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Abstract Background Accumulation of β-amyloid (Aβ) in the brain is a hallmark of Alzheimer’s Disease (AD). Cerebral deposition of Aβ initiates deteriorating pathways which eventually can lead to AD. However, the exact mechanisms are not known. A possible pathway could be that Aβ affects the cerebral vessels, causing inadequate cerebrovascular function. In the present study, we examined if Aβ accumulation is associated with a reduced cerebral blood flow response (CBF) to neuronal activation by visual stimulation (ΔCBFVis.Act.) in cognitively normal subjects from the Metropolit Danish Male Birth Cohort. Methods 64 subjects participated in the present study. ΔCBFVis.Act. was measured using arterial spin labelling (ASL) combined with blood-oxygen-level-dependent (BOLD) MRI. Neuronal activation was obtained by visual stimulation by a flickering checkerboard presented on a screen in the MRI-scanner. Brain Aβ accumulation and cerebral glucose metabolism were assessed by PET imaging using the radiotracers [11C]Pittsburgh Compound-B (PiB) and [18F]Fluorodeoxyglucose (FDG), respectively. Cortical thickness was measured from structural MRI. Results ΔCBFVis.Act. correlated negatively ( $$\beta$$ β = -32.1 [95% confidence interval (CI): -60.2; -4.1], r = -0.30, p = 0.025) with PiB standardized uptake value ratio (SUVr) in the brain regions activated by visual stimulation. ΔCBFVis.Act. did not correlate with FDG SUVr ( $$\beta$$ β = 1.9 [CI: -23.8; 27.6], r = 0.02, p = 0.88) or cortical thickness ( $$\beta$$ β = 10.3 [CI: -8.4; 29.0], r = 0.15, p = 0.27) in the activated brain regions. Resting CBF did not correlate with PiB SUVr neither in the regions activated by visual stimulation ( $$\beta$$ β = -17.8 [CI:-71.9; 36.2], r =- 0.09, p = 0.51) nor in the remaining cortex ( $$\beta$$ β = 5.2 [CI:-3.9; 14.2], r = 0.15, p = 0.26). Conclusion We found a correlation between high PiB SUVr and reduced CBF response to neuronal activation, indicating a link between Aβ accumulation and impaired cerebrovascular function. The impairment was not associated with cortical thinning or hypometabolism, suggesting that Aβ accumulation affecting brain vessel function could be a very early pathology leading to neurodegenerative disease.

Abstract Background Treatment of patients with low-grade and high-grade gliomas is highly variable due to the large difference in survival expectancy. New non-invasive tools are needed for risk stratification prior to treatment. The urokinase plasminogen activator receptor (uPAR) is expressed in several cancers, associated with poor prognosis and may be non-invasively imaged using uPAR-PET. We aimed to investigate the uptake of the uPAR-PET tracer [68Ga]Ga-NOTA-AE105 in primary gliomas and establish its prognostic value regarding overall survival (OS), and progression-free survival (PFS). Additionally, we analyzed the proportion of uPAR-PET positive tumors to estimate the potential number of candidates for future uPAR-PRRT. Methods In a prospective phase II clinical trial, 24 patients suspected of primary glioma underwent a dynamic 60-min PET/MRI following the administration of approximately 200 MBq (range: 83–222 MBq) [68Ga]Ga-NOTA-AE105. Lesions were considered uPAR positive if the tumor-to-background ratio, calculated as the ratio of TumorSUVmax-to-Normal-BrainSUVmean tumor-SUVmax-to-background-SUVmean, was ≥ 2.0. The patients were followed over time to assess OS and PFS and stratified into high and low uPAR expression groups based on TumorSUVmax. Results Of the 24 patients, 16 (67%) were diagnosed with WHO grade 4 gliomas, 6 (25%) with grade 3, and 2 (8%) with grade 2. Two-thirds of all patients (67%) presented with uPAR positive lesions and 94% grade 4 gliomas. At median follow up of 18.8 (2.1–45.6) months, 19 patients had disease progression and 14 had died. uPAR expression dichotomized into high and low, revealed significant worse prognosis for the high uPAR group for OS and PFS with HR of 14.3 (95% CI, 1.8-112.3; P = 0.011), and HR of 26.5 (95% CI, 3.3–214.0; P = 0.0021), respectively. uPAR expression as a continuous variable was associated with worse prognosis for OS and PFS with HR of 2.7 (95% CI, 1.5–4.8; P = 0.0012), and HR of 2.5 (95% CI, 1.5–4.2; P = 0.00073), respectively. Conclusions The majority of glioma patients and almost all with grade 4 gliomas displayed uPAR positive lesions underlining the feasibility of 68Ga-NOTA-AE105 PET/MRI in gliomas. High uPAR expression is significantly correlated with worse survival outcomes for patients. Additionally, the high proportion of uPAR positive gliomas underscores the potential of uPAR-targeted radionuclide therapy in these patients. Trail Registration EudraCT No: 2016-002417-21; the Scientific Ethics Committee: H-16,035,303; the Danish Data Protection Agency: 2012-58-0004; clinical trials registry: NCT02945826, 26Oct2016, URL: https://classic.clinicaltrials.gov/ct2/show/NCT02945826 .

Abstract Background O-(2-[18F]fluoroethyl)-l-tyrosine positron emission tomography ([18F]FET PET) scanning is used in routine clinical management and evaluation of gliomas with a recommended 4 h prior fasting. Knowledge of test–retest variation of [18F]FET PET imaging uptake metrics and the impact of accidental protein intake can be critical for interpretation. The aim of this study was to investigate the repeatability of [18F]FET-PET metrics and to assess the impact of protein-intake prior to [18F]FET PET scanning of gliomas. Results Test–retest variability in the non-protein group was good with absolute (and relative) upper and lower limits of agreement of + 0.15 and − 0.13 (+ 9.7% and − 9.0%) for mean tumour-to-background ratio (TBRmean), + 0.43 and − 0.28 (+ 19.6% and − 11.8%) for maximal tumour-to-background ratio (TBRmax), and + 2.14 cm3 and − 1.53 ml (+ 219.8% and − 57.3%) for biological tumour volume (BTV). Variation was lower for uptake ratios than for BTV. Protein intake was associated with a 27% increase in the total sum of plasma concentration of the l-type amino acid transporter 1 (LAT1) relevant amino acids and with decreased standardized uptake value (SUV) in both healthy appearing background brain tissue (mean SUV − 25%) and in tumour (maximal SUV − 14%). Oral intake of 24 g of protein 1 h prior to injection of tracer tended to increase variability, but the effects on derived tumour metrics TBRmean and TBRmax were only borderline significant, and changes generally within the variability observed in the group with no protein intake. Conclusion The test–retest repeatability was found to be good, and better for TBRmax and TBRmean than BTV, with the methodological limitation that tumour growth may have influenced results. Oral intake of 24 g of protein one hour before a [18F]FET PET scan decreases uptake of [18F]FET in both tumour and in healthy appearing brain, with no clinically significant difference on the most commonly used tumour metrics.

ObjectiveWe present an algorithm to estimate the delay between a tissue time-activity curve and a blood input curve at a single-voxel level tested on whole-body data from a long-axial field-of-view scanner with tracers of different noise characteristics.MethodsWhole-body scans of 15 patients divided equally among three tracers, namely [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, which were used in development and testing of the algorithm. Delay times were estimated by fitting the cumulatively summed input function and tissue time-activity curve with special considerations for noise. To evaluate the performance of the algorithm, it was compared against two other algorithms also commonly applied in delay estimation: name cross-correlation and a one-tissue compartment model with incorporated delay. All algorithms were tested on both synthetic time-activity curves produced with the one-tissue compartment model with increasing levels of noise and delays between the tissue activity curve and the blood input curve. Whole-body delay maps were also calculated for each of the three tracers with data acquired on a long-axial field-of-view scanner with high time resolution.ResultsOur proposed model performs better for low signal-to-noise ratio time-activity curves compared to both cross-correlation and the one-tissue compartment models for non-[15O]H2O tracers. Testing on synthetically produced time-activity curves showed only a small and even residual delay, while the one-tissue compartment model with included delay showed varying residual delays.ConclusionThe algorithm is robust to noise and proves applicable on a range of tracers as tested on [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, and hence is a viable option offering the ability for delay correction across various organs and tracers in use with kinetic modeling.

Abstract Introduction Estimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images. Methods A total of 1309 patients with cognitive impairment scanned with [11C]PIB PET/CT or PET/MRI were included. Two convolutional neural networks (CNNs) for reading-based amyloid status and SUVR prediction were trained using 75% of the PET/CT data. The remaining PET/CT (n = 300) and all PET/MRI (n = 100) data was used for evaluation. Results The prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader’s classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R 2 = 0.96). Both reference and predicted SUVR had an accuracy of 97% compared to expert classification when applying a predetermined SUVR threshold of 1.35 for binary classification of amyloid status. Conclusion The proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline.

The accurate estimation of the tracer arterial blood concentration is crucial for reliable quantitative kinetic analysis in PET. In the current work, we demonstrate the automatic extraction of an image-derived input function (IDIF) from a CT AI-based aorta segmentation subsequently resliced to a dynamic PET series acquired on a Siemens Vision Quadra long-axial field of view scanner in 10 human subjects scanned with [15O]H2O. We demonstrate that the extracted IDIF is quantitative and in excellent agreement with a delay- and dispersion-corrected sampled arterial input function (AIF). Perfusion maps in the brain are calculated and compared from the IDIF and AIF, respectively, showed a high degree of correlation. The results demonstrate the possibility of defining a quantitatively correct IDIF compared with AIFs from the new-generation high-sensitivity and high-time-resolution long-axial field-of-view PET/CT scanners.

Abstract Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct anatomical–functional interpretation: They measure the deformation caused by the tumor on the surrounding brain structures, comparing the shape of various structures in the patient’s brain to their expected shape in healthy individuals. To obtain the required segmentations, we use an automatic method that is contrast-adaptive and robust to missing modalities, making the features generalizable across scanners and imaging protocols. Since the features we propose do not depend on characteristics of the tumor region itself, they are also applicable to post-operative images, which have been much less studied in the context of survival prediction. Using experiments involving both pre- and post-operative data, we show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features.

Abstract Background Dopamine transporter (DAT) imaging of striatum is clinically used in Parkinson’s disease (PD) and neurodegenerative parkinsonian syndromes (PS) especially in the early disease stages. The aim of the present study was to evaluate the diagnostic performance of the recently developed tracer for DAT imaging [18F]FE-PE2I PET/CT to the reference standard [123I]FP-CIT SPECT. Methods Ninety-eight unselected patients referred for DAT imaging were included prospectively and consecutively and evaluated with [18F]FE-PE2I PET/CT and [123I]FP-CIT SPECT on two separate days. PET and SPECT scans were categorized independently by two blinded expert readers as either normal, vascular changes, or mixed. Semiquantitative values were obtained for each modality and compared regarding effect size using Glass’ delta. Results Fifty-six of the [123I]FP-CIT SPECT scans were considered abnormal (52 caused by PS, 4 by infarctions). Using [18F]FE-PE2I PET/CT, 95 of the 98 patients were categorized identically to SPECT as PS or non-PS with a sensitivity of 0.94 [0.84–0.99] and a specificity of 1.00 [0.92–1.00]. Inter-reader agreement for [18F]FE-PE2I PET with a kappa of 0.97 [0.89–1.00] was comparable to the agreement for [123I]FP-CIT SPECT of 0.96 [0.76–1.00]. Semiquantitative values for short 10-min reconstructions of [18F]FE-PE2I PET/CT were comparable to longer reconstructions. The effect size for putamen/caudate nucleus ratio was significantly increased using PET compared to SPECT. Conclusions The high correspondence of [18F]FE-PE2I PET compared to reference standard [123I]FP-CIT SPECT establishes [18F]FE-PE2I PET as a feasible PET tracer for clinical use with favourable scan logistics.

Recent advancements in PET/CT, including the emergence of long axial field-of-view (LAFOV) PET/CT scanners, have increased PET sensitivity substantially. Consequently, there has been a significant reduction in the required tracer activity, shifting the primary source of patient radiation dose exposure to the attenuation correction (AC) CT scan during PET imaging. This study proposes a parameter-transferred conditional generative adversarial network (PT-cGAN) architecture to generate synthetic CT (sCT) images from non-attenuation corrected (NAC) PET images, with separate networks for [18F]FDG and [15O]H2O tracers. The study includes a total of 1018 subjects (n = 972 [18F]FDG, n = 46 [15O]H2O). Testing was performed on the LAFOV scanner for both datasets. Qualitative analysis found no differences in image quality in 30 out of 36 cases in FDG patients, with minor insignificant differences in the remaining 6 cases. Reduced artifacts due to motion between NAC PET and CT were found. For the selected organs, a mean average error of 0.45% was found for the FDG cohort, and that of 3.12% was found for the H2O cohort. Simulated low-count images were included in testing, which demonstrated good performance down to 45 s scans. These findings show that the AC of total-body PET is feasible across tracers and in low-count studies and might reduce the artifacts due to motion and metal implants.

PurposeConventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [18F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants.MethodsThe MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [18F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC.ResultsNinety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method.ConclusionWe evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.

PurposeBrain 2-Deoxy-2-[18F]fluoroglucose ([18F]FDG-PET) is widely used in the diagnostic workup of Alzheimer’s disease (AD). Current tools for uptake analysis rely on non-personalized templates, which poses a challenge as decreased glucose uptake could reflect neuronal dysfunction, or heterogeneous brain morphology associated with normal aging. Overcoming this, we propose a deep learning method for synthesizing a personalized [18F]FDG-PET baseline from the patient’s own MRI, and showcase its applicability in detecting AD pathology.MethodsWe included [18F]FDG-PET/MRI data from 123 patients of a local cohort and 600 patients from ADNI. A supervised, adversarial model with two connected Generative Adversarial Networks (GANs) was trained on cognitive normal (CN) patients with transfer-learning to generate full synthetic baseline volumes (sbPET) (192 × 192 × 192) which reflect healthy uptake conditioned on brain anatomy. Synthetic accuracy was measured by absolute relative %-difference (Abs%), relative %-difference (RD%), and peak signal-to-noise ratio (PSNR). Lastly, we deployed the sbPET images in a fully personalized method for localizing metabolic abnormalities.ResultsThe model achieved a spatially uniform Abs% of 9.4%, RD% of 0.5%, and a PSNR of 26.3 for CN subjects. The sbPET images conformed to the anatomical information dictated by the MRI and proved robust in presence of atrophy. The personalized abnormality method correctly mapped the pathology of AD subjects while showing little to no anomalies for CN subjects.ConclusionThis work demonstrated the feasibility of synthesizing fully personalized, healthy-appearing [18F]FDG-PET images. Using these, we showcased a promising application in diagnosing AD, and theorized the potential value of sbPET images in other neuroimaging routines.

Purpose: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim was to investigate the feasibility of using activity reduction in clinical [18F]FE-PE2I and [11C]PiB PET/CT scans, simulating low injected activity or scanning time reduction, in combination with AI-assisted denoising. Methods: A total of 162 patients with clinically uncertain Alzheimer's disease underwent amyloid [11C]PiB PET/CT and 509 patients referred for clinically uncertain Parkinson's disease underwent dopamine transporter (DAT) [18F]FE-PE2I PET/CT. Simulated low-activity data were obtained by random sampling of 5% of the events from the list-mode file and a 5% time window extraction in the middle of the scan. A three-dimensional convolutional neural network (CNN) was trained to denoise the resulting PET images for each disease cohort. Results: Noise reduction of low-activity PET images was successful for both cohorts using 5% of the original activity with improvement in visual quality and all similarity metrics with respect to the ground-truth images. Clinically relevant metrics extracted from the low-activity images deviated

Abstract Oscura is a planned light-dark matter search experiment using Skipper-CCDs with a total active mass of 10 kg. As part of the detector development, the collaboration plans to build the Oscura Integration Test (OIT), an engineering test with 10% of the total mass. Here we discuss the early science opportunities with the OIT to search for millicharged particles (mCPs) using the NuMI beam at Fermilab. mCPs would be produced at low energies through photon-mediated processes from decays of scalar, pseudoscalar, and vector mesons, or direct Drell-Yan productions. Estimates show that the OIT would be a world-leading probe for mCPs in the ∼MeV mass range.

Software plays an important role in contemporary research. Aside from its use for administering traditional instruments like surveys and in data analysis, the widespread use of mobile and web apps for social, medical and lifestyle engagement has led to software becoming a research intervention in its own right. For example, it is not unusual to find apps being studied for their utility as interventions in health and social life. Since the software may persist in use beyond the life of an investigation, this raises questions as to the extent of ethical duties for researchers involved in its production and/or study towards the participants involved. Key factors identified include the extent of affect created by the software, the effect it has on a participant’s life, the length of investigation, cost of maintenance and participant agency. In this article we discuss the issues raised in such situations, considering them in the context of post-research duties of care and suggesting strategies to balance the burden on researchers with the need for ongoing participant support.

Wineinformatics involves the application of data science techniques to wine-related datasets generated during the grape growing, wine production, and wine evaluation processes. Its aim is to extract valuable insights that can benefit wine producers, distributors, and consumers. This study highlights the potential of neural networks as the most effective black-box classification algorithm in wineinformatics for analyzing wine reviews processed by the Computational Wine Wheel (CWW). Additionally, the paper provides a detailed overview of the enhancements made to the CWW and presents a thorough comparison between the latest version and its predecessors. In comparison to the highest accuracy results obtained in the latest research work utilizing an elite Bordeaux dataset, which achieved approximately 75% accuracy for Robert Parker’s reviews and 78% accuracy for the Wine Spectator’s reviews, the combination of neural networks and CWW3.0 consistently yields improved performance. Specifically, this combination achieves an accuracy of 82% for Robert Parker’s reviews and 86% for the Wine Spectator’s reviews on the elite Bordeaux dataset as well as a newly created dataset that contains more than 10,000 wines. The adoption of machine learning algorithms for wine reviews helps researchers understand more about quality wines by analyzing the end product and deconstructing the sensory attributes of the wine; this process is similar to reverse engineering in the context of wine to study and improve the winemaking techniques employed.

Summary: Background: Hypertensive disorders of pregnancy (HDP) are a leading cause of maternal death in low- to middle-income countries (LMIC). The American College of Obstetricians and Gynecologists (ACOG) updated diagnostic guidelines to align signs and symptoms with those associated with maternal death. We performed an observational study to ask whether ACOG guidelines were employed and associated with adverse outcomes in La Paz-El Alto, Bolivia, an LMIC. Methods: Medical records for all HDP discharge diagnoses (n = 734) and twice as many controls (n = 1647) were reviewed for one year at the three largest delivery sites. For the 690 cases and 1548 controls meeting inclusion criteria (singleton, 18–45 maternal age, local residence), health history, blood pressures, symptoms, lab tests, HDP diagnoses (i.e., gestational hypertension [GH]; preeclampsia [PE]; haemolysis, low platelets, high liver enzymes [HELLP] syndrome, eclampsia), and adverse outcomes were recorded. Bolivian diagnoses were compared to ACOG guidelines using accuracy analysis and associated with adverse outcomes by logistic regression. Findings: Both systems agreed with respect to eclampsia, but only 27% of all Bolivian HDP diagnoses met ACOG criteria. HDP increased adverse maternal- or perinatal-outcome risks for both systems, but ACOG guidelines enabled more pre-delivery diagnoses, graded maternal-risk assessment, and targeting of HDP terminating in maternal death. Interpretation: Bolivia diagnoses agreed with ACOG guidelines concerning end-stage disease (eclampsia) but not the other HDP due mainly to ACOG's recognition of a broader range of severe features. ACOG guidelines can aid in identifying pregnancies at greatest risk in LMICs, where most maternal and perinatal deaths occur. Funding: NIH TW010797, HD088590, HL138181, UL1 TR002535.

A Delphi study has been undertaken to address eight specific areas relating to the management of hand-arm vibration syndrome and carpal tunnel syndrome, with the aim of providing consensus guidelines.