Positron emission tomography compartmental models

In vivo quantification of [11C]BIO-1819578 in non-human primates, a novel radioligand for O-GlcNAcase

Neurofibrillary tangles (NFTs), composed of aggregated tau protein, in the brain is a neuropathological hallmark and feature of Alzheimer's disease (AD) and other tauopathies. One promising approach to prevent tau aggregates is to inhibit O-GlcNAcase (OGA), an enzyme that regulates tau O-GlcNAcylation. [11C]BIO-1819578 has emerged as a promising candidate to determine target occupancy of such OGA inhibitor drugs. The aim of this study was to further evaluate the pharmacokinetic properties of [11C]BIO-1819578 in non-human primates (NHPs) and to estimate its effective dose. Kinetic compartment analyses of [11C]BIO-1819578 binding to OGA in the brain were performed on positron emission tomography (PET) measurements conducted in three cynomolgus NHPs. Whole-body PET measurements were carried out in two NHPs to estimate the effective radiation dose. Both the 1-tissue-compartment (1TCM) and 2-tissue-compartment model (2TCM) could describe the regional time activity curves of [11C]BIO-1819578. The 2TCM was the statistically preferred model. The effective radiation dose was estimated to be 0.0033 mSv/MBq. The results showed that [11C]BIO-1819578 has suitable characteristics for reliable quantification of OGA using full kinetic modelling. The effective dose was on par with other 11C radioligands and is unlikely to pose an issue for human use.

Preclinical in vitro and in vivo evaluation of [11C]ORM-13070 as PET ligand for alpha-2C adrenergic receptor occupancy using PET imaging in non-human primates

This paper describes the preclinical validation of the radioligand [11C]ORM-13070 and its tritiated analog for addressing selectivity and occupancy of the selective alpha-2C adrenergic receptor (α2CR) antagonist BAY 292 in the cynomolgus brain. BAY 292 is a novel drug candidate being developed for the treatment of obstructive sleep apnea (OSA) via binding to central α2CR. In vitro autoradiography studies with sections from non-diseased post-mortem human caudate revealed an excellent specific binding window (>80%) using [3H]ORM-13070. BAY 292 bound to the same binding site as [3H]ORM-13070 and generated a good specific binding signal, with greater selectivity for α2CR. In non-human primates in vivo, [11C]ORM-13070 demonstrated a reversible behavior, with uptake at baseline highest in striatum (putamen, caudate, ventral striatum, and pallidum) and low in the cerebellar cortex, consistent with the known distribution of the α2CR. A dose dependent increase in receptor occupancy after BAY 292 administration was observed, confirming BBB penetration and target engagement. The estimated EC50 for BAY 292 is 33.39 ± 11.91 ng/mL. This study aimed to demonstrate the suitability of [11C]ORM-13070 as a PET-radioligand for the study of α2CR in the non-human primate brain, and to pave the way for future clinical PET tracer studies with BAY 292.

A deep learning method for total-body dynamic PET imaging with dual-time-window protocols

PURPOSE

Prolonged scanning durations are one of the primary barriers to the widespread clinical adoption of dynamic Positron Emission Tomography (PET). In this paper, we developed a deep learning algorithm that capable of predicting dynamic images from dual-time-window protocols, thereby shortening the scanning time.

METHODS

This study includes 70 patients (mean age ± standard deviation, 53.61 ± 13.53 years; 32 males) diagnosed with pulmonary nodules or breast nodules between 2022 to 2024. Each patient underwent a 65-min dynamic total-body [18F]FDG PET/CT scan. Acquisitions using early-stop protocols and dual-time-window protocols were simulated to reduce the scanning time. To predict the missing frames, we developed a bidirectional sequence-to-sequence model with attention mechanism (Bi-AT-Seq2Seq); and then compared the model with unidirectional or non-attentional models in terms of Mean Absolute Error (MAE), Bias, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) of predicted frames. Furthermore, we reported the comparison of concordance correlation coefficient (CCC) of the kinetic parameters between the proposed method and traditional methods.

RESULTS

The Bi-AT-Seq2Seq significantly outperform unidirectional or non-attentional models in terms of MAE, Bias, PSNR, and SSIM. Using a dual-time-window protocol, which includes a 10-min early scan followed by a 5-min late scan, improves the four metrics of predicted dynamic images by 37.31%, 36.24%, 7.10%, and 0.014% respectively, compared to the early-stop protocol with a 15-min acquisition. The CCCs of tumor' kinetic parameters estimated with recovered full time-activity-curves (TACs) is higher than those with abbreviated TACs.

CONCLUSION

The proposed algorithm can accurately generate a complete dynamic acquisition (65 min) from dual-time-window protocols (10 + 5 min).

PET Mapping of Receptor Occupancy Using Joint Direct Parametric Reconstruction

Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed separately for each scan by first reconstructing dynamic PET scan data before fitting a kinetic model to time activity curves. This approach fails to properly model the noise in PET measurements, resulting in poor RO estimates, especially in low receptor density regions.

OBJECTIVE

In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and post-drug injection dynamic PET scans.

METHODS

The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions.

RESULTS

Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK-6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators.

CONCLUSION

The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions.

SIGNIFICANCE

This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.

Assessment of fully quantitative and simplified methods for analysis of [68Ga]Ga-FAPI-46 uptake in patients with pancreatobiliary cancer using LAFOV PET/CT

PURPOSE

The aim of this study was to validate simplified methods for quantifying [68Ga]Ga-FAPI-46 uptake against full pharmacokinetic modeling.

METHODS

Ten patients with pancreatobiliary cancer underwent a 90-min dynamic PET/CT scan using a long axial field of view system. Arterial blood samples were used to establish calibrated plasma-input function from both continuous arterial sampling and image-derived input function (IDIF). Lesional [68Ga]Ga-FAPI-46 kinetics were described using conventional non-linear plasma-input tissue-compartment models. Logan plots using 30-90 min and 30-60 min post-injection (p.i), image-based target-to-whole blood ratio (TBR), mean standardized uptake values (SUVmean) normalized to body weight, lean body mass, and body surface area, at 20-30 min, 60-70 min and 80-90 min p.i were assessed.

RESULTS

One patient was excluded due to discontinued scan acquisition and missing arterial sampling. Thirteen tumoral lesions and 11 non-tumoral lesions were included. A reversible 2-tissue-compartment model showed most preferrable fits for all types of [68Ga]Ga-FAPI-46 positive lesions. The distribution volume (VT) results obtained using arterial sampling plasma-input function and those using plasma-IDIF (VT_plasma_IDIF) showed an excellent correlation (Spearman rank correlation coefficient (rs) = 0.949). Logan VT using both time intervals were highly correlated with VT_plasma_IDIF (rs ≥ 0.938). The correlation values with VT_plasma_IDIF for image-based TBR and SUVmean parameters were higher at 80-90 min (rs ≥ 0.839) and at 60-70 min (rs ≥ 0.835) p.i than at 20-30 min p.i (rs ≤ 0.774).

CONCLUSION

Image-based TBR and SUVmean at 60-70 min p.i are suitable for quantifying [68Ga]Ga-FAPI-46 uptake.

TRIAL REGISTRATION

EudraCT, EudraCT 2022-001867-29. Registered 02 November 2022.

Distinguishing benign from malignant lesions with high [68 Ga]Ga-FAPI-04 uptake in oncology patients: Insights from dynamic total-body [68 Ga]Ga-FAPI-04 PET/CT

BACKGROUND AND PURPOSE

While [68 Ga]Ga-FAPI-04 PET/CT is widely used in various malignant tumors diagnosis, its specificity is challenged by high uptake in benign lesions such as inflammatory lymphadenopathy, bone fractures, and degenerative changes. This study aimed to quantitatively assess and characterize the metabolic heterogeneity of [68 Ga]Ga-FAPI-04 uptake in benign and malignant lesions using dynamic total-body PET/CT.

METHODS

Dynamic total-body [68 Ga]Ga-FAPI-04 PET/CT scans (0-60 min post-injection) were performed on 17 oncology patients. Time-activity curves (TACs) were generated for benign and malignant lesions with high [68 Ga]Ga-FAPI-04 uptake. The reversible two-tissue compartment model (2T4k) was used to derive kinetic metrics, including K1, k2, k3, k4, vB and VT. Receiver operating characteristic (ROC) curve analysis was used to determine the cut-off values for differentiating benign and malignant lesions.

RESULTS

The study included 58 malignant and 55 inflammatory lesions with high [68 Ga]Ga-FAPI-04 uptake. Malignant lesions exhibited higher K1 (0.277 ± 0.217 ml/ccm/min vs. 0.221 ± 0.216 ml/ccm/min, P = 0.011), vB (0.042 ± 0.007 vs. 0.013 ± 0.004, P < 0.001), and lower k3 (0.267 ± 0.041 1/min vs. 0.481 ± 0.085 1/min, P = 0.008) compared to benign lesions. Lesions were classified into low, medium, and high-probability groups for being malignant based on K1, k3 and vB values, with probabilities of 0%, 50.7%, and 92.0%, respectively (P < 0.001).

CONCLUSIONS

Kinetic metrics, particularly K1, k3 and vB values, show promise as imaging biomarkers for distinguishing between benign and malignant lesions with high [68 Ga]Ga-FAPI-04 uptake in oncology patients. These metrics reflect the metabolic heterogeneity of the lesions and may improve diagnostic accuracy in oncological imaging.

Exploration of (R)-[11C]YH168 as a PET tracer for imaging monoacylglycerol lipase in the brain: from mice to non-human primates

PURPOSE

The monoacylglycerol lipase (MAGL) plays a pivotal role in modulating the endocannabinoid system and is considered an attractive therapeutic target for diseases in both the central nervous system and periphery. The current study aimed to develop and evaluate a suitable carbon-11 labeled tracer for imaging MAGL in preclinical studies.

METHODS

(R)-YH168 was synthesized via a multi-step pathway and its half-maximal inhibitory concentration (IC50) values were measured using an enzymatic assay. Radiosynthesis of (R)-[11C]YH168 was accomplished by 11C-methylation via Suzuki cross-coupling of a pinacol boron precursor. In vitro autoradiography was performed using brain tissues from MAGL knockout and the corresponding wild-type mice. The metabolic stability of (R)-[11C]YH168 in mouse brain and plasma was assessed 5 min after injection. Dynamic PET scans were conducted on anesthetized mice and rhesus monkey. For studies in non-human primates, arterial blood samples were analyzed to obtain the input function for kinetic modeling. Blocking studies with the irreversible MAGL inhibitor PF-06795071 were performed to assess the binding specificity of (R)-[11C]YH168.

RESULTS

(R)-[11C]YH168 was synthesized via Suzuki coupling of the phenyl boronic ester with [11C]CH3I in the presence of palladium catalyst. In vitro autoradiography revealed a heterogeneous distribution pattern of (R)-[11C]YH168 with higher binding to MAGL-rich brain regions in wild-type mouse brain slices compared to that of MAGL knockout mice. Dynamic PET imaging in wild-type and MAGL knockout mice confirmed its high specificity and selectivity in mouse brains. In the rhesus monkey, (R)-[11C]YH168 displayed good brain permeability. High levels of radioactivity uptake were seen in the cingulate cortex, frontal cortex, cerebellum, occipital cortex, and hippocampus, consistent with MAGL expression. The one-tissue compartment model was appropriate for fitting the regional time-activity curves and provided reliable volume of distribution values across all brain regions. Pretreatment with PF-06795071 (0.1 mg/kg) resulted in almost complete blockade (> 95%) of radioactivity uptake, demonstrating binding specificity of (R)-[11C]YH168 to MAGL in the non-human primate brain. The regional non-displaceable binding potential follows the rank order of cingulate cortex ~ frontal cortex ~ insula > putamen > temporal cortex > caudate ~ occipital cortex ~ thalamus > nucleus accumbens ~ hippocampus ~ cerebellum ~ globus pallidus > substantia nigra > amygdala.

CONCLUSION

(R)-[11C]YH168 is a promising PET probe for imaging and quantifying MAGL in the brains of mice and non-human primates. This 11C-labeled tracer holds great potential for translation into human subjects and offers the possibility of performing multiple PET scans on the same subject within a single day.

A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma

BACKGROUND

18F fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) pharmacokinetics is an approach for efficiently quantifying perfusion and metabolic processes in the liver, but the conventional single-individual optimization algorithms and single-population optimization algorithms have difficulty obtaining reasonable physiological characteristics from estimated parameters. A prior-based multi-population multi-objective optimization (p-MPMOO) approach using two sub-populations based on two categories of prior information was preliminarily proposed for estimating the 18F-FDG PET/CT pharmacokinetics of patients with hepatocellular carcinoma.

METHODS

PET data from 24 hepatocellular carcinoma (HCC) tumors of 5-min dynamic PET/CT supplemented with 1-min static PET at 60 min were prospectively collected. A reversible double-input three-compartment model and kinetic parameters (K1, k2, k3, k4, fa, and [Formula: see text]) were used to quantify the metabolic information. The single-individual Levenberg-Marquardt (LM) algorithm, single-population algorithms (Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA)) and p-MPMO optimization algorithms (p-MPMOPSO, p-MPMODE, and p-MPMOGA) were used to estimate the parameters.

RESULTS

The areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in K1 and k4 (P < 0.05 in the DeLong test) and the single population optimization in k2 and k3 (P < 0.05), and did not differ from other methods in fa and vb (P > 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between K1, k2, k3, and k4. The p-MPMOPSO showed significant differences (P < 0.05) in the parameter estimation of k2, k3, k4, and fa. The p-MPMODE is implemented on K1, k2, k3, k4, and fa; The p-MPMOGA does it on all six parameters.

CONCLUSIONS

The p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.

FireVoxel: Interactive Software for Multi-Modality Analysis of Dynamic Medical Images

This article provides an overview of the FireVoxel software for quantitative analysis of medical images and its applications in the field. We describe FireVoxel's user interface, multi-layer design, dynamic parametric models, and several turn-key workflows. Additionally, we discuss its application in recent imaging projects. We outline basic image analysis tools such as segmentation, non-uniformity correction, and coregistration through a pictorial overview, with a focus on deformable coregistration and motion correction. Several example workflows and image-based dynamic modeling are also highlighted. Furthermore, we analyze peer-reviewed studies that utilized FireVoxel for image processing, categorizing published papers based on body structures/organs, image processing methods, and imaging modalities. For comparison, we searched the Ovid MEDLINE database to assess the general use of medical image analysis software. FireVoxel is used by over 3000 users worldwide, with 528 articles, including 413 in English, published in the past 15 years. MRI is the most commonly used imaging modality (78.2%), followed by CT (14.5%) and PET (7.3%). The most frequently used methods are dynamic modeling, segmentation, texture analysis, and coregistration. FireVoxel is commonly used in abdominal and genitourinary imaging studies, where it appears to fill a niche due to the lack of alternative software. The search of the Ovid MEDLINE suggests that quantitative medical imaging studies, on the other hand, focus on the brain and cardiovascular system. FireVoxel offers an effective set of quantitative tools, particularly for abdominal and genitourinary imaging, likely due to its ability to manage patient motion and correct for MR artifacts. The software is especially valuable for processing dynamic studies. The steady increase in publications utilizing FireVoxel reflects growing interest in this software and its relevance for image-based research.

PET Imaging of Sphingosine-1-Phosphate Receptor 1 with [18F]TZ4877 in Nonhuman Primates

PURPOSE

The sphingosine-1-phosphate receptor-1 (S1PR1) is involved in regulating responses to neuroimmune stimuli. There is a need for S1PR1-specific radioligands with clinically suitable brain pharmcokinetic properties to complement existing radiotracers. This work evaluated a promising S1PR1 radiotracer, [18F]TZ4877, in nonhuman primates.

PROCEDURES

[18F]TZ4877 was produced via nucleophilic substitution of tosylate precursor with K[18F]/F- followed by deprotection. Brain PET imaging data were acquired with a Focus220 scanner in two Macaca mulatta (6, 13 years old) for 120-180 min following bolus injection of 118-163 MBq [18F]TZ4877, with arterial blood sampling and metabolite analysis to measure the parent input function and plasma free fraction (fP). Each animal was scanned at baseline, 15-18 min after 0.047-0.063 mg/kg of the S1PR1 inhibitor ponesimod, 33 min after 0.4-0.8 mg/kg of the S1PR1-specific compound TZ82112, and 167-195 min after 1 ng/kg of the immune stimulus endotoxin. Kinetic analysis with metabolite-corrected input function was performed to estimate the free fraction corrected total distribution volume (VT/fP). Whole-body dosimetry scans were acquired in 2 animals (1M, 1F) with a Biograph Vision PET/CT System, and absorbed radiation dose estimates were calculated with OLINDA.

RESULTS

[18F]TZ4877 exhibited fast kinetics that were described by the reversible 2-tissue compartment model. Baseline [18F]TZ4877 fP was low (<1%), and [18F]TZ4877 VT/fP values were 233-866 mL/cm3. TZ82112 dose-dependently reduced [18F]TZ4877 VT/fP, while ponesimod and endotoxin exhibited negligible effects on VT/fP, possibly due to scan timing relative to dosing. Dosimetry studies identified the critical organs of gallbladder (0.42 (M) and 0.31 (F) mSv/MBq) for anesthetized nonhuman primate.

CONCLUSIONS

[18F]TZ4877 exhibits reversible kinetic properties, but the low fP value limits reproducible quantification with this radiotracer. S1PR1 is a compelling PET imaging target, and these data support pursuing alternative F-18 labeled radiotracers for potential future human studies.

Preclinical characterization of [18F]D2-LW223: an improved metabolically stable PET tracer for imaging the translocator protein 18 kDa (TSPO) in neuroinflammatory rodent models and non-human primates

Positron emission tomography (PET) targeting translocator protein 18 kDa (TSPO) can be used for the noninvasive detection of neuroinflammation. Improved in vivo stability of a TSPO tracer is beneficial for minimizing the potential confounding effects of radiometabolites. Deuteration represents an important strategy for improving the pharmacokinetics and stability of existing drug molecules in the plasma. This study developed a novel tracer via the deuteration of [18F]LW223 and evaluated its in vivo stability and specific binding in neuroinflammatory rodent models and nonhuman primate (NHP) brains. Compared with LW223, D2-LW223 exhibited improved binding affinity to TSPO. Compared with [18F]LW223, [18F]D2-LW223 has superior physicochemical properties and favorable brain kinetics, with enhanced metabolic stability and reduced defluorination. Preclinical investigations in rodent models of LPS-induced neuroinflammation and cerebral ischemia revealed specific [18F]D2-LW223 binding to TSPO in regions affected by neuroinflammation. Two-tissue compartment model analyses provided excellent model fits and allowed the quantitative mapping of TSPO across the NHP brain. These results indicate that [18F]D2-LW223 holds significant promise for the precise quantification of TSPO expression in neuroinflammatory pathologies of the brain.

Imaging the 5-HT2C receptor with PET: Evaluation of 5-HT2C and 5-HT2A affinity of pimavanserin in the primate brain

Two complimentary techniques were used to estimate occupancy of pimavanserin (a selective 5-HT2A/2C inverse agonist) to 5-HT2A and 5-HT2C receptors in non-human primate brains. One employed the 5-HT2A/2C selective radioligand [11C]CIMBI-36 combined with quantification of binding potentials in brain regions known to be enriched in 5-HT2A (cortex) or 5-HT2C (choroid plexus) receptors to estimate occupancy. Pimavanserin was 6-10 fold more potent displacing [11C]CIMBI-36 from cortex (ED50 = 0.007 mg/kg; EC50 = 0.6 ng/ml) than from choroid plexus (ED50 =0.046 mg/kg; EC50 = 6.0 ng/ml). The assignment of [11C]CIMBI-36 binding to 5-HT2A and 5-HT2C receptors by anatomical brain structure was confirmed using the 5-HT2A selective inverse agonist MDL 100,907 and the 5-HT2C selective antagonist SB 242584 to displace [11C]CIMBI-36. The second technique employed a novel, 5-HT2C selective tracer called [11C]AC1332. [11C]AC1332 bound robustly to choroid plexus, moderately to hippocampus, and minimally to cortex. Pimavanserin displaced [11C]AC1332 with similar potency (ED50 = 0.062 mg/kg; EC50 = 2.5 ng/ml) as its potency displacing [11C]CIMBI-36 binding from choroid plexus. These results demonstrate the feasibility of simultaneously estimating drug occupancy of 5-HT2A and 5-HT2C receptors in vivo, and the utility of a novel 5-HT2C receptor selective tracer ligand.

Pharmacokinetic analysis and simplified uptake measures for tumour lesion [18F]F-AraG PET imaging in patients with non-small cell lung cancer

INTRODUCTION

The novel positron emission tomography (PET) imaging tracer, [18F]F-AraG, targets activated T-cells, offering a potential means to improve our understanding of immune-oncological processes. The aim of this study was to determine the optimal pharmacokinetic model to quantify tumour lesion [18F]F-AraG uptake in patients with non-small cell lung cancer (NSCLC), and to validate simplified measures at different time intervals against the pharmacokinetic uptake parameter.

METHODS

Ten patients with early-stage NSCLC and three patients with advanced NSCLC underwent a dynamic PET scan of minimal 60 min. Venous and/or arterial blood sampling was obtained at maximum seven time points. Tumour lesion time activity curves and metabolite-corrected input functions were analysed using single-tissue reversible (1T2k), two-tissue irreversible (2T3k) and two-tissue reversible (2T4k) plasma input models. Simplified uptake measures, such as standardised uptake value (SUV) and tumour-to-blood (TBR) or tumour-to-plasma ratio (TPR), were evaluated for different time intervals.

RESULTS

Whole-blood and plasma radioactivity concentrations showed rapid clearance of [18F]F-AraG. Metabolite analysis revealed a low rate of metabolism, at 70 min p.i., on average, 79% (SD = 9.8%) of the total radioactivity found in blood corresponded to intact [18F]F-AraG. The time activity curves were best fitted by the 2T3k model. Strong positive correlations were found for SUV (body weight (BW), lean body mass (LBM) or body surface area (BSA) corrected), TBR and TPR for any time interval between 20 and 70 min p.i. against the 2T3k-derived Ki. The correlation of TBR at 60-70 min p.i. with 2T3K-derived Ki (r (df = 20) = 0.87, p < 0.01), was stronger than for SUVBW (r (df = 20) = 0.80, p < 0.01).

CONCLUSION

Tumour lesion [18F]F-AraG uptake in patients with NSCLC is characterised by a 2T3k model. TBR and TPR show most potential for simplified quantification of tumour lesion [18F]F-AraG uptake in patients with NSCLC.

Cerebral and myocardial kinetics of [11C]acetoacetate and [11C]β-hydroxybutyrate: A comparative crossover study in healthy rats

BACKGROUND

Ketone metabolism has been studied using positron emission tomography (PET) with the radiotracers [11C]acetoacetate and [11C]β-hydroxybutyrate. However, whether these two radiotracers actually yield equivalent estimates of cerebral and myocardial ketone metabolism has not yet been investigated. This study aimed to investigate and compare the kinetics of both tracers in the brain and heart of healthy rats under varying levels of circulating ketones at baseline and after a single-dose exogenous ketone ester (KE) supplement.

METHODS

Six healthy Sprague-Dawley rats each underwent two scans with each tracer: one following oral KE administration and one with a placebo. Cerebral kinetic parameters (Ki, VT, and cerebral metabolic rate (CMR)) were obtained using the Patlak method, whereas myocardial kinetic parameters (K1, k2, and VT) were derived using a 1-tissue compartment model. Parameters were compared through mixed-effects, correlation, and Bland-Altman analyses.

RESULTS

Global CMR increased 3-4-fold in the KE group versus placebo, with strong positive correlations between CMR and plasma ketone levels for both tracers. Correlations between [11C]acetoacetate and [11C]β-hydroxybutyrate were moderate and non-significant for relative cerebral uptake expressed as Ki (ρ = 0.40) and for VT (ρ = 0.38) but strongly positive for absolute uptake, CMR (r = 0.84), with a non-significant mean bias of -0.03. In contrast, myocardial kinetics showed only non-significant weak to moderate correlations between the radiotracers (K1 (r = 0.04), k2 (r = -0.27), and VT (ρ = 0.43)), with no systematic biases.

CONCLUSION

[11C]acetoacetate and [11C]β-hydroxybutyrate can be used interchangeably for measuring global CMR in healthy rats but differ in certain cerebral and myocardial kinetics. Whether these findings are generalizable to pathological conditions warrants further studies to explore the kinetics of these tracers in disease models.

Comparative benefits of Ki and SUV images in lesion detection during PET/CT imaging

BACKGROUND

Clinical application of the tracer net influx rate (Ki) imaging in PET/CT remains limited, due to a lack of evidence demonstrating the superiority of Ki images in lesion detection, and guidelines on when to utilize Ki images. This study aims to compare the benefits of Ki and standardized uptake value (SUV) images in lesion detection during PET/CT imaging. By analyzing the performance of both techniques in identifying tumor lesions, the study seeks to provide guidance for the clinical application of Ki images.

RESULTS

This retrospective study included 134 patients with 244 pathologically confirmed lesions (200 malignant and 44 benign). Patients with a histopathological diagnosis received a weight-based 18F-FDG injection and underwent 60-min total-body PET/CT dynamic imaging. SUV images were reconstructed using data collected from the last 10 min of the scans. Ki images were generated using the Patlak methods with data from minutes 12-60. The background SUVmax, SUVmean, SUVSD, Kimax, Kimean, and KiSD values were recorded. The signal-to-noise ratios of the SUV (SUVSNR) and Ki (KiSNR) images were calculated. The lesion detection rate and sensitivity of the SUV and Ki images were evaluated. The lesion-detection rates were 97.7% (214/219) and 99.5% (218/219) for the SUV and Ki images, respectively (p = .22). Five false-negative lesions on the SUV images were true-positive on the Ki images (3 hepatic malignancies and 2 metastatic lymph nodes). The sensitivity (94.0% vs. 96.0%, p = .22), specificity (41.9% vs. 41.9%, p > .99), accuracy (84.4% vs. 86.1%, p = .61), positive predictive value (87.9% vs. 88.1%, p = .94), negative predictive value (60.0% vs. 69.2%, p = .47), and the area under the curve [0.68 (95% confidence interval, 0.61-0.73) vs. 0.69 (95% confidence interval, 0.62-0.74)] were similar in the SUV and Ki images (all p ≥ .10).

CONCLUSION

Ki images exhibit benefits in lesion detection compared to SUV images, particularly in organs with high background such as liver. The enhanced contrast provided by Ki imaging is recommended to clinically improve detection rates in such cases.

Evaluation of [11C]UCB-A positron emission tomography in human brains

BACKGROUND

In preclinical studies, the positron emission tomography (PET) imaging with [11C]UCB-A provided promising results for imaging synaptic vesicle protein 2A (SV2A) as a proxy for synaptic density. This paper reports the first-in-human [11C]UCB-A PET study to characterise its kinetics in healthy subjects and further evaluate SV2A-specific binding.

RESULTS

Twelve healthy subjects underwent 90-min baseline [11C]UCB-A scans with PET/MRI, with two subjects participating in an additional blocking scan with the same scanning procedure after a single dose of levetiracetam (1500 mg). Our results indicated abundant [11C]UCB-A brain uptake across all cortical regions, with slow elimination. Kinetic modelling of [11C]UCB-A PET using various compartment models suggested that the irreversible two-tissue compartment model best describes the kinetics of the radioactive tracer. Accordingly, the Patlak graphical analysis was used to simplify the analysis. The estimated SV2A occupancy determined by the Lassen plot was around 66%. Significant specific binding at baseline and comparable binding reduction as grey matter precludes the use of centrum semiovale as reference tissue.

CONCLUSIONS

[11C]UCB-A PET imaging enables quantifying SV2A in vivo. However, its slow kinetics require a long scan duration, which is impractical with the short half-life of carbon-11. Consequently, the slow kinetics and complicated quantification methods may restrict its use in humans.

PET Imaging of Sphingosine-1-Phosphate Receptor 1 with [18F]TZ4877 in Nonhuman Primates

Purpose

The sphingosine-1-phosphate receptor-1 (S1PR1) is involved in regulating responses to neuroimmune stimuli. There is a need for S1PR1-specific radioligands with clinically suitable brain pharmcokinetic properties to complement existing radiotracers. This work evaluated a promising S1PR1 radiotracer, [18F]TZ4877, in nonhuman primates.

Procedures

[18F]TZ4877 was produced via nucleophilic substitution of tosylate precursor with K[18F]/F- followed by deprotection. Brain PET imaging data were acquired with a Focus220 scanner in two Macaca mulatta (6, 13 years old) for 120-180 min following bolus injection of 118-163 MBq [18F]TZ4877, with arterial blood sampling and metabolite analysis to measure the parent input function and plasma free fraction (f P). Each animal was scanned at baseline, 15-18 min after 0.047-0.063 mg/kg of the S1PR1 inhibitor ponesimod, 33 min after 0.4-0.8 mg/kg of the S1PR1-specific compound TZ82112, and 167-195 min after 1 ng/kg of the immune stimulus endotoxin. Kinetic analysis with metabolite-corrected input function was performed to estimate the free fraction corrected total distribution volume (V T/f P). Whole-body dosimetry scans were acquired in 2 animals (1M, 1F) with a Biograph Vision PET/CT System, and absorbed radiation dose estimates were calculated with OLINDA.

Results

[18F]TZ4877 exhibited fast kinetics that were described by the reversible 2-tissue compartment model. Baseline [18F]TZ4877 f P was low (< 1%), and [18F]TZ4877 V T/f P values were 233-866 mL/cm3. TZ82112 dose-dependently reduced [18F]TZ4877 V T/f P, while ponesimod and endotoxin exhibited negligible effects on V T/f P, possibly due to scan timing relative to dosing. Dosimetry studies identified the critical organs of gallbladder (0.42 (M) and 0.31 (F) mSv/MBq) for anesthetized nonhuman primate.

Conclusions

[18F]TZ4877 exhibits reversible kinetic properties, but the low f P value limits quantification with this radiotracer. S1PR1 is a compelling PET imaging target, and these data support pursuing alternative F-18 labeled radiotracers for potential future human studies.

Signal separation of simultaneous dual-tracer PET imaging based on global spatial information and channel attention

BACKGROUND

Simultaneous dual-tracer positron emission tomography (PET) imaging efficiently provides more complete information for disease diagnosis. The signal separation has long been a challenge of dual-tracer PET imaging. To predict the single-tracer images, we proposed a separation network based on global spatial information and channel attention, and connected it to FBP-Net to form the FBPnet-Sep model.

RESULTS

Experiments using simulated dynamic PET data were conducted to: (1) compare the proposed FBPnet-Sep model to Sep-FBPnet model and currently existing Multi-task CNN, (2) verify the effectiveness of modules incorporated in FBPnet-Sep model, (3) investigate the generalization of FBPnet-Sep model to low-dose data, and (4) investigate the application of FBPnet-Sep model to multiple tracer combinations with decay corrections. Compared to the Sep-FBPnet model and Multi-task CNN, the FBPnet-Sep model reconstructed single-tracer images with higher structural similarity, peak signal-to-noise ratio and lower mean squared error, and reconstructed time-activity curves with lower bias and variation in most regions. Excluding the Inception or channel attention module resulted in degraded image qualities. The FBPnet-Sep model showed acceptable performance when applied to low-dose data. Additionally, it could deal with multiple tracer combinations. The qualities of predicted images, as well as the accuracy of derived time-activity curves and macro-parameters were slightly improved by incorporating a decay correction module.

CONCLUSIONS

The proposed FBPnet-Sep model was considered a potential method for the reconstruction and signal separation of simultaneous dual-tracer PET imaging.

Dual time-point [18F]FDG PET imaging for quantification of metabolic uptake rate: Evaluation of a simple, clinically feasible method

PURPOSE

We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (Ki) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy.

METHODS

We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.). We determined Ki for a total of 81 lesions. Ki quantification from full dynamic PET data (Patlak-Ki) and Ki from DTP (DTP-Ki) were compared. In addition, we scaled a population-based input function (PBIFscl) with the image-derived blood pool activity sampled at different time points to assess the best scaling time-point for Ki quantifications in the simulation data.

RESULTS

In the simulation study, Ki estimated using DTP via (30,60-min), (30,90-min), (60,90-min), and (60,120-min) samples showed strong correlations (r ≥ 0.944, P < 0.0001) with the true value of Ki. The DTP results with the PBIFscl at 60-min time-point in (30,60-min), (60,90-min), and (60,120-min) were linearly related to the true Ki with a slope of 1.037, 1.008, 1.013 and intercept of -6 × 10-4, 2 × 10-5, 5 × 10-5, respectively. In a clinical study, strong correlations (r ≥ 0.833, P < 0.0001) were observed between Patlak-Ki and DTP-Ki. The Patlak-derived mean values of Ki, tumor-to-background-ratio, signal-to-noise-ratio, and contrast-to-noise-ratio were linearly correlated with the DTP method.

CONCLUSIONS

Besides calculating the retention index as a commonly used quantification parameter inDTP imaging,our DTP method can accurately estimate Ki.

Single-voxel delay map from long-axial field-of-view PET scans

Objective

We present an algorithm to estimate the delay between a tissue time-activity curve and a blood input curve at a single-voxel level tested on whole-body data from a long-axial field-of-view scanner with tracers of different noise characteristics.

Methods

Whole-body scans of 15 patients divided equally among three tracers, namely [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, which were used in development and testing of the algorithm. Delay times were estimated by fitting the cumulatively summed input function and tissue time-activity curve with special considerations for noise. To evaluate the performance of the algorithm, it was compared against two other algorithms also commonly applied in delay estimation: name cross-correlation and a one-tissue compartment model with incorporated delay. All algorithms were tested on both synthetic time-activity curves produced with the one-tissue compartment model with increasing levels of noise and delays between the tissue activity curve and the blood input curve. Whole-body delay maps were also calculated for each of the three tracers with data acquired on a long-axial field-of-view scanner with high time resolution.

Results

Our proposed model performs better for low signal-to-noise ratio time-activity curves compared to both cross-correlation and the one-tissue compartment models for non-[15O]H2O tracers. Testing on synthetically produced time-activity curves showed only a small and even residual delay, while the one-tissue compartment model with included delay showed varying residual delays.

Conclusion

The algorithm is robust to noise and proves applicable on a range of tracers as tested on [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, and hence is a viable option offering the ability for delay correction across various organs and tracers in use with kinetic modeling.

Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F]Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Kinetic analysis of cardiac dynamic 18F-Florbetapir PET in healthy volunteers and amyloidosis patients: A pilot study

Objectives

This study aimed to explore the potential of full dynamic PET kinetic analysis in assessing amyloid binding and perfusion in the cardiac region using 18F-Florbetapir PET, establishing a quantitative approach in the clinical assessment of cardiac amyloidosis disease.

Materials & methods

The distribution volume ratios (DVRs) and the relative transport rate constant (R1), were estimated by a pseudo-simplified reference tissue model (pSRTM2) and pseudo-Logan plot (pLogan plot) with kidney reference for the region of interest-based and voxel-wise-based analyses. The parametric images generated using the pSRTM2 and linear regression with spatially constrained (LRSC) algorithm were then evaluated. Semi-quantitative analyses include standardized uptake value ratios at the early phase (SUVREP, 0.5-5 min) and late phase (SUVRLP, 50-60 min) were also calculated.

Results

Ten participants [7 healthy controls (HC) and 3 cardiac amyloidosis (CA) subjects] underwent a 60-min dynamic 18F-Florbetapir PET scan. The DVRs estimated from pSRTM2 and Logan plot were significantly increased (HC vs CA; DVRpSRTM2: 0.95 ± 0.11 vs 2.77 ± 0.42, t'(2.13) = 7.39, P = 0.015; DVRLogan: 0.80 ± 0.12 vs 2.90 ± 0.55, t'(2.08) = 6.56, P = 0.020), and R1 were remarkably decreased in CA groups, as compared to HCs (HC vs CA; 1.08 ± 0.37 vs 0.56 ± 0.10, t'(7.63) = 3.38, P = 0.010). The SUVREP and SUVRLP were highly correlated to R1 (r = 0.97, P < 0.001) and DVR(r = 0.99, P < 0.001), respectively. The DVRs in the total myocardium region increased slightly as the size of FWHM increased and became stable at a Gaussian filter ≥6 mm. The secular equilibrium of SUVR was reached at around 50-min p.i. time.

Conclusion

The DVR and R1 estimated from cardiac dynamic 18F-Florbetapir PET using pSRTM with kidney pseudo-reference tissue are suggested to quantify cardiac amyloid deposition and relative perfusion, respectively, in amyloidosis patients and healthy controls. We recommend a dual-phase scan: 0.5-5 min and 50-60 min p.i. as the appropriate time window for clinically assessing cardiac amyloidosis and perfusion measurements using 18F-Florbetapir PET.

Non-specific irreversible 89Zr-mAb uptake in tumours: evidence from biopsy-proven target-negative tumours using 89Zr-immuno-PET

BACKGROUND

Distribution of mAbs into tumour tissue may occur via different processes contributing differently to the 89Zr-mAb uptake on PET. Target-specific binding in tumours is of main interest; however, non-specific irreversible uptake may also be present, which influences quantification. The aim was to investigate the presence of non-specific irreversible uptake in tumour tissue using Patlak linearization on 89Zr-immuno-PET data of biopsy-proven target-negative tumours. Data of two studies, including target status obtained from biopsies, were retrospectively analysed, and Patlak linearization provided the net rate of irreversible uptake (Ki).

RESULTS

Two tumours were classified as CD20-negative and two as CD20-positive. Four tumours were classified as CEA-negative and nine as CEA-positive. Ki values of CD20-negative (0.43 µL/g/h and 0.92 µL/g/h) and CEA-negative tumours (mdn = 1.97 µL/g/h, interquartile range (IQR) = 1.50-2.39) were higher than zero. Median Ki values of target-negative tumours were lower than CD20-positive (1.87 µL/g/h and 1.90 µL/g/h) and CEA-positive tumours (mdn = 2.77 µL/g/h, IQR = 2.11-3.65).

CONCLUSION

Biopsy-proven target-negative tumours showed irreversible uptake of 89Zr-mAbs measured in vivo using 89Zr-immuno-PET data, which suggests the presence of non-specific irreversible uptake in tumours. Consequently, for 89Zr-immuno-PET, even if the target is absent, a tumour-to-plasma ratio always increases over time.

Kinetic Modeling of Brain [18-F]FDG Positron Emission Tomography Time Activity Curves with Input Function Recovery (IR) Method

Accurate positron emission tomography (PET) data quantification relies on high-quality input plasma curves, but venous blood sampling may yield poor-quality data, jeopardizing modeling outcomes. In this study, we aimed to recover sub-optimal input functions by using information from the tail (5th-100th min) of curves obtained through the frequent sampling protocol and an input recovery (IR) model trained with reference curves of optimal shape. Initially, we included 170 plasma input curves from eight published studies with clamp [18F]-fluorodeoxyglucose PET exams. Model validation involved 78 brain PET studies for which compartmental model (CM) analysis was feasible (reference (ref) + training sets). Recovered curves were compared with original curves using area under curve (AUC), max peak standardized uptake value (maxSUV). CM parameters (ref + training sets) and fractional uptake rate (FUR) (all sets) were computed. Original and recovered curves from the ref set had comparable AUC (d = 0.02, not significant (NS)), maxSUV (d = 0.05, NS) and comparable brain CM results (NS). Recovered curves from the training set were different from the original according to maxSUV (d = 3) and biologically plausible according to the max theoretical K1 (53//56). Brain CM results were different in the training set (p < 0.05 for all CM parameters and brain regions) but not in the ref set. FUR showed reductions similarly in the recovered curves of the training and test sets compared to the original curves (p < 0.05 for all regions for both sets). The IR method successfully recovered the plasma inputs of poor quality, rescuing cases otherwise excluded from the kinetic modeling results. The validation approach proved useful and can be applied to different tracers and metabolic conditions.

Improving SUVR quantification by correcting for radiotracer clearance in tissue

Standardized Uptake Value Ratio (SUVR) is a widely reported semi-quantitative positron emission tomography (PET) outcome measure, partly because of its ease of measurement from short scan durations. However, in brain, SUVR is often a biased estimator of the gold-standard distribution volume ratio (DVR) due to non-equilibrium conditions, i.e., clearance of the radiotracer in relevant tissues. Factors that affect radiotracer metabolism and clearance such as medication or subject groups could lead to artificial differences in SUVR. This work developed a correction that reduces the bias in SUVR (estimated from a short 15-30 min PET imaging session) by accounting for the effects of tracer clearance observed during the late SUVR time window. The proposed correction takes the form of a one-step non-linear algebraic transform of SUVR that is a function of radiotracer dependent parameters such as clearance rates from the reference and target tissues, and population averaged reference region clearance rate (k 2 , ref ). An important observation was the need for accurate estimation of radiotracer clearance rate in target tissue, which was addressed with a regression based model. Simulations and human data from two different radiotracers (healthy controls for [11C]LSN3172176, healthy controls and Parkinson's disease subjects for [18F]FE-PE2I) were used to validate the correction and evaluate its benefits and limitations. SUVR correction in human data significantly reduced mean SUVR bias across brain regions and subjects (from ∼25% for SUVR to <10% for corrected SUVR). This correction also significantly reduced the variability of this bias across brain regions for both tracers (approximately 50% for [11C]LSN3172176, 20% for [18F]FE-PE2I). Future work should investigate the benefits of using corrected SUVR in other populations and with different tracers.

Assessing dynamic metabolic heterogeneity in prostate cancer patients via total-body [68Ga]Ga-PSMA-11 PET/CT imaging: quantitative analysis of [68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs

PURPOSE

This study aimed to quantitatively assess [68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs in prostate cancer using the total-body [68Ga]Ga-PSMA-11 PET/CT and to characterize the dynamic metabolic heterogeneity of prostate cancer.

METHODS

Dynamic total-body [68Ga]Ga-PSMA-11 PET/CT scans were performed on ten prostate cancer patients. Manual delineation of volume-of-interests (VOIs) was performed on multiple normal organs displaying high [68Ga]Ga-PSMA-11 uptake, as well as pathological lesions. Time-to-activity curves (TACs) were generated, and the four compartment models including one-tissue compartmental model (1T1k), reversible one-tissue compartmental model (1T2k), irreversible two-tissue compartment model (2T3k) and reversible two-tissue compartmental model (2T4k) were fitted to each tissue TAC. Various rate constants, including K1 (forward transport rate from plasma to the reversible compartment), k2 (reverse transport rate from the reversible compartment to plasma), k3 (tracer binding on the PSMA-receptor and its internalization), k4 (the externalization rate of the tracer) and Ki (net influx rate), were obtained. The selection of the optimal model for describing the uptake of both lesions and normal organs was determined using the Akaike information criteria (AIC). Receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off values for differentiating physiological and pathological [68Ga]Ga-PSMA-11 uptake.

RESULTS

Both 1T1k and 1T2k models showed relatively high AIC values compared to the 2T3k and 2T4k models in both pathological lesions and normal organs. The kinetic behavior of pathological lesions was better described by the 2T3k model compared to the 2T4k model, while the normal organs were better described by the 2T4k model. Significant variations in kinetic metrics, such as K1, k2, and k3, and Ki, were observed among normal organs with high [68Ga]Ga-PSMA-11 uptake and pathological lesions. The high Ki value in normal organs was primarily determined by elevated K1 and low k3, rather than k2. Conversely, the high Ki value in pathological lesions, ranking second to the kidney and similar to salivary glands and spleen, was predominantly determined by the highest k3 value. Notably, k3 exhibited the highest performance in distinguishing between physiological and pathological [68Ga]Ga-PSMA-11 uptake, with an area under the curve (AUC) of 0.844 (95% CI, 0.773-0.915), sensitivity of 82.9%, and specificity of 74.1%. The k3 values showed better performance than SUVmean (AUC, 0.659), SUVmax (AUC, 0.637), and other kinetic parameter including K1 (AUC, 0.604), k2 (AUC, 0.634), and Ki (AUC, 0.651).

CONCLUSIONS

Significant discrepancies in kinetic metrics were detected between pathological lesions and normal organs, despite their shared high uptake of [68Ga]Ga-PSMA-11. Notably, the k3 value exhibits a noteworthy capability to distinguish between pathological lesions and normal organs with elevated [68Ga]Ga-PSMA-11 uptake. This discovery implies that k3 holds promise as a prospective imaging biomarker for distinguishing between pathologic and non-specific [68Ga]Ga-PSMA-11 uptake in patients with prostate cancer.

Effect of mitochondrial circulation on mitochondrial age density distribution

Recent publications report that although the mitochondria population in an axon can be quickly replaced by a combination of retrograde and anterograde axonal transport (often within less than 24 hours), the axon contains much older mitochondria. This suggests that not all mitochondria that reach the soma are degraded and that some are recirculating back into the axon. To explain this, we developed a model that simulates mitochondria distribution when a portion of mitochondria that return to the soma are redirected back to the axon rather than being destroyed in somatic lysosomes. Utilizing the developed model, we studied how the percentage of returning mitochondria affects the mean age and age density distributions of mitochondria at different distances from the soma. We also investigated whether turning off the mitochondrial anchoring switch can reduce the mean age of mitochondria. For this purpose, we studied the effect of reducing the value of a parameter that characterizes the probability of mitochondria transition to the stationary (anchored) state. The reduction in mitochondria mean age observed when the anchoring probability is reduced suggests that some injured neurons may be saved if the percentage of stationary mitochondria is decreased. The replacement of possibly damaged stationary mitochondria with newly synthesized ones may restore the energy supply in an injured axon. We also performed a sensitivity study of the mean age of stationary mitochondria to the parameter that determines what portion of mitochondria re-enter the axon and the parameter that determines the probability of mitochondria transition to the stationary state. The sensitivity of the mean age of stationary mitochondria to the mitochondria stopping probability increases linearly with the number of compartments in the axon. High stopping probability in long axons can significantly increase mitochondrial age.

Evaluation of an Image-Derived Input Function for Kinetic Modeling of Nicotinic Acetylcholine Receptor-Binding PET Ligands in Mice

Positron emission tomography (PET) radioligands that bind with high-affinity to α4β2-type nicotinic receptors (α4β2Rs) allow for in vivo investigations of the mechanisms underlying nicotine addiction and smoking cessation. Here, we investigate the use of an image-derived arterial input function and the cerebellum for kinetic analysis of radioligand binding in mice. Two radioligands were explored: 2-[18F]FA85380 (2-FA), displaying similar pKa and binding affinity to the smoking cessation drug varenicline (Chantix), and [18F]Nifene, displaying similar pKa and binding affinity to nicotine. Time-activity curves of the left ventricle of the heart displayed similar distribution across wild type mice, mice lacking the β2-subunit for ligand binding, and acute nicotine-treated mice, whereas reference tissue binding displayed high variation between groups. Binding potential estimated from a two-tissue compartment model fit of the data with the image-derived input function were higher than estimates from reference tissue-based estimations. Rate constants of radioligand dissociation were very slow for 2-FA and very fast for Nifene. We conclude that using an image-derived input function for kinetic modeling of nicotinic PET ligands provides suitable results compared to reference tissue-based methods and that the chemical properties of 2-FA and Nifene are suitable to study receptor response to nicotine addiction and smoking cessation therapies.

Quantitation of dynamic total-body PET imaging: recent developments and future perspectives

BACKGROUND

Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades.

CHALLENGES

The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results.

CONCLUSIONS

In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.

Carimas: An Extensive Medical Imaging Data Processing Tool for Research

Carimas is a multi-purpose medical imaging data processing tool, which can be used to visualize, analyze, and model different medical images in research. Originally, it was developed only for positron emission tomography data in 2009, but the use of this software has extended to many other tomography imaging modalities, such as computed tomography and magnetic resonance imaging. Carimas is especially well-suited for analysis of three- and four-dimensional image data and creating polar maps in modeling of cardiac perfusion. This article explores various parts of Carimas, including its key features, program structure, and application possibilities.

Multivariate analysis of PET pharmacokinetic parameters improves inferential efficiency

PURPOSE

In positron emission tomography quantification, multiple pharmacokinetic parameters are typically estimated from each time activity curve. Conventionally all but the parameter of interest are discarded before performing subsequent statistical analysis. However, we assert that these discarded parameters also contain relevant information which can be exploited to improve the precision and power of statistical analyses on the parameter of interest. Properly taking this into account can thereby draw more informative conclusions without collecting more data.

METHODS

By applying a hierarchical multifactor multivariate Bayesian approach, all estimated parameters from all regions can be analysed at once. We refer to this method as Parameters undergoing Multivariate Bayesian Analysis (PuMBA). We simulated patient-control studies with different radioligands, varying sample sizes and measurement error to explore its performance, comparing the precision, statistical power, false positive rate and bias of estimated group differences relative to univariate analysis methods.

RESULTS

We show that PuMBA improves the statistical power for all examined applications relative to univariate methods without increasing the false positive rate. PuMBA improves the precision of effect size estimation, and reduces the variation of these estimates between simulated samples. Furthermore, we show that PuMBA yields performance improvements even in the presence of substantial measurement error. Remarkably, owing to its ability to leverage information shared between pharmacokinetic parameters, PuMBA even shows greater power than conventional univariate analysis of the true binding values from which the parameters were simulated. Across all applications, PuMBA exhibited a small degree of bias in the estimated outcomes; however, this was small relative to the variation in estimated outcomes between simulated datasets.

CONCLUSION

PuMBA improves the precision and power of statistical analysis of PET data without requiring the collection of additional measurements. This makes it possible to study new research questions in both new and previously collected data. PuMBA therefore holds great promise for the field of PET imaging.

Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images

When no arterial input function is available, quantification of dynamic PET images requires a previous step devoted to the extraction of a reference time-activity curve (TAC). Factor analysis is often applied for this purpose. This paper introduces a novel approach that conducts a new kind of nonlinear factor analysis relying on a compartment model, and computes the kinetic parameters of specific binding tissues jointly. To this end, it capitalizes on data-driven parametric imaging methods to provide a physical description of the underlying PET data, directly relating the specific binding with the kinetics of the non-specific binding in the corresponding tissues. This characterization is introduced into the factor analysis formulation to yield a novel nonlinear unmixing model designed for PET image analysis. This model also explicitly introduces global kinetic parameters that allow for a direct estimation of a binding potential that represents the ratio at equilibrium of specifically bound radioligand to the concentration of nondisplaceable radioligand in each non-specific binding tissue. The performance of the method is evaluated on synthetic and real data to demonstrate its potential interest.

Tumor-to-blood ratio for assessment of fibroblast activation protein receptor density in pancreatic cancer using [68Ga]Ga-FAPI-04

PURPOSE

[⁶⁸Ga]Ga-FAPI PET/CT has been widely used in clinical diagnosis and radiopharmaceutical therapy. In this study, tumor-to-blood ratio (TBR) was evaluated as a powerful tool for semiquantitative assessment of [⁶⁸Ga]Ga-FAPI-04 tumor uptake and as an effective index for tumors with high FAP expression in theranostics.

METHODS

Nine patients with pancreatic cancer underwent a 60-min dynamic PET/CT scan by total-body PET/CT (with a long AFOV of 194 cm) after injection of [⁶⁸Ga]Ga-FAPI-04. After dynamic PET/CT scan, three patients received chemotherapy and underwent the second dynamic scan to evaluate treatment response. Time-activity curves (TACs) were obtained by drawing regions of interest for primary pancreatic lesions and metastatic lesions. The lesion TACs were fitted using four compartment models by the software PMOD PKIN kinetic modeling. The preferred pharmacokinetic model for [⁶⁸Ga]Ga-FAPI-04 was evaluated based on the Akaike information criterion. The correlations between simplified methods for quantification of [⁶⁸Ga]Ga-FAPI-04 (SUVs; tumor-to-blood ratios [TBRs]) and the total distribution volume (V_t) estimates obtained from pharmacokinetic analysis were calculated.

RESULTS

In total, 9 primary lesions and 25 metastatic lesions were evaluated. The reversible two-tissue compartment model (2TCM) was the most appropriate model among the four compartment models. The total distribution volume V_t values derived from 2TCM varied significantly in pathological lesions and background regions. A strong positive correlation was observed between TBR_mean and V_t from the 2TCM model in pathological lesions (R²=0.92, P<0.001). The relative difference range for TBR_mean was 2.1% compared to the reduction rate of V_t in the patients who were treated with chemotherapy.

CONCLUSIONS

A strong positive correlation was observed between TBR_mean and V_t for [⁶⁸Ga]Ga-FAPI-04. TBR_mean reflects FAP receptor density better than SUV_mean and SUV_max, and would be the preferred measurement tool for semiquantitative assessment of [⁶⁸Ga]Ga-FAPI-04 tumor uptake and as a means for evaluating treatment response.

An automatic segmentation method with self-attention mechanism on left ventricle in gated PET/CT myocardial perfusion imaging

OBJECTIVES

We aimed to propose an automatic segmentation method for left ventricular (LV) from 16 electrocardiogram (ECG) -gated ¹³N-NH₃ PET/CT myocardial perfusion imaging (MPI) to improve the performance of LV function assessment.

METHODS

Ninety-six cases with confirmed or suspected obstructive coronary artery disease (CAD) were enrolled in this research. The LV myocardial contours were delineated by physicians as ground truth. We developed an automatic segmentation method, which introduces the self-attention mechanism into 3D U-Net to capture global information of images so as to achieve fine segmentation of LV. Three cross-validation tests were performed on each gate (64 vs. 32 for training vs. validation). The effectiveness was validated by quantitative metrics (modified hausdorff distance, MHD; dice ratio, DR; 3D MHD) as well as cardiac functional parameters (end-systolic volume, ESV; end-diastolic volume, EDV; ejection fraction, EF). Furthermore, the feasibility of the proposed method was also evaluated by intra- and inter-observers with DR and 3D-MHD.

RESULTS

Compared with backbone network, the proposed approach improved the average DR from 0.905 ± 0.0193 to 0.9202 ± 0.0164, and decreased the average 3D MHD from 0.4611 ± 0.0349 to 0.4304 ± 0.0339. The average relative error of LV volume between proposed method and ground truth is 1.09±3.66%, and the correlation coefficient is 0.992 ± 0.007 (P < 0.001). The EDV, ESV, EF deduced from the proposed approach were highly correlated with ground truth (r ≥ 0.864, P < 0.001), and the correlation with commercial software is fair (r ≥ 0.871, P < 0.001). DR and 3D MHD of contours and myocardium from two observers are higher than 0.899 and less than 0.5194.

CONCLUSION

The proposed approach is highly feasible for automatic segmentation of the LV cavity and myocardium, with potential to benefit the precision of LV function assessment.

Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

BACKGROUND

Simultaneous dual-tracer positron emission tomography (PET) imaging can observe two molecular targets in a single scan, which is conducive to disease diagnosis and tracking. Since the signals emitted by different tracers are the same, it is crucial to separate each single tracer from the mixed signals. The current study proposed a novel deep learning-based method to reconstruct single-tracer activity distributions from the dual-tracer sinogram.

METHODS

We proposed the Multi-task CNN, a three-dimensional convolutional neural network (CNN) based on a framework of multi-task learning. One common encoder extracted features from the dual-tracer dynamic sinogram, followed by two distinct and parallel decoders which reconstructed the single-tracer dynamic images of two tracers separately. The model was evaluated by mean squared error (MSE), multiscale structural similarity (MS-SSIM) index and peak signal-to-noise ratio (PSNR) on simulated data and real animal data, and compared to the filtered back-projection method based on deep learning (FBP-CNN).

RESULTS

In the simulation experiments, the Multi-task CNN reconstructed single-tracer images with lower MSE, higher MS-SSIM and PSNR than FBP-CNN, and was more robust to the changes in individual difference, tracer combination and scanning protocol. In the experiment of rats with an orthotopic xenograft glioma model, the Multi-task CNN reconstructions also showed higher qualities than FBP-CNN reconstructions.

CONCLUSIONS

The proposed Multi-task CNN could effectively reconstruct the dynamic activity images of two single tracers from the dual-tracer dynamic sinogram, which was potential in the direct reconstruction for real simultaneous dual-tracer PET imaging data in future.

Patient-Adaptive Population-Based Modeling of Arterial Input Functions

Kinetic modeling of dynamic PET data requires knowledge of tracer concentration in blood plasma, described by the arterial input function (AIF). Arterial blood sampling is the gold standard for AIF measurement, but is invasive and labour intensive. A number of methods have been proposed to accurately estimate the AIF directly from blood sampling and/or imaging data. Here we consider fitting a patient-adaptive mixture of historical population time course profiles to estimate individual AIFs. Travel time of a tracer atom from the injection site to the right ventricle of the heart is modeled as a realization from a Gamma distribution, and the time this atom spends in circulation before being sampled is represented by a subject-specific linear mixture of population profiles. These functions are estimated from independent population data. Individual AIFs are obtained by projection onto this basis of population profile components. The model incorporates knowledge of injection duration into the fit, allowing for varying injection protocols. Analyses of arterial sampling data from 18F-FDG, 15O-H2O and 18F-FLT clinical studies show that the proposed model can outperform reference techniques. The statistically significant gain achieved by using population data to train the basis components, instead of fitting these from the single individual sampling data, is measured on the FDG cohort. Kinetic analyses of simulated data demonstrate the reliability and potential benefit of this approach in estimating physiological parameters. These results are further supported by numerical simulations that demonstrate convergence and stability of the proposed technique under varying training population sizes and noise levels.

PET imaging of kappa opioid receptors and receptor expression quantified in neuron-derived extracellular vesicles in socially housed female and male cynomolgus macaques

Recent positron emission tomography (PET) studies of kappa opioid receptors (KOR) in humans reported significant relationships between KOR availability and social status, as well as cocaine choice. In monkey models, social status influences physiology, receptor pharmacology and behavior; these variables have been associated vulnerability to cocaine abuse. The present study utilized PET imaging to examine KOR availability in socially housed, cocaine-naïve female and male monkeys, and peripheral measures of KORs with neuron-derived extracellular vesicles (NDE). KOR availability was assessed in dominant and subordinate female and male cynomolgus macaques (N = 4/rank/sex), using PET imaging with the KOR selective agonist [11C]EKAP. In addition, NDE from the plasma of socially housed monkeys (N = 13/sex; N = 6-7/rank) were isolated by immunocapture method and analyzed for OPRK1 protein expression by ELISA. We found significant interactions between sex and social rank in KOR availability across 12 of 15 brain regions. This was driven by female data, in which KOR availability was significantly higher in subordinate monkeys compared with dominant monkeys; the opposite relationship was observed among males, but not statistically significant. No sex or rank differences were observed for NDE OPRK1 concentrations. In summary, the relationship between brain KOR availability and social rank was different in female and male monkeys. This was particularly true in female monkeys. We hypothesize that lower [11C]EKAP binding potentials were due to higher concentrations of circulating dynorphin, which is consistent with greater vulnerability in dominant compared with subordinate females. These findings suggest that the KOR is an important target for understanding the neurobiology associated with vulnerability to abused drugs and sex differences, and detectable in peripheral circulation.

Evaluation of [18F]F-TZ3108 for PET Imaging of Metabolic-Associated Fatty Liver Disease

PURPOSE

Sigma-1 receptor (Sig-1R), a chaperone that resides at the mitochondrion-associated endoplasmic reticulum (ER) membrane, is an ER stress biomarker. It is thought that ER stress plays a critical role in the progression of metabolic-associated fatty liver disease (MAFLD). The aim of this study was to evaluate a positron emission tomography (PET) tracer [¹⁸F]F-TZ3108 targeting Sig-1R for MAFLD.

PROCEDURES

The mouse model of MAFLD was established by feeding high-fat diet (HFD) for 12 weeks. Dynamic (0-60 min) PET/CT scans were performed after intravenous injection of 2-deoxy-2[¹⁸F]fluoro-D-glucose ([¹⁸F]-FDG) and [¹⁸F]F-TZ3108. Tracer kinetic modeling was performed for quantification of the PET/CT imaging of the liver. Post-PET biodistribution, the liver tissue western blotting (WB), and immunofluorescence (IF) were performed to compare the expression of Sig-1R levels in the organs harvested from both MAFLD and age-matched control mice.

RESULTS

The micro PET/CT imaging revealed a significantly decreased uptake of [¹⁸F]F-TZ3108 in the livers of the MAFLD group compared to the healthy controls, while the uptake of [¹⁸F]-FDG in the livers was not significantly different between the two groups. Based on the tracer kinetic modeling, the binding disassociate rate (k₄) for [¹⁸F]F-TZ3108 was significantly increased in MAFLD group compared to healthy controls. The volume distribution (V_T), and the non-displacement binding potential (BP_ND) revealed significantly decrease in MAFLD compared to healthy controls respectively. The post-PET biodistribution (%ID/g) of [¹⁸F]F-TZ3108 in the livers of MAFLD mice was significantly reduced nearly twofold than that in the livers of control mice. WB and IF experiments further confirmed the reduction of Sig-1R expression in the MAFLD group.

CONCLUSIONS

The expression of Sig-1R in the liver, measured by the PET tracer, [¹⁸F]F-TZ3108, was significantly decreased in mouse model of MAFLD. The [¹⁸F]F-TZ3108 PET/CT imaging may provide a novel means of visualization for ER stress in MAFLD or other diseases in vivo.

Nuclear medicine imaging methods as novel tools in the assessment of pulmonary drug disposition

INTRODUCTION

Drugs for the treatment of respiratory diseases are commonly administered by oral inhalation. Yet surprisingly little is known about the pulmonary pharmacokinetics of inhaled molecules. Nuclear medicine imaging techniques (i.e. planar gamma scintigraphy, single-photon emission computed tomography [SPECT] and positron emission tomography [PET]) enable the noninvasive dynamic measurement of the lung concentrations of radiolabeled drugs or drug formulations. This review discusses the potential of nuclear medicine imaging techniques in inhalation biopharmaceutical research.

AREAS COVERED

(i) Planar gamma scintigraphy studies with radiolabeled inhalation formulations to assess initial pulmonary drug deposition; (ii) imaging studies with radiolabeled drugs to assess their intrapulmonary pharmacokinetics; (iii) receptor occupancy studies to quantify the pharmacodynamic effect of inhaled drugs.

EXPERT OPINION

Imaging techniques hold potential to bridge the knowledge gap between animal models and humans with respect to the pulmonary disposition of inhaled drugs. However, beyond the mere assessment of the initial lung deposition of inhaled formulations with planar gamma scintigraphy, imaging techniques have rarely been employed in pulmonary drug development. This may be related to several technical challenges encountered with such studies. Considering the wealth of information that can be obtained with imaging studies their use in inhalation biopharmaceutics should be further investigated.

Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data

Positron emission tomography (PET) is an in vivo imaging method essential for studying the neurochemical pathophysiology of psychiatric and neurological disease. However, its high cost and exposure of participants to radiation make it unfeasible to employ large sample sizes. The major shortcoming of PET imaging is therefore its lack of power for studying clinically-relevant research questions. Here, we introduce a new method for performing PET quantification and analysis called SiMBA, which helps to alleviate these issues by improving the efficiency of PET analysis by exploiting similarities between both individuals and regions within individuals. In simulated [11C]WAY100635 data, SiMBA greatly improves both statistical power and the consistency of effect size estimation without affecting the false positive rate. This approach makes use of hierarchical, multifactor, multivariate Bayesian modelling to effectively borrow strength across the whole dataset to improve stability and robustness to measurement error. In so doing, parameter identifiability and estimation are improved, without sacrificing model interpretability. This comes at the cost of increased computational overhead, however this is practically negligible relative to the time taken to collect PET data. This method has the potential to make it possible to test clinically-relevant hypotheses which could never be studied before given the practical constraints. Furthermore, because this method does not require any additional information over and above that required for traditional analysis, it makes it possible to re-examine data which has already previously been collected at great expense. In the absence of dramatic advancements in PET image data quality, radiotracer development, or data sharing, PET imaging has been fundamentally limited in the scope of research hypotheses which could be studied. This method, especially combined with the recent steps taken by the PET imaging community to embrace data sharing, will make it possible to greatly improve the research possibilities and clinical relevance of PET neuroimaging.

Cardiac amyloidosis characterization by kinetic model fitting on [18F]florbetaben PET images

OBJECTIVE

To evaluate the feasibility of kinetic modeling-based approaches from [18F]-Flobetaben dynamic PET images as a non-invasive diagnostic method for cardiac amyloidosis (CA) and to identify the two AL- and ATTR-subtypes.

METHODS AND RESULTS

Twenty-one patients with diagnoses of CA (11 patients with AL-subtype and 10 patients with ATTR-subtype of CA) and 15 Control patients with no-CA conditions underwent PET/CT imaging after [18F]Florbetaben bolus injection. A two-tissue-compartment (2TC) kinetic model was fitted to time-activity curves (TAC) obtained from left ventricle wall and left atrium cavity ROIs to estimate kinetic micro- and macro-parameters. Combinations of kinetic parameters were evaluated with the purpose of distinguishing Control subjects and CA patients, and to correctly label the last ones as AL- or ATTR-subtype. Resulting sensitivity, specificity, and accuracy for Control subjects were: 0.87, 0.9, 0.89; as far as CA patients, the sensitivity, specificity, and accuracy were respectively 0.9, 1, and 0.97 for AL-CA patients and 0.9, 0.92, 0.97 for ATTR-CA patients.

CONCLUSION

Pharmacokinetic analysis based on a 2TC model allows cardiac amyloidosis characterization from dynamic [18F]Florbetaben PET images. Estimated model parameters allows to not only distinguish between Control subjects and patients, but also between AL- and ATTR-amyloid patients.

Regional Characterization of the Gottingen Minipig Brain by [18 F]FDG Dynamic Pet Modeling

Purpose

To determine the best kinetic model to be applied on dynamic brain [18 F]FDG PET images by characterizing the regional brain glucose metabolism of normal Göttingen minipigs.

Methods

Nine Göttingen minipigs were scanned with a clinical PET/CT tomograph, starting from the injection of an intravenous bolus of [18 F]FDG, for about 25 min. Dynamic images were reconstructed and nine brain regions of interest (ROI), plus a vascular region, were defined and time-activity curves (TAC) were determined.

Three kinetic models were considered for fitting with experimental TACs: one-tissue compartment model 1TC, two-tissue irreversible compartment model 2TCi and two-tissue reversible model 2TC. Akaike Information Criterion was considered to evaluate the goodness of each model fitting. Regional and global kinetic parameter values were evaluated, in addition to the partition coefficient, net influx rate and retention index (RI).

Results

Both 2TCi and 2TC models turned out to be good choices for the next analysis. Parameter values were very similar between the different brain regions, with similar values to when the brain as a whole is considered (kinetic parameters mean values, from 2TCi model: K1 = 1.0 ml/g/min, k2 = 0.49 min− 1, k3 = 0.034 min− 1, K1/k2 = 2.14ml/g, Ki =0.069 ml/g/min; from 2TC model: K1 = 1.10 ml/g/min, k2 = 0.54 min− 1, k3 = 0.058 min− 1, k4 = 0.039 min− 1, K1/k2 = 2.18 ml/g, Ki = 0.10 ml/g/min; RI mean ± sd: 0.147 ± 0.037 min− 1), with the exception of the cerebellum (mean values from the 2TCi model: K1 = 0.52 ml/g/min, k2 = 0.56 min− 1, k3 = 0.025 min− 1, K1/k2 = 0.98ml/g, Ki=0.022 ml/g/min; from 2TC model: K1 = 0.54 ml/g/min, k2 = 0.61 min− 1, k3 = 0.044 min− 1, k4 = 0.038 min− 1, K1/k2 = 0.95ml/g, Ki=0.032 ml/g/min; RI mean ± sd: 0.071 ± 0.018 min− 1).

Conclusion

The two-tissue model is able to describe the regional brain metabolism in Göttingen minipigs. Compared to the 2TCi model, in the 2TC model the k4 micro-parameter was also evaluated. This led to adjustments of the other microparameters, especially k3 and consequently the net influx rate Ki. For healthy minipigs, the glucose metabolism was similar in all of the brain regions analyzed, with the exception of the cerebellum, where the FDG uptake was lower.

Practical considerations for navigating the regulatory landscape of non-clinical studies for clinical translation of radiopharmaceuticals

Background

The development of radiopharmaceuticals requires extensive evaluation before they can be applied in a diagnostic or therapeutic setting in Nuclear Medicine. Chemical, radiochemical, and pharmaceutical parameters must be established and verified to ensure the quality of these novel products.

Main body

To provide supportive evidence for the expected human in vivo behaviour, particularly related to safety and efficacy, additional tests, often referred to as “non-clinical” or “preclinical” are mandatory. This document is an outcome of a Technical Meeting of the International Atomic Energy Agency. It summarises the considerations necessary for non-clinical studies to accommodate the regulatory requirements for clinical translation of radiopharmaceuticals. These considerations include non-clinical pharmacology, radiation exposure and effects, toxicological studies, pharmacokinetic modelling, and imaging studies. Additionally, standardisation of different specific clinical applications is discussed.

Conclusion

This document is intended as a guide for radiopharmaceutical scientists, Nuclear Medicine specialists, and regulatory professionals to bring innovative diagnostic and therapeutic radiopharmaceuticals into the clinical evaluation process in a safe and effective way.

Unsupervised PET logan parametric image estimation using conditional deep image prior

Recently, deep learning-based denoising methods have been gradually used for PET images denoising and have shown great achievements. Among these methods, one interesting framework is conditional deep image prior (CDIP) which is an unsupervised method that does not need prior training or a large number of training pairs. In this work, we combined CDIP with Logan parametric image estimation to generate high-quality parametric images. In our method, the kinetic model is the Logan reference tissue model that can avoid arterial sampling. The neural network was utilized to represent the images of Logan slope and intercept. The patient's computed tomography (CT) image or magnetic resonance (MR) image was used as the network input to provide anatomical information. The optimization function was constructed and solved by the alternating direction method of multipliers (ADMM) algorithm. Both simulation and clinical patient datasets demonstrated that the proposed method could generate parametric images with more detailed structures. Quantification results showed that the proposed method results had higher contrast-to-noise (CNR) improvement ratios (PET/CT datasets: 62.25%±29.93%; striatum of brain PET datasets : 129.51%±32.13%, thalamus of brain PET datasets: 128.24%±31.18%) than Gaussian filtered results (PET/CT datasets: 23.33%±18.63%; striatum of brain PET datasets: 74.71%±8.71%, thalamus of brain PET datasets: 73.02%±9.34%) and nonlocal mean (NLM) denoised results (PET/CT datasets: 37.55%±26.56%; striatum of brain PET datasets: 100.89%±16.13%, thalamus of brain PET datasets: 103.59%±16.37%).

Radiogenomic analysis of primary breast cancer reveals [18F]-fluorodeoxglucose dynamic flux-constants are positively associated with immune pathways and outperform static uptake measures in associating with glucose metabolism

BACKGROUND

PET imaging of 18F-fluorodeoxygucose (FDG) is used widely for tumour staging and assessment of treatment response, but the biology associated with FDG uptake is still not fully elucidated. We therefore carried out gene set enrichment analyses (GSEA) of RNA sequencing data to find KEGG pathways associated with FDG uptake in primary breast cancers.

METHODS

Pre-treatment data were analysed from a window-of-opportunity study in which 30 patients underwent static and dynamic FDG-PET and tumour biopsy. Kinetic models were fitted to dynamic images, and GSEA was performed for enrichment scores reflecting Pearson and Spearman coefficients of correlations between gene expression and imaging.

RESULTS

A total of 38 pathways were associated with kinetic model flux-constants or static measures of FDG uptake, all positively. The associated pathways included glycolysis/gluconeogenesis ('GLYC-GLUC') which mediates FDG uptake and was associated with model flux-constants but not with static uptake measures, and 28 pathways related to immune-response or inflammation. More pathways, 32, were associated with the flux-constant K of the simple Patlak model than with any other imaging index. Numbers of pathways categorised as being associated with individual micro-parameters of the kinetic models were substantially fewer than numbers associated with flux-constants, and lay around levels expected by chance.

CONCLUSIONS

In pre-treatment images GLYC-GLUC was associated with FDG kinetic flux-constants including Patlak K, but not with static uptake measures. Immune-related pathways were associated with flux-constants and static uptake. Patlak K was associated with more pathways than were the flux-constants of more complex kinetic models. On the basis of these results Patlak analysis of dynamic FDG-PET scans is advantageous, compared to other kinetic analyses or static imaging, in studies seeking to infer tumour-to-tumour differences in biology from differences in imaging. Trial registration NCT01266486, December 24th 2010.

PET Imaging in Preclinical Anti-Aβ Drug Development

Positron emission tomography (PET), a medical imaging technique allowing for studies of the living human brain, has gained an important role in clinical trials of novel drugs against Alzheimer’s disease (AD). For example, PET data contributed to the conditional approval in 2021 of aducanumab, an antibody directed towards amyloid-beta (Aβ) aggregates, by showing a dose-dependent reduction in brain amyloid after treatment. In parallel to clinical studies, preclinical studies in animal models of Aβ pathology may also benefit from PET as a tool to detect target engagement and treatment effects of anti-Aβ drug candidates. PET is associated with a high level of translatability between species as similar, non-invasive protocols allow for longitudinal rather than cross-sectional studies and can be used both in a preclinical and clinical setting. This review focuses on the use of preclinical PET imaging in genetically modified animals that express human Aβ, and its present and potential future role in the development of drugs aimed at reducing brain Aβ levels as a therapeutic strategy to halt disease progression in AD.

Overview of positron emission tomography in functional imaging of the lungs for diffuse lung diseases

Positron emission tomography (PET) is a quantitative molecular imaging modality increasingly used to study pulmonary disease processes and drug effects on those processes. The wide range of drugs and other entities that can be radiolabeled to study molecularly targeted processes is a major strength of PET, thus providing a noninvasive approach for obtaining molecular phenotyping information. The use of PET to monitor disease progression and treatment outcomes in DLD has been limited in clinical practice, with most of such applications occurring in the context of research investigations under clinical trials. Given the high costs and failure rates for lung drug development efforts, molecular imaging lung biomarkers are needed not only to aid these efforts but also to improve clinical characterization of these diseases beyond canonical anatomic classifications based on computed tomography. The purpose of this review article is to provide an overview of PET applications in characterizing lung disease, focusing on novel tracers that are in clinical development for DLD molecular phenotyping, and briefly address considerations for accurately quantifying lung PET signals.

Relationship between Biodistribution and Tracer Kinetics of 11C-Erlotinib, 18F-Afatinib and 11C-Osimertinib and Image Quality Evaluation Using Pharmacokinetic/Pharmacodynamic Analysis in Advanced Stage Non-Small Cell Lung Cancer Patients

Background: Patients with non-small cell lung cancer (NSCLC) driven by activating epidermal growth factor receptor (EGFR) mutations are best treated with therapies targeting EGFR, i.e., tyrosine kinase inhibitors (TKI). Radiolabeled EGFR-TKI and PET have been investigated to study EGFR-TKI kinetics and its potential role as biomarker of response in NSCLC patients with EGFR mutations (EGFRm). In this study we aimed to compare the biodistribution and kinetics of three different EGFR-TKI, i.e., 11C-erlotinib, 18F-afatinib and 11C-osimertinib. Methods: Data of three prospective studies and 1 ongoing study were re-analysed; data from thirteen patients (EGFRm) were included for 11C-erlotinib, seven patients for 18F-afatinib (EGFRm and EGFR wild type) and four patients for 11C-osimertinib (EGFRm). From dynamic and static scans, SUV and tumor-to-blood (TBR) values were derived for tumor, lung, spleen, liver, vertebra and, if possible, brain tissue. AUC values were calculated using dynamic time-activity-curves. Parent fraction, plasma-to-blood ratio and SUV values were derived from arterial blood data. Tumor-to-lung contrast was calculated, as well as (background) noise to assess image quality. Results: 11C-osimertinib showed the highest SUV and TBR (AUC) values in nearly all tissues. Spleen uptake was notably high for 11C-osimertinib and to a lesser extent for 18F-afatinib. For EGFRm, 11C-erlotinib and 18F-afatinib demonstrated the highest tumor-to-lung contrast, compared to an inverse contrast observed for 11C-osimertinib. Tumor-to-lung contrast and spleen uptake of the three TKI ranked accordingly to the expected lysosomal sequestration. Conclusion: Comparison of biodistribution and tracer kinetics showed that 11C-erlotinib and 18F-afatinib demonstrated the highest tumor-to-background contrast in EGFRm positive tumors. Image quality, based on contrast and noise analysis, was superior for 11C-erlotinib and 18F-afatinib (EGFRm) scans compared to 11C-osimertinib and 18F-afatinib (EGFR wild type) scans.