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.

Abstract (250 words) Background: Glutamine addiction is a hallmark of aggressive tumors, yet glutaminase (GLS1) inhibitor CB-839 showed disappointing anti-tumor efficacy in clinical trials. L-5-[11C]-glutamine ([11C]glutamine) PET enables non-invasive assessment of glutamine metabolism in vivo, providing a tool to test mechanistic hypotheses, and identify tumors likely to respond to GLS1 inhibition: focusing on compartmentation of GLS1-derived glutamate, CB-839 impact on flux, and reciprocal glutamine synthesis. Methods: Glutaminolytic TNBC (HCC1806) and poorly glutaminolytic ER+ (MCF-7) xenograft mice with or without CB-839, underwent dynamic [11C]glutamine PET. HPLC quantified fractional radioactivity of [11C]glutamine, soluble metabolites ([11C]glutamate, [11C]CO2), and macromolecule-incorporated metabolites from blood and tumor. A four-tissue compartment model characterized GLS1 activity (k_GLS) and flux, glutamine synthetase activity (k_GS), and subcellular glutamate distribution by comparing single vs. dual glutamate pool models. Averaged tumor curves and HPLC-derived tumor metabolites were fit. Monte Carlo simulations assessed parameter estimation performance. Results: The single glutamate pool model showed high correlations between k_GLS and other parameters, yielding inflated k_GLS estimates. The dual glutamate pool model reduced correlations, improved k_GLS recovery, and yielded subcellular glutamate distributions consistent with in vitro measurements. In TNBC, k_GLS was 3-fold higher than ER+ tumors (non-overlapping 95% CI) with glutamate concentrated in the mitochondrial compartment. CB-839 reduced k_GLS in TNBC and depleted mitochondrial glutamate (non-overlapping 95% CI), though glutaminolytic flux showed no distinguishable change. ER+ tumors showed higher k_GS compared to TNBC. Conclusion: [11C]glutamine PET kinetic analysis reveals distinct glutamine metabolic phenotypes in breast cancer subtypes. Preserved glutaminolytic flux and cytosolic glutamate in TNBC provide mechanistic hypotheses for clinical failure of GLS1 inhibitors, informing ongoing studies.

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: Mu-opioid receptors (MORs) are expressed throughout the body including in the brain and gastrointestinal (GI) tract. Total-body PET imaging of the brain and GI tract offers a promising approach for cross-sectional in vivo evaluation of the MOR brain-GI axis. However, intestinal motility and bladder filling introduce motion throughout the GI tract over the scan window. Here we establish analysis methodology to account for motion for dynamic imaging of the brain-GI axis, to further characterize peripheral MORs throughout the body and provide a framework for semi-automatic total-body PET modeling. Methods: 4 subjects underwent 90-min dynamic [11C]-carfentanil (cfn) total-body PET acquisitions at baseline, after intravenous naloxone (central antagonist) administration, and after orally administered loperamide (peripheral agonist and P-glycoprotein substrate). Thalamic MOR availability was measured using the Logan reference tissue model. Using CT-based segmentation, the GI tract was subdivided into anatomical segments, in addition to other peripheral organs (e.g., liver, psoas muscle). Frame-by-frame semi-automatic motion correction was performed with three distinct reference frames (11-14 min post-injection, p.i., 35-40 min p.i., and 85-90 min p.i.). The performance of these three were compared to manual correction. Compartment modeling and Logan graphical analysis were performed to estimate relevant kinetic parameters (K1, VT, VTLogan). Results: Across the 4 subjects and regions, kinetic parameter estimates were highly correlated (r>0.7) for K1, VT and VT Logan when comparing semi-automatic (reference frame at 35-40 min p.i.) and manual correction. With semi-automatic motion correction, graphical-based estimation of VTLogan in the gastrointestinal tract was significantly decreased with loperamide relative to baseline (p<0.05). As expected, naloxone decreased brain thalamic MOR availability but loperamide did not. Conclusions: With semi-automatic motion correction and [11C]-cfn total-body PET, pharmacologic perturbations of MOR brain-GI axis can be quantitatively characterized, reducing the burden of image analysis for these studies.

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.

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.

Objective: Deep cervical lymphatic-venous anastomosis (LVA) has shown promise in treating Alzheimer's disease (AD), yet no preoperative tool exists to identify potential responders. We developed and evaluated a novel A {beta} PET based scoring system that quantifies regional amyloid burden according to anatomical proximity to the meningeal lymphatic vessels (MLVs) to predict treatment response. Methods: We retrospectively enrolled 58 AD patients who had undergone upper cervical LVA. Eleven regions of interest (ROIs) adjacent to the superior sagittal and straight sinuses were scored based on anatomical proximity to MLVs (higher = closer) and functional relevance to AD (functional score = 1 for AD-related ROIs), yielding a regional assigned score (RAS). Standardized uptake value ratios (SUVRs) were obtained for each ROI. The total SUVR (Stotal) was calculated as {sum}(SUVR x RAS) over all ROIs, and S4+5 was defined as the same sum restricted to ROIs with RAS 4 or 5. These scores, along with baseline demographic characteristics, were evaluated for their ability to predict treatment response using LASSO-logistic regression and receiver operating characteristic (ROC) curve analysis. Results: Forty-one patients (70.7%) were responders. At baseline, responders had significantly higher SUVR of the associative visual cortex (SAVC) (1.68{+/-}0.26 vs. 1.53{+/-}0.12, P=0.0394) and higher S4+5 (32.69{+/-}4.45 vs. 30.14{+/-}3.07, P=0.0358) than non-responders. In univariate analysis, S4+5 was the only significant predictor (OR=1.183, 95% CI: 1.005-1.391, P=0.0433); SAVC was borderline significant (OR=16.654, 95% CI: 0.999-277.63, P=0.0501), while SUVR of the posterior cingulate cortex (SPCC) and Mini-Mental State Examination (MMSE) showed only weak trends (P=0.0714 and P=0.0889, respectively). In the multivariable model, MMSE was independently associated with treatment response (adjusted OR = 1.43, 95% CI: 1.06-1.93, P = 0.022); with SPCC and SUVR of the superior parietal cortex (SsPL) reaching marginal significance (P=0.055 and P=0.051, respectively). The apparent AUC was 0.920, decreasing to a Bootstrap-corrected AUC of 0.780 (95% CI: 0.708-0.884) after optimism correction (optimism = 0.139). The Brier score was 0.097. The covariates-only model yielded a corrected AUC of only 0.574, confirming the incremental value of PET DOI data. Conclusion: This exploratory study introduces a novel A{beta} PET scoring system grounded in MLV anatomy that, combined with baseline MMSE, demonstrates modest predictive potential for LVA response in AD. The findings warrant validation in larger, multicenter cohorts.

Background: Nuclear medicine and radiopharmaceutical development require coordinated radiochemistry, dosimetry, molecular imaging, radiation-safety and clinical decision processes. Current workflows remain fragmented, difficult to audit and poorly standardised for evaluating domain-specific AI support. Methods: We developed RadGuide AI, a nuclear medicine agent built around a traceable data-model-tool loop. Patent, literature and clinical-trial records were converted into 15,596 initial QA items; relevance screening, completeness checks, semantic deduplication and cross-validation retained 5,474 core QA items. MedGemma-27B-Instruct served as the foundation model and was adapted with LoRA. The system incorporated 55 MCP-wrapped tools covering radiopharmaceutical R&D, clinical decision support, imaging analysis and radiation-safety/dosimetry. Evaluation used a locked N=200 benchmark with predefined denominators, leakage control, expert scoring, statistical procedures, factuality audits and tool-execution metrics. Results: RadGuide-LLM achieved 88.5% answer accuracy (177/200; 95% CI, 83.3-92.2%) and a Macro-Average score of 21.5/25 (bootstrap 95% CI, 20.9-22.0), exceeding GPT-4o, DeepSeek-V3.2 and the base MedGemma model in this technical evaluation. Supplementary audits reported guideline compliance, terminology recall, knowledge coverage, tool-routing success and preclinical/phantom dosimetry agreement with explicit denominators and confidence intervals. Interpretation: RadGuide AI converts nuclear medicine queries into auditable retrieval, tool selection, calculation, verification and reporting workflows. The findings support technical feasibility, not definitive patient-level clinical validation; prospective multicentre studies and external benchmark release remain required before clinical deployment.

Background. Ortho-positronium (o-Ps) lifetime, now measurable in vivo on long-axial-field-of-view (LAFOV) PET/CT, has been proposed as a biomarker of tissue oxygenation and hypoxia. Because o-Ps lifetime is dominated by tissue free-volume structure while the oxygen- specific contribution is small, whether an in-vivo lifetime contrast reflects oxygenation rather than anatomy is an open, identifiability-limited question. Aim. To test the oxygenation hypothesis directly using the heart's natural arterial/venous oxygenation contrast, with a built-in anatomical control. Methods. We re-analysed a public [82Rb]Cl human cardiac LAFOV PET/CT dataset (5.30 x 10^8 evaluated three-photon events). Per-compartment o-Ps lifetimes were extracted with a background-plus-two-component exponentially-modified-Gaussian (EMG) model. The list-mode to image mapping and right/left ventricle (RV/LV) identity were established lifetime-free (the mapping reproduces the provider's reconstructed image at block-correlation 0.998 and wins a joint multi-organ alignment panel). We applied a confound battery: registration stress test, blood-core vs wall, lung-air and wall-myocardium partial-volume, tissue density; and a structure/position-matched control (pulmonary artery, deoxygenated, vs aorta, oxygenated). An isotope-matched 82Rb uniform-quartz reference bounded the instrument's positional behaviour. All results were produced by two independent analysis pipelines. Results. RV o-Ps lifetime exceeded LV by delta tau = +0.304 ns (RV 1.700 +/- 0.172, LV 1.396 +/- 0.130 ns; about 1.4 sigma), in the oxygen-expected direction; the contrast was stable across +/-16 mm registration perturbation (sign preserved in 100% of 342 shifts) and resided in the blood core, not the wall. However, the matched-vessel control was null: pulmonary artery minus aorta = -0.011 +/- 0.344 ns. Lung-air and wall-myocardium partial-volume were disfavoured, and the effect fell within the isotope-matched 82Rb instrumental positional envelope (about 0.1-0.35 ns over 40 mm in uniform material). Conclusion. On this single subject, the cardiac o-Ps lifetime contrast does not provide a clean readout of blood oxygenation: an oxygenation effect of the observed (about 0.3 ns) magnitude is ruled out by the matched control, while a small physiological effect cannot be excluded. We provide a reusable confound-control battery for evaluating future in-vivo o-Ps oxygenation claims. Multi-subject replication with anatomy decoupled from oxygenation is required.

Positron emission tomography (PET) tracers targeting amyloid-{beta} (A{beta}) are central to the diagnosis and staging of Alzheimers disease (AD). However, growing evidence indicates that these tracers can engage off-target molecules, complicating signal interpretation. Sulfotransferases (SULTs) have been experimentally identified as binding partners of 11C-Pittsburgh Compound-B (PiB). However, whether the clinically used fluorinated PiB derivatives flutemetamol and flutafuranol interact with brain-expressed SULTs is yet unexplored. Here, we combined multi-omic transcriptomic profiling with molecular docking and molecular dynamics (MD) simulations to assess the structural interactions of SULT-tracer complexes. Analysis of the Genotype-Tissue Expression project and the Human Protein Atlas identified SULT1A1, SULT1A3, and SULT4A1 as the SULT isoforms predominantly expressed in the human brain. Docking and MD simulations showed that all three tracers form energetically comparable complexes within the catalytic pockets of these isoforms, yet their dynamic stability varied in an enzyme- and tracer-specific manner. PiB and flutemetamol were stably accommodated in SULT1A1, but PiB lost its initial pose in SULT4A1. Flutafuranol showed weaker binding in SULT1A1, yet formed stable complexes in SULT1A3 and SULT4A1. Notably, SULT1A1, SULT1A3, and SULT4A1 are all expressed in the cerebellum, the brain region used as a reference for A{beta} PET signal normalization. These findings provide a structural framework for off-target tracer interaction with brain SULTs and suggest that the intracellular enzymatic environment may contribute to variability in A{beta} PET signals beyond fibrillar A{beta} deposition.

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.

BackgroundThermally ablative focused ultrasound (T-FUS) offers a noninvasive, spatially precise strategy for local tumor destruction, with the added potential to remodel tumor architecture and immune dynamics in ways that influence downstream therapeutic delivery and efficacy. Despite promising preclinical and clinical findings, the T-FUS parameters that best balance tumor debulking with preservation of local biologic, e.g. immunotherapy, penetrance remain unclear. Thermal dose, defined by the relationship between tissue heating, exposure duration, and biological effect, is likely a critical determinant of this balance. Excessive thermal dose may eliminate the vascular and stromal features needed to support immunotherapy access, whereas insufficient thermal dose may fail to achieve meaningful cytoreduction. Here, we deploy multimodal PET, contrast-enhanced ultrasound, and tissue profiling to define a "Goldilocks Zone" for T-FUS that balances bulk tumor destruction with immunotherapy delivery. MethodSubtotal T-FUS was applied to 4T1 tumors using three thermal dose regimens resolved by in silico modeling. Ablation was quantified by H&E and TTC staining. Post-ablative perfusion and microvascular coverage were assessed by contrast-enhanced ultrasound and immunofluorescence, respectively. Tumor oxygenation was measured by intravenous hypoxyprobe labeling. After T-FUS, mice underwent dynamic [18F]-FDG PET and immunoPET with a model tumor-targeted antibody, [89Zr]-CD47, to relate cytoreduction to antibody penetrance. ImmunoPET findings were further evaluated by ex vivo biodistribution analysis. ResultsIn silico modeling established three T-FUS regimens that generated distinct thermal dose profiles and were deployed in vivo in a solid breast tumor model. Histopathology, perfusion imaging, and hypoxia analysis revealed dose-dependent and dose-divergent biological effects that informed a candidate Goldilocks thermal window. Low thermal dose produced measurable but limited tumor debulking, whereas high thermal dose caused disproportionate functional perfusion collapse. An intermediate thermal dose achieved robust partial ablation, broad hypoxia relief, and preservation of residual tumor physiology sufficient to support antibody access. Dynamic [18F]-FDG PET confirmed a marked reduction in metabolically active tumor burden after Goldilocks T-FUS. Serial [89Zr]-CD47 immunoPET showed that bulk antibody signal was maintained after ablation, and integration of immunoPET with matched [18F]-FDG PET revealed approximately 3-fold enrichment of antibody exposure within the residual viable tumor compartment of ablated tumors. These findings demonstrate that appropriately tuned thermal ablation can debulk tumor while preserving, and potentially concentrating, immunotherapy access within the remaining targetable tumor niche. ConclusionThis study identifies thermal dose as a critical consideration for T-FUS immunotherapy combinations and establishes a PET-informed framework for balancing cytoreduction with therapeutic delivery. Rather than functioning solely as a local debulking modality, we demonstrate that T-FUS can be tuned to yield a post-ablation tumor state that remains accessible to large biologics. These findings provide timely, translationally relevant guidance for tailoring T-FUS regimens to achieve local tumor destruction while preserving an immunotherapy-permissive niche for combination treatment.

PurposeTo develop and evaluate a multi-model consensus deep learning approach for automated gold fiducial marker (FM) segmentation in T1-weighted prostate MRI. Materials and MethodsIn this retrospective study, T1-weighted MRI and CT-derived reference standard segmentations were collected from 127 prostate cancer patients (all male; mean age, 70 years {+/-} 7 [standard deviation]; age range, 50-88 years; collected between October 2020 and January 2026) who each had three implanted gold FMs. A 3D U-Net was trained on 93 subjects using four random seeds to produce an ensemble. At inference, marker-class probability maps were averaged across models and the top three connected components selected. Performance was evaluated on 34 temporally held-out subjects (9 tuning, 25 test) using marker-level sensitivity and precision with exact (Clopper-Pearson) 95% confidence intervals (CIs). A model count ablation study was performed. The pipeline was deployed for on-scanner processing on Siemens MRI systems via the OpenRecon framework and as a browser-based application using WebAssembly, executing entirely client-side. ResultsThe four-model consensus achieved 96% (70 of 73) sensitivity and 95% (70 of 74) precision on 25 test subjects, with 29 of 34 (85%) subjects achieving perfect marker detection. Single models had a mean sensitivity of 84% (SD, 9%), improving to 96% with four-model consensus (SD, <1%). ConclusionMulti-model consensus deep learning substantially improved FM segmentation reliability over individual models, achieving high sensitivity and precision using only routinely acquired T1-weighted MRI.

The design of radiotheranostic agents has been constrained by a fundamental chemical incompatibility: existing strategies for fluorine-18 incorporation -- nucleophilic substitution, silicon-fluoride exchange, aluminum fluoride chelation, and prosthetic conjugation -- either require conditions incompatible with macrocyclic metal coordination, consume the chelator site needed for radiometal labeling, or introduce non-native structural appendages that alter pharmacokinetic behavior. Here we show that sulfur(VI) fluoride exchange (SuFEx) chemistry and macrocyclic metal coordination define non-overlapping reactivity domains co-embeddable within a single peptide precursor. An aryl fluorosulfate on tyrosine accepts 18F under mildly basic conditions; a spatially distinct macrocyclic chelator, DOTA, NOTA, NODAGA, or any compatible macrocycle, independently coordinates diagnostic (68Ga, 64Cu) or therapeutic (177Lu) radiometals under mildly acidic conditions. The labeling pathways proceed independently under mutually compatible conditions, and both preserve receptor-binding affinity. Validated across PD-L1- and CD38-targeting scaffolds, this platform delivers nanomolar target affinity, high radiochemical yields, and matched pharmacokinetics, establishing isotopic orthogonality as a designable, intrinsic property of synthetic molecular radiopharmaceuticals.

Large language models (LLMs) are increasingly used for automated quality control (QC) of radiology reports. However, the reliability of LLMs on reports in Mandarin, and the relative performance of domestic versus international flagship models, remain unknown. We benchmarked 14 LLM configurations, seven Chinese-developed ("domestic") and seven international models, on 1,000 whole-body 18F-FDG PET/CT reports split into an error-injected "junior-docto" arm and a low-residual "finalised" arm (500 each), using a controlled error-injection gold standard. Under each blinded zero-shot prompt, each model flagged six error types and assigned a 1-5 overall score. Two distinct abilities: error-detection macro-F1 (0.356-0.667) and overall-score calibration (ICC[2,1] 0.099-0.627), were weakly and not significantly correlated across models (Spearman {rho} = 0.38, p = 0.18); the dissociation was instead evident in sharp rank reversals, the strongest detector (Claude-Opus-4.8 0.667) calibrating poorly (0.491), while the three best-calibrated models were all domestic (MiMo 0.627, GLM-5 0.612, DeepSeek 0.609). Once the access channel was controlled, domestic and international error detection were statistically indistinguishable ({Delta}macro-F1= -0.011, P = 0.84); domestic models showed consistent but not significant advantages in calibration ({Delta}ICC = +0.142) and Chinese-character-error detection ({Delta}F1 = +0.109), accompanied with large reductions in cost (US$0.09-2.71 vs $0.26-14.5 per 1,000 reports) and on-premise deployability. Re-running two flagships through both agent channels and clean APIs showed that agent channel inflated both detection and calibration (GPT-5.5 {Delta}ICC = +0.098, 95% CI 0.070-0.128), confirming that uncontrolled benchmarks over-credit agent-channel models. Missed-diagnosis detection was the universal weakness (best 0.467) and the one category where the human physicians outperformed every model. Raw detection ability does not guarantee a trustworthy score, and domestic and international models differ by deployment-relevant profile rather than by overall performance rank; both essential distinctions for performing clinical nuclear-medicine QC.

PurposeIn this work, we aimed to establish a flow phantom for multi-modal PET and spectral CT imaging to improve blood flow quantification. MethodsA modular flow phantom was built with materials compatible with both PET and spectral CT. A peristaltic pump was used to allow for recirculation. Pores were installed through the aorta to allow for tissue exchange between the blood and tissue compartments, and valves were placed in line with the aorta to control the pressure gradient between compartments. We characterized the system using saline bolus experiments, dynamic PET imaging, and iodine-based spectral CT acquisitions. A blood flow (K1) of 1.0 mL/min/mL with a pressure range of approximately 1.0-3.0 psi was targeted. Using compartmental modeling, we estimate K1 across phantom configurations and evaluate the consistency of perfusion-related parameters derived from saline, PET, and spectral CT measurements. ResultsWith the four pore, two valve configuration, target K1 of 1.0 mL/min/mL was achieved with a physiologic pressure range (2.2-3.5 psi) and a pump speed of 150 rpm. Further, the flow phantom was also able to recapitulate K1 across a range of values through adjustable modifications to the phantom configuration. ConclusionsWe present a modular multimodal flow phantom with a tissue-mimicking compartment, vascular tubing with an adjustable number of pores and valves, and 3D-printed components to support tunable exchange between blood-pool and tissue compartments and controlled dynamic perfusion imaging with same-session PET and spectral CT. Such a setup will enable the development of multi-modal approaches for evaluating tissue perfusion.

PurposeTo apply voxel-wise tracer kinetic model selection, characterise the spatial distribution of best-fitting models across the brain, and evaluate whether model selection improves sensitivity for differentiating normal-appearing tissue from pathological tissue compared to the Patlak model. MethodsExtended Tofts, Patlak, and intravascular models were fit to DCE-MRI data from stroke survivors and controls, as well as simulated data. The best-fitting model was chosen for each voxel using the Akaike Information Criterion, and model selection Ktrans (estimates from the best-fitting model for each voxel) compared to Patlak model Ktrans. ResultsIn simulated data, the Extended Tofts model was best-fitting at Ktrans>10-3 min-1, where the Patlak model systematically underestimated Ktrans. Patlak was optimal at Ktrans between 10-4-10-3 min-1, where Extended Tofts estimates had greater variability. The intravascular model was selected for Ktrans[~]10-4 min-1. The Patlak model was chosen in most control voxels. In chronic stroke, the Extended Tofts model was preferred in most cortical and white matter hyperintensity voxels, while the Patlak model was selected in most deep grey matter and normal-appearing white matter voxels. Model selection Ktrans estimates were significantly greater than Patlak estimates in the cortex and white matter hyperintensities, with greater inter-patient variability, likely reflecting biological variability in blood-brain barrier leakage resulting from stroke. ConclusionVoxel-wise model selection may provide more accurate estimates of a wider range of Ktrans values than any single model, revealing greater differences between normal and pathological tissue and offering a more sensitive and physiologically appropriate framework for DCE-MRI analysis of blood-brain barrier dysfunction.

BackgroundReliable tracking of extracellular vesicles (EVs), key biological nanocarriers in nanomedicine, remains a major technical challenge due to the limitations of conventional lipophilic dyes, including aggregation, micelle formation, and nonspecific background signals that compromise biodistribution analyses. MethodsHere, we present a fluorogenic labeling strategy based on Aco-600, a water-soluble probe exhibiting a "light-on" activation in hydrophobic environments. Medium/large EVs (m/lEVs) derived from murine BV2 microglial cells were labeled and intranasally administered to adult C57BL/6 mice. EV biodistribution and brain uptake were quantitatively assessed by ex vivo fluorescence imaging on brain cryosections at multiple time points (5-1440 min), focusing on the cortex and hippocampus. ResultsAco-600 labeling enabled high signal-to-noise detection with minimal background and no evidence of dye aggregation artifacts. Quantitative analysis revealed a consistent spatiotemporal distribution profile across brain regions, with peak signal intensity at 60 minutes post-administration, followed by progressive clearance. This approach provided reproducible and sensitive tracking of EV biodistribution following a clinically relevant intranasal delivery route. ConclusionsOur findings establish fluorogenic labeling as a robust and artifact-minimizing strategy for in vivo EV tracking. This method enhances the accuracy of biodistribution studies and supports the development of EV-based nanomedicine platforms, particularly for central nervous system delivery applications.

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality in men and women. Timely detection and diagnosis are key to management of CRC, which is under-diagnosed because colorectal aberrant crypt foci, hyperplastic polyps, and microadenomas are often missed with conventional colonoscopy. The enzyme cyclooxygenase-2 (COX-2) is overexpressed in early stages of colorectal carcinogenesis and plays an important regulatory role in the process, suggesting that it could be a valuable target for enhanced imaging of nascent disease. Thus, we have generated an alpaca-derived library of 73 COX-2-specific nanobody clones. Here, we describe one such nanobody, F9-K45Q-K77Q-ROX, in which two native lysine residues have been mutated followed by conjugation to a fluorophore at the N-terminus with retention of COX-2-selective binding. The site of fluorophore conjugation and COX-2 binding affinity of F9-K45Q-K77Q-ROX were determined by proteomic and microscale thermophoretic analyses, respectively. In cell culture studies using 1483 human head and neck squamous cell carcinoma cells, F9-K45Q-K77Q-ROX accumulated inside cells and bound to intracellular COX-2, as visualized by fluorescence microscopy. In vivo pharmacokinetic, and toxicological analyses revealed that F9-K45Q-K77Q-ROX is detectable in circulation with a plasma half-life of 17.9 min and there is no short-term toxicity associated with single injections of 10 mg/kg, 20 mg/kg, or 40 mg/kg doses at 24 h post-administration. Noninvasive in vivo fluorescence endoscopic imaging validated tumor-specific accumulation of F9-K45Q-K77Q-ROX in azoxymethane/dextran sodium sulfate-induced colorectal adenomas in mice. This work demonstrates the first COX-2-targeted nanobodies including a fluorescent derivative that offers significant promise for targeted endoscopic imaging of COX-2-expressing neoplasms. Significance StatementCurrent colorectal cancer screening procedures, such as white-light colonoscopy, chromoendoscopy, and narrow-band imaging aim to detect solid colon tumors and precursor lesions. However, these methods tend to detect only raised solid tumors and mature cancers, whereas precursor lesions, such as aberrant crypt foci, hyperplastic polyps, and small adenomas are frequently missed. To address the need for better visualization of early lesions, we developed a library of alpaca-derived nanobodies targeted to cyclooxygenase-2 (COX-2), an enzyme that is overexpressed in colorectal adenomas. COX-2-targeted nanobodies bearing a fluorescent tag accumulate and are retained in colonic adenomas, facilitating their endoscopic visualization. This novel COX-2-targeted nanobody platform may also be valuable for early detection of other neoplastic diseases in which COX-2 overexpression occurs. (Word counts 119, limit 120)

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.