European Journal of Nuclear Medicine and Molecular Imaging

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.

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.

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.

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.

Neuroinflammation mediated by reactive microgliosis is a central driver of Parkinsons disease (PD) pathogenesis. This inflammatory process unfolds years before clinical symptoms, creating an opportunity for early intervention. In vivo imaging technologies that could detect and quantify microglial reactivity are therefore essential for early diagnosis, patient stratification, and evaluating emerging immunomodulatory therapies that target this fundamental driver of PD progression. Yet no standardized, sensitive, and specific technology currently achieves this goal. Molecular magnetic resonance imaging (mMRI) is uniquely suitable to address this problem because it integrates inherent high spatial resolution and soft tissue contrast of conventional MRI with molecularly targeted contrast agents, enabling simultaneous acquisition of anatomical detail and functional/biological information at submillimeter isotropic resolution. Here we present a novel mMRI probe designed to specifically target colony stimulating factor-1 receptor, expressed primarily on microglia in the brain. In silico data show that the targeting ligand binds the extracellular Ig domain of the receptor. In vitro cell uptake studies with both murine and human microglia cell lines show that the probe binds the receptor triggering active cell uptake and in vivo MRI enabled effective separation of the A53T mouse model of PD from control mice using radiomics-assisted MR image analysis. Ex-vivo immunohistochemical analysis showed signal from the probe largely in the cytosolic compartment of IBA-1 reactive cells, confirming that the observed in vivo MRI signal is due primarily to retention of the agent by microglia. This novel technology has the potential to interrogate the regional presentation of microglial activation in PD.

Leptomeningeal disease (LD), involving the metastasis of cells to the leptomeningeal membranes in the central nervous system (CNS), can be a deadly complication of several different types of cancer originating in the periphery or CNS, including breast cancer (BC) and pediatric medulloblastoma (MB). Targeted therapy represents a promising new approach to improve overall survival for LD patients. To this date, angiopep-2 (Ang2) and transactivating transcriptional activator (TAT), two well-known peptides for their brain delivery capability, have been reported to transport therapeutic cargos into the CNS for treatment of disease. Current administration strategies, however, still rely on oral delivery or intravenous injection (IV), where the substances need to travel through complex biological barriers to reach the subarachnoid space (SAS), which is the primary location of LD. Our research group has focused on the intrathecal (IT) route of administration as an alternative approach that can potentially enable high exposure of drug to CSF exposed tissues. However, there is a major field gap in understanding how targeting peptides can access (or not access) LD as a function of their route of administration. Therefore, our work was focused on comparing the targeting capability of Ang2 vs TAT by IT vs IV routes of administration. We first generated two xenograft models of LD by directly infusing breast cancer cells (MDA-MB231) or medulloblastoma cells (HDMB03) into the SAS via intracisternal magna injection (ICM) to form BC-LD and MB-LD models, respectively. These tumor models were characterized for overall survival, tumor growth patterns, and presence of hydrocephalus. Second, we further administered fluorescently labeled Ang2 or TAT peptides either IV or ICM into tumor bearing mice. Neuraxial fluorescence images were examined to evaluate the targeting ability of these two peptides based on colocalization between peptide signal and tumor tissues ex vivo. We discovered that the median survival of both models was negatively related to the number of the cells infused. While HDMB03 cells tended to metastasize preferentially to the brain region, MDA-MB231 cells tended to metastasize preferentially to the spinal cord. Both models present hydrocephalus as one of the common clinical symptoms in LD patients. Compared to the healthy control, MB-LD yielded a 7.3-fold increase and BC-LD a 26.5-fold increase in ventricular volume. Furthermore, targeting achieved by TAT was significantly higher than targeting achieved by Ang2 in thoracic spine for the MB-LD model. For BC-LD model, TAT signal was found to be significantly higher than Ang2 signal in the olfactory bulbs, brain stem, thoracic spine, and lumbar spine regions. While both peptides showed a strong signal at 2 hours post ICM injection, signal was not detectable 24 hours after administration, reflecting washout or degradation. Significantly, these data provide evidence that ICM will be a preferable route of administration over IV for the purpose of maximally targeting LD.

Background: Gadolinium-based contrast agents (GBCAs) are routinely used in magnetic resonance imaging (MRI). Although macrocyclic GBCAs were initially considered biologically inert, it is now known that a fraction of patients retains gadolinium (Gd) for prolonged periods in tissues such as blood, bone, and brain. Because the first cellular interactions of GBCAs occur in the bloodstream, this study aimed to elucidate the uptake mechanism but also the intracellular persistence and release dynamics of gadoterate meglumine, one of the most widely used macrocyclic agents, in white blood cells (WBCs). Methodology and principal findings: WBCs and K562 cells were incubated with gadoterate meglumine under different conditions to investigate its cellular entry mechanisms. Uptake of the contrast agent was quantified by measuring intracellular Gd using single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS). Time and concentration-dependent incubation of K562 cells revealed saturable uptake kinetics consistent with a Michaelis-Menten model which is independent of the phase of the cell cycle. Gadoterate meglumine uptake in both WBCs and K562 cells was shown to be an active process, as uptake was strongly reduced or abolished at low temperature (16C and 4C) and in the presence of metabolic inhibitors (sodium azide and 2-deoxyglucose). Co-incubation with multiple endocytosis inhibitors (Dyngo 4a, Dynole 2-24 and chlorpromazine) did not significantly decrease intracellular Gd levels in K562 cells and caused only a slight reduction in WBCs, indicating that endocytosis is not the main entry pathway for gadoterate meglumine in these cells. Furthermore, we assessed the retention time of the Gd inside the cells, showing that only after 24 hours post incubation 80% percent of the intracellular Gd was released through an active process. Finally, we demonstrate that one of the mechanisms of Gd release from WBCs involves extracellular vesicles, which may substantially increase its potential for downstream accumulation in different tissues, including immunoprivileged tissues like brain. Significance: The observed time-dependent accumulation, temperature and energy dependence of gadoterate meglumine uptake demonstrate that active cellular mechanisms are primarily responsible for GBCA internalization. Furthermore, our results indicate that macropinocytosis, phagocytosis, and clathrin-mediated endocytosis are not the primary routes of gadoterate meglumine entry. Hereby, we also describe that Gd externalization is an active process involving extracellular vesicles which may influence the Gd distribution in different tissues and its consequent long-term retention. Further studies are required to explore strategies to block this process in order to mitigate potential long-term gadolinium retention.

Purpose: {beta}-hydroxybutyrate (BHB), a ketone body and alternative cerebral energy substrate, can be measured in vivo using J-difference edited proton magnetic resonance spectroscopy (1H-MRS). Oral ketone supplementation with substrates such as the ketone monoester (R)-3-hydroxybutyl-(R)-3-hydroxybutyrate (KME) and 1,3-butanediol (BD) have gained attention as a mechanism to elevate circulating BHB and induce ketosis without dietary restrictions. Elevated brain ketone availability is of growing therapeutic interest as a strategy to support neuronal energetics in conditions such as epilepsy, neurodegenerative disease, and alcohol use disorder (AUD). However, both pathways introduce BD into the bloodstream, which crosses the blood-brain barrier. Critically, BD exhibits a spectral signature that closely resembles the prominent BHB peak in JDE-MR spectroscopic imaging (MRSI), identified in a pilot AUD study. Methods: Two separate JDE-MRSI acquisitions tailored for BHB and BD editing were implemented, exploiting frequency separation between the BHB (4.14ppm) and BD (3.95ppm) coupling partners of the observed 1.2ppm resonance to independently quantify each metabolite. Results: Brain BD concentrations (0.25-0.58mM) were comparable to or exceeded corresponding BHB concentrations (0.20-0.27mM) in all volunteers after consumption of a single dose of the KME, indicating that BD constitutes a major fraction of the signal conventionally attributed to BHB. Combined BHB+BD concentrations (~0.45-0.85mM) were consistent with brain BHB values reported in prior studies employing similar doses of the KME, indicating that those measurements likely reflect a combined BHB+BD signal. Conclusions: Separate quantification of the two metabolites is important for interpreting brain ketone studies and for understanding the full pharmacology of KME supplementation.

Objectives: Natural language processing (NLP) can enable scalable extraction of clinically relevant information from unstructured radiology reports retrieved from electronic healthcare data warehouses, but reliance on externally hosted models may pose cost, privacy, and deployment challenges. We compared self-hosted discriminative and generative NLP pipelines for automated extraction of Prostate Imaging and Reporting Data System (PIRADS) scores from multiparametric magnetic resonance imaging (mpMRI) reports used in prostate cancer risk assessment. Materials and Methods: We identified 44,511 mpMRI reports across 68 Veterans Affairs (VA) healthcare systems. A stratified random sample of 1,973 reports was used to train, test, and evaluate multiple pipeline configurations combining Named Entity Recognition (NER) models and large language models (LLMs). Performance was assessed by accuracy of maximum PI-RADS extraction and processing speed using self-hosted implementations of spaCy NER, Transformers NER, and generative LLMs Llama 3, Qwen3, and Gemma3. Results: Across the top 10 pipeline configurations, accuracy for maximum PI-RADS extraction ranged from 89.3% to 95.5%, with processing times spanning 150 milliseconds to 70 seconds per report. Generative LLM pipelines achieved the highest accuracy (up to 95.5%) but were substantially slower (2 to 70 seconds), whereas NER based pipelines demonstrated lower accuracy (88.5%) with faster performance (50 to 150 milliseconds). Discussion: Discriminative NER pipelines achieved high accuracy while offering advantages in speed and potential scalability. Accuracy gains from LLMs were accompanied by significantly higher computational cost, potentially limiting feasibility in high-volume clinical environments. Conclusion: Discriminative methods were more efficient than generative models in annotating PIRADS from mpMRI report text, providing insights into configurations for optimal clinical deployment when volume is a limiting factor. However, generative AI offered improved accuracy with less upfront development.

Purpose: To develop an accelerated motion-compensated diffusion-weighted pseudo-continuous arterial spin labeling (MCDW-pCASL) method using a spatial subspace low-rank reconstruction method for efficient quantification of blood-brain barrier (BBB) water exchange (kw) and permeability (PSw). Methods: An accelerated multidelay MCDW-pCASL sequence was developed to simultaneously encode intravascular and extravascular diffusion-weighted ASL signals across multiple post-labeling delays (PLDs). A spatial subspace low-rank reconstruction framework was optimized to enable joint estimation of cerebral blood flow (CBF) and BBB water exchange rate and permeability. Fourteen young healthy adults underwent test-retest scans (separated by ~1 week) at 3T with both the accelerated MCDW-pCASL and a conventional diffusion-prepared (DP) pCASL sequence. Whole-brain, gray-matter, and white-matter CBF and kw values were quantified to assess test-retest repeatability and cross-method agreement. An additional cohort of 30 older adults underwent single-session MCDW and DP scans to evaluate age-related perfusion and BBB kw/PSw differences. Intraclass correlation coefficients (ICCs) were used to assess reliability and agreement. Results: Accelerated MCDW-pCASL demonstrated excellent agreement with DP-pCASL for CBF (ICC = 0.89) and fair agreement for kw (ICC = 0.56). Test-retest repeatability of MCDW-pCASL was good for CBF, BBB kw and PSw (ICC {approx} 0.6). Across both sequences, younger subjects exhibited significantly higher CBF and kw compared with older adults. Conclusion: Incorporating a spatial low-rank subspace reconstruction enables accelerated MCDW-pCASL acquisition with reliable simultaneous quantification of CBF, BBB kw and PSw. Clinical applications of this method for assessing perfusion and BBB function are warranted.

Longitudinal monitoring of circulating tumor DNA (ctDNA) has emerged as a promising framework for characterizing treatment response dynamics in cancer. Scalable tumor-naive approaches for quantifying ctDNA often involve whole-genome sequencing (WGS) or DNA methylation profiling, but their comparative performance and capacity for complementary integration remain poorly understood. Here we systematically benchmarked tumor-naive WGS- and methylation-based ctDNA quantification methods using plasma from 150 patients with colorectal, lung and breast cancer. Using paired high-depth WGS and EM-seq data, we generated 40,000 in silico samples and evaluated detection accuracy, limits of detection (LoD) and quantification (LoQ) across cancer types and sequencing depths (0.1x-30x). We further assessed single- and multimodal method combinations, identifying conditions under which integrated approaches enhance analytical performance for detection and quantification relative to single modalities. This benchmark delineates key performance trade-offs and provides a practical framework to support method development and guide future research applications in ctDNA-based biomarker studies.

Introduction Parkinson's disease (PD) is a multifactorial disorder, affecting multiple neurotransmitter systems, including the cholinergic system. Cholinergic denervation is heterogeneous across patients and difficult to predict based on clinical presentation. In this study, we assessed the sensitivity of structural MRI (sMRI) and functional MRI (fMRI) to cholinergic degeneration related to PD and to cognitive functioning in PD. We compared our results to results from previously reported [18F]Fluoroethoxybenzovesamicol ([18F]FEOBV) PET imaging, which is considered the gold standard for cholinergic imaging. Methods 34 PD patients and 10 healthy controls underwent structural T1-weighted MRI. A subset of 14 patients and 9 controls also underwent resting-state fMRI. We extracted the bilateral volumes of the nucleus basalis of Meynert (NBM) from the sMRI images. Functional connectivity (FC) from the NBM to the cortex (NBM-FC) was determined using fMRI data. Principal component analysis (PCA) was applied to reduce the dimensionality of the NBM-FC images. We assessed performances for NBM-FC in distinguishing patients from controls using stepwise logistic regression. Similarly, NBM volume was used using logistic regression. Furthermore, the relation between these measures and cognitive function in several domains was investigated with (stepwise) linear regression. Leave-one-out cross validation (LOOCV) and bootstrapping was performed to assess robustness of the results. Results NBM-FC was well able to discriminate patients from controls with an AUC of 0.84 (95% CI: 0.62-1). NBM volume showed lower performance, but was still better than chance: AUC: 0.75 (95% CI: 0.57-0.93). Significant correlations were found between 1) cognition in the attentional domain and NBM-FC (r=0.63; p=.015) and 2) global cognition and NBM volume (r=0.55, p=.001). These results were inferior to those previously reported using [18F]FEOBV tracer uptake (see Chapter 6). Bootstrapping revealed that NBM volume of only the left hemisphere was stably related to PD diagnosis and global cognition in PD patients. We found that a lower NBM-FC in specific brain areas, including the fusiform gyrus, supramarginal gyrus and dorsolateral prefrontal cortex, was related to PD diagnosis. Bootstrapping revealed no stable NBM-FC pattern related to attention. Conclusion Although MRI results were slightly inferior to [18F]FEOBV PET data, MRI may provide a cheaper and more widely available alternative for cholinergic imaging. We recommend testing the utility of MRI as predictor and monitor of cholinergic treatment effect in a longitudinal study.

In vivo infrared thermography is limited by the inherently poor spatial resolution at long wavelengths, low contrast, and the lack of biocompatible contrast agents. Here, we present 3-5 m mid-wave infrared (MWIR) thermography enhanced by an artificial intelligence (AI) network and cold phosphate-buffered saline (PBS) as a thermal contrast agent for noninvasive in vivo imaging with high contrast and resolution. MWIR imaging enabled high thermal sensitivity with microscale spatial resolution, strong relative thermal contrast, and facilitated visualization of the subcutaneous vasculature in the human arm, hand, ankle, the femoral artery and vein in rats, and the femoral vessels in mice, with image contrast further enhanced by AI networks. In a 4T1 tumor-bearing mouse model, AI-enhanced MWIR resolved early-stage tumors of ~2.3 mm and metastases as small as ~1.7 mm. Using cold PBS as a MWIR thermal contrast agent, we achieved precise tumor boundary visualization and real-time imaging-guided tumor resection. AI-enhanced MWIR offers a promising solution for early diagnosis and improved surgical precision.

BackgroundQuantitative dynamic contrast-enhanced myocardial perfusion cardiovascular magnetic resonance (CMR) enables estimation of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). These measurements require an arterial input function (AIF), which is typically derived from the left ventricular blood pool. However, the contrast agent bolus undergoes dispersion during transport through the coronary vasculature before reaching the myocardial microcirculation. This may introduce systematic and spatially heterogeneous errors in MBF and MPR estimates. PurposeThis work provides an extended segmental analysis of bolus-dispersion-induced errors in quantitative myocardial perfusion MRI using previously established computational fluid dynamics (CFD) simulations in realistic porcine coronary artery models. The focus of the present analysis is the assignment of coronary outlets to myocardial segments and the resulting segmental variability of MBF and MPR errors. MethodsRealistic three-dimensional models of the left and right coronary arteries were extracted from an ex-vivo porcine imaging cryomicrotome dataset. The models extended down to the pre-arteriolar level and included 364 outlets for the left coronary artery and 104 outlets for the right coronary artery, with an average outlet diameter of 383 {+/-} 85 {micro}m. Blood flow was simulated under rest and stress conditions using OpenFOAM. Contrast agent transport was then modeled by solving the advection-diffusion equation using a gamma-variate bolus as input. Outlet concentration-time curves were analyzed using an indicator-dilution model to estimate MBF and MPR errors. Outlets were assigned to standardized myocardial segments, and segmental averages were evaluated with respect to coronary supply territory and travel distance from the model inlet. ResultsThe simulations demonstrated marked segmental heterogeneity of volume blood flow and bolus-dispersion-induced MBF and MPR errors. Errors increased with travel distance from the coronary artery inlet and were more pronounced in regions supplied by the right coronary artery, consistent with lower flow velocities and stronger bolus dispersion. The resulting systematic errors led to underestimation of MBF and overestimation of MPR, with segmental deviations reaching up to approximately 60%. ConclusionBolus dispersion in the coronary vasculature may lead to substantial segmental and location-dependent errors in quantitative myocardial perfusion MRI. This extended analysis indicates that dispersion-related bias is not spatially uniform, but depends on coronary supply territory, travel distance, and flow conditions. These effects should be considered when interpreting regional MBF and MPR estimates, particularly as automated quantitative myocardial perfusion CMR becomes more widely used.

ObjectiveDisease progression modeling (DPM) or "amyloid time" is increasingly used to stage Alzheimer disease (AD). DPM performance depends on within-individual heterogeneity in rates of pathological accumulation as well as test-retest reliability of the biomarker. The relative contributions of these variabilities have not been systematically assessed. This would be particularly relevant if extrapolations from DPM were to be used to make individual-level predictions for research, clinical trials, or potentially future clinical practice. MethodsWe conducted simulation studies incorporating empirically-derived noise properties from amyloid biomarkers to assess the contributions of inter- and intra-individual variability. Findings generalized in an autosomal dominant AD cohort with amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), and plasma biomarkers and in a sporadic AD cohort with both amyloid PET and plasma biomarkers. We assessed group level DPM performance via mean average error (MAE) and root mean squared error (RMSE). At the individual level, we evaluated distinctness of distributions of biomarker levels associated with specific disease timings. ResultsInter-individual variability was the dominant source of error in temporal estimates. Intra-individual variability reduced estimate stability. Optimal performance occurred in biomarkers with positive average accumulation rates where a subset of individuals had exceptionally high levels of accumulation. In research study data, amyloid PET outperformed CSF and plasma biomarkers. InterpretationDPM is fundamentally constrained by dynamic range, variability, and test-retest reliability of the biomarker of interest. Current DPM approaches are more robust at the group level, particularly when applied to biomarkers with more than 10-15% variability like fluid biomarkers. FundingNational Institute on Aging, Alzheimers Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Korean Ministry of Health & Welfare and Ministry of Science and ICT, Spanish Institute of Health.

IntroductionChemogenetic tools such as Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) provide a powerful means to causally manipulate defined neuronal populations in vivo. While chemogenetic fMRI studies have consistently demonstrated robust hemodynamic responses following circuit perturbation, considerably less is known about the accompanying metabolic consequences. Functional magnetic resonance spectroscopy (fMRS) offers the potential to probe these neurochemical processes, yet the relationship between hemodynamic and metabolic responses remains poorly understood. Here, we combined chemogenetics, pharmacological fMRI (ph-fMRI), and proton magnetic resonance spectroscopy (1H-MRS/fMRS) at 7 T to investigate the temporal evolution of metabolic and hemodynamic responses in the rat motor cortex. MethodsFemale Fischer rats received viral injections in the motor cortex to express either a pan-neuronal hM3D(Gq) DREADD construct (hSyn-hM3Dq) or an interneuron-targeted construct (hDlx-hM3Dq). Ph-fMRI, fMRS, and 1H-MRS measurements were performed before, during, and following systemic administration of clozapine-N-oxide (CNO, 1 mg/kg). Functional MRS was acquired during the acute response phase (0-60 min post-injection), while conventional 1H-MRS measurements were obtained at a delayed time point (70 min post-injection). ResultsChemogenetic modulation produced robust and opposing hemodynamic responses. Pan-neuronal activation elicited focal positive BOLD responses (+3.5 {+/-} 1.5%), whereas interneuron-targeted activation generated significant negative BOLD responses (-3.3 {+/-} 0.8%). In contrast, acute fMRS measurements revealed no significant changes in Glx or GABA concentrations during the first hour following CNO administration, despite the presence of strong hemodynamic effects. However, delayed metabolic alterations were detected 70 min after CNO administration. Animals expressing the pan-neuronal construct exhibited significant increases in GABA (+14.4%) and total choline compounds (+57.8%), whereas interneuron-targeted animals displayed reductions in several metabolites, including Glx (-15.6%), total NAA (-16.9%), glucose (-25.9%), and total creatine (-25.4%). ConclusionChemogenetic perturbation of cortical circuits produced robust hemodynamic responses but more subtle and temporally complex metabolic effects. The absence of detectable acute changes in Glx and GABA despite strong BOLD responses, together with the emergence of delayed neurochemical alterations, highlights the challenges of interpreting metabolic signals in relation to circuit activity.

Background: Cognitive impairment is a prevalent long-term sequela in glioma patients, yet its cerebrovascular correlates remain poorly characterized. Arterial spin labeling (ASL) perfusion MRI offers a non-invasive means to quantify cerebral blood flow (CBF) and may serve as a sensitive correlate of radiotherapy (RT)-induced neurovascular injury. Methods: Fifty WHO Grade 2/3 glioma patients and 50 matched healthy controls underwent pseudo-continuous ASL (pCASL) MRI and a standardized cognitive test battery. Regional CBF was compared between patients (n=44, after quality control) and controls (n=50) using ANCOVA with age, sex, and deep white matter CBF as covariates. In irradiated patients (~5 years post-RT), RT dose-CBF associations were assessed using region-wise regression, and regional CBF was compared among controls and low-dose ([&le;]15 Gy) versus high-dose ([&ge;]40 Gy) regional RT exposure groups. Cognition-CBF associations were evaluated in a priori domain-specific regions of interest. Results: Compared with controls, patients showed frontoparietal cortical hypoperfusion, with significantly lower CBF in middle frontal and superior/inferior parietal cortices (all q<0.01; partial -squared=0.128-0.147). Region-wise regression showed no significant linear RT dose-CBF associations after correction. However, subgroup analyses identified RT dose-sensitive regions with [&ge;]40 Gy exposure that showed lower adjusted CBF than controls, most prominently in the left precentral and caudal middle frontal cortices (q<0.01; adjusted-{Delta}CBF{approx}-27.2--28.8 mL/100g/min). Perfusion in the left precentral and postcentral gyri of irradiated patients correlated positively with motor performance. Conclusions: pCASL reveals persistent cortical hypoperfusion in glioma patients that spatially corresponds with RT dose exposure and associates with cognitive performance, positioning ASL as a promising non-invasive biomarker of RT-related neurovascular injury.

Background: Net Water Uptake (NWU) is a non-contrast CT (NCCT) biomarker of early cerebral edema in ischemic stroke, calculated from attenuation differences between ischemic and contralateral non-ischemic brain regions. Manual NWU quantification is labor-intensive and prone to inter-operator variability, limiting clinical uptake and research scalability. We developed and internally validated a fully automated NWU evaluation pipeline. Methods: We analyzed 24-hour follow-up NCCT scans from the AcT (Alteplase compared to Tenecteplase) trial. Infarcts were automatically obtained by segmentation framework based on a synchronous image-label diffusion probability model. The images and extracted infarcts were registered to the standard MNI152 space, allowing us to mirror the infarct onto the contralateral hemisphere symmetrically, regardless of size or tilt angle. Subsequently, the mirrored region was inversely transformed to return to its original space. Voxels outside the range of 20-80 Hounsfield Units (HU) were excluded to remove non-parenchymal tissue. Automated NWU was computed as the percentage difference in mean HU between infarct and mirrored contralateral regions. The agreement with manually determined NWU was evaluated using Pearson correlation, mean absolute error (MAE), and Bland-Altman analysis. Results: Of 1,327 patients in the trial, 298 (22.5%) met predefined imaging-quality criteria for the manual validation analysis, including well-aligned raw NCCT scans in the axial plane and clear parenchymal infarct segmentations. Automated 24-hour NWU showed excellent agreement with manual measurements (r = 0.99). Mean absolute error was 0.18% (95% CI: 0.01-0.46). Bland-Altman analysis demonstrated minimal bias (0.09%) and satisfactory limits of agreement (-4.05% to +4.24%). Ninety-nine percent of cases fell within {+/-}5% of the manually determined value. Conclusions: Our automated mirrored segmentation pipeline enables accurate and reproducible NWU quantification from routine 24-hour NCCT scans, matching expert manual measurements with minimal bias.

Background: Plasma phosphorylated tau-217 (pTau-217) measurements for use in Alzheimer disease (AD) identification require thresholds to define positivity and there exist different approaches to operationally defining the boundary. We compared amyloid {beta} (AB) PET-anchored and distribution-based positivity cut-off values and explored how these mapped onto latent biomarker states. Methods: We analysed plasma pTau-217 measured in the Bio-Hermes-001 cohort (N = 990) using an immunoassay (Lilly) and mass spectrometry assay (University of Gothenburg). Gaussian mixture models were used to identify latent classes and thresholds were derived in two ways: achieving 90% specificity for AB PET positivity and exceeding the mean + 2SDs of the lowest latent class. We explore classes in reference to AB PET status and clinical diagnosis, as well as agreement between approaches using Cohen kappa for both assays. Results: In both assays, three latent biomarker classes were identified with monotonic increases in AD clinical diagnosis and AB PET positivity. PET-anchored thresholds showed lower specificity but higher sensitivity to amyloid positivity than distribution-based thresholds. Overall agreement between the approaches was acceptable (k = 0.678 for Lilly and 0.575 for University of Gothenburg), with disagreement concentrated in the intermediate latent class. Classes with the lowest and highest pTau-217 concentrations were classified consistently using both thresholds Discussion: The two thresholding approaches yielded similar classifications at both the negative and positive tail of the observed biomarker distribution, but classify intermediate concentrations differently. The boundary definition influenced pTau-217 positivity more than the analytical platform itself. Thresholding approaches may capture different pTau-217 biomarker states, therefore such methodological decisions should be grounded in the context of the intended application.