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The Compact Muon Solenoid (CMS) is a general-purpose detector for use with CERN's Large Hadron Collider (LHC).Tests show that the CSM's strands meet required specifications and perform well.

A progress report on the development of outer cables for the Superconducting Super Collider Laboratory's dipole magnets is presented. The work has been subcontracted to Outokumpu Superconductors Oy under the facility's Vendor Qualification Program. So far, Outokumpu has manufactured 18 and 15 billets under Phase 1A and Phase 1B of the program, respectively. A characterization of the superconducting wires is discussed.

Gating of positron emission tomography images has been shown to reduce the motion effects, especially when imaging small targets, such as coronary plaques. However, the selection of optimal number of gates for gating remains a challenge. Selecting too high number of gates results in a loss of signal-to-noise ratio, while too low number of gates does remove only part of the motion. Here, we introduce a respiratory-cardiac motion model to determine the optimal number of respiratory and cardiac gates. We evaluate the model using a realistic heart phantom and data from 12 cardiac patients (47–77 years, 64.5 on average). To demonstrate the benefits of our model, we compared it with an existing respiratory model. Based on our study, the optimal number of gates was determined to be five respiratory and four cardiac gates in the phantom and patient studies. In the phantom study, the diameter of the most active hot spot was reduced by 24% in the dual gated images compared to non-gated images. In the patient study, the thickness of myocardium wall was reduced on average by 21%. In conclusion, the motion model can be used for estimating the optimal number of respiratory and cardiac gates for dual gating. [ABSTRACT FROM AUTHOR]

Background: Cardiac positron emission tomography (PET) can visualize and quantify the molecular and physiological pathways of cardiac function. However, cardiac and respiratory motion can introduce blurring that reduces PET image quality and quantitative accuracy. Dual cardiac- and respiratory-gated PET reconstruction can mitigate motion artifacts but increases noise as only a subset of data are used for each time frame of the cardiac cycle., Aim: The objective of this study is to create a zero-shot image denoising framework using a conditional generative adversarial networks (cGANs) for improving image quality and quantitative accuracy in non-gated and dual-gated cardiac PET images., Methods: Our study included retrospective list-mode data from 40 patients who underwent an 18 F-fluorodeoxyglucose ( 18 F-FDG) cardiac PET study. We initially trained and evaluated a 3D cGAN-known as Pix2Pix-on simulated non-gated low-count PET data paired with corresponding full-count target data, and then deployed the model on an unseen test set acquired on the same PET/CT system including both non-gated and dual-gated PET data., Results: Quantitative analysis demonstrated that the 3D Pix2Pix network architecture achieved significantly (p value<0.05) enhanced image quality and accuracy in both non-gated and gated cardiac PET images. At 5%, 10%, and 15% preserved count statistics, the model increased peak signal-to-noise ratio (PSNR) by 33.7%, 21.2%, and 15.5%, structural similarity index (SSIM) by 7.1%, 3.3%, and 2.2%, and reduced mean absolute error (MAE) by 61.4%, 54.3%, and 49.7%, respectively. When tested on dual-gated PET data, the model consistently reduced noise, irrespective of cardiac/respiratory motion phases, while maintaining image resolution and accuracy. Significant improvements were observed across all gates, including a 34.7% increase in PSNR, a 7.8% improvement in SSIM, and a 60.3% reduction in MAE., Conclusion: The findings of this study indicate that dual-gated cardiac PET images, which often have post-reconstruction artifacts potentially affecting diagnostic performance, can be effectively improved using a generative pre-trained denoising network., (© 2024. The Author(s), under exclusive licence to The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine.)

Background: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 H 2 O perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imaging. The classifier uses polar map images with numerical data and visualizes data findings., Methods: A DLmodel was implemented and evaluated on 138 individuals, consisting of a combined image-and data-based classifier considering 35 clinical, CTA, and PET variables. Data from invasive coronary angiography were used as reference. Performance was evaluated with clinical classification using accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE), precision (PRE), net benefit, and Cohen's Kappa. Statistical testing was conducted using McNemar's test., Results: The DL model had a median ACC = 0.8478, AUC = 0.8481, F1S = 0.8293, SEN = 0.8500, SPE = 0.8846, and PRE = 0.8500. Improved detection of true-positive and false-negative cases, increased net benefit in thresholds up to 34%, and comparable Cohen's kappa was seen, reaching similar performance to clinical reading. Statistical testing revealed no significant differences between DL model and clinical reading., Conclusions: The combined DL model is a feasible and an effective method in detection of CAD, allowing to highlight important data findings individually in interpretable manner., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Several factors may decrease the accuracy of quantitative PET myocardial perfusion imaging (MPI). It is therefore essential to ensure that myocardial blood flow (MBF) values are reproducible and accurate, and to design systematic protocols to achieve this. Until now, no systematic phantom protocols have been available to assess the technical factors affecting measurement accuracy and reproducibility in MPI., Materials and Methods: We implemented a standard measurement protocol, which applies a flow phantom in order to compare image-derived flow values with respect to a ground truth flow value with [ 15 O]H 2 O MPI performed on both a Discovery MI (DMI-20, GE Healthcare) and a Biograph Vision 600 (Vision-600, Siemens Healthineers) system. Both systems have automatic [ 15 O]H 2 O radio water generators (Hidex Oy) individually installed, allowing us to also study the differences occurring due to two different bolus delivery systems. To investigate the technical factors contributing to the modelled flow values, we extracted the [ 15 O]H 2 O bolus profiles, the flow values from the kinetic modeling (Qin and Qout), and finally calculated their differences between test-retest measurements on both systems., Results: The measurements performed on the DMI-20 system produced Qin and Qout values corresponging to each other as well as to the reference flow value across all test-retest measurements. The repeatability differences on DMI-20 were 2.1% ± 2.6% and 3.3% ± 4.1% for Qin and Qout, respectively. On Vision-600 they were 10% ± 8.4% and 11% ± 10% for Qin and Qout, respectively. The measurements performed on the Vision-600 system showed more variation between Qin and Qout values across test-retest measurements and exceeded 15% difference in 7/24 of the measurements., Conclusions: A preliminary protocol for measuring the accuracy and reproducibility of flow values in [ 15 O]H 2 O MPI between digital PET/CT systems was assessed. The test-retest reproducibility falls below 15% in majority of the measurements conducted between two individual injector systems and two digital PET/CT systems. This study highlights the importance of implementing a standardized bolus injection and delivery protocol and importance of assessing technical factors affecting flow value reproducibility, which should be carefully investigated in a multi-center setting., (© 2024. The Author(s).)

Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images., (© 2024. The Author(s).)

Aims: To evaluate the incremental value of positron emission tomography (PET) myocardial perfusion imaging (MPI) over coronary computed tomography angiography (CCTA) in predicting short- and long-term outcome using machine learning (ML) approaches., Methods and Results: A total of 2411 patients with clinically suspected coronary artery disease (CAD) underwent CCTA, out of whom 891 patients were admitted to downstream PET MPI for haemodynamic evaluation of obstructive coronary stenosis. Two sets of Extreme Gradient Boosting (XGBoost) ML models were trained, one with all the clinical and imaging variables (including PET) and the other with only clinical and CCTA-based variables. Difference in the performance of the two sets was analysed by means of area under the receiver operating characteristic curve (AUC). After the removal of incomplete data entries, 2284 patients remained for further analysis. During the 8-year follow-up, 210 adverse events occurred including 59 myocardial infarctions, 35 unstable angina pectoris, and 116 deaths. The PET MPI data improved the outcome prediction over CCTA during the first 4 years of the observation time and the highest AUC was at the observation time of Year 1 (0.82, 95% confidence interval 0.804-0.827). After that, there was no significant incremental prognostic value by PET MPI., Conclusion: PET MPI variables improve the prediction of adverse events beyond CCTA imaging alone for the first 4 years of follow-up. This illustrates the complementary nature of anatomic and functional information in predicting the outcome of patients with suspected CAD., Competing Interests: Conflict of interest: J.K. received consultancy fees from GE Healthcare and AstraZeneca and speaker fees from GE Healthcare, Bayer, Lundbeck, Boehringer-Ingelheim, Pfizer, and Merck, outside of the submitted work. A.S. received consultancy fees from Amgen and Astra Zeneca, Boehringer-Ingelheim, and Pfizer, and speaker fees from Abbott, Astra Zeneca, and Bayer. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

[ 68 Ga]Ga-NODAGA-Arg-Gly-Asp (RGD) is a PET tracer targeting α v β 3 integrin, which is upregulated during angiogenesis soon after acute myocardial infarction (AMI). We prospectively evaluated determinants of myocardial uptake of [ 68 Ga]Ga-NODAGA-RGD and its associations with left ventricular (LV) function in patients after AMI. Methods: Myocardial blood flow and [ 68 Ga]Ga-NODAGA-RGD uptake (60 min after injection) were evaluated by PET in 31 patients 7.7 ± 3.8 d after primary percutaneous coronary intervention for ST-elevation AMI. Transthoracic echocardiography of LV function was performed on the day of PET and at the 6-mo follow-up. Results: PET images showed increased uptake of [ 68 Ga]Ga-NODAGA-RGD in the ischemic area at risk (AAR), predominantly in injured myocardial segments. The SUV in the segment with the highest uptake (SUV max ) in the ischemic AAR was higher than the SUV mean of the remote myocardium (0.73 ± 0.16 vs. 0.51 ± 0.11, P < 0.001). Multivariable predictors of [ 68 Ga]Ga-NODAGA-RGD uptake in the AAR included high peak N-terminal pro-B-type natriuretic peptide ( P < 0.001), low LV ejection fraction, low global longitudinal strain ( P = 0.01), and low longitudinal strain in the AAR ( P = 0.01). [ 68 Ga]Ga-NODAGA-RGD uptake corrected for myocardial blood flow and perfusable tissue fraction in the AAR predicted improvement in global longitudinal strain at follow-up ( P = 0.002), independent of peak troponin, N-terminal pro-B-type natriuretic peptide, and LV ejection fraction. Conclusion: [ 68 Ga]Ga-NODAGA-RGD uptake shows increased α v β 3 integrin expression in the ischemic AAR early after AMI that is associated with regional and global systolic dysfunction, as well as increased LV filling pressure. Increased [ 68 Ga]Ga-NODAGA-RGD uptake predicts improvement of global LV function 6 mo after AMI., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: Machine Learning (ML) allows integration of the numerous variables delivered by cardiac PET/CT, while traditional survival analysis can provide explainable prognostic estimates from a restricted number of input variables. We implemented a hybrid ML-and-survival analysis of multimodal PET/CT data to identify patients who developed myocardial infarction (MI) or death in long-term follow up., Methods: Data from 739 intermediate risk patients who underwent coronary CT and selectively stress 15 O-water-PET perfusion were analyzed for the occurrence of MI and all-cause mortality. Images were evaluated segmentally for atherosclerosis and absolute myocardial perfusion through 75 variables that were integrated through ML into an ML-CCTA and an ML-PET score. These scores were then modeled along with clinical variables through Cox regression. This hybridized model was compared against an expert interpretation-based and a calcium score-based model., Results: Compared with expert- and calcium score-based models, the hybridized ML-survival model showed the highest performance (CI .81 vs .71 and .64). The strongest predictor for outcomes was the ML-CCTA score., Conclusion: Prognostic modeling of PET/CT data for the long-term occurrence of adverse events may be improved through ML imaging score integration and subsequent traditional survival analysis with clinical variables. This hybridization of methods offers an alternative to traditional survival modeling of conventional expert image scoring and interpretation., (© 2023. The Author(s).)

Recently, PET systems with a long axial field of view have become the current state of the art. Total-body PET scanners enable unique possibilities for scientific research and clinical diagnostics, but this new technology also raises numerous challenges. A key advantage of total-body imaging is that having all the organs in the field of view allows studying biologic interaction of all organs simultaneously. One of the new, promising imaging techniques is total-body quantitative perfusion imaging. Currently, 15 O-labeled water provides a feasible option for quantitation of tissue perfusion at the total-body level. This review summarizes the status of the methodology and the analysis and provides examples of preliminary findings on applications of quantitative parametric perfusion images for research and clinical work. We also describe the opportunities and challenges arising from moving from single-organ studies to modeling of a multisystem approach with total-body PET, and we discuss future directions for total-body imaging., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: New Block-Sequential-Regularized-Expectation-Maximization (BSREM) image reconstruction technique has been introduced for clinical use mainly for oncologic use. Accurate and quantitative image reconstruction is essential in myocardial perfusion imaging with positron emission tomography (PET) as it utilizes absolute quantitation of myocardial blood flow (MBF). The aim of the study was to evaluate BSREM reconstruction for quantitation in patients with suspected coronary artery disease (CAD)., Methods and Results: We analyzed cardiac [ 15 O]H 2 O PET studies of 177 patients evaluated for CAD. Differences between BSREM and Ordered-Subset-Expectation-Maximization with Time-Of-Flight (TOF) and Point-Spread-Function (PSF) modeling (OSEM-TOF-PSF) in terms of MBF, perfusable tissue fraction, and vascular volume fraction were measured. Classification of ischemia was assessed between the algorithms. OSEM-TOF-PSF and BSREM provided similar global stress MBF in patients with ischemia (1.84 ± 0.21 g⋅ml -1 ⋅min -1 vs 1.86 ± 0.21 g⋅ml -1 ⋅min -1 ) and no ischemia (3.26 ± 0.34 g⋅ml -1 ⋅min -1 vs 3.28 ± 0.34 g⋅ml -1 ⋅min -1 ). Global resting MBF was also similar (0.97 ± 0.12 g⋅ml -1 ⋅min -1 and 1.12 ± 0.06 g⋅ml -1 ⋅min -1 ). The largest mean relative difference in MBF values was 7%. Presence of myocardial ischemia was classified concordantly in 99% of patients using OSEM-TOF-PSF and BSREM reconstructions CONCLUSION: OSEM-TOF-PSF and BSREM image reconstructions produce similar MBF values and diagnosis of myocardial ischemia in patients undergoing [ 15 O]H 2 O PET due to suspected obstructive coronary artery disease., (© 2023. The Author(s).)

Carimas is a multi-purpose medical imaging data processing tool, which can be used to visualize, analyze, and model different medical images in research. Originally, it was developed only for positron emission tomography data in 2009, but the use of this software has extended to many other tomography imaging modalities, such as computed tomography and magnetic resonance imaging. Carimas is especially well-suited for analysis of three- and four-dimensional image data and creating polar maps in modeling of cardiac perfusion. This article explores various parts of Carimas, including its key features, program structure, and application possibilities., (© 2023. The Author(s).)

Introduction: Non-invasive imaging techniques such as positron emission tomography (PET) are extremely important for cancer detection and characterization especially for difficult to biopsy or extremely delicate organs such as the brain. The folate analogue 1,4,7-triazacylononane-1,4,7-triacetic acid-conjugated folate radiolabeled with aluminum fluoride-18 ([ 18 F]FOL) has been previously shown to accumulate preferentially in tumor cells with an overexpression of folate receptors (FRs) and here was investigated for its ability to detect orthotopic gliomas in a rat model. In addition, we studied the expression of FRs in human glioblastoma samples to investigate if an analogous relationship may exist., Methods: Nine BDIX rats were injected with BT4C rat glioma cells into the right hemisphere of the brain. Animals were imaged with gadolinium-enhanced magnetic resonance imaging at on days prior to PET/computed tomography (CT) imaging. Animals were divided into two groups, and were PET/CT imaged with either [ 18 F]FOL or 2-deoxy-2-18F-fluoro-D-glucose ([ 18 F]FDG) on 19 and 32-days post glioma grafting. Two subjects were also PET/CT imaged with [ 18 F]FOL on day 16. Biodistribution was studied and brains were cryosectioned for autoradiography, immunofluorescence, and histological studies. Patient-derived paraffin-embedded glioblastomas were sectioned and stained with similar methods., Results: PET imaging showed an increase of [18F]FOL tumor-to-brain uptake ratio (TBR) over the study duration from day 16/19 (3.3 ± 0.9) increasing to 5.7 ± 1.0 by day 32. [ 18 F]FDG PET-imaged rats had a consistent TBR of 1.6 ± 0.1 throughout the study. Ex vivo autoradiography results revealed an exceptionally high TBR of 116.1 ± 26.9 for [ 18 F]FOL while the [18F]FDG values were significantly lower giving 2.9 ± 0.6 (P<0.0001). Immunostaining demonstrated an increased presence of FR-α in the BT4C gliomas versus the contralateral brain tissue, while FR-β was present only on glioma periphery. Human sections assayed showed similar FRs expression characteristics., Conclusion: This study shows upregulation of FR-α inside glioma regions in both human and animal tissue, providing a biochemical basis for the observed increased [ 18 F]FOL uptake in animal PET images. These results suggest that FRs targeting imaging and therapeutic compounds may possess clinically relevant translational abilities for the detection and treatment of gliomas., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Miner, Liljenbäck, Virta, Kärnä, Viitanen, Elo, Gardberg, Teuho, Saipa, Rajander, Mansour, Cleveland, Low, Li and Roivainen.)

Purpose: This systematic review provides a consensus on the clinical feasibility of machine learning (ML) methods for brain PET attenuation correction (AC). Performance of ML-AC were compared to clinical standards., Methods: Two hundred and eighty studies were identified through electronic searches of brain PET studies published between January 1, 2008, and August 1, 2022. Reported outcomes for image quality, tissue classification performance, regional and global bias were extracted to evaluate ML-AC performance. Methodological quality of included studies and the quality of evidence of analysed outcomes were assessed using QUADAS-2 and GRADE, respectively., Results: A total of 19 studies (2371 participants) met the inclusion criteria. Overall, the global bias of ML methods was 0.76 ± 1.2%. For image quality, the relative mean square error (RMSE) was 0.20 ± 0.4 while for tissues classification, the Dice similarity coefficient (DSC) for bone/soft tissue/air were 0.82 ± 0.1 / 0.95 ± 0.03 / 0.85 ± 0.14., Conclusions: In general, ML-AC performance is within acceptable limits for clinical PET imaging. The sparse information on ML-AC robustness and its limited qualitative clinical evaluation may hinder clinical implementation in neuroimaging, especially for PET/MRI or emerging brain PET systems where standard AC approaches are not readily available., Competing Interests: Declaration of Competing Interest The authors declare that they have no existing conflict of interest., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

Background: Dual-gating reduces respiratory and cardiac motion effects but increases noise. With motion correction, motion is minimized and image quality preserved. We applied motion correction to create end-diastolic respiratory motion corrected images from dual-gated images., Methods: [ 18 F]-fluorodeoxyglucose ([ 18 F]-FDG) PET images of 13 subjects were reconstructed with 4 methods: non-gated, dual-gated, motion corrected, and motion corrected with 4D-CT (MoCo-4D). Image quality was evaluated using standardized uptake values, contrast ratio, signal-to-noise ratio, coefficient of variation, and contrast-to-noise ratio. Motion minimization was evaluated using myocardial wall thickness., Results: MoCo-4D showed improvement for contrast ratio (2.83 vs 2.76), signal-to-noise ratio (27.5 vs 20.3) and contrast-to-noise ratio (14.5 vs 11.1) compared to dual-gating. The uptake difference between MoCo-4D and non-gated images was non-significant (P > .05) for the myocardium (2.06 vs 2.15 g/mL), but significant (P < .05) for the blood pool (.80 vs .86 g/mL). Non-gated images had the lowest coefficient of variation (27.3%), with significant increase for all other methods (31.6-32.5%). MoCo-4D showed smallest myocardial wall thickness (16.6 mm) with significant decrease compared to non-gated images (20.9 mm)., Conclusions: End-diastolic respiratory motion correction and 4D-CT resulted in improved motion minimization and image quality over standard dual-gating., (© 2021. The Author(s).)

In Myocardial Perfusion Imaging (MPI) with Positron Emission Tomography/Computed Tomography (PET/CT) systems, accurate quantification is essential. We assessed flow quantification accuracy over various injected activities using a flow phantom., Methods: The study was performed on the digital 4-ring Discovery MI (DMI-20) and analog Discovery 690 (D690) PET/CT systems, using 325-1257 MBq of [ 15 O]H 2 O. PET performance and flow quantification accuracy were assessed in terms of count-rates, dead-time factors (DTF), scatter fractions (SF), time-activity curves (TACs), areas-under-the-curves (AUCs) and flow values., Results: On DMI-20, prompts of 12.8 Mcps, DTF of 2.06 and SF of 46.1% were measured with 1257 MBq of activity. On the D690, prompts of 6.85 Mcps, DTF of 1.57 and SF of 32.5% were measured with 1230 MBq of activity. AUC values were linear over all activities. Mean wash-in flow error was - 9% for both systems whereas wash-out flow error was - 5% and - 6% for DMI-20 and D690. With the highest activity, wash-out flow error was - 12% and - 7% for the DMI-20 and D690., Conclusion: DMI-20 and D690 preserved accurate flow quantification over all injected activities, with maximum error of - 12%. In the future, flow quantification accuracy over the activities and count-rates evaluated in this study should be assessed., (© 2021. The Author(s).)

Background: Attenuation correction is crucial in quantitative positron emission tomography-magnetic resonance (PET-MRI) imaging. We evaluated three methods to improve the segmentation and modelling of the attenuation coefficients in the nasal sinus region. Two methods (cuboid and template method) included a MRI-CT conversion model for assigning the attenuation coefficients in the nasal sinus region, whereas one used fixed attenuation coefficient assignment (bulk method)., Methods: The study population consisted of data of 10 subjects which had undergone PET-CT and PET-MRI. PET images were reconstructed with and without time-of-flight (TOF) using CT-based attenuation correction (CTAC) as reference. Comparison was done visually, using DICE coefficients, correlation, analyzing attenuation coefficients, and quantitative analysis of PET and bias atlas images., Results: The median DICE coefficients were 0.824, 0.853, 0.849 for the bulk, cuboid and template method, respectively. The median attenuation coefficients were 0.0841 cm -1 , 0.0876 cm -1 , 0.0861 cm -1 and 0.0852 cm -1 , for CTAC, bulk, cuboid and template method, respectively. The cuboid and template methods showed error of less than 2.5% in attenuation coefficients. An increased correlation to CTAC was shown with the cuboid and template methods. In the regional analysis, improvement in at least 49% and 80% of VOI was seen with non-TOF and TOF imaging. All methods showed errors less than 2.5% in non-TOF and less than 2% in TOF reconstructions., Conclusions: We evaluated two proof-of-concept methods for improving quantitative accuracy in PET/MRI imaging and showed that bias can be further reduced by inclusion of TOF. Largest improvements were seen in the regions of olfactory bulb, Heschl's gyri, lingual gyrus and cerebellar vermis. However, the overall effect of inclusion of the sinus region as separate class in MRAC to PET quantification in the brain was considered modest., (© 2022. The Author(s).)

We implemented a two-dimensional convolutional neural network (CNN) for classification of polar maps extracted from Carimas (Turku PET Centre, Finland) software used for myocardial perfusion analysis. 138 polar maps from 15 O-H 2 O stress perfusion study in JPEG format from patients classified as ischemic or non-ischemic based on finding obstructive coronary artery disease (CAD) on invasive coronary artery angiography were used. The CNN was evaluated against the clinical interpretation. The classification accuracy was evaluated with: accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE) and precision (PRE). The CNN had a median ACC of 0.8261, AUC of 0.8058, F1S of 0.7647, SEN of 0.6500, SPE of 0.9615 and PRE of 0.9286. In comparison, clinical interpretation had ACC of 0.8696, AUC of 0.8558, F1S of 0.8333, SEN of 0.7500, SPE of 0.9615 and PRE of 0.9375. The CNN classified only 2 cases differently than the clinical interpretation. The clinical interpretation and CNN had similar accuracy in classifying false positives and true negatives. Classification of ischemia is feasible in 15 O-H 2 O stress perfusion imaging using JPEG polar maps alone with a custom CNN and may be useful for the detection of obstructive CAD., (© 2022. The Author(s).)

We studied the association between episodic memory and cortical fibrillar β-amyloid pathology within twin pairs. Using telephone-administered cognitive screening of 1415 twin pairs in a population-based older Finnish Twin Cohort study, we identified 45 (mean [SD] age 72.9 [4.0] years, 40% women) cognitively discordant same-sex twin pairs (24 dizygotic and 21 monozygotic) without neurological or psychiatric disorders other than AD or mild cognitive impairment. In-person neuropsychological testing was conducted. Cortical amyloid was measured with carbon 11-labelled Pittsburgh compound B ([ 11 C]PiB) positron emission tomography imaging and quantified as the average standardized uptake value ratio in cortical regions affected in AD. Larger within-twin pair differences in verbal immediate (r = -0.42) and delayed free recall (r = -0.41), and visual delayed free recall (r = -0.46) were associated with larger within-twin pair differences in [ 11 C]PiB uptake (p's < 0.01). Correlations were not significantly different in dizygotic and monozygotic pairs suggesting that the episodic memory-cortical amyloid relationship is not confounded by genetic effects. However, larger samples are needed to draw more definitive conclusions., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: The three positron emission tomography (PET) imaging compounds: (2 S ,4 R )-4-[ 18 F]Fluoroglutamine ([ 18 F]FGln), L -[methyl- 11 C]Methionine ([ 11 C]Met), and 2-deoxy-2-[ 18 F]fluoro- D -glucose ([ 18 F]FDG) were investigated to contrast their ability to image orthotopic BT4C gliomas in BDIX rats. Two separate small animal imaging systems were compared for their tumor detection potential. Dynamic acquisition of [ 18 F]FGln was evaluated with multiple pharmacokinetic models for future quantitative comparison., Procedures: Up to four imaging studies were performed on each orthotopically grafted BT4C glioma-bearing BDIX rat subject (n = 16) on four consecutive days. First, a DOTAREM ® contrast enhanced MRI followed by attenuation correction CT and dynamic PET imaging with each radiopharmaceutical (20 min [ 11 C]Met, 60 min [ 18 F]FDG, and 60 min [ 18 F]FGln with either the Molecubes PET/CT (n = 5) or Inveon PET/CT cameras (n = 11). Ex vivo brain autoradiography was completed for each radiopharmaceutical and [ 18 F]FGln pharmacokinetics were studied by injecting 40 MBq into healthy BDIX rats (n = 10) and collecting blood samples between 5 and 60 min. Erythrocyte uptake, plasma protein binding and plasma parent-fraction were combined to estimate the total blood bioavailability of [ 18 F]FGln over time. The corrected PET-image blood data was then applied to multiple pharmacokinetic models., Results: Average BT4C tumor-to-healthy brain tissue uptake ratios (TBR) for PET images reached maxima of: [ 18 F]FGln TBR: 1.99 ± 0.19 (n = 13), [ 18 F]FDG TBR: 1.41 ± 0.11 (n = 6), and [ 11 C]Met TBR: 1.08 ± 0.08, (n = 12) for the dynamic PET images. Pharmacokinetic modeling in dynamic [ 18 F]FGln studies suggested both reversible and irreversible uptake play a similar role. Imaging with Inveon and Molecubes yielded similar end-result ratios with insignificant differences (p > 0.25)., Conclusions: In orthotopic BT4C gliomas, [ 18 F]FGln may offer improved imaging versus [ 11 C]Met and [ 18 F]FDG. No significant difference in normalized end-result data was found between the Inveon and Molecubes camera systems. Kinetic modelling of [ 18 F]FGln uptake suggests that both reversible and irreversible uptake play an important role in BDIX rat pharmacokinetics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Miner, Liljenbäck, Virta, Helin, Eskola, Elo, Teuho, Seppälä, Oikonen, Yang, Kindler-Röhrborn, Minn, Li and Roivainen.)

Background: Assessment of myocardial viability is often needed in patients with chest pain and reduced ejection fraction. We evaluated the performance of reduced resting MBF, perfusable tissue fraction (PTF), and perfusable tissue index (PTI) in the assessment of myocardial viability in a pig model of myocardial infarction (MI)., Methods and Results: Pigs underwent resting [ 15 O]water PET perfusion study 12 weeks after surgical (n = 16) or 2 weeks after catheter-based (n = 4) occlusion of the proximal left anterior descending coronary artery. MBF, PTF, and PTI were compared with volume fraction of MI in matched segments as assessed by triphenyl tetrazolium chloride staining of LV slices. MBF and PTF were lower in infarcted than non-infarcted segments. Segmental analysis of MBF showed similar area under the curve (AUC) of 0.85, 0.86, and 0.90 with relative MBF, PTF, and PTI for the detection of viable myocardium defined as infarct volume fraction of < 75%. Cut-off values of relative MBF of ≥ 67% and PTF of ≥ 66% resulted in accuracies of 90% and 81%, respectively., Conclusions: Our results indicate that resting MBF, PTF, and PTI based on [ 15 O]water PET perfusion imaging are useful for the assessment of myocardial viability., (© 2019. The Author(s).)

We present a novel method for estimating respiratory motion using inertial measurement units (IMUs) based on microelectromechanical systems (MEMS) technology. As an application of the method we consider the amplitude gating of positron emission tomography (PET) imaging, and compare the method against a clinically used respiration motion estimation technique. The presented method can be used to detect respiratory cycles and estimate their lengths with state-of-the-art accuracy when compared to other IMU-based methods, and is the first based on commercial MEMS devices, which can estimate quantitatively both the magnitude and the phase of respiratory motion from the abdomen and chest regions. For the considered test group consisting of eight subjects with acute myocardial infarction, our method achieved the absolute breathing rate error per minute of 0.44 ± 0.23 1/min, and the absolute amplitude error of 0.24 ± 0.09 cm, when compared to the clinically used respiratory motion estimation technique. The presented method could be used to simplify the logistics related to respiratory motion estimation in PET imaging studies, and also to enable multi-position motion measurements for advanced organ motion estimation.

Contrast-enhanced magnetic resonance imaging (MRI) is a promising method for estimating myocardial blood flow (MBF). However, it is often affected by noise from imaging artefacts, such as dark rim artefact obscuring relevant features. Machine learning enables extracting important features from such noisy data and is increasingly applied in areas where traditional approaches are limited. In this study, we investigate the capacity of machine learning, particularly support vector machines (SVM) and random forests (RF), for estimating MBF from tissue impulse response signal in an animal model. Domestic pigs (n = 5) were subjected to contrast enhanced first pass MRI (MRI-FP) and the impulse response at different regions of the myocardium (n = 24/pig) were evaluated at rest (n = 120) and stress (n = 96). Reference MBF was then measured using positron emission tomography (PET). Since the impulse response may include artefacts, classification models based on SVM and RF were developed to discriminate noisy signal. In addition, regression models based on SVM, RF and linear regression (for comparison) were developed for estimating MBF from the impulse response at rest and stress. The classification and regression models were trained on data from 4 pigs (n = 168) and tested on 1 pig (n = 48). Models based on SVM and RF outperformed linear regression, with higher correlation (R SVM 2  = 0.81, R RF 2  = 0.74, R linear&#95;regression 2  = 0.60; ρ SVM  = 0.76, ρ RF  = 0.76, ρ linear&#95;regression  = 0.71) and lower error (RMSE SVM  = 0.67 mL/g/min, RMSE RF  = 0.77 mL/g/min, RMSE linear&#95;regression  = 0.96 mL/g/min) for predicting MBF from MRI impulse response signal. Classifier based on SVM was optimal for detecting impulse response signals with artefacts (accuracy = 92%). Modified dual bolus MRI signal, combined with machine learning, has potential for accurately estimating MBF at rest and stress states, even from signals with dark rim artefacts. This could provide a protocol for reliable and easy estimation of MBF, although further research is needed to clinically validate the approach.

Background: We investigated the image quality of 11 C, 68 Ga, 18 F and 89 Zr, which have different positron fractions, physical half-lifes and positron ranges. Three small animal positron emission tomography/computed tomography (PET/CT) systems were used in the evaluation, including the Siemens Inveon, RAYCAN X5 and Molecubes β-cube. The evaluation was performed on a single scanner level using the national electrical manufacturers association (NEMA) image quality phantom and analysis protocol. Acquisitions were performed with the standard NEMA protocol for 18 F and using a radionuclide-specific acquisition time for 11 C, 68 Ga and 89 Zr. Images were assessed using percent recovery coefficient (%RC), percentage standard deviation (%STD), image uniformity (%SD), spill-over ratio (SOR) and evaluation of image quantification., Results: 68 Ga had the lowest %RC (< 62%) across all systems. 18 F had the highest maximum %RC (> 85%) and lowest %STD for the 5 mm rod across all systems. For 11 C and 89 Zr, the maximum %RC was close (> 76%) to the %RC with 18 F. A larger SOR were measured in water with 11 C and 68 Ga compared to 18 F on all systems. SOR in air reflected image reconstruction and data correction performance. Large variation in image quantification was observed, with maximal errors of 22.73% ( 89 Zr, Inveon), 17.54% ( 89 Zr, RAYCAN) and - 14.87% ( 68 Ga, Molecubes)., Conclusions: The systems performed most optimal in terms of NEMA image quality parameters when using 18 F, where 11 C and 89 Zr performed slightly worse than 18 F. The performance was least optimal when using 68 Ga, due to large positron range. The large quantification differences prompt optimization not only by terms of image quality but also quantification. Further investigation should be performed to find an appropriate calibration and harmonization protocol and the evaluation should be conducted on a multi-scanner and multi-center level.