured using a reference region-based analysis to evaluate whether it can distinguish high-affinity (HAB) and mixed-affinity (MAB) binders. Results: In the arterial blood-sampling cohort, VT derived from IDIF was found to be an accurate surrogate of the VT from AIF. The bias of VT, IDIF was −5.8 ± 7.8% when compared to VT,AIF, and the linear mixed effect model showed a high correlation between VT,AIF and VT, IDIF (p < 0.001). In the nonblood-sampling cohort, VT, IDIF showed a significance difference between the HAB and MAB healthy controls. VT, IDIF and standard uptake values (SUV) showed superior results in distinguishing HAB from MAB subjects than VT,REF. Conclusions: A novel IDIF method for 18F-DPA-714 PET quantification was developed and evaluated in this study. This IDIF provides a noninvasive alternative measurement of VT to quantify the TSPO binding of 18F-DPA-714 in the human brain through dynamic PET scans.

Reduced Acquisition Time [18F]GE-180 PET Scanning Protocol Replaces Gold-Standard Dynamic Acquisition in a Mouse Ischemic Stroke Model

Aim

Understanding neuroinflammation after acute ischemic stroke is a crucial step on the way to an individualized post-stroke treatment. Microglia activation, an essential part of neuroinflammation, can be assessed using [18F]GE-180 18 kDa translocator protein positron emission tomography (TSPO-PET). However, the commonly used 60–90 min post-injection (p.i.) time window was not yet proven to be suitable for post-stroke neuroinflammation assessment. In this study, we compare semi-quantitative estimates derived from late time frames to quantitative estimates calculated using a full 0–90 min dynamic scan in a mouse photothrombotic stroke (PT) model.

Materials and Methods

Six mice after PT and six sham mice were included in the study. For a half of the mice, we acquired four serial 0–90 min scans per mouse (analysis cohort) and calculated standardized uptake value ratios (SUVRs; cerebellar reference) for the PT volume of interest (VOI) in five late 10 min time frames as well as distribution volume ratios (DVRs) for the same VOI. We compared late static 10 min SUVRs and the 60–90 min time frame of the analysis cohort to the corresponding DVRs by linear fitting. The other half of the animals received a static 60–90 min scan and was used as a validation cohort. We extrapolated DVRs by using the static 60–90 min p.i. time window, which were compared to the DVRs of the analysis cohort.

Results

We found high linear correlations between SUVRs and DVRs in the analysis cohort for all studied 10 min time frames, while the fits of the 60–70, 70–80, and 80–90 min p.i. time frames were the ones closest to the line of identity. For the 60–90 min time window, we observed an excellent linear correlation between SUVR and DVR regardless of the phenotype (PT vs. sham). The extrapolated DVRs of the validation cohort were not significantly different from the DVRs of the analysis group.

Conclusion

Simplified quantification by a reference tissue ratio of the late 60–90 min p.i. [18F]GE-180 PET image can replace full quantification of a dynamic scan for assessment of microglial activation in the mouse PT model.

TSPO imaging in animal models of brain diseases

Over the last 30 years, the 18-kDa TSPO protein has been considered as the PET imaging biomarker of reference to measure increased neuroinflammation. Generally assumed to image activated microglia, TSPO has also been detected in endothelial cells and activated astrocytes. Here, we provide an exhaustive overview of the recent literature on the TSPO-PET imaging (i) in the search and development of new TSPO tracers and (ii) in the understanding of acute and chronic neuroinflammation in animal models of neurological disorders. Generally, studies testing new TSPO radiotracers against the prototypic [11C]-R-PK11195 or more recent competitors use models of acute focal neuroinflammation (e.g. stroke or lipopolysaccharide injection). These studies have led to the development of over 60 new tracers during the last 15 years. These studies highlighted that interpretation of TSPO-PET is easier in acute models of focal lesions, whereas in chronic models with lower or diffuse microglial activation, such as models of Alzheimer's disease or Parkinson's disease, TSPO quantification for detection of neuroinflammation is more challenging, mirroring what is observed in clinic. Moreover, technical limitations of preclinical scanners provide a drawback when studying modest neuroinflammation in small brains (e.g. in mice). Overall, this review underlines the value of TSPO imaging to study the time course or response to treatment of neuroinflammation in acute or chronic models of diseases. As such, TSPO remains the gold standard biomarker reference for neuroinflammation, waiting for new radioligands for other, more specific targets for neuroinflammatory processes and/or immune cells to emerge.

Reduced microglia activity in patients with long-term immunosuppressive therapy after liver transplantation

PURPOSE

Calcineurin inhibitors (CNI) can cause long-term impairment of brain function. Possible pathomechanisms include alterations of the cerebral immune system. This study used positron emission tomography (PET) imaging with the translocator protein (TSPO) ligand 18F-GE-180 to evaluate microglial activation in liver-transplanted patients under different regimens of immunosuppression.

METHODS

PET was performed in 22 liver-transplanted patients (3 CNI free, 9 with low-dose CNI, 10 with standard-dose CNI immunosuppression) and 9 healthy controls. The total distribution volume (VT) estimated in 12 volumes-of-interest was analyzed regarding TSPO genotype, CNI therapy, and cognitive performance.

RESULTS

In controls, VT was about 80% higher in high affinity binders (n = 5) compared to mixed affinity binders (n = 3). Mean VT corrected for TSPO genotype was significantly lower in patients compared to controls, especially in patients in whom CNI dose had been reduced because of nephrotoxic side effect.

CONCLUSION

Our results provide evidence of chronic suppression of microglial activity in liver-transplanted patients under CNI therapy especially in patients with high sensitivity to CNI toxicity.

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

PURPOSE

To identify a reliable alternative to the full blood [¹¹C]PBR28 quantification method that would be easily replicated in multiple research and clinical settings.

PROCEDURES

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 (V_Ts) were calculated using PBIF, PBIF30, PBIF15, PBIF60, Totals, TPF30-IF, and the individual AIF (V_T^AIF). Distribution volume ratios (DVR) were computed using the cerebellum and the centrum semiovale (CSO), as pseudo-reference regions (DVR^Cereb, DVR^CSO). Results obtained with each method were compared to V_T^AIF. 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).

RESULTS

V_Ts computed using PBIF15 and PBIF30 showed the best correlation to V_T^AIF (r > 0.90), while V_T derived from the blood-free-scaled PBIF showed poor correlation (r = 0.46) and DVR^CSO correlated the least (r = 0.26). In the pharmacological challenge study, most population-derived V_T values were comparable to V_T^AIF at baseline and showed varied sensitivity to challenges at acute and post-recovery evaluation. DVR values did not detect relevant changes.

CONCLUSIONS

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.

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

INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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