Comparison of Multi-Organ Segmentation Tools for Whole-Body [18F]FDG-PET/CT Clinical Imaging
Typically, delineation of volumes of interest (VOI) within clinical PET/CT imaging is performed manually. This is time consuming [1] and is highly vulnerable to inter/intra-operator variability resulting in differing delineations for the same patient [1]. Application of automated segmentation aims to address these issues in the VOI delineation process; increasing image throughput and reducing the current manual stochasticity involved. Moreover, automation in the segmentation process has the potential to galvanise downstream analyses, including network analysis [2]. Among the various automated multi-organ segmentation approaches, two methodologies Multiple-Organ Objective Segmentation (MOOSE) [3] and TotalSegmentator (TS) [4] have emerged as state-of-the-art (SOTA). Both methods leverage the nnU-Net framework as their underlying architecture. However, they differ in training dataset, nnU-Net configurations, and weights. Recently, Julie et al. [5] compared MOOSE and TotalSegmentator on a metastatic breast cancer dataset. However, gold-standard labels were not generated for this study and as a result, comparison against a verified gold-standard is not available, instead the authors focus on the degree of agreement and differences in feature values. Concurrent with Julie et al. [5], we look to compare MOOSE and TS, on a dataset of clinical stage IIB/III non-small cell lung carcinoma (NSCLC). We compare both methods versus gold-standard manual delineation and evaluate using current technical segmentation metrics alongside PET/CT outcome metrics (e.g. Hounsfield units (HU) and Standardised Uptake Values (SUV)) to assess if the automated methods introduce quantitative bias versus manual annotations. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Cameron Wheeler, Phyo H. Khaing, Eleonora D’Arnese , Adriana A.S. Tavares https://creativecommons.org/licenses/by/4.0
Evaluating the potential of cis- and trans-4-[18F]fluoro-L-proline positron emission tomography as biomarkers of active collagen biosynthesis in cardiometabolic diseases
The increased prevalence of obesity and its associated comorbidities has coincided with an upsurge in cardiometabolic diseases, such as heart failure with preserved ejection fraction, metabolic dysfunction-associated steatotic liver disease, and chronic kidney disease. Active tissue remodelling and fibrosis following cellular damage and inflammation, characterised by aberrant collagen deposition, is a common feature of cardiometabolic disease. Currently, there are no established probes for non-invasive whole-body imaging of active collagen biosynthesis to study the multisystem consequences of cardiometabolic diseases. We aimed to evaluate the potential of cis¬- and trans-4-[18F]fluoro-L-proline positron emission tomography (PET) as biomarkers of active misfolded and stable triple helical collagen biosynthesis, respectively, using a preclinical Western-style diet (WD)-induced rat model of cardiometabolic diseases. Animals fed a WD had significantly greater uptake of both cis¬- and trans-4-[18F]fluoro-L-proline in the heart compared to age-matched controls across the time course, reflecting the increased histological accumulation of collagen, a profibrotic gene expression profile, and cardiac dysfunction on echocardiography. Although histological and transcriptional alterations were also noted in the liver, there were no detectable differences in hepatic cis- and trans-4-[18F]fluoro-L-proline PET signal. We hypothesise that the organ-specific differences in relative [18F]fluoro-L-proline PET uptake reflect disease-associated perturbations to the free-proline pool. Previous reports showed that WD feeding markedly increased the proline content of plasma and liver tissue1 which could in turn alter tracer kinetics. Currently, we are developing and validating new quantification strategies for [18F]fluoro-L-proline PET studies based on amino acid blood concentrations, similar to established plasma glucose corrections in [18F]fluorodeoxyglucose PET studies2, in order to improve [18F]fluoro-L-proline PET outcome reporting. Overall, our data suggests that the rates of active collagen biosynthesis in cardiometabolic diseases are organ-specific, likely indicating differences in susceptibility to injury and fibrosis. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Angus Jacobs, Victoria J. M. Reid, Carlos-Alcaide Corral, Islay Cranston, Callum Sutherland, Heeyoun Hur, Adrian Thomson, Timothy J. Kendall, Timaeus E. F. Morgan, Kerry M. O’Rourke, Leanne M. Riley, Andrew Sutherland, Mark G. Macaskill, David E. Newby, Laura Denby, Jonathan A. Fallowfield, Adriana A. S. Tavares https://creativecommons.org/licenses/by/4.0
Tumour Segmentation and Volume estimation from PET Lymphoma Data Using a Pre-trained U-Net Model and Probability Contour Framework
Accurate tumour delineation is critical for utilizing total metabolic tumour volume (TMTV) as a reproducible prognostic biomarker in lymphoma PET imaging [1]. This study focuses on achieving robust tumour segmentation specifically from PET data [1] by applying a pre-trained U-Net model [2] and a kernel-smoothed probability contour framework [3]. Additionally, ongoing segmentation experiments on various other PET datasets are underway to validate and generalize this approach further Tumour segmentation was conducted using a pre-trained U-Net model, initially developed on the AutoPET II PET challenge dataset [4]. The model achieved a Dice similarity coefficient of 0.65 on the new lymphoma PET benchmark dataset [1]. To enhance interpretability and reliability, a kernel-smoothed probability contour framework was applied as a post-processing step, generating voxel-wise uncertainty information for each segmented subvolume without compromising Dice accuracy. The U-Net model effectively segmented lymphoma tumours across multiple PET datasets, consistently demonstrating stable and reproducible performance. The probability contour framework successfully provided uncertainty quantification, highlighting regions of high and low confidence in PET segmentation results. This combined methodology presents a robust, interpretable solution for tumour delineation specifically optimized for PET lymphoma imaging. Integrating a pre-trained U-Net model with a kernel-smoothed probability contour approach enables standardized and reproducible tumour segmentation from PET data in lymphoma. The provision of uncertainty metrics significantly enhances the segmentation's reliability, supporting the potential of TMTV as a robust clinical biomarker. Future research leveraging this PET-focused framework will continue to explore and validate its clinical utility across additional PET lymphoma datasets. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Baixiang Zhao, Surajit Ray, Wenhui Zhang https://creativecommons.org/licenses/by/4.0
Investigating the effect of different CT protocols on material maps for preclinical PET imaging
Every Positron Emission Tomography (PET) reconstruction needs a material map of the imaged object to estimate the attenuation and scatter. In the preclinical setting, this is a Computed Tomography (CT)-based binary map. The dose delivered needs to be compliant with animal welfare requirements1. Currently, there is limited literature on preclinical CT-based material maps optimisation2,3. This study aimed to evaluate the effect of different CT acquisition and reconstruction protocols on the material maps generated and, consequently, on the quality of the PET image. A 3D printed (tango-black) hollow rat phantom with a 3 cm diameter NEMA IQ phantom4 was used (Fig.1A) with 3.7 MBq activity. Helical (H), semi-circular (SC), and Zig-Zag (Z) trajectories were investigated. The projection number for SC1, SC3, H1, Z1 and Z2 was 360, while for SC2 it was 720. All of the non-helical scans were full scans, except the SC3 and Z2 as they were half scans. The helical pitch was 1. The tube voltage was 50 kV and the tube current was 980 mA for all the scans, expect for Z2 where these numberers were 35 kV and 180 mA. The dose of each protocol was measured by an ionizing chamber-based detector. FBP (Filtered-Back-Projection), MLTR (Maximum-Likelihood-Transmission-Reconstruction), and MSIRT (Modelled-Simultaneous-Iterative-Reconstruction-Technique) reconstruction were used. The generated maps were subtracted from each other to quantify the differences. The PET image was reconstructed with all the material maps, and the NEMA image quality phantom analysis was carried out. The low dose protocols (SC3, Z2) (Fig.1B) with FBP reconstruction resulted in slightly different maps (Fig.1C), which also influenced the uniformity and accuracy (Fig.1D-E). The iterative reconstructions (MLTR, MSIRT) helped to overcome this issue (Fig.1B-E). With iterative reconstruction even low dose CT protocols (SC3 and Z2) can produce accurate material maps for PET imaging. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Daniel Reti, Adriana Tavares, Axel Montagne, Maurits Jansen, Peter Major https://creativecommons.org/licenses/by/4.0
Multi-center image analysis study to assess the interobserver variability of SUV analysis in preclinical [18F]FDG PET/CT
This multi-centre FDG-PET/(CT) image analysis project aimed to evaluate the impact of the image analysis method on dynamic preclinical FDG-PET/(CT) data comparability. FDG-PET (n=6) and FDG-PET/CT (n=7) datasets from tumour-bearing mice were analyzed by trained and untrained investigators (n=8) from in total 7 different laboratories using their individual standard image analysis software and method for the respective organs. Apart from one investigator, the analysis was performed blinded. The investigators were asked to delineate the tumour, whole brain, muscle, heart or left ventricle, kidneys, liver and urinary bladder. Reporting included the used software program, intensity levels of the radiation scale, ROI/VOI size, delineation method, used image frame, activity concentration in the ROI/VOI (mean and max) and information on co-registration (PET/CT datasets). The used image analysis software included three different programs. Images were analyzed in %ID/cc, SUV or kBq/ml. Organ delineation methods ranged from fixed objects (e.g. spheres) to manual delineation and semi-automatic methods using thresholding. For both datasets (FDG-PET and FDG-PET/CT), the smallest variation in the VOI sizes was obtained in the heart (50% CoV and 32% CoV), whereas the largest was obtained in the liver (234% CoV and 154% CoV), respectively. However, the different investigators obtained liver time-activity curves (TACs) given in SUVmean, which were nearly identical, whereas huge variations in the muscle, left ventricle and urinary bladder SUVmean TACs were obtained. Tumour SUVmean TACs also revealed high differences in the analysis methods (delineating the whole tumour or only the active volume). The inclusion of the CT data led to an improvement in the brain SUVmean TACs (reduction of the CoV from 14% to 5%, respectively). SUVmax TACs were almost identical in the tumour, kidney and urinary bladder but exhibited some variations in all the other organs. This is the first multi-centre study focusing on the influence of the image analysis method on the obtained results. We could show that the outcome of an FDG-PET/(CT) study heavily depends on the employed image analysis method. Especially the SUVmean changes with the region's position (e.g. muscle) and/or size (e.g. urinary bladder). Therefore, a standardized method for image analysis will be proposed based on the findings of this study. The authors would like to acknowledge the contribution of the COST Action CA17121. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Carlos Corral, Claudia Kuntner, Herve Boutin, David Brasse, Filipe Elvas, Duncan Forster, Lena Ivancic, Islay Cranston, Adriana Tavares, Thomas Wanek, Julia G. Mannheim https://creativecommons.org/licenses/by/4.0
Simultaneous Multi-Bed MAP Reconstruction with CT-Guided Directional TV Prior for Y-90 PET SIRT
Text Yttrium-90 (Y-90) microspheres are used clinically for selective internal radiation therapy (SIRT) for unresectable liver cancers and have been proposed for glioblastoma1,2. Accurate dosimetry is critical but remains challenging due to low-count PET data arising from Y-90’s low positron branching ratio, as well as accurate treatment of bremsstrahlung for SPECT. Anatomically guided edge-preserving regularisation has shown promise for improving reconstruction quality under such conditions3. Whole-organ PET scans often exceed the axial field-of-view, requiring acquisition across multiple, partially overlapping bed positions (BPs). A common approach reconstructs each BP separately and merges them via sensitivity-weighted averaging. However, in overlap regions, low counts and edge-preserving priors can interact to produce intensity discontinuities that obscure image features. We introduce a maximum-a-posteriori (MAP) framework that reconstructs all BPs jointly, with a CT-guided directional total variation (DTV) prior function applied over a combined image volume4. We evaluate this method for Y-90 PET following SIRT. Y-90 SIRT PET data was acquired on a GE Discovery 690 using two BPs, with overlap centred on the liver. We compared two workflows: Separate + Fuse – independent reconstructions with sensitivity-weighted merging post-reconstruction. Joint – Simultaneous reconstruction of both bed positions using a combined image volume, with separate updates computed for the data-fit terms per bed position and a single joint update for the prior term. Visual assessment shows that Separate + Fuse obtains abrupt changes in intensity in the overlap region due to noise. The Joint method suppresses these artefacts and yields a smoother, anatomically consistent distribution (Figure 1). Simultaneous reconstruction of multiple bed positions with a CT-guided DTV prior reduces overlap artefacts and enhances qualitative fidelity in Y-90 SIRT PET. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Sam Porter, Daniel Deidda, Daniel McGowan, Simon Arridge, Kris Thielemans https://creativecommons.org/licenses/by/4.0
Early experiences of monitoring healthy controls with total-body FDG-PET/CT
FDG-PET/CT provides essential metabolic information for oncology diagnostics and may help assess systemic health in asymptomatic individuals. Reliable baseline values from healthy subjects are needed as references for disease detection, while acknowledging public concerns about radiation exposure. In this study, we performed test-retest scans in healthy subjects using total-body FDG-PET/CT and evaluated how much the effective dose (ED) could be reduced while maintaining quantitative accuracy. 47 (25F/22M) participants underwent two 5-min TB-PET/CT scans (test/retest) on a Siemens Biograph Vision Quadra system following an injected activity of 100 MBq of FDG. Test and retest scans were ~35 days apart. MOOSE [1] was used for automatic organ delineation and extracting organ-specific readouts (HU, SUV, volume). Imaging repeatability was assessed by comparing test/retest readouts with % difference and Student's T-test per-subject and group levels, and with coefficients of variation (COV). To determine the lowest achievable PET-related radiation exposure, listmode emission data were reduced to 50%, 25%, 10%, and 5% of their original counts. Synthetic attenuation maps (ED=0 mSv) were used for attenuation correction at the various count levels [2] (Figure 1A). The %-differences of organ readouts between the reference and synthetic dose levels were statistically compared (Figure 1B). For all assessed organs and readouts, test/retest %-differences were below 5% on a group level and 9% on a per-subject level, except in the heart and kidneys. Quantitatively accurate organ readouts were achieved at 25% of the original 100MBq (resulting in 0.5 mSv total ED) while preserving image integrity (COV < 15%). Total-body FDG PET/CT provides reproducible measurements in healthy subjects and retains quantitative reliability at significantly lower doses, endorsing PET for longitudinal health monitoring and establishing metabolic references. Future work could integrate physiological and wearable data to expand this framework toward a broader model of systemic health tracking. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Sebastian Gutschmayer, Daria Ferrara, Barbara Katharina Geist, Öykü Özer, Josef Yu, Manuel Pires, Ivo Rausch, Marcus Hacker, Lalith Kumar Shiyam Sundar, Werner Langsteger, Thomas Beyer https://creativecommons.org/licenses/by/4.0
Using an autoencoder to determine the input function for dynamic PET data in the presence of radiometabolites
Previously, we have demonstrated that an estimate of the arterial input function can be found from dynamic positron emission tomography (PET) data using a machine learning tool called an autoencoder. [1] Our initial study made use of an inflammation tracer ([18F]LW223) which is not greatly influenced by radiometabolites. [2] This means that an input function estimate derived from the image data without further correction has some hope of being accurate. Here we focus on the synaptic density tracer [18F]SynVesT-1 and pre-clinical studies of rodent models. For this radiotracer initially, 100% of the radioactivity is associated with the tracer, however as the scan progresses an increasing proportion of the radioactivity originates from radiometabolites. [3] We use the autoencoder to decompose the dynamic signals from the PET data into three characteristic components. We associate the component with the earliest peak with the input function. Finally, we re-train the autoencoder while constraining the input function to be similar to a population curve. This provides the estimate of the input function which we then use to analyse time activity curves from different tissues. [1] Microparameters characteristic of different tissues were obtained using a linearisation approach due to Ito (Figure 1) which produces values for K1 (rate of entry of tracer from blood to tissue) and VT (the volume of distribution). [4] Estimating K1 relies on the earliest part of the input function. This part of the curve is least influenced by radiometabolites; we find good agreement between our K1 values and those derived using invasive blood sampling and radiometabolite correction. By contrast, VT is much more dependent on the later parts of the curve which are most corrupted. Unsurprisingly we were unable to capture reasonable values without taking account of radiometabolites. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Iona McGowan, Catriona Wimberley, Paul Clegg https://creativecommons.org/licenses/by/4.0
TOF PET/MR attenuation correction of flexible MR-hardware utilizing MLAA and template alignment
Accurate attenuation correction (AC) of hardware components used during Magnetic Resonance Imaging (MR) remains a challenge in PET/MR, as these components are not visible in MR-derived attenuation maps. CT-based template μ-maps are widely used for stationary hardware such as the patient table, but flexible MR hardware is currently not corrected for in clinical protocols. Previously, the influence of such MR hardware (MR-HW) on PET quantification was investigated using phantom [1] and patient [2] measurements performed on the Siemens Biograph mMR. These initial studies explored the simultaneous estimation of attenuation and activity (MLAA) [3] together with registration of a CT-based template μ-map on non-time of flight data to correct for the coil attenuation, similar to [4]. It has been shown that time-of-flight (TOF) information can stabilize the joint estimation problem for patient attenuation correction, eliminating crosstalk artefacts in the presence of errors in initial attenuation estimates [5]. Building on these findings, the current work evaluates AC strategies for MR hardware using a state-of-the-art TOF PET/CT system (Biograph Vision 600, Siemens Healthineers) to explore how TOF information influences the robustness and accuracy of MRAC for recent PET/MR systems. Measurements of a NEMA body phantom—with and without an MR-hardware coil placed on top—were acquired using the TOF PET/CT system. MLAA, initialized with a phantom (and patient table) μ-map only, was employed to reconstruct the attenuation distribution outside the phantom, i.e., the coil attenuation. These results were compared to the known coil position obtained from a dedicated CT acquisition of the setup. Additionally, a CT scan of the coil in a different position was converted into a PET μ-map and registered to the MLAA output to assess registration-based correction. The feasibility and accuracy of the registration and the impact of the different MR-hardware attenuation correction strategies on the reconstructed PET images were evaluated. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Nicole Jurjew, Cameron Anderson, Belinda Stiles, Mohammadreza Teimoorisichani, Paul Schleyer, David Atkinson, Kris Thielemans https://creativecommons.org/licenses/by/4.0
Q Amyloid: an open-access platform for automated amyloid-beta quantification
Amyloid-beta (Aβ) deposition is one of the earliest neuropathological hallmarks of Alzheimer's disease (AD), and Positron Emission Tomography (PET) is commonly used to detect Aβ in vivo. However, an automated PET-based pipeline for quantification of brain Aβ accumulation is still missing. This project aims to develop Q Amyloid, an open-access platform to automatically quantify brain Aβ burden. Q Amyloid quantifies Aβ load using the Centiloid (CL) scale [1]. Structural Magnetic Resonance Imaging (T1-MRI) and PET data were downloaded from the GAAIN website for algorithm development, along with CL values, adopted as gold standards. The pipeline included MRI and PET scan preprocessing following the “standard method” [1], and a quality control step to assess its outcome. Individual Standardized Uptake Value ratios (SUVr) were then computed and converted to CL units [1]. The pipeline was evaluated on the 11C-PiB GAAIN dataset and subsequently calibrated on three additional ¹⁸F-labeled radiotracers using the corresponding data available on GAAIN. Evaluation of the Q Amyloid pipeline on the 11C-PiB GAAIN dataset demonstrated that the group mean SUVr values were within 2% of the gold standard, and that individual SUVr were highly correlated with the other fluorinated tracers (R2= [0.902-0.962]) (Figure 1). Once operational, the platform will receive subject-specific T1-MRI and PET scans as input and generate a report detailing the amyloidosis status. Q Amyloid will serve as a valuable tool in clinical trials, providing quantitative biomarkers to support early AD diagnosis. This project is supported by the cascading grant “Q Amyloid – Quantitative Amyloid Imaging” under PNRR ECS00000017 “THE-Tuscany Health Ecosystem,” Spoke 6: “Precision Medicine & Personalized Healthcare” funded by the European Commission through the NextGeneration EU programme. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Mattia Veronese, Benedetta Marin, Chiara Da Villa, Francesco Piva, Lucia Maccioni, Manuela Moretto, Luigi Lorenzini https://creativecommons.org/licenses/by/4.0
Organocatalytic Asymmetric Synthesis of SynVesT-1, a PET Imaging Agent of the SV2A Receptor
[18F]SynVesT-1 is a potent and selective positron emission tomography imaging agent for synaptic vesicle glycoprotein 2 (SV2A).1 SV2A is an integral transmembrane glycoprotein widely expressed in the brain. Although the exact role of SV2A has not been confirmed, it is known that SV2A participates in key vesicular processes. Furthermore, SV2A is a validated target for epilepsy and a biomarker of synaptic density.2 The established synthetic strategy to obtain [18F]SynVesT-1 involves the multistep synthesis of a racemic intermediate, requiring late-stage separation of the two enantiomers via chiral HPLC.3 Our aim was to develop an asymmetric synthetic route to access [18F]SynVesT-1. In this work, we optimised a seven-step route to an organotin precursor of [18F]SynVesT-1 starting with a Wittig reaction of 3-bromo-5-fluorobenzaldehyde.4 This was followed by the asymmetric conjugate addition of nitromethane to the resulting cinnamaldehyde utilising the Hayashi-Jørgensen organocatalyst (Scheme 1). Subsequently, a number of standard transformations facilitated the synthesis of the organotin precursor which was then subjected to automated copper(II)-mediated fluoro-destannylation for the preparation of [18F]SynVesT-1. This work will be discussed, along with a second-generation route detailing the synthesis of a boronic ester-derived precursor to [18F]SynVesT-1. Please click on the 'PDF' for the full abstract!
Derechos de autor 2025 Holly McErlain, Valeria K. Burianova, Timaeus E. F. Morgan, Adriana A. S. Tavares, Andrew Sutherland https://creativecommons.org/licenses/by/4.0
Chemoradiotherapy treatment increases cardiac and aortic [18F]FDG uptake ratios in lung cancer patients
[18F]Fluorodeoxyglucose (FDG) positron emission tomography (PET) is an indispensable non-invasive imaging tool to aid diagnosis, prognostication, and therapeutic monitoring in oncology, but it can also evaluate cardiovascular inflammation1,2. Previously, cardiac metabolic changes using [18F]FDG with chemoradiotherapy have been explored3 but changes in the rest of the cardiovascular system remain undetermined. To investigate the cardiovascular metabolic changes pre- and post-chemoradiotherapy in stage 3b non-small cell lung cancer (NSCLC) patients. A retrospective analysis of 26 patients (43-82 years) with stage 3b NSCLC from the American College of Radiology Imaging Network (ACRIN 6668) trial was performed3. All available pre- and post-treatment imaging were retrieved from the Cancer Imaging Archive (TCIA)4 and regions of interest for the left ventricle and calcified large arteries were contoured on all PET-CT scans using PMOD version 4.0 software. The mean, maximum standard uptake value (SUVmean and SUVmax) and target-to-background ratio (TBR) were quantified on PMOD. At approximately 14 weeks post-chemoradiotherapy, there was a higher SUVmean (mean difference 0.81, p=0.01) and SUVmax (mean difference 2.20, p=0.006) in the left ventricle compared with pretreatment scans. TBR for aorta was higher post-treatment (mean difference 0.06, p=0.005). However, SUVmean for carotid arteries and right brachiocephalic artery was reduced post-chemoradiotherapy (mean difference 0.15, p=0.008 and mean difference 0.28, p=0.03). A reduction in SUVmax was also seen for aortic arch (mean difference 0.58, p=0.03) and right brachiocephalic artery (mean difference 0.42, p=0.03 and mean difference 0.42, p=0.03). Cardiovascular glucose metabolism is selectively increased in the left ventricle and aorta post-chemoradiotherapy. Changes in glucose metabolism of atherosclerotic plaques vary across different vessels throughout the body. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Phyo Khaing, David Newby, Adriana Tavares https://creativecommons.org/licenses/by/4.0
[18F]FDG Brain Imaging Assessment in Preclinical Acute and Chronic Stress Models Using Statistical Parametric Mapping
Previous studies have used [18F]FDG as a biomarker for depression, anxiety, or chronic stress in cancer patients1,2. However, they lack specific brain patterns and assessment in preclinical models, limiting their translational potential. Hence, we set out to establish preclinical [18F]FDG brain PET as a method to evaluate stress in vivo. Lean C57BL/6J mice were exposed to acute and chronic cooling, restraint, and surgical stress. Obese mice were on a high-fat diet for eight weeks and exposed to cooling and restraint stress. [18F]FDG was injected into fasted animals under brief isoflurane anesthesia via the tail vein. Following a 60-minute distribution phase during acute stress or one day after stress exposure (chronic), animals underwent static μPET/CT scans (Siemens® Inveon). Brain images were spatially normalized, and intensity normalization was performed using proportional and histogram-based scaling3 with PMOD (V. 3.8). Normalized images were analyzed using a brain atlas (M. Mirrione) to extract regional SUV and statistical parametric mapping (SPM12) for voxel-wise comparison. Overall, in lean mice, total brain [18F]FDG-uptake was significantly reduced across all stress models except for acute restraint, whereas total uptake was unaffected in obese mice. Subsequent brain atlas analysis of lean mice revealed changes in regional uptake for all stressors except for chronic cooling, while obese mice were affected by only acute and chronic restraint. Using SPM, we validated stress-specific hypo- and hypermetabolic areas in the brains of lean mice upon acute cooling, acute and chronic restraint, and surgery and in obese mice after chronic restraint (see Figure). We also identified overlapping areas in the brain stem and hypothalamus region following acute cooling and surgery, possibly due to post-surgical hypothermia. Our findings show that static [18F]FDG-imaging is a valuable tool for noninvasively assessing brain activity in a multitude of stress models and a suitable translational tool for stress research in mice. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Maximilian Krisch, Jayne Louise Wilson, Thomas Wanek, Paul Ettel, Claudia Kuntner, Oeykue Oezer, Thomas Weichhart, Cécile Philippe, Marcus Hacker, Chrysoula Vraka https://creativecommons.org/licenses/by/4.0
Assessing overall oxidative metabolism of high-metabolic-rate abdominal organs in diet-induced obesity using compartmental modeling of [11C]acetate-PET
[11C]Acetate PET imaging is a powerful tool for visualizing and studying metabolic processes in various tissues and organs, particularly in the context of heart disease and prostate cancer. The tracer can reflect tissue perfusion, oxidative metabolism, and fatty acid synthesis[1]. Male Sprague Dawley rats on standard (SD n=6) and after 8-10 weeks on a high-fat diet (HFD n=6) were studied for [11C]acetate turnover. Longitudinal utilization of the tracer in the abdomen was recorded using Siemens Inveon µ-PET/CT. Under isoflurane anesthesia, rats underwent 60 min dynamic [11C]acetate-PET scans and additionally on the next week venous blood sampling for [11C]CO2-metabolite analysis (7-time points; SD n=3; HFD n=3) accordingly[2]. Delineation of kidneys, myocardium and liver; SUV calculations; 1-tissue compartmental modeling with blood-to-plasma ratio and [11C]CO2-metabolite correction were performed in PMOD3.8; clearance rate was calculated in Carimas2.1. Semi-quantification methods demonstrated contrasting outcomes between the groups. Increased renal (right p=0.04, left p=0.03) and myocardial (p=0.01) uptake (Fig.A) was observed in the HFD group, while clearance rates in kidneys (right p=0.01, left p=0.04) and liver (p=0.03) were decreased (Fig.B). Blood sampling for [11C]CO2-metabolite analysis revealed differing profiles between the groups, with a maximum peak at 30 min in SD and two peaks at 5 and 40 min in HFD animals (Fig.C). Deeper analysis using 1-tissue compartmental modeling shed light on the hepatic uptake and showed a difference in the volume of distribution (Vt), which was significantly decreased in the HFD group (p<0.001) (Fig.D). The study indicates [11C]acetate accumulation in metabolically active organs: kidneys, heart, and liver. Compartmental modeling demonstrated the necessity of metabolite correction for quantitative analysis. Previously, Sprague Dawley rats on HFD were studied for fatty acid uptake and decline of VLDL synthesis[3] that can correlate with the variation of Vt in the liver and confirm pathological changes in organ functioning. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Usevalad Ustsinau, Marius Ozenil , Antonia Grosinger, Thomas Wanek, Markus Hacker, Martin Krššák, Cecile Philippe https://creativecommons.org/licenses/by/4.0
Performance measurements of Total Body PET scanner simulations using the Geant4 toolkit
Total-Body PET (TB PET) is a novel imaging modality that can image the whole body in a single scan. It has increased sensitivity, improved spatial resolution, reduced imaging times compared to conventional clinical PET scanners, and has the potential to detect smaller lesions and multiorgan diseases. Commercially available TB PET scanners include the Siemens Biograph Vision Quadra [1] and the United Imaging uEXPLORER [2]. This study uses the Geant4 toolkit [3] to create a simulation model of two different TB PET detector geometries; Siemens Biograph Vision Quadra and uEXPLORER. Analysis of simulated data and performance measurements were carried out in Python. The study follows the NEMA NU-2 2018 standard [4]. The simulation was used to explore the effect of different scintillator crystals and evaluates the count rate performance and the sensitivity of the scanner. The simulated data was compared to published experimental data from Siemens and Explorer. Good agreement between the count rates of simulated and experimental data was observed for both geometries. The simulated Siemens Quadra measured a system sensitivity value of 178.6 cps/kBq showing a 2% difference compared to the experimental data of 175.3 cps/kBq for LSO crystals. Axial sensitivity profiles plotted also match well. The comparison of different scintillators showed that BGO performs best as it has the highest peak NECR, thus good signal-to-noise ratio. LSO and LYSO also perform well and their physical properties are similar. NaI and CsI exhibit the worst performance; with low true count rates and the random count rates increased almost linearly with activity concentration. Our simulations compared well against experimental data, allowing us to test further scintillator materials and radioisotopes. After implementing image reconstruction and relevant corrections, this simulation can be used for validation of various research ideas in the future. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Aparna Jayaraj, Hanna Borecka-Bielska, Matthew Needham, Adriana Tavares, Benjamin Wynne, Catriona Wimberley, Franz Muheim https://creativecommons.org/licenses/by/4.0
Comparison of preclinical PET scanners performance with varying axial fields-of-view
In preclinical Positron Emission Tomography (PET) imaging, understanding a PET system's performance is crucial for comparing its results with other systems’. In multicentre studies, the performance of the participating systems is essential when planning the experiments [1,2]. In this study, a comprehensive performance evaluation of three PET systems was carried out. The Mediso PET systems were installed in 2023, 2022, and 2014. The newest system is a three-ring system, with 150 mm axial field-of-view (FOV), while the others are two-ring systems. The reconstruction software has undergone multiple updates over time for each system. Other hardware specifications and the crystal matrix are consistent across the three systems. The NEMA NU-4 2008 assessments [3], and linearity measurements were performed. The resolution was similar across the three systems, with filtered-back-projection (FBP) and with iterative reconstruction at the centre of the FOV. The absolute sensitivity within the 250-750 keV range was highest in the newest 3-ring system, while it was similar with the other 2-ring scanners. The mouse 250-750 keV NEC peak was considerably higher with the newest system. Image quality results are presented in Figure 1. The linearity of all systems was within acceptable limits in the 0.5-120 MBq range. All results of the newest system are preliminary, before publication. The greatest differences arose from the extended FOV of the newest system. The impact of software advancements and system ageing is evident in our results, particularly in the image quality phantom. Although the systems are similar, their performances vary significantly. When designing a study involving these systems, factors such as ageing, software version, and the length of the FOV must be considered. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Daniel Reti, Adriana Tavares, Axel Montagne, Maurits Jansen, Peter Major https://creativecommons.org/licenses/by/4.0
Quantification of extra-cardiac damage following acute myocardial infarction using cis- and trans-4-[18F]fluoro-L-proline PET imaging
The preclinical surgically-induced models of myocardial infarction (MI) have been extensively utilised for studying the wound healing response in the mammalian heart. It is well-established that these invasive procedures induce marked acute systemic inflammation and adverse haemodynamic derangements, which can result in extra-cardiac tissue injury and remodelling; however, the extent of this has not yet been robustly characterised. Here, using a rat permanent coronary artery ligation (PL) model of MI and the cis- and trans-4-[18F]fluoro-l-proline positron emission tomography (PET) probes, targeting active misfolded and triple-helical collagen biosynthesis, respectively, we conducted an exploratory time-course analysis of the associated extra-cardiac tissue remodelling post-injury. Our results showed an increased cis-4-[18F]fluoro-l-proline PET signal in the liver (P = 0.02; two sample t-test) and lung (P = 0.03; two sample t-test) at the acute day 7 timepoint in the MI group compared to the Sham group (n = 3-6), which was not apparent at the later timepoints (days 14, 28, and 84). Ex vivo quantification of the deposited collagen in extra-cardiac tissues using Sirius red histochemical staining and the total hydroxyproline assay validated the apparent time course trends observed with [18F]fluoro-l-proline PET. These findings suggest that the liver and lungs are susceptible to acute injury in the rat PL model of MI and align with numerous clinical reports of extracardiac injury in MI patients. Moreover, our results highlight the suitability of [18F]fluoro-l-proline PET as a systems-based imaging technique for investigating active collagen biosynthesis across multiple organs.
Derechos de autor 2024 Angus Jacobs, Victoria J M Reid, Mark G Macaskill, Kalyani Pandya, Carolos Alcaide-Coral, Timaeus E F Morgan, Andrew Sutherland, Adriana A S Tavares https://creativecommons.org/licenses/by/4.0
Validation of network-based correlation analysis with [11C]glyburide whole-body PET
Network analysis is a form of correlation analysis that is particularly useful in performing whole-body studies with positron emission tomography (PET), as it provides a way to quantify the similarities and differences in the data from a range of organs and patients. Network analysis was performed on dynamic whole-body PET with [11C]glyburide in humans, with and without infusion of rifampicin [1]. Glyburide is a substrate of several Organic Anion-Transporting Polypeptides (OATPs), which are proteins that control the uptake of drugs into the liver, although OATP expression in other organs has been reported. Rifampicin is a strong OATP inhibitor that has been shown to decrease the body’s ability to eliminate glyburide [1]. Time-activity curves (TACs) in kBq/mL from several organs of interest were used to perform network analysis with the Pearson correlation coefficient employed as the linear correlation measure. In the liver, network analysis was able to differentiate the control and rifampicin-treated groups (Fig. 1A). In this network, each node represents an average TAC from the liver of one patient (Fig. 1C), and the edges between nodes represent significant correlations. The edges are colour-coded to the strength of the Pearson value, showing stronger, more dense correlations within the control group. Additionally, network analysis was performed on static PET data (Fig. 1B), where each node represents a patient curve comprised of the average activity in each organ at equilibrium (Fig. 1D). With the static data, network analysis was able to separate the control and treatment groups into two components. This is highly dependent on the liver though and removing it from the curves leads to reduced differentiation. Overall, network analysis is sensitive to the differing uptake of [11C]glyburide throughout the body under normal and rifampicin-treated conditions. It provides confirmation that the control and treatment groups differ largely through the uptake in the liver, which is the predominant site of OATP function. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Abigail F. Hellman, Paul S. Clegg, Solène Marie, Nicolas Tournier, Adriana A. S. Tavares https://creativecommons.org/licenses/by/4.0
Comparing total-body metabolic PET imaging signatures of lung cancer cachexia to other wasting conditions
Cancer cachexia (CC) is a debilitating wasting condition. While anorexia and muscle wasting are features of cachexia, CC is a distinct and poorly understood metabolic syndrome1. We harnessed preclinical models of lung cancer to study how glucose uptake changes during CC and performed comparative analyses with anorexia and muscle wasting. Lung K-rasG12D/+;Lkb1-/-(KL) mice suffering CC were imaged using [18F]fluorodeoxyglucose (FDG) PET at 15-19% weight loss alongside K-ras wild-type (WT) non-tumour bearing controls. Mice were injected with 12-18MBq of FDG, imaging 80-100 minutes post injection with the Mediso nanoScan® PET/MRI 1T. In non-tumour bearing male animals, we modelled anorexia, muscle wasting, and circulation of cachexia factor GDF15 as follows, respectively: fasted overnight for 20 hr, treated with dexamethasone 21-phosphate (dexa) (2mg/kg, i.p. daily, 21 days) and single injection of recombinant human GDF15 hormone (0.1 mg/kg s.c.). FDG ex vivo biodistribution showed increased uptake in myocardium (p<0.05), brain and liver (both p<0.01) of KL cachexic animals with no significant change in FDG blood pool. Like cachexic KL animals, FDG uptake in fasted animals increased in brain (p<0.0001), perhaps due to increased FDG blood pool. FDG uptake in skeletal muscles and brown adipose tissue decreased (both p<0.05) in fasted state suggesting lower muscle activity and decreased thermogenesis respectively. Dexa-induced muscle atrophy also resulted in decreased FDG uptake in gastrocnemius/soleus muscles (SUVmean 0.63 vehicle vs. 0.45 dexa, p<0.05) but no changes in skeletal muscle FDG uptake were noted in cachexic animals. In contrast to KL animals, GDF15 injection resulted in decreased FDG uptake in myocardium (p<0.05). Overall, the cachexic FDG signature showed differences to other wasting conditions in this mouse model, suggesting different underlying mechanisms. Total-body PET can reveal diverse metabolic wasting states. We aim to elucidate mechanisms of metabolic changes in cachexia to allow metabolic subtyping and personalised treatment options. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Emma Brown, Lisa Duff, Federico Bernuzzi, Robert Bielik, Abdullah Alyamani, Chrysoula Vraka, Nesibe Peker, Fraser Edgar, Dmitry Soloviev, Johan Vande Voorde, David Lewis https://creativecommons.org/licenses/by/4.0
Using total-body PET to investigate relationships between excretory organs time activity curves and the metabolite fraction for [18F]SynVesT-1
Accurate quantification of dynamic PET data using kinetic modelling requires assessment of the metabolism of the tracer to obtain the quantity of unchanged parent tracer in the blood. Several image derived input functions methods have been demonstrated to be effective for kinetic modelling; however, currently no image derived metabolite correction method exists. Finding an image derived metabolite correction would remove the need for invasive blood sampling. This study uses 7 full body 18F-SynVesT-1 total body mouse scans. Initially, the time activity curves (TACs) for a range of organs including the liver, kidney and bladder were studied. Factor analysis (FA) was investigated to accurately separate contributions from the kidney – cortex, medulla and collecting duct. Following this, the relationships between each of the organ TACs and the parent plasma fraction (PPf) was investigated. FA analysis successfully discriminates between contributions from the cortex, medulla and collecting duct of the kidneys. In the context of total body scans, FA removes the need for manual drawing of volumes of interest allowing for more consistency amongst studies. By separating out these regions, a relationship between the kidney medulla and the metabolites also came to light. In addition, the average of the seven bladder time activity curves against the plasma parent fraction [Figure 1] shows a potential relationship between the excretion of the tracer and its metabolism. Combining excretory organ relationships with the parent fraction and potentially liver data opens the possibility for a model to be produced to fit a metabolite curve when we have access to a total body PET scan. While this research is in its early stages, it has shown relationships between excretory organ TACs and the metabolite fraction. This has the potential to allow for metabolite correction models to be created directly from the image data. Model architectures are under investigation. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Bernadette Andrews, Paul S. Clegg, Adriana A. S. Tavares, Catriona Wimberley https://creativecommons.org/licenses/by/4.0
Valvular and Myocardial Fibroblast Activation in Aortic Stenosis
Activated fibroblasts drive leaflet thickening and left ventricular decompensation in aortic stenosis. Gallium-68 Fibroblast Activation Protein Inhibitor (68Ga-FAPI) binds to these key effector cells, and provides a readout of fibroblast activation. We aimed to describe the role of activated fibroblasts in patients with aortic stenosis in vivo using 68Ga-FAPI. In a prospective observational study, patients with aortic stenosis and control subjects underwent echocardiography, 68Ga-FAPI PET, CT, and MRI. Valvular and myocardial 68Ga-FAPI uptake was quantified using maximal standardised uptake values (SUVmax), and target-to-background ratio (TBRmax). Aortic stenosis severity was measured by peak velocity on echocardiography and the CT calcium score. Myocardial fibrosis was quantified by late gadolinium enhancement (LGE) on MRI. 86 patients with aortic valve disease (72±11 years, 68% male) plus 9 matched control subjects (72±9 years, 67%% male) participated. Increased 68Ga-FAPI uptake was observed in the aortic valves of patients with aortic stenosis compared with controls (p<0.001). 68Ga-FAPI TBRmax correlated with peak velocity (r=0.532, p<0.0010) and calcium score (r = 0.577, p<0.001). 54 patients (69%) had myocardial 68Ga-FAPI uptake, of whom 30 (38%) also had LGE corresponding to the region of 68Ga-FAPI uptake. Myocardial 68Ga-FAPI uptake correlated with increased indexed left ventricular mass (r=0.429, p<0.001) and indexed Extracellular Volume (r=0.404, p<0.001). For the first time, we have described valvular and myocardial fibroblast activation in patients with aortic stenosis in vivo. Valvular fibroblast activation is increased in patients with aortic stenosis, correlating with increased disease severity. Myocardial fibroblast activation is seen in the majority of patients with aortic stenosis in areas with and without established fibrosis, and is associated with adverse left ventricular remodelling. 68Ga-FAPI PET can visualise the key effector cell driving aortic valve disease, and may have a role in monitoring disease modifying treatments, as well as identifying patients at high risk of myocardial decompensation. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Neil Craig, Michael McDermott, Jolien Geers, Krithika Loganath, Menaka Mahendran, Laura Clark, Audrey White, Beth Whittington, Anna Barton, Craig Balmforth, Joel Lenell, Michelle Williams, Edwin van Beek, Damini Dey, Piotr Slomka, David Newby, Marc Dweck https://creativecommons.org/licenses/by/4.0
Automated segmentation pipeline for segmentation and characterising metabolic dysregulation in cancer cachexia
Cancer cachexia disrupts metabolic homeostasis across various organs. Positron Emission Tomography (PET) is valuable for investigating cachexia in preclinical models and patients[1]. However, manually segmenting multiple organs is laborious and hinders quantitative analysis of whole-body PET. This study establishes an automated pipeline for segmenting and characterizing organs in preclinical PET/MRI. Acute metabolic changes were induced in non-tumour bearing mice through three approaches: a single dose of Growth Differentiation Factor-15 (GDF-15; n=3 treated, control), daily dexamethasone for 24 days (n=4 treated, control), and 20 hour fasting (n=2 fasted, n=2 fed). [18F]flurodeoxy-glucose PET was used to image whole-body glucose utilisation. To overcome manual segmentation limitations, an automated multi-organ segmentation method was developed for preclinical MRI using nnU-net, an artificial intelligence architecture[2,3]. The nnU-net was trained on 18 preclinical MRI scans with 21 manual tissue annotations. Evaluation on 8 additional mice revealed high accuracy (Dice Coefficients: 0.72-0.91 for 17 tissues). Adipose tissues had lower accuracy but retained good specificity. Due to low accuracy, pancreas segmentation was excluded. All mice used for training and testing the nnU-net were imaged with the same protocol, sourced from the described datasets, with additional scans from different time points. After segmenting the scans, we measured mean Standardized Uptake Values (quantitative measure of metabolic activity) for each tissue. Significant differences (p<0.05) were observed following the various interventions (GDF-15, dexamethasone, fasting) compared to control. Network analysis provided insights into potential inter-organ interactions, while Linear Discriminant Analysis successfully distinguished between each metabolic state. This study presents a novel method for automated segmentation and analysis within preclinical PET/MRI. This method significantly improves efficiency and provides new analysis of total-body metabolic phenotypes. We are currently applying this approach to explore metabolic changes associated with cancer cachexia, aiming to improve our understanding of systemic metabolic dysregulation in both preclinical models and clinical settings. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Lisa Duff, Emma Brown, Fraser Edgar, Manuel Pires, Lalith Kumar Shiyam Sundar , Dmitry Solovyev , David Lewis https://creativecommons.org/licenses/by/4.0
The utility of PET-CT in baseline and sequential characterisation of Pulmonary Pleomorphic Carcinoma
Pulmonary Pleomorphic Carcinomas (PPCs) represent a rare and aggressive subtype of Non-Small Cell Lung Cancer (NSCLC) that can only be definitively diagnosed on a surgical specimen. This study utilised PET-CT to evaluate radiological characteristics of PPCs. This study retrospectively evaluated the radiological characteristics of PCCs diagnosed in St James’s Hospital Dublin between 2012-2023. Computed Tomography (CT) and Positron Emission Tomography (FDG-PET) imaging features (size, location, density, shape, invasion, and growth kinetics) and standard uptake value for each lesion were evaluated. 39 PCCs were identified with a mean age of 66.5 years (range: 49-82 years). FDG-PET was performed in all 39 cases. Tumours demonstrated a high FDG uptake at baseline with a mean (SUV) of 12.6 (range: 1.4 - 36.9). A second interval PET-CT on average 3.3 months after the first in 3 cases demonstrated over 120% increase in SUV. The mean tumour size was 4.3 cm (range: 1.0 - 14.5 cm). Tumours developed rapid interval growth, reaching a mean maximum diameter of 6.6 cm (53.4%) within a mean of 2.1 months. Tumours were predominantly located in the upper lobe (71.8 %) and displayed necrotic features in 53.8 % of cases. 82.1% of tumours invaded the mediastinum. This study describes the largest cohort of Pulmonary Pleomorphic Carcinoma in the literature. Tumours demonstrate a high SUV on baseline imaging and demonstrate rapid growth on interval imaging and central necrosis. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Dr. Darragh Garrahy, Teh Rui Qian, Dr. Danielle Byrne, Professor Peter Beddy https://creativecommons.org/licenses/by/4.0
Tracing proliferating potential of lung tumours by [11C]Nicotinamide
Many tumours rely on aerobic glycolysis, known as the Warburg effect, to satisfy energy needs and sustain rapid proliferation [1]. High demands of glucose in tumours can be measured by [18F[fluorodeoxyglucose, [18F]FDG, positron emission tomography (PET). Nicotinamide adenine dinucleotide (NAD) is a coenzyme involved in redox reactions in many metabolic pathways, including glycolysis. To maintain the high rates of glycolysis and proliferation, tumours need increased NAD+ levels and use nicotinamide for the upregulated NAD+ salvage pathway that is the main source of NAD+ in proliferative cancer cells [2]. Herein, we show an improved synthesis method of [11C]Nicotinamide, [11C]NAM, and studied the uptake of [11C]NAM and [18F]FDG to measure proliferating capacity in a lung cancer model. [Carboxyl-11C]nicotinamide was synthesized by a one-pot radiolabeling method [3] on the SYNTHRA synthesizer (Synthra GmbH, Germany) (see Figure 1A). We used a KrasG12D/+ genetically engineered mouse model of lung cancer and evaluated the uptake of [11C]nicotinamide and [18F]FDG with dual-tracer sequential imaging protocol (see Figure 1B) using a NanoScan PET/MRI scanner (Mediso Medical Imaging Systems, Hungary). We obtained [carboxyl-11C]nicotinamide in a radiochemical yield of 15 ± 5 %, volumic activity of 50 ± 10 MBq/ml, and radiochemical purity was > 99 %. We optimized the imaging protocol and found the uptake of [11C]nicotinamide co-localized with the uptake of [18F]FDG in the KrasG12D/+ model of lung cancer as shown in Figure 1C suggesting the higher NAM uptake was associated with high NAD+ production in highly proliferating tumours. We demonstrated that metabolic PET/MRI imaging with [11C]NAM can complement [18F]FDG PET and be useful for probing proliferative potential in lung cancer. This method could be translated and applied in clinical practice to enable visualization of NAD+ metabolism in lung tumours and targeted therapeutically. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Robert Bielik, Gavin Brown, Dmitry Solovyev, David Lewis https://creativecommons.org/licenses/by/4.0
A Systems-Level Analysis of 2-[18F]FDG PET in Pigs Reveals Complex Skeletal Metabolism Networks in vivo which are altered with Osteomyelitis
Our understanding of complex tissue interactions at a systems level remains rudimentary, limiting our ability to dissect mechanisms underlying diseases and develop novel therapeutics. Skeletal research has focused on the pathogenicity of obesity and diabetes mellitus on the skeleton; however, it has been recently revealed that the skeleton is not merely an endocrine target but also a secretory organ, modulating systemic energy homeostasis1,2. We recently discovered that different bones within the murine skeleton have a unique glucose metabolism and form a complex metabolic network3. Thus, we hypothesised that different bones within the porcine skeleton would have unique molecular signatures and form a distinct metabolic network, and these networks would be altered by osteomyelitis, an inflammatory condition of the bone. Five healthy mini and three Danish Landrace-Yorkshire pigs (inoculated with S. aureus to induce osteomyelitis) underwent static 2-[18F]FDG PET/CT and were analysed to measure bone and bone marrow adipose tissue (BMAT) glucose metabolism. Analyze was used for image analysis and processing. Bone and bone marrow volumes of interest were segmented in CT images using Hounsfield Unit (HU) > 300 (bone) and HU -200 to 115 (BMAT; Fig1). PET images were used to extract average standard uptake values. Network analysis was performed using Graphia. Osteomyelitis pigs had a lower bone density and increased BMAT volume compared to healthy controls. There was an increased incorporation of 2-[18F]FDG into the bone and BMAT of osteomyelitis pigs. Similar to what has previously been observed in the mouse3 and human4, there was an increased incorporation of 2-[18F]FDG in the axial skeleton compared to the appendicular skeleton. Network analysis indicated a shift in bone metabolic profiles between healthy and osteomyelitis pigs. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Karla Suchacki, Chloe Brown, Adriana Tavares, Ruth Morgan, Aage Kristian Olsen Alstrup, Svend Borup Jensen, Lars Jødal, Pia Afzeliuss https://creativecommons.org/licenses/by/4.0
Imaging Mutant Huntingtin Aggregates using [18F]CHDI-650 PET in a preclinical model of Huntington’s disease
Huntington’s disease (HD) is a fatal neurodegenerative disorder caused by a CAG repeat expansion in the huntingtin gene (HTT) that encodes pathogenic mutant huntingtin (mHTT) protein. Therapeutic strategies aimed at lowering mHTT would benefit from non-invasive PET-based methods to evaluate treatment response. The CHDI foundation developed the first-generation PET ligands labelled with 11C, which advanced to clinical evaluation. Further optimization generated an 18F radioligand with improved metabolic stability, brain exposure and specificity, shown by PET imaging in wild-type mice and autoradiography in HD brain samples1,2. Here, we characterised the PET radioligand [18F]CHDI-650 in the R6/1 mouse model of HD to assess its sensitivity for monitoring treatment response of mHTT lowering therapies. [18F]CHDI-650 was produced according to an established method3 using a TRACERlab system. Radiochemical purity/molar activity of the final [18F]CHDI-650 product was detected by reverse-phase HPLC analysis. Dynamic PET imaging was conducted using R6/1 mice and wild-type littermates at various disease stages. Image processing and kinetic modelling (2TCM) analysis were performed using PMOD software. Ex vivo brain radioactivity was measured using gamma counting. Brains were cryo-sectioned and processed for immunofluorescence to identify HTT load. [18F]CHDI-650 was produced (1.3 ± 0.2 GBq) with an average radiochemical purity of 99.9 ± 0.1 % and a molar activity of 32.4 ± 5.2 GBq/µmol (n = 9). The radioligand was able to discriminate R6/1 HET from WT mice at an early presymptomatic age for both dynamic PET imaging (striatal) and ex vivo whole brain gamma. A well-defined age associated increase was also evident using both measures. Dual immunofluorescence spatially defined the level of aggregated HTT in striatal and hippocampal regions across the age groups. [18F]CHDI-650 PET shows great potential as a non-invasive tool for visualising the development of mHTT pathology and as a biomarker for preclinical efficacy studies. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Michael Fairclough, Dhifaf Jasim, Tracy Hall, Rosemary Shoop, Herve Barjat, Tiffany Allen, Kerry Shea, Gayle Marshall, Juliana Maynard, Longbin Liu, Paul Sharp https://creativecommons.org/licenses/by/4.0
An instantly applicable machine learning model for extraprostatic extension prediction in prostate cancer: A post-hoc analysis of the RAPID 68Ga-PSMA PET/MRI trial
Radical prostatectomy (RP) is a common first-line treatment for patients with localized prostate cancer (PCa), with nerve-sparing techniques recommended to minimize adverse effects on quality of life. Accurate pre-operative detection of extraprostatic extension (EPE) remains difficult, often resulting in suboptimal treatment choices. Machine learning (ML) has the potential to improve pre-operative EPE detection through the integration of multimodal data, but its clinical adoption is hindered by implementation complexities and explainability issues. This study conducted a post-hoc analysis of a prospective clinical trial (NCT02659527) involving patients who underwent [68Ga]Ga-PSMA-11 PET/MRI from 2014 to 2019. Only the clinically used parameters were collected, including PET-derived features, blood-based markers, histology-derived parameters, and patient demographics. ML models were developed using either only non-invasive features or a combination of non-invasive and invasive features for EPE detection and were compared to conventional PET image readings. Among the 77 patients included in the study, 44 (57%) had EPE based on post-surgical findings. The ML models outperformed traditional PET image readings in diagnostic accuracy, with the explainable boosting machine (EBM) model achieving an AUC of 0.88. The ML model incorporating invasive features showed superior predictive ability for EPE compared to visual clinical assessments (AUC 0.88 vs. 0.71, p 0.02), while the difference between the ML model with non-invasive features and clinical assessments was not significant (AUC 0.83 vs. 0.71, p 0.34). Explainable ML models utilizing routinely acquired clinical data can greatly enhance the pre-operative detection of EPE in PCa patients, potentially leading to more accurate clinical staging, better treatment decisions, and improved patient outcomes. Please click on the 'PDF' for the full abstract!
Derechos de autor 2024 Jing Ning, Clemens Spielvogel, Kilian Kluge, David Haberl, Gabriel Wasinger, Laszlo Papp, Bernhard Grubmüller, Shahrokh Shariat, Lukas Kenner, Marcus Hacker, Alexander Haug, Sazan Rasul https://creativecommons.org/licenses/by/4.0