European Journal of Nuclear Medicine and Molecular Imaging

Background: Prostate-specific membrane antigen (PSMA) PET/CT is central to prostate cancer staging and theranostic workflows. To our knowledge, no direct within-patient comparison of [18F]FC303 ([18F]Florastamin) and [68Ga]Ga-PSMA-11 has been reported. We performed a preliminary paired method-comparison study under non-harmonized acquisition protocols. Patients and Methods: Twenty patients with histologically confirmed prostate cancer underwent [68Ga]Ga-PSMA-11 PET/CT (185 +/- 37 MBq, 60 +/- 10 min) followed by [18F]FC303 PET/CT (370 +/- 37 MBq, 105 +/- 15 min) on the same PET/CT system within each patient (median interval, 29.5 days). Index targets were anatomically matched to the biopsied or surgically sampled lesion or target region. The primary malignant set included 18 histologically malignant targets; two histology-negative or indeterminate targets were included only in sensitivity analysis. Fixed [68Ga]Ga-PSMA-11-first scan order and the 45-min uptake-time difference were central interpretive constraints. Results: Across five predefined reference organs, [18F]FC303 showed lower SUVmean than [68Ga]Ga-PSMA-11 (all Benjamini-Hochberg-adjusted p < 0.001; [68Ga]/[18F]FC303 geometric mean ratio [GMR], 1.29-3.89). In the primary malignant set, [18F]FC303 lesion SUVmax was lower than [68Ga]Ga-PSMA-11 (median, 11.3 vs 18.1; paired median difference, -5.50; 95% CI, -6.85 to -2.90; Wilcoxon p = 8.4 x 10-4), with strong rank correlation (Spearman {rho} = 0.90). Passing-Bablok regression yielded {beta} = 1.13 (95% CI, 1.04-1.45), and log-Bland-Altman GMR (FC303/[68Ga]) was 0.75, consistent with proportional non-interchangeability. Tumor-to-liver and tumor-to-mediastinum ratios did not differ significantly (GMR, 1.17 [95% CI, 0.94-1.45] and 0.96 [0.80-1.15], respectively); the study was not powered for equivalence. The n = 20 sensitivity analysis showed consistent directionality. Conclusions: Under non-harmonized acquisition conditions, [18F]FC303 showed lower physiologic reference-organ SUVmean and malignant target-region SUVmax than [68Ga]Ga-PSMA-11, whereas tumor-to-liver and tumor-to-mediastinum ratios were not significantly different. Absolute SUVs were not interchangeable; [68Ga]Ga-PSMA-11-derived SUV thresholds should not be directly transferred to [18F]FC303 without tracer-specific calibration.

The synaptic vesicle protein SV2C, predominantly found in the basal ganglia, has been associated with Parkinsons disease through genetic studies. It plays a crucial role in regulating dopamine release and has been shown to be disrupted in PD animal models and brain tissues from PD patients. In the context of PD-related synaptopathy, SV2C may serve as a potential imaging target for monitoring disease progression and response to treatment. [18F]UCB-F is a radioligand binding to SV2C developed by UCB. Preliminary autoradiography and PET studies in rats showed that [18F]UCB-F displays a brain distribution consistent with the expression of SV2C in vitro but does not display any specific binding in vivo. This study was therefore designed to further investigate the affinity and selectivity of [18F]UCB-F for SV2C and to examine the in vitro and in vivo properties of the radioligand in non-human primates. In vitro binding studies were performed to measure the affinity of UCB-F to SV2A, SV2B, and SV2C. Insilico modeling was used to assess the binding mode and energy of UCB-F. Autoradiography studies on rat and non-human primate (NHP) brain tissues were performed to confirm that [18F]UCB-F showed similar distribution in rat and NHP tissue. Finally, PET studied in NHPs were performed to examine the in vivo pharmacokinetic properties of [18F]UCB-F. [18F]UCB-F was successfully synthesized from the corresponding precursor with high yield. Autoradiography on brain slices from rats and NHPs demonstrated specific binding of [18F]UCB-F in the pallidum, striatum, substantia nigra, and brainstem, consistent with the known brain expression of SV2C. In NHPs, [18F]UCB-F rapidly crossed the blood-brain barrier, reaching peak uptake values of 2.8 %ID in NHP1 and 2.1 %ID in NHP2 at 4 minutes post-injection. The tracer wasrapidly washed out from the brain, with no clear regional distribution. Radiometabolite analysis revealed the formation of only more polar radiometabolites, with approximately 15% of unchanged radioligand remaining in plasma at 15 minutes post-injection. In vitro and in-silico studies demonstrated that the affinity of [18F]UCB-F decreased by approximately one factor of magnitude with increase of temperature from 4{degrees} to 37{degrees} C. This temperature-related decrease of the affinity for SV2C together with rapid in vivo radiometabolism might explain the discrepancy between in vitro and in vivo performance of [18F]UCB-F. Overall, these findings suggest that [18F]UCB-F is not a suitable PET radioligand for imaging SV2C. Further research is needed to identify alternative candidates with improved in vivo stability and brain retention.

Background: Meningiomas are the most common primary intracranial tumors in adults, and volumetric assessment increasingly guides surveillance and treatment decisions. Automated segmentation could enable standardized volumetry but requires robust validation. Purpose: To develop a fully automated three-dimensional deep learning model for meningioma segmentation on multiparametric MRI, and to evaluate segmentation accuracy, external generalizability, failure modes, radiologist-rated clinical plausibility, and workflow feasibility. Methods: From 2024 to 2026, this retrospective study trained a custom 3D nnU-Net residual encoder model. Expert segmentations covered enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Dice similarity coefficient (DSC) was the primary metric. External validation used an independent single-institution dataset (n = 310 intracranial cases) with incomplete MRI protocols. Failure modes, model equity, and inference time were assessed. A blinded multi-rater study (10 radiologists; 510 cases) rated TC segmentations using a 0-10 Likert scale, analyzed with linear mixed-effects models. Results: Model training used the BraTS Meningioma 2023 dataset (n = 1000; mean age 60.2 {+/-} 14.5; 705 female). In cross-validation, mean DSC was 0.939 for ET, 0.937 for TC, and 0.921 for WT. In external validation, mean DSC was 0.872 for TC and 0.842 for WT, despite heterogeneous protocols and incomplete sequences. Predicted TC volumes correlated strongly with reference volumes in cross-validation (r = 0.995) and external validation (r = 0.971). Most common failure modes were skull base and intraosseous tumors with performance equitable across demographic subgroups. Mean inference time was 1.2 seconds. In blinded evaluation (1120 ratings), model segmentations received higher scores than reference annotations (+0.32 BraTS; +1.38 external validation). Conclusion: A fully automated deep-learning model achieved high meningioma segmentation accuracy across multi-institutional training data and external clinical imaging. In a blinded study, model segmentation quality exceeded reference annotations, and 1.2-second inference supported workflow integration. Prospective evaluation is warranted before routine deployment.

BackgroundRealistic PET/CT phantoms are essential for system evaluation, protocol optimization, and validation of advanced reconstruction methods. However, existing phantoms are often limited by simplified geometries, spatially uniform activity patterns, and complex preparation procedures. PurposeTo develop and evaluate PixelPrintPET, a 3D printing-based method for fabricating anatomically realistic PET/CT phantoms with spatially heterogeneous radiotracer distributions and a single-solution filling workflow that avoids physical compartmentalization. MethodsPixelPrintPET generates voxel-based printing instructions that encode spatially varying infill, which is realized during printing through modulation of filament extrusion, enabling heterogeneous activity distributions without compartmentalization of radioactivity at different activity concentrations. Calibration phantoms and anatomically structured phantoms were designed and printed using high-flow polylactic acid (PLA), with anatomical inputs derived from either digital atlas-based models or patient imaging data. The printed phantoms were subsequently filled by immersion in a radioactive solution, allowing activity distribution to be controlled by the internal porous structure. A bottom-up filling procedure with reduced surface tension was developed to ensure uniform infiltration and minimize air entrapment. Phantoms were imaged on the PennPET Explorer PET/CT system, and quantitative performance was evaluated using contrast recovery coefficient (CRC), target-to-background ratio (TBR), and comparisons with simulated or patient-derived reference data. ResultsA strong linear relationship between infill ratio and normalized signal (R2 = 0.998) was demonstrated by the calibration phantom, enabling reliable mapping between structure and activity. Additionally, air entrapment was minimized to less than 1% of the total phantom volume. In the contrast recovery phantom, CRC values were consistent with measurements using traditional phantoms. The brain phantom reproduced atlas-derived contrast patterns, with gray-to-white matter differences within 5% after accounting for resolution and other system effects. The patient-based thorax phantom showed high reproducibility across repeated scans, with differences within 3%, and closely matched the input patient image with regional differences within 10% in all regions except the lung. ConclusionsPixelPrintPET enables the fabrication of realistic, reproducible, and versatile PET/CT phantoms with a voxel-level control of the activity distribution. This approach provides a practical solution for generating patient-specific and application-specific phantoms, with the potential to accelerate system validation, protocol development, and clinical translation of advanced PET/CT technologies.

Background and Purpose2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (static PET) has mixed specificity and sensitivity in targeting epileptic zones in the noninvasive stage of epilepsy surgery evaluations. We compared the signal quality of static PET compared to a method of interictal dynamic PET (iD-PET). Materials and MethodsWe calculated the signal quality of static PET and iD-PET obtained from a cohort of patients with focal epilepsy. We developed a Bayesian regional estimated signal quality (BRESQ) technique to objectively compare signal-to-noise ratios (SNRs) by region of interest (ROI) within subjects. ResultsAdjusted for ROI size and neighboring regions, iDPET was superior to sPET with probability >95% in 8/36 regions; >90% in 21/36 regions; >80% in 29/36 regions. The top five regions with the largest adjusted SNR differences (greatest magnitude of iDPET superiority) were the Temporal Mesial (Left and Right), Occipital Lateral (Left and Right), and the Left Frontal Inferior Base. ConclusionsWe found that iDPET yielded a superior SNR in most ROI. BRESQ offers a scalable and generalizable method to quantify signal quality between brain mapping modalities.

BackgroundQuantitative cerebral blood flow (CBF) measured with [15O]water positron emission tomography (PET) is the reference standard for quantifying brain perfusion. However, clinical interpretation of individual CBF measurements is limited by the absence of large normative datasets accounting for physiological variability across the adult lifespan. Long-axial field-of-view PET enables high-sensitivity quantitative [15O]water perfusion imaging without arterial blood sampling, allowing normative characterization of cerebral perfusion at unprecedented scale. The aim of this study was to establish normative and covariate-adjusted models of cerebral blood flow across the adult lifespan using total-body [15O]water PET. MethodsQuantitative CBF measurements were obtained in 302 neurologically healthy adults (age 21-86 years) using total-body [15O]water PET. Linear mixed-effects models were used to evaluate the effects of age, sex, body mass index (BMI), and blood hemoglobin concentration on CBF and to generate normative prediction models across the adult lifespan. Between-subject and within-subject variability were estimated from repeated scans in a subset of participants (n=51). ResultsMean grey matter CBF was 46.1 mL/(min*dL), with substantial inter-individual variability but high within-subject reproducibility (intraclass correlation coefficients 0.78-0.89). Advancing age was associated with a decline in CBF of approximately 7% per decade (p_FDR < 10-12). Higher BMI was associated with lower CBF (approximately -6% per 10 kg/m2; p_FDR < 0.01). Women exhibited higher CBF than men (approximately 7.5%), but this difference was largely explained by lower blood hemoglobin concentration in women. Covariate-adjusted models were used to generate normative predictions and prediction intervals describing expected CBF across adulthood. ConclusionThis study establishes a normative database of quantitative cerebral blood flow across the adult lifespan using high-sensitivity [15O]water PET. Age, BMI, and hemoglobin are major determinants of inter-individual variability in CBF. The resulting generative models provide a quantitative reference framework for interpreting cerebral perfusion measurements and may enable automated detection of abnormal brain perfusion in clinical PET imaging.

Whole-body SPECT bone scintigraphy reflects skeletal metabolic activity throughout the body and plays an indispensable role in the screening, treatment evaluation, and prognostic assessment of bone metastases in tumors. However, the automatic detection and segmentation of hypermetabolic bone lesions remain challenging due to low contrast, limited spatial resolution, and complex lesion distributions. In this study, we proposed Bone-Segnet, a dual-view guided automatic segmentation network for hypermetabolic bone lesions that integrated multi-scale feature modeling, global context modeling, and view-conditioned modulation. Pixel-level annotated anterior and posterior whole-body bone scintigraphy images were used for model training and prediction. The proposed network enhanced the recognition of low-contrast and small-scale lesions through small-lesion enhancement and multi-scale contextual modeling. A Transformer module was further introduced to strengthen global feature representation, while cross-view collaborative modeling was achieved by incorporating the complementary characteristics of anterior and posterior imaging. Experimental results demonstrated that the proposed method outperformed existing approaches across multiple evaluation metrics, with the Dice score improving from 0.7440 to 0.8750, indicating a substantial improvement in segmentation performance. Further quantitative analysis based on the segmentation results revealed significant differences among disease types in lesion count, pixel burden, and spatial distribution patterns, reflecting the heterogeneity of disease-related skeletal metabolic activity. Overall, the proposed method improved automatic lesion segmentation performance and enabled quantitative analysis of lesion burden and spatial distribution patterns, providing objective data support for the assessment of related diseases. Index Terms--Whole-body SPECT, bone lesion segmentation, dual-view modeling, quantitative analysis.

It has been hypothesized that effective cellular internalization is required for the retention of 225Ac daughter radionuclides. The complex decay chain of 225Ac and recoil-mediated release of daughters, particularly 213Bi (half-life (t1/2) = 46 min), raise concerns about redistribution that may reduce tumor absorbed dose (TAD) and increase off-target radiation exposure. Because somatostatin receptor subtype 2 (SSTR2) antagonists such as SSO110 are not internalized, it has been proposed that the daughter radionuclides are less effectively retained compared to internalizing agonists such as DOTA-TATE. We therefore performed a direct and quantitative comparison of daughter radionuclide redistribution following administration of [225Ac]Ac-SSO110 and [225Ac]Ac-DOTA-TATE. MethodsBiodistribution and 213Bi redistribution were evaluated in Balb/c nude mice bearing NCI-H69 small cell lung cancer xenografts. Repeated gamma counting combined with bi-exponential modeling was used to quantify 225Ac and 213Bi activity in tumor, blood, bone marrow, kidneys, liver, and intestines up to 96 h post-injection. TAD was calculated with and without accounting for experimentally-derived 213Bi redistribution. Real-time in vitro binding assays were conducted to characterize cellular retention of [225Ac]Ac-SSO110. Results[225Ac]Ac-SSO110 demonstrated higher tumor uptake and prolonged retention compared with [225Ac]Ac-DOTA-TATE, resulting in a 1.9-fold higher tumor-to-kidney ratio at 96 h and a 2.8-fold higher TAD. Redistribution of 213Bi from tumor was minimal and comparable between agonist and antagonist, with maximum tumor loss of 3.5% for [225Ac]Ac-SSO110 and 2% for [225Ac]Ac-DOTA-TATE. Accounting for daughter redistribution reduced TAD by less than 5% for both radioconjugates. No sustained 213Bi accumulation was observed in blood, kidneys, or liver, and only minimal activity was detected in bone marrow and intestines. Real-time binding studies demonstrated sustained cell-associated {beta}- signal following incubation with [225Ac]Ac-SSO110. ConclusionReceptor-mediated internalization is not required for effective retention of 225Ac daughter radionuclides. Despite negligible internalization, [225Ac]Ac-SSO110 achieved superior TAD and higher tumor-to-kidney ratio without increased daughter redistribution compared with the internalizing agonist [225Ac]Ac-DOTA-TATE. These findings question the necessity of internalization for daughter retention and support further evaluation of antagonist-based 225Ac radioligand therapy.

Assessment of pathologic complete response (pCR) following neoadjuvant chemotherapy (NAC) remains an unmet clinical need in breast cancer. Fibroblast activation protein inhibitor (FAPI) PET targets the tumor microenvironment and may therefore enhance response evaluation after NAC. This study aimed to compare the performance of [68Ga]Ga-FAPI-04 PET, [18F]FDG PET, and contrast-enhanced MRI for predicting pathologic response after NAC in breast cancer, with separate analyses for primary breast lesions and axillary lymph nodes. MethodsIn this prospective single-center diagnostic accuracy study, women with biopsy-confirmed stage II-III breast cancer underwent baseline and post-therapy [68Ga]Ga-FAPI-04 PET/MRI, [18F]FDG PET/CT, and contrast-enhanced MRI before surgery. Quantitative PET parameters were evaluated for primary tumors and axillary lymph nodes. pCR was defined as ypT0/isN0. Significant variables identified in univariable analyses were further explored using least absolute shrinkage and selection operator (LASSO) analysis, and receiver-operating-characteristic (ROC) analysis was performed to assess diagnostic performance. Fibroblast activation protein expression was also assessed by immunohistochemistry in paired pre- and post-therapy tumor specimens from a subset of patients. ResultsTwenty-four patients completed the study protocol, yielding 25 primary lesions and 44 metastatic lymph nodes across 27 axillary compartments. Overall patient-level pCR was achieved in 13 of 24 patients (54.17%). The lesion-level pCR rate was 60.00% (15/25) for primary breast lesions, and the node-level pCR rate was 72.73% (32/44) for axillary lymph nodes. For primary tumor response, post-therapy [68Ga]Ga-FAPI-04 SUVmax showed the highest diagnostic performance (AUC, 0.84; sensitivity, 80.00%; specificity, 80.00%; accuracy, 80.00%), whereas the optimal [18F]FDG parameter was {Delta} TBR% (AUC, 0.747). For nodal response, post-therapy [68Ga]Ga-FAPI-04 SULmean showed the highest diagnostic performance (AUC, 0.89; sensitivity, 91.67%; specificity, 81.25%; accuracy, 84.09%) and was significantly different from the best [18F]FDG parameter ({Delta} SULmax%, AUC, 0.669) on DeLong testing (P < 0.05). MRI achieved AUCs of 0.733 for primary lesions and 0.770 for lymph nodes. Stromal FAP expression positively correlated with [68Ga]Ga-FAPI-04 SUVmax and was markedly reduced in lesions achieving pCR. ConclusionPost-therapy [68Ga]Ga-FAPI-04 PET may serve as a promising adjunctive imaging biomarker for predicting pathologic response after NAC in breast cancer, particularly for axillary nodal assessment. These findings suggest that FAPI PET may provide clinically relevant information for preoperative evaluation of residual disease burden, potentially contributing to more individualized surgical planning and treatment decision-making.

BackgroundPlasma biomarkers demonstrate strong within-cohort performance for identifying cerebral amyloid pathology, but their real-world clinical utility depends on generalization across populations and assay platforms. The impact of cross-cohort deployment on clinically actionable metrics such as negative predictive value (NPV) remains poorly characterized. ObjectiveTo evaluate the performance and portability of plasma biomarker-based machine learning models for amyloid PET prediction across independent cohorts, with emphasis on calibration and clinically relevant predictive values. MethodsData from ADNI (n=885) and A4 (n=822) were analyzed. Machine learning models were trained within each cohort to predict amyloid PET status and continuous amyloid burden (centiloids). Performance was assessed using ROC AUC, accuracy, R{superscript 2}, and RMSE. Cross-cohort generalizability was evaluated using bidirectional transfer without retraining. Calibration, predictive values, and decision curve analysis were used to assess clinical utility. ResultsWithin-cohort discrimination was high (AUC up to 0.913 in ADNI and 0.870 in A4), with moderate performance for centiloid prediction (R{superscript 2} up to 0.628 and 0.535, respectively). Cross-cohort deployment resulted in modest attenuation of AUC ([~]4-7%) but substantially greater degradation in clinically actionable performance. NPV declined from 0.831 to 0.644 under ADNI[-&gt;]A4 transfer ([~]19 percentage points) despite preserved discrimination. Calibration analyses demonstrated systematic probability misestimation, and decision curve analysis showed reduced net clinical benefit. Biomarker distribution differences across cohorts were consistent with dataset shift. ConclusionPlasma biomarker models retain discrimination across cohorts but exhibit clinically meaningful degradation in predictive value under deployment. Calibration instability and prevalence differences critically affect NPV, highlighting the need for cross-cohort validation, calibration assessment, and assay harmonization before clinical implementation.

Quantitative analysis of positron emission tomography (PET) neuroimaging data is essential for studying neurodegenerative diseases, yet existing processing pipelines often rely on computationally intensive software packages such as FreeSurfer, limiting accessibility for many research groups. Here I introduce BrainPET Studio, an open-source desktop application for atlas-based regional PET quantification that operates entirely in Montreal Neurological Institute (MNI) standard space. BrainPET Studio integrates affine registration, optional Muller-Gartner (MG) partial volume correction (PVC), interactive quality control (QC), and standardized uptake value ratio (SUVR) calculation into a single graphical user interface (GUI), eliminating the requirement for FreeSurfer-based cortical reconstruction. I validated BrainPET Studio against two established pipelines: (1) the UC Berkeley Alzheimers Disease Neuroimaging Initiative (ADNI) AV1451 (flortaucipir) pipeline, which employs FreeSurfer v7.1.1 parcellation, SPM-based coregistration, and Geometric Transfer Matrix (GTM) PVC in native subject space; and (2) the volBrain/petBrain online platform. Region-of-interest (ROI) SUVR values were compared across 322 subjects. Overall Pearson correlation coefficients for meta-ROI composites ranged from r = 0.83-0.96 versus ADNI and r = 0.86-0.94 versus volBrain/petBrain. Detailed per-subject validation on four representative cases across 112 FreeSurfer-defined regions demonstrated strong agreement for large cortical composites and acceptable variability for smaller medial temporal structures. These results establish BrainPET Studio as a reliable, accessible, and extensible tool for multi-site PET research, educational applications, and studies where FreeSurfer-based processing is impractical.

IntroductionMagnetic resonance imaging (MRI) is central to neurological care, yet access remains profoundly inequitable in low- and middle-income countries, especially in rural health facilities where high costs and fragile electricity supply limit services. Ultra-low-field (ULF) portable MRI offers a way to expand access, but deployment in weak-grid settings requires robust affordable power. We characterized the power needs of a 0.064T portable ULF MRI system and assessed the feasibility of a solar-powered MRI-capable facility in a rural Zimbabwean clinic, which we believe to be the first of its kind in the world. MethodsWe measured the power draw of an ultra-low-field MRI session from a portable photovoltaic (PV) battery kit in the UK, quantifying scan, standby and energy use. We then monitored a PV-battery micro-grid supplying a protected circuit at an MRI-capable clinic in Shurugwi, Zimbabwe. Inverter telemetry was used to derive PV generation, load, battery state of charge (SoC) and grid import for working days in October-November 2025, spanning the end of the dry season and onset of the rainy season. ResultsIn the portable configuration, a 64-minute MRI session consumed [~]0.21 kWh, with standby demand of [~]1.44 kWh per 24 hours. In clinic, mean PV generation was 9.10 kWh (SD=1.34) and load 9.91 kWh, with zero recorded grid import and minimum daily SoC typically [&ge;]60%, including during the early rainy season. ConclusionAn affordable PV-battery micro-grid can reliably support ULF MRI and associated research power loads in a rural, weak-grid clinic, offering a reproducible blueprint to narrow diagnostic equity gaps in resource-limited settings.

Background: Amyloid-beta PET provides critical biomarker data for Alzheimer's disease diagnosis and anti-amyloid therapy evaluation, yet low spatial resolution and partial volume effects result in decreased interpretability, particularly in cases with low or borderline cortical amyloid burden. While quantitative metrics (SUVr, Centiloid) aid in interpretation of amyloid burden, disagreement between visual reads and quantitative burden does occur, further blurring the line between positive or negative scans. We evaluated whether a vendor-neutral MR-guided PET denoising and resolution enhancement method (MRG) that uses Bowsher regularization improves image interpretability and reader performance while preserving established quantitative biomarkers across multiple amyloid tracers, leading to increased concordance among visual reads and quantitative metrics. Methods: Standard (STN) and MRG PET images were compared for four tracers ([18F]AV-45 ([18F]florbetapir, FBP), [18F]florbetaben (FBB), [18F]flutemetamol (FMM), and [11C]Pittsburgh compound-B (PiB) collectively from 24 MRI and 33 PET scanners. Quantitative equivalence was assessed by comparing Standardized Uptake Value ratio (SUVr) and Centiloid scores. In three of the four tracers (FBP, FBB, FMM), visual-quantitative concordance (AUC) and reader performance were evaluated in a blinded multi-reader study by four highly experienced brain PET readers who assessed image quality, artifact severity, reader confidence, and binary amyloid positivity. Results: Across all tracers, MRG preserved quantitative SUVr and Centiloid metrics relative to STN (R2 >0.90 for all tracers) without introducing bias to the SUVr metric. Concordance between visual reads and quantitative burden measures significantly improved with MRG. In the multi-reader study, MRG resulted in significantly higher image quality, lower artifact burden, and greater reader confidence compared to STN (p < 0.0001). Reader accuracy increased from 0.89 to 0.94, and the false-negative rate decreased from 0.08 to 0.04. Crucially, improvements in reader confidence, accuracy, and the reduction in false negative reads were most pronounced in cases with low amyloid burden near the threshold of visual positivity. Conclusions: MRG denoising and resolution enhancement improved perceived image quality, reader confidence, and accuracy for amyloid PET while preserving standard quantitative behavior across tracers. By improving cortical definition in visually challenging low-burden cases without disrupting established SUVr/Centiloid behavior, MRG may reduce visual-quantitative discordance and support more confident amyloid PET interpretation near the threshold of positivity.

PURPOSEGlycolytic production of HDO from the metabolism of perdeuterated glucose provides a means for metabolic imaging with 2H MRI. The present study compared HDO production from a cost-efficient [2,3,4,6,6-2H5]glucose with [2H7]glucose in vitro and in vivo. METHODS2H NMR spectroscopy was performed to measure glucose consumption, lactate, and HDO production in the SFxL glioblastoma cell line. In vivo studies in healthy mice using 2H magnetic resonance spectroscopy were performed at 11.1 T after administering a bolus of either metabolic contrast agent. In vivo metabolite levels were quantified using unlocalized and slice-selective localized spectra. RESULTSOur in vitro results demonstrated similar glucose consumption and HDO production kinetics, although significant differences in lactate labeling were observed. The in vivo study showed comparable glucose consumption and HDO production kinetics following tail-vein bolus administration of either metabolic contrast agent, while lactate was not detected in the brain. CONCLUSION[2,3,4,6,6-2H5]glucose shows comparable HDO production to [2H7]glucose, while offering lower cost and reduced spectral complexity. These findings place [2,3,4,6,6-2H5]glucose as an alternative to [2H7]glucose for HDO-based DMI studies.

Colony stimulating factor 1 receptor (CSF1R) is a tyrosine kinase receptor that is expressed exclusively in microglia within the CNS. Its endogenous ligands, colony stimulating factor-1 (CSF1) and interleukin-34 (IL-34), are released from neurons, positioning CSF1R as a key mediator receptor of neuron-glia communication. CSF1R is considered not only a potential drug target, but also a biomarker of neuroinflammation. From that perspective, selective radioligands for neuroimaging are of great interest for imaging neuroinflammation and determining drug occupancy. In this study, we have validated the binding characteristics of a CSF1R inhibitor, 4-((5-MethOxy-6-((5-methoxypyridin-2-yl)methoxy)pyridin-3-yl)methyl)-2-(1-methyl-1H-pyrazol-4-yl)pyrimidine (5-MOP) as a novel CSF1R radioligand, by performing in vitro saturation binding experiments in human and murine tissues. 5-MOP was found to be selective for CSF1R among a broad range of kinases. Autoradiography revealed that [3H]5-MOP binds with high affinity (KD = 9.8 nM) to a single saturable binding site in human meningioma tissues, and this binding was displaced with known CSF1R inhibitors, including CPPC, sCSF1inh and GW-2580. In contrast, CPPC, which has been extensively used as a CSF1R radioligand showed substantial cross-reactivity to other brain kinases, including Trk A/B/C, and [3H]CPPC could only be displaced with CPPC itself, not by other ligands, including 5-MOP. These results identify [3H]5-MOP as the most selective radioligand currently available, enabling accurate detection of drug occupancy and activated microglia. Significance of the studyThis study identifies and validates a novel selective radioligand that binds CSF1R with high selectivity and low nanomolar affinity. Because CSF1R is selectively expressed in activated microglia, this radioligand could be useful for detecting neuroinflammatory activity.

Somatostatin receptor 2 (SSTR2) is highly expressed in neuroendocrine tumors including small cell lung cancer (SCLC) and represents a validated target for peptide receptor radionuclide therapy. The SSTR2 agonist [177Lu]Lu-DOTA-TATE is clinically approved, however, treatment resistance and relapse occur. The SSTR2 antagonist SSO110 (DOTA-JR11, OPS201) demonstrates higher tumor uptake and longer retention than DOTA-TATE both pre-clinically and clinically. We performed a systemic head-to-head comparison of SSO110 labeled with various radionuclides of distinct emission characteristics to identify the optimal radionuclide for SSO110 and to compare antagonist with agonist performance. MethodsSSO110 was radiolabeled with 177Lu, 161Tb, 212Pb, and 225Ac. Biodistribution was assessed in AR42J and NCI-H69 xenograft models. Therapeutic efficacy of single and fractionated [212Pb]Pb-SSO110 was compared with [177Lu]Lu-SSO110 in NCI-H69 tumors. Single-dose efficacy of 225Ac-, 161Tb-, and 177Lu-labeled SSO110 was evaluated in both models. [{superscript 2}{superscript 2}Ac]Ac-DOTA-TATE served as agonist comparator. Tumor growth, survival, safety parameters, and tumor absorbed doses were analyzed. ResultsAll SSO110 radioconjugates demonstrated comparable biodistribution with high tumor uptake and favorable tumor-to-kidney ratios. In NCI-H69 tumors, [212Pb]Pb-SSO110 induced dose-dependent tumor growth delay but did not improve anti-tumor efficacy compared with [177Lu]u-SSO110 under single or fractionated regimens. [161Tb]Tb-SSO110 showed efficacy comparable to [177Lu]Lu-SSO110 in NCI-H69 model and significantly improved tumor growth delay in high-SSTR2-expressing AR42J tumors. Across both models, [225Ac]Ac-SSO110 demonstrated the highest therapeutic potency, inducing durable tumor regression and 100% survival at clinically relevant activities. [225Ac]Ac-SSO110 also outperformed the agonist comparator [225Ac]Ac-DOTA-TATE. Dosimetry analysis revealed a 63-fold higher tumor absorbed dose per injected administered activity for [225Ac]Ac-SSO110 compared with [212Pb]Pb-SSO110. All treatments were well tolerated without significant renal or hepatic toxicity. ConclusionTherapeutic efficacy of SSTR2-targeted peptide receptor radionuclide therapy appears to benefit from alignment between radionuclide physical half-life and ligand tumor residence time. Among the radionuclides evaluated, [225Ac]Ac-SSO110 demonstrated the most pronounced and durable anti-tumor efficacy, outperforming [161Tb]Tb-SSO110, [177Lu]Lu-SSO110, and the short-lived -emitter [212Pb]Pb-SSO110. These findings support clinical investigation of [225Ac]Ac-SSO110 in SSTR2-positive malignancies.

Non-invasive tracking of natural killer (NK) cells remains a major challenge in cancer immunotherapy, limiting our understanding of their in vivo migration and persistence. Magnetic particle imaging (MPI) offers a quantitative, real-time method for visualizing labeled cells, yet optimal labeling protocols for NK cells have not been established. Here, we evaluate commercially available iron oxide nanoparticles (IONPs) for MPI labeling of both NK92MI cells and primary human NK cells. Labeled cells retained viability and cytotoxicity, including activity against three-dimensional tumor spheroids, and were detectable by MPI. To further examine imaging performance in a biologically relevant context, we employed mouse phantoms that recapitulate organ-specific signal distributions, enabling evaluation of quantification and liver spillover effects. We identify key tradeoffs between particle colloidal stability and per-cell iron content: VivoTrax and VivoTrax Plus provided higher MPI signal but required post-labeling purification, reducing cell recovery, whereas Synomag-D and Perimag were more stable and preserved cell yield despite lower signal intensity per cell. These results provide a framework for selecting nanoparticles that balance detection sensitivity, cell viability, and workflow practicality, advancing non-invasive NK cell tracking.

Besides immunotherapy, inhibitors of the DNA damage response (DDR) are currently one of the most promising contributors to improved cancer therapy. They exploit elevated replicative stress in cancer cells and often rely on synthetic lethality with existing gene deficiencies or between targeted pathways. In view of the absence of reliable histological biomarkers for replicative stress, this study examined [18F]-fluorothymidine (FLT) positron emission tomography (PET) as alternative or complementary approach to predict treatment response to DDR inhibitors. Using orthotopic and syngeneic triple negative breast cancer mouse models and treatment with combined AZD6738 and AZD1775 (inhibiting ATR and Wee1, respectively) this study found that: a) Sequential [18F]FLT-PET in the early phase of treatment was able to predict ATR/Wee1 inhibitor treatment efficacy, whereas b) [18F]FLT tumor uptake at onset of therapy was unable to predict treatment outcome, despite c) [18F]FLT tumor uptake positively correlating with Ki-67 staining, the clinically used proliferation marker. Importantly, non-invasive monitoring of changes in tumor biology by [18F]FLT-PET predicted which tumor model responds to combined AZD6738/AZD1775 treatment and established a quantitative correlation in [18F]FLT tumor uptake with tumor shrinkage in individual responders. Since the inhibitors AZD6738 and AZD1775 are already in phase I/II clinical trials, this knowledge could soon be translated into the clinic. To our knowledge this is the first study to correlate non-invasive PET imaging with treatment efficacy of DDR inhibitors. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/710900v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1e18b7eorg.highwire.dtl.DTLVardef@8d306corg.highwire.dtl.DTLVardef@1663a21org.highwire.dtl.DTLVardef@7261c2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Access to positron emission tomography (PET) remains limited in rural and low-resource healthcare settings due to high infrastructure cost and radiotracer logistics. This restricts early oncologic screening in underserved populations. The study proposes a rural-optimized conditional diffusion framework for synthetic PET generation directly from widely available CT scans. The architecture employs a two-stage residual design consisting of a lightweight coarse predictor followed by computationally efficient diffusion refinement with reduced timesteps and deterministic sampling. A multi-objective SUV-aware loss ensures metabolic consistency. To emulate rural deployment conditions, this study simulates low-dose noise, Hounsfield unit miscalibration, and resolution degradation. Clinical validation demonstrates strong structural fidelity (SSIM 0.81) and stable SUVmean preservation. Domain-matched training achieves SUVmax error as low as 0.61. Cross-dataset analysis highlights the importance of SUV harmonization for robust rural deployment. This work presents a resource-sensitive AI frame-work supporting equitable oncology screening in rural healthcare systems. HighlightsO_LITwo-stage residual conditional diffusion for CT-to-PET translation. C_LIO_LISUV-aware multi-objective optimization preserves metabolic biomarkers. C_LIO_LIFew-shot adaptation improves cross-dataset SUV calibration. C_LI

Digital health technologies (DHT) offer a promising solution to the timely identification of early Alzheimer's disease (eAD) to enable early treatment. This study evaluated the feasibility, acceptability, adherence, reliability, and preliminary clinical and content validity of the novel AD Digital Assessment Suite (AD-DAS). 123 individuals (32 healthy controls (HC), 31 amyloid-PET negative (SCDn), 30 amyloid-PET positive (SCDp) with subjective cognitive decline, and 30 early AD (eAD)) participated. AD-DAS was remotely deployed for 28 days. Remote testing was feasible (97.6% completers), acceptable (>85% ''good''), and associated with high adherence (96%). Metrics showed moderate to excellent test-retest reliability (ICC 0.53-0.91), associations with clinical comparators (adjusted R2 0.01-0.24), differentiated eAD from other known groups (absolute log odds differences 0.6-3.28), and correlated with brain atrophy in expected regions. Episodic and working memory AD-DAS metrics differentiated SCDp from SCDn participants. These preliminary findings suggest that AD-DAS may be a promising tool for detecting cognitive impairments in early AD stages.