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The interpretation of clinical oncologic PET studies has historically used static reconstructions based on SUVs. SUVs and SUV-based images have important limitations, including dependence on uptake times and reduced conspicuity of tracer-avid lesions in organs with high background uptake. The acquisition of dynamic PET images enables additional PET reconstructions via Patlak modeling, which assumes that a tracer is irreversibly trapped by tissues of interest. The resulting multiparametric PET images capture a tracer's net trapping rate and apparent volume of distribution, separating the contributions of bound and free tracer fractions to the PET signal captured in the SUV. Potential benefits of multiparametric PET include higher quantitative stability, superior lesion conspicuity, and greater accuracy for differentiating malignant and benign lesions. However, the imaging protocols necessary for multiparametric PET are inherently more complex and time intensive, despite the recent introduction of automated or semiautomated scanner-based reconstruction packages. In this Review, we examine the current state of multiparametric PET in whole-body oncologic imaging. We summarize the Patlak method and relevant tracer kinetics, discuss clinical workflows and protocol considerations, and highlight clinical challenges and opportunities. We aim to help oncologic imagers make informed decisions about whether to implement multiparametric PET in their clinical practices.

Background: In modern positron emission tomography (PET) with multi-modality imaging (e.g., PET/CT and PET/MR), the attenuation correction (AC) is the single largest correction factor for image reconstruction. One way to assess AC methods and other reconstruction parameters is to utilize software-based simulation tools, such as a lesion insertion tool. Extensive validation of these simulation tools is required to ensure results of the study are clinically meaningful., Purpose: To evaluate different PET AC methods using a synthetic lesion insertion tool that simulates lesions in a patient cohort that has both PET/MR and PET/CT images. To further demonstrate how lesion insertion tool may be used to extend knowledge of PET reconstruction parameters, including but not limited to AC., Methods: Lesion quantitation is compared using conventional Dixon-based MR-based AC (MRAC) to that of using CT-based AC (CTAC, a "ground truth"). First, the pre-existing lesions were simulated in a similar environment; a total of 71 lesions were identified in 18 pelvic PET/MR patient images acquired with a time-of-flight simultaneous PET/MR scanner, and matched lesions were inserted contralaterally on the same axial slice. Second, synthetic lesions were inserted into four anatomic target locations in a cohort of four patients who didn't have any observed clinical lesions in the pelvis., Results: The matched lesion insertions resulted in unity between the lesion error ratios (mean SUVs), demonstrating that the inserted lesions successfully simulated the original lesions. In the second study, the inserted lesions had distinct characteristics by target locations and demonstrated negative max-SUV%diff trends for bone-dominant sites across the patient cohort., Conclusions: The current work demonstrates that the applied lesion insertion tool can simulate uptake in pelvic lesions and their expected SUV values, and that the lesion insertion tool can be extended to evaluate further PET reconstruction corrections and algorithms and their impact on quantitation accuracy and precision., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by SPECT, typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis due to SPECT/CT misalignment. To address these issues, we developed a method for cardiac SPECT AC using deep learning and emission scatter-window photons without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans. Pre-defined attenuation coefficients are assigned to these regions, yielding the attenuation map used for AC. We objectively evaluated this method in a retrospective study with anonymized clinical SPECT/CT stress MPI images on the clinical task of detecting defects with an anthropomorphic model observer. CTLESS yielded statistically non-inferior performance compared to a CT-based AC (CTAC) method and significantly outperformed a non-AC (NAC) method on this clinical task. Similar results were observed in stratified analyses with different sexes, defect extents and severities. The method was observed to generalize across two SPECT scanners, each with a different camera. In addition, CTLESS yielded similar performance as CTAC and outperformed NAC method on the metrics of root mean squared error and structural similarity index measure. Moreover, as we reduced the training dataset size, CTLESS yielded relatively stable AUC values and generally outperformed another DL-based AC method that directly estimated the attenuation coefficient within each voxel. These results demonstrate the capability of the CTLESS method for transmission-less AC in SPECT and motivate further clinical evaluation.

Background and Purpose: Integrated PET/MR allows the simultaneous acquisition of PET biomarkers and structural and functional MRI to study Alzheimer disease (AD). Attenuation correction (AC), crucial for PET quantification, can be performed using a deep learning approach, DL-Dixon, based on standard Dixon images. Longitudinal amyloid PET imaging, which provides important information about disease progression or treatment responses in AD, is usually acquired over several years. Hardware and software upgrades often occur during a multiple-year study period, resulting in data variability. This study aims to harmonize PET/MR DL-Dixon AC amid software and head coil updates and evaluate its accuracy and longitudinal consistency., Materials and Methods: Tri-modality PET/MR and CT images were obtained from 329 participants, with a subset of 38 undergoing tri-modality scans twice within approximately three years. Transfer learning was employed to fine-tune DL-Dixon models on images from two scanner software versions (VB20P and VE11P) and two head coils (16-channel and 32-channel coils). The accuracy and longitudinal consistency of the DL-Dixon AC were evaluated. Power analyses were performed to estimate the sample size needed to detect various levels of longitudinal changes in the PET standardized uptake value ratio (SUVR)., Results: The DL-Dixon method demonstrated high accuracy across all data, irrespective of scanner software versions and head coils. More than 95.6% of brain voxels showed less than 10% PET relative absolute error in all participants. The median [interquartile range] PET mean relative absolute error was 1.10% [0.93%, 1.26%], 1.24% [1.03%, 1.54%], 0.99% [0.86%, 1.13%] in the cortical summary region, and 1.04% [0.83%, 1.36%], 1.08% [0.84%, 1.34%], 1.05% [0.72%, 1.32%] in cerebellum using the DL-Dixon models for the VB20P-16-channel-coil, VE11P-16-channel-coil and VE11P-32-channel-coil data, respectively. The within-subject coefficient of variation and intra-class correlation coefficient of PET SUVR in the cortical regions were comparable between the DL-Dixon and CT AC. Power analysis indicated that similar numbers of participants would be needed to detect the same level of PET changes using DL-Dixon and CT AC., Conclusions: DL-Dixon exhibited excellent accuracy and longitudinal consistency across the two software versions and head coils, demonstrating its robustness for longitudinal PET/MR neuroimaging studies in AD., Abbreviations: AC = attenuation correction; AD = Alzheimer disease; HU = Hounsfield unit; ICC = intraclass correlation coefficient; MAE = mean absolute error; MRAE = mean relative absolute error; pCT = pseudo-CT; PiB = Pittsburgh Compound B; SD = standard deviation; SUVR = standardized uptake value ratio; wCV = within-subject coefficient of variation., (© 2024 by American Journal of Neuroradiology.)

In oncologic PET, the SUV and standardized uptake ratio (SUR) of a viable tumor generally increase during the postinjection period. In contrast, the net influx rate ( K i ), which is derived from dynamic PET data, should remain relatively constant. Uptake-time-corrected SUV (cSUV) and SUR (cSUR) have been proposed as uptake-time-independent, static alternatives to K i Our primary aim was to quantify the intrascan repeatability of K i , SUV, cSUV, SUR, and cSUR among malignant lesions on PET/CT. An exploratory aim was to assess the ability of cSUR to estimate K i Methods: This prospective, single-center study enrolled adults undergoing standard-of-care oncologic PET/CT. SUV and K i images were reconstructed from dynamic PET data obtained before (∼35-50 min after injection) and after (∼75-90 min after injection) standard-of-care imaging. Tumors were manually segmented. Quantitative metrics were extracted. cSUVs and cSURs were calculated for a 60-min postinjection reference uptake time. The magnitude of the intrascan test-retest percent change (test-retest |%Δ|) was calculated. Coefficients of determination ( R 2 ) and intraclass correlation coefficients (ICC) were also computed. Differences between metrics were assessed via the Wilcoxon signed-rank test (α, 0.05). Results: This study enrolled 78 subjects; 41 subjects (mean age, 63.8 y; 24 men) with 116 lesions were analyzed. For both tracers, SUV max and maximum SUR (SUR max ) had large early-to-late increases (i.e., poor intrascan repeatability). Among [ 18 F]FDG-avid lesions ( n = 93), there were no differences in intrascan repeatability (median test-retest |%Δ|; ICC) between the maximum K i ( K i ,max ) (13%; 0.97) and either the maximum cSUV (cSUV max ) (12%, P = 0.90; 0.96) or the maximum cSUR (cSUR max ) (13%, P = 0.67; 0.94). For DOTATATE-avid lesions ( n = 23), there were no differences in intrascan repeatability between the K i ,max (11%; 0.98) and either the cSUV max (13%, P = 0.41; 0.98) or the cSUR max (11%, P = 0.08; 0.94). The SUV max , cSUV max , SUR max , and cSUR max were all strongly correlated with the K i ,max for both [ 18 F]FDG ( R 2 , 0.81-0.92) and DOTATATE ( R 2 , 0.88-0.96), but the cSUR max provided the best agreement with the K i ,max across early-to-late time points for [ 18 F]FDG (ICC, 0.69-0.75) and DOTATATE (ICC, 0.90-0.91). Conclusion: K i ,max , cSUV max , and cSUR max had low uptake time dependence compared with SUV max and SUR max The K i ,max can be predicted from cSUR max ., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Objective. Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently. Approach . FAST-PET simulates PET images by performing precise forward projection, scatter, and random estimation that match the scanner geometry and statistics. Although the same process should be applicable to other scanner models, we focus on the Siemens Biograph Vision-600 in this work. Calibration and validation of FAST-PET were performed through comparison with an experimental scan of a National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Further validation was conducted between FAST-PET and Geant4 Application for Tomographic Emission (GATE) quantitatively in clinical image simulations in terms of intensity-based and texture-based features and task-based tumor segmentation. Main results. According to the NEMA IQ phantom simulation, FAST-PET's simulated images exhibited partial volume effects and noise levels comparable to experimental images, with a relative bias of the recovery coefficient RC within 10% for all spheres and a coefficient of variation for the background region within 6% across various acquisition times. FAST-PET generated clinical PET images exhibit high quantitative accuracy and texture comparable to GATE (correlation coefficients of all features over 0.95) but with ∼100-fold lower computation time. The tumor segmentation masks comparison between both methods exhibited significant overlap and shape similarity with high concordance CCC > 0.97 across measures. Significance. FAST-PET generated PET images with high quantitative accuracy comparable to GATE, making it ideal for applications requiring extensive PET image simulations such as virtual imaging trials, and the development and validation of image processing pipelines., (Creative Commons Attribution license.)

Thorium-227 ( 227 Th)-based α-particle radiopharmaceutical therapies (α-RPTs) are currently being investigated in several clinical and pre-clinical studies. After administration, 227 Th decays to 223 Ra, another α-particle-emitting isotope, which redistributes within the patient. Reliable dose quantification of both 227 Th and 223 Ra is clinically important, and SPECT may perform this quantification as these isotopes also emit X- and γ-ray photons. However, reliable quantification is challenging for several reasons: the orders-of-magnitude lower activity compared to conventional SPECT, resulting in a very low number of detected counts, the presence of multiple photopeaks, substantial overlap in the emission spectra of these isotopes, and the image-degrading effects in SPECT. To address these issues, we propose a multiple-energy-window projection-domain quantification (MEW-PDQ) method that jointly estimates the regional activity uptake of both 227 Th and 223 Ra directly using the SPECT projection data from multiple energy windows. We evaluated the method with realistic simulation studies conducted with anthropomorphic digital phantoms, including a virtual imaging trial, in the context of imaging patients with bone metastases of prostate cancer who were treated with 227 Th-based α-RPTs. The proposed method yielded reliable (accurate and precise) regional uptake estimates of both isotopes and outperformed state-of-the-art methods across different lesion sizes and contrasts, as well as in the virtual imaging trial. This reliable performance was also observed with moderate levels of intra-regional heterogeneous uptake as well as when there were moderate inaccuracies in the definitions of the support of various regions. Additionally, we demonstrated the effectiveness of using multiple energy windows and the variance of the estimated uptake using the proposed method approached the Cramér-Rao-lower-bound-defined theoretical limit. These results provide strong evidence in support of this method for reliable uptake quantification in 227 Th-based α-RPTs.

Background: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties., Purpose: We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images. The performance of the ARD-Net framework was evaluated for numerous low count realizations using fidelity-based qualitative metrics, task-based segmentation, and quantitative metrics., Method: Patient Derived tumor Xenograft (PDX) with tumors implanted in the mammary fat-pad were subjected to preclinical [ 18 F]-Fluorodeoxyglucose (FDG)-PET/CT imaging. SC-PET images were derived from a 10 min static FDG-PET acquisition, 50 min post administration of FDG, and were resampled to generate four distinct LC-PET realizations corresponding to 10%, 5%, 1.6%, and 0.8% of SC-PET count-level. ARD-Net was trained and optimized using 48 preclinical FDG-PET datasets, while 16 datasets were utilized to assess performance. Further, the performance of ARD-Net was benchmarked against two leading DL-based methods (Residual UNet, RU-Net; and Dilated Network, D-Net) and non-DL methods (Non-Local Means, NLM; and Block Matching 3D Filtering, BM3D). The performance of the framework was evaluated using traditional fidelity-based image quality metrics such as Structural Similarity Index Metric (SSIM) and Normalized Root Mean Square Error (NRMSE), as well as human observer-based tumor segmentation performance (Dice Score and volume bias) and quantitative analysis of Standardized Uptake Value (SUV) measurements. Additionally, radiomics-derived features were utilized as a measure of quality assurance (QA) in comparison to true SC-PET. Finally, a performance ensemble score (EPS) was developed by integrating fidelity-based and task-based metrics. Concordance Correlation Coefficient (CCC) was utilized to determine concordance between measures. The non-parametric Friedman Test with Bonferroni correction was used to compare the performance of ARD-Net against benchmarked methods with significance at adjusted p-value ≤0.01., Results: ARD-Net-generated SC-PET images exhibited significantly better (p ≤ 0.01 post Bonferroni correction) overall image fidelity scores in terms of SSIM and NRMSE at majority of photon-count levels compared to benchmarked DL and non-DL methods. In terms of task-based quantitative accuracy evaluated by SUV Mean  and SUV Peak, ARD-Net exhibited less than 5% median absolute bias for SUV Mean compared to true SC-PET and lower degree of variability compared to benchmarked DL and non-DL based methods in generating SC-PET. Additionally, ARD-Net-generated SC-PET images displayed higher degree of concordance to SC-PET images in terms of radiomics features compared to non-DL and other DL approaches. Finally, the ensemble score suggested that ARD-Net exhibited significantly superior performance compared to benchmarked algorithms (p ≤ 0.01 post Bonferroni correction)., Conclusion: ARD-Net provides a robust framework to generate SC-PET from LC-PET images. ARD-Net generated SC-PET images exhibited superior performance compared other DL and non-DL approaches in terms of image-fidelity based metrics, task-based segmentation metrics, and minimal bias in terms of task-based quantification performance for preclinical PET imaging., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Patlak slope (PS) images have the potential to improve lesion conspicuity compared with standardized uptake value (SUV) images but may be more artifact-prone. This study compared PS versus SUV image quality and hepatic tumor-to-background ratios (TBRs) at matched time points. Early and late SUV and PS images were reconstructed from dynamic positron emission tomography (PET) data. Two independent, blinded readers scored image quality metrics (a four-point Likert scale) and counted tracer-avid lesions. Hepatic lesions and parenchyma were segmented and quantitatively analyzed. Differences were assessed via the Wilcoxon signed-rank test (alpha, 0.05). Forty-three subjects were included. For overall quality and lesion detection, early PS images were significantly inferior to other reconstructions. For overall quality, late PS images (reader 1 [R1]: 3.95, reader 2 [R2]: 3.95) were similar ( p > 0.05) to early SUV images (R1: 3.88, R2: 3.84) but slightly superior ( p ≤ 0.002) to late SUV images (R1: 2.97, R2: 3.44). For lesion detection, late PS images were slightly inferior to late SUV images (R1 only) but slightly superior to early SUV images (both readers). PS-based TBRs were significantly higher than SUV-based TBRs at the early time point, with opposite findings at the late time point. In conclusion, late PS images are similar to early/late SUV images in image quality and lesion detection; the superiority of SUV versus PS hepatic TBRs is time-dependent.

There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners ( N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5%, and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic deep learning-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT., Competing Interests: All authors declare that they have no known conflicts of interest in terms of competing financial interests or personal relationships that could have an influence or are relevant to the work reported in this article.

Purpose: We aimed to determine the test-retest repeatability of quantitative metrics based on the Patlak slope (PS) versus the standardized uptake value (SUV) among lesions and normal organs on oncologic [ 18 F]FDG-PET/CT., Procedures: This prospective, single-center study enrolled adults undergoing standard-of-care oncologic [ 18 F]FDG-PET/CTs. Early (35-50 min post-injection) and late (75-90 min post-injection) SUV and PS images were reconstructed from dynamic whole-body PET data. Repeat imaging occurred within 7 days. Relevant quantitative metrics were extracted from lesions and normal organs. Repeatability was assessed via mean test-retest percent changes [T-RT %Δ], within-subject coefficients of variation (wCVs), and intra-class correlation coefficients (ICCs)., Results: Nine subjects (mean age, 61.7 ± 6.2 years; 6 females) completed the test-retest protocol. Four subjects collectively had 17 [ 18 F]FDG-avid lesions. Lesion wCVs were higher (i.e., worse repeatability) for PS-early-max (16.2%) and PS-early-peak (15.6%) than for SUV-early-max (8.9%) and SUV-early-peak (8.1%), with similar early metric ICCs (0.95-0.98). Lesion wCVs were similar for PS-late-max (8.5%) and PS-late-peak (6.4%) relative to SUV-late-max (9.7%) and SUV-late-peak (7.2%), with similar late metric ICCs (0.93-0.98). There was a significant bias toward higher retest SUV and PS values in the lesion analysis (T-RT %Δ [95% CI]: SUV-late-max, 10.0% [2.6%, 17.0%]; PS-late-max, 20.4% [14.3%, 26.4%]) but not in the normal organ analysis., Conclusions: Among [ 18 F]FDG-avid lesions, the repeatability of PS-based metrics is similar to equivalent SUV-based metrics at late post-injection time points, indicating that PS-based metrics may be suitable for tracking response to oncologic therapies. However, further validation is required in light of our study's limitations, including small sample size and bias toward higher retest values for some metrics., (© 2024. The Author(s), under exclusive licence to World Molecular Imaging Society.)

90 Y-microsphere radioembolization has become a well-established treatment option for liver malignancies and is one of the first U.S. Food and Drug Administration-approved unsealed radionuclide brachytherapy devices to incorporate dosimetry-based treatment planning. Several different mathematical models are used to calculate the patient-specific prescribed activity of 90 Y, namely, body surface area (SIR-Spheres only), MIRD single compartment, and MIRD dual compartment (partition). Under the auspices of the MIRDsoft initiative to develop community dosimetry software and tools, the body surface area, MIRD single-compartment, MIRD dual-compartment, and MIRD multicompartment models have been integrated into a MIRDy90 software worksheet. The worksheet was built in MS Excel to estimate and compare prescribed activities calculated via these respective models. The MIRDy90 software was validated against available tools for calculating 90 Y prescribed activity. The results of MIRDy90 calculations were compared with those obtained from vendor and community-developed tools, and the calculations agreed well. The MIRDy90 worksheet was developed to provide a vetted tool to better evaluate patient-specific prescribed activities calculated via different models, as well as model influences with respect to varying input parameters. MIRDy90 allows users to interact and visualize the results of various parameter combinations. Variables, equations, and calculations are described in the MIRDy90 documentation and articulated in the MIRDy90 worksheet. The worksheet is distributed as a free tool to build expertise within the medical physics community and create a vetted standard for model and variable management., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Although a high amount of brown adipose tissue (BAT) is associated with low plasma triglyceride concentration, the mechanism responsible for this relationship in people is not clear. Here, we evaluate the interrelationships among BAT, very-low-density lipoprotein triglyceride (VLDL-TG), and free fatty acid (FFA) plasma kinetics during thermoneutrality in women with overweight/obesity who had a low (<20 mL) or high (≥20 mL) volume of cold-activated BAT (assessed by using positron emission tomography in conjunction with 2-deoxy-2-[ 18 F]-fluoro-glucose). We find that plasma TG and FFA concentrations are lower and VLDL-TG and FFA plasma clearance rates are faster in women with high BAT than low BAT volume, whereas VLDL-TG and FFA appearance rates in plasma are not different between the two groups. These findings demonstrate that women with high BAT volume have lower plasma TG and FFA concentrations than women with low BAT volumes because of increased VLDL-TG and FFA clearance rates. This study was registered at ClinicalTrials.gov (NCT02786251)., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Challenges in PET/MRI quantitative accuracy for neurological uses arise from PET attenuation correction accuracy. We proposed and evaluated an automatic pipeline to assess the quantitative accuracy of four MRI-derived PET AC methods using analytically simulated PET brain lesions and ROIs as ground truth for PET activity., Methods: Our proposed pipeline, integrating a synthetic lesion insertion tool and the FreeSurfer neuroimaging framework, inserts simulated spherical and brain ROIs into PET projection space, reconstructing them via four PET MRAC techniques. Utilizing an 11-patient brain PET dataset, we compared the quantitative accuracy of four MRACs (DIXON, DIXONbone, UTE AC, and DL-DIXON) against the gold standard PET CTAC, evaluating MRAC to CTAC activity bias in spherical lesions and brain ROIs with and without background activity against original (lesion free) PET reconstructed images., Results: The proposed pipeline yielded accurate results for spherical lesions and brain ROIs, adhering to the MRAC to CTAC pattern of original brain PET images. Among the MRAC methods, DIXON AC exhibited the highest bias, followed by UTE, DIXONBone, and DL-DIXON showing the least. DIXON, DIXONbone, UTE, and DL-DIXON showed MRAC to CTAC biases of - 5.41%, - 1.85%, - 2.74%, and 0.08% respectively for ROIs inserted in background activity; - 7.02%, - 2.46%, - 3.56%, and - 0.05% for lesion ROIs without background; and - 6.82%, - 2.08%, - 2.29%, and 0.22% for the original brain PET images' 16 FreeSurfer brain ROIs., Conclusion: The proposed pipeline delivers accurate results for synthetic spherical lesions and brain ROIs, with and without background activity consideration, enabling the evaluation of new attenuation correction approaches without utilizing measured PET emission data. Additionally, it offers a consistent method to generate realistic lesion ROIs, potentially applicable in assessing further PET correction techniques., (© 2023. The Author(s).)

Among the diverse populations of myeloid cells that reside within the healthy and diseased heart, C-C chemokine receptor 2 (CCR2) is specifically expressed on inflammatory populations of monocytes and macrophages that contribute to the development and progression of heart failure 1-4 . Here, we evaluated a peptide-based imaging probe ( 64 Cu-DOTA-ECL1i) that specifically recognizes CCR2 + monocytes and macrophages for human cardiac imaging. Compared to healthy controls, 64 Cu-DOTA-ECL1i heart uptake was increased in subjects following acute myocardial infarction, predominately localized within the infarct area, and was associated with impaired myocardial wall motion. These findings establish the feasibility of molecular imaging of CCR2 expression to visualize inflammatory monocytes and macrophages in the injured human heart., Competing Interests: Competing interests K.J.L. serves as a consultant for Implicit Biosciences and Medtronic and is the recipient of sponsored research agreements with Amgen and Novartis. K.J.L., D.K., S.L.B., R.J.G., and Y.L. have a pending patent entitled “Methods for detecting CCR2 receptors” (application number: US17/001,857). The remaining authors declare no competing interests

Activation of metalloprodrugs or prodrug activation using transition metal catalysts represents emerging strategies for drug development; however, they are frequently hampered by poor spatiotemporal control and limited catalytic turnover. Here, we demonstrate that metal complex-mediated, autolytic release of active metallodrugs can be successfully employed to prepare clinical grade (radio-)pharmaceuticals. Optimization of the Lewis-acidic metal ion, chelate, amino acid linker, and biological targeting vector provides means to release peptide-based (radio-)metallopharmaceuticals in solution and from the solid phase using metal-mediated, autolytic amide bond cleavage (MMAAC). Our findings indicate that coordinative polarization of an amide bond by strong, trivalent Lewis acids such as Ga 3+ and Sc 3+ adjacent to serine results in the N, O acyl shift and hydrolysis of the corresponding ester without dissociation of the corresponding metal complex. Compound [ 68 Ga]Ga- 10 , incorporating a cleavable and noncleavable functionalization, was used to demonstrate that only the amide bond-adjacent serine effectively triggered hydrolysis in solution and from the solid phase. The corresponding solid-phase released compound [ 68 Ga]Ga- 8 demonstrated superior in vivo performance in a mouse tumor model compared to [ 68 Ga]Ga- 8 produced using conventional, solution-phase radiolabeling. A second proof-of-concept system, [ 67 Ga]Ga- 17A (serine-linked) and [ 67 Ga]Ga- 17B (glycine-linked) binding to serum albumin via the incorporated ibuprofen moiety, was also synthesized. These constructs demonstrated that complete hydrolysis of the corresponding [ 68 Ga]Ga-NOTA complex from [ 67 Ga]Ga- 17A can be achieved in naïve mice within 12 h, as traceable in urine and blood metabolites. The glycine-linked control [ 68 Ga]Ga- 17B remained intact. Conclusively, MMAAC provides an attractive tool for selective, thermal, and metal ion-mediated control of metallodrug activation compatible with biological conditions.

Background: Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been the use of deep-learning (DL)-based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application., Purpose: DL-based approaches for denoising nuclear-medicine images have typically been evaluated using fidelity-based figures of merit (FoMs) such as root mean squared error (RMSE) and structural similarity index measure (SSIM). However, these images are acquired for clinical tasks and thus should be evaluated based on their performance in these tasks. Our objectives were to: (1) investigate whether evaluation with these FoMs is consistent with objective clinical-task-based evaluation; (2) provide a theoretical analysis for determining the impact of denoising on signal-detection tasks; and (3) demonstrate the utility of virtual imaging trials (VITs) to evaluate DL-based methods., Methods: A VIT to evaluate a DL-based method for denoising myocardial perfusion SPECT (MPS) images was conducted. To conduct this evaluation study, we followed the recently published best practices for the evaluation of AI algorithms for nuclear medicine (the RELAINCE guidelines). An anthropomorphic patient population modeling clinically relevant variability was simulated. Projection data for this patient population at normal and low-dose count levels (20%, 15%, 10%, 5%) were generated using well-validated Monte Carlo-based simulations. The images were reconstructed using a 3-D ordered-subsets expectation maximization-based approach. Next, the low-dose images were denoised using a commonly used convolutional neural network-based approach. The impact of DL-based denoising was evaluated using both fidelity-based FoMs and area under the receiver operating characteristic curve (AUC), which quantified performance on the clinical task of detecting perfusion defects in MPS images as obtained using a model observer with anthropomorphic channels. We then provide a mathematical treatment to probe the impact of post-processing operations on signal-detection tasks and use this treatment to analyze the findings of this study., Results: Based on fidelity-based FoMs, denoising using the considered DL-based method led to significantly superior performance. However, based on ROC analysis, denoising did not improve, and in fact, often degraded detection-task performance. This discordance between fidelity-based FoMs and task-based evaluation was observed at all the low-dose levels and for different cardiac-defect types. Our theoretical analysis revealed that the major reason for this degraded performance was that the denoising method reduced the difference in the means of the reconstructed images and of the channel operator-extracted feature vectors between the defect-absent and defect-present cases., Conclusions: The results show the discrepancy between the evaluation of DL-based methods with fidelity-based metrics versus the evaluation on clinical tasks. This motivates the need for objective task-based evaluation of DL-based denoising approaches. Further, this study shows how VITs provide a mechanism to conduct such evaluations computationally, in a time and resource-efficient setting, and avoid risks such as radiation dose to the patient. Finally, our theoretical treatment reveals insights into the reasons for the limited performance of the denoising approach and may be used to probe the effect of other post-processing operations on signal-detection tasks., (© 2023 American Association of Physicists in Medicine.)

Background: Single-photon emission computed tomography (SPECT) provides a mechanism to perform absorbed-dose quantification tasks for [Formula: see text]-particle radiopharmaceutical therapies ([Formula: see text]-RPTs). However, quantitative SPECT for [Formula: see text]-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. Towards addressing these challenges, we propose a low-count quantitative SPECT reconstruction method for isotopes with multiple emission peaks., Methods: Given the low-count setting, it is important that the reconstruction method extracts the maximal possible information from each detected photon. Processing data over multiple energy windows and in list-mode (LM) format provide mechanisms to achieve that objective. Towards this goal, we propose a list-mode multi energy window (LM-MEW) ordered-subsets expectation-maximization-based SPECT reconstruction method that uses data from multiple energy windows in LM format and include the energy attribute of each detected photon. For computational efficiency, we developed a multi-GPU-based implementation of this method. The method was evaluated using 2-D SPECT simulation studies in a single-scatter setting conducted in the context of imaging [[Formula: see text]Ra]RaCl[Formula: see text], an FDA-approved RPT for metastatic prostate cancer., Results: The proposed method yielded improved performance on the task of estimating activity uptake within known regions of interest in comparison to approaches that use a single energy window or use binned data. The improved performance was observed in terms of both accuracy and precision and for different sizes of the region of interest., Conclusions: Results of our studies show that the use of multiple energy windows and processing data in LM format with the proposed LM-MEW method led to improved quantification performance in low-count SPECT of isotopes with multiple emission peaks. These results motivate further development and validation of the LM-MEW method for such imaging applications, including for [Formula: see text]-RPT SPECT., (© 2023. The Author(s).)

Background: First-pass perfusion imaging in magnetic resonance imaging (MRI) is an established method to measure myocardial blood flow (MBF). An obstacle for accurate quantification of MBF is the saturation of blood pool signal intensity used for arterial input function (AIF). The objective of this project was to validate a new simplified method for AIF estimation obtained from single-bolus and single sequence perfusion measurements. The reference MBF was measured simultaneously on 13 N-ammonia positron emission tomography (PET)., Methods: Sixteen patients with clinically confirmed myocardial ischemia were imaged in a clinical whole-body PET-MRI system. PET perfusion imaging was performed in a 10-min acquisition after the injection of 10 mCi of 13 N-ammonia. The MRI perfusion acquisition started simultaneously with the start of the PET acquisition after the injection of a 0.075 mmol/kg gadolinium contrast agent. Cardiac stress imaging was initiated after the administration of regadenoson 20 s prior to PET-MRI scanning. The saturation part of the MRI AIF data was modeled as a gamma variate curve, which was then estimated for a true AIF by minimizing a cost function according to various boundary conditions. A standard AHA 16-segment model was used for comparative analysis of absolute MBF from PET and MRI., Results: Overall, there were 256 segments in 16 patients, mean resting perfusion for PET was 1.06 ± 0.34 ml/min/g and 1.04 ± 0.30 ml/min/g for MRI (P = 0.05), whereas mean stress perfusion for PET was 2.00 ± 0.74 ml/min/g and 2.12 ± 0.76 ml/min/g for MRI (P < 0.01). Linear regression analysis in MBF revealed strong correlation (r = 0.91, slope = 0.96, P < 0.001) between PET and MRI. Myocardial perfusion reserve, calculated from the ratio of stress MBF over resting MBF, also showed a strong correlation between MRI and PET measurements (r = 0.82, slope = 0.81, P < 0.001)., Conclusion: The results demonstrated the feasibility of the simplified AIF estimation method for the accurate quantification of MBF by MRI with single sequence and single contrast injection. The MRI MBF correlated strongly with PET MBF obtained simultaneously. This post-processing technique will allow easy transformation of clinical perfusion imaging data into quantitative information., (© 2023. The Author(s).)

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 18 F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training, we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET (sPET) time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic 18 F-FDG uptake, e.g., high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart, and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this sPET data can be used interchangeably with real PET data for the PET quantification task of comparing CTAC and MRAC methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed sPET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.

To expand the single-dose duration over which noninvasive clinical and preclinical cancer imaging can be conducted with high sensitivity, and well-defined spatial and temporal resolutions, a facile strategy to prepare ultrasmall nanoparticulate X-ray contrast media (nano-XRCM) as dual-modality imaging agents for positron emission tomography (PET) and computed tomography (CT) has been established. Synthesized from controlled copolymerization of triiodobenzoyl ethyl acrylate and oligo(ethylene oxide) acrylate monomers, the amphiphilic statistical iodocopolymers (ICPs) could directly dissolve in water to afford thermodynamically stable solutions with high aqueous iodine concentrations (>140 mg iodine/mL water) and comparable viscosities to conventional small molecule XRCM. The formation of ultrasmall iodinated nanoparticles with hydrodynamic diameters of ca. 10 nm in water was confirmed by dynamic and static light scattering techniques. In a breast cancer mouse model, in vivo biodistribution studies revealed that the 64 Cu-chelator-functionalized iodinated nano-XRCM exhibited extended blood residency and higher tumor accumulation compared to typical small molecule imaging agents. PET/CT imaging of tumor over 3 days showed good correlation between PET and CT signals, while CT imaging allowed continuous observation of tumor retention even after 10 days post-injection, enabling longitudinal monitoring of tumor retention for imaging or potentially therapeutic effect after a single administration of nano-XRCM., (© 2022 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.)

Objective. Synthetic images generated by simulation studies have a well-recognized role in developing and evaluating imaging systems and methods. However, for clinically relevant development and evaluation, the synthetic images must be clinically realistic and, ideally, have the same distribution as that of clinical images. Thus, mechanisms that can quantitatively evaluate this clinical realism and, ideally, the similarity in distributions of the real and synthetic images, are much needed. Approach. We investigated two observer-study-based approaches to quantitatively evaluate the clinical realism of synthetic images. In the first approach, we presented a theoretical formalism for the use of an ideal-observer study to quantitatively evaluate the similarity in distributions between the real and synthetic images. This theoretical formalism provides a direct relationship between the area under the receiver operating characteristic curve, AUC, for an ideal observer and the distributions of real and synthetic images. The second approach is based on the use of expert-human-observer studies to quantitatively evaluate the realism of synthetic images. In this approach, we developed a web-based software to conduct two-alternative forced-choice (2-AFC) experiments with expert human observers. The usability of this software was evaluated by conducting a system usability scale (SUS) survey with seven expert human readers and five observer-study designers. Further, we demonstrated the application of this software to evaluate a stochastic and physics-based image-synthesis technique for oncologic positron emission tomography (PET). In this evaluation, the 2-AFC study with our software was performed by six expert human readers, who were highly experienced in reading PET scans, with years of expertise ranging from 7 to 40 years (median: 12 years, average: 20.4 years). Main results. In the ideal-observer-study-based approach, we theoretically demonstrated that the AUC for an ideal observer can be expressed, to an excellent approximation, by the Bhattacharyya distance between the distributions of the real and synthetic images. This relationship shows that a decrease in the ideal-observer AUC indicates a decrease in the distance between the two image distributions. Moreover, a lower bound of ideal-observer AUC = 0.5 implies that the distributions of synthetic and real images exactly match. For the expert-human-observer-study-based approach, our software for performing the 2-AFC experiments is available athttps://apps.mir.wustl.edu/twoafc. Results from the SUS survey demonstrate that the web application is very user friendly and accessible. As a secondary finding, evaluation of a stochastic and physics-based PET image-synthesis technique using our software showed that expert human readers had limited ability to distinguish the real images from the synthetic images. Significance. This work addresses the important need for mechanisms to quantitatively evaluate the clinical realism of synthetic images. The mathematical treatment in this paper shows that quantifying the similarity in the distribution of real and synthetic images is theoretically possible by using an ideal-observer-study-based approach. Our developed software provides a platform for designing and performing 2-AFC experiments with human observers in a highly accessible, efficient, and secure manner. Additionally, our results on the evaluation of the stochastic and physics-based image-synthesis technique motivate the application of this technique to develop and evaluate a wide array of PET imaging methods., (Creative Commons Attribution license.)

There remains an unmet need for molecularly targeted imaging agents for multiple myeloma (MM). The integrin very late antigen 4 (VLA4), is differentially expressed in malignant MM cells and in pathogenic inflammatory microenvironmental cells. [ 64 Cu]Cu-CB-TE1A1P-LLP2A ( 64 Cu-LLP2A) is a VLA4-targeted, high-affinity radiopharmaceutical with promising utility for managing patients diagnosed with MM. Here, we evaluated the safety and human radiation dosimetry of 64 Cu-LLP2A for potential use in MM patients. Methods: A single-dose [ nat Cu]Cu-LLP2A (Cu-LLP2A) tolerability and toxicity study was performed on CD-1 (Hsd:ICR) male and female mice. 64 Cu-LLP2A was synthesized in accordance with good-manufacturing-practice-compliant procedures. Three MM patients and six healthy participants underwent 64 Cu-LLP2A-PET/CT or PET/MRI at up to 3 time points to help determine tracer biodistribution, pharmacokinetics, and radiation dosimetry. Time-activity curves were plotted for each participant. Mean organ-absorbed doses and effective doses were calculated using the OLINDA software. Tracer bioactivity was evaluated via cell-binding assays, and metabolites from human blood samples were analyzed with analytic radio-high-performance liquid chromatography. When feasible, VLA4 expression was evaluated in the biopsy tissues using 14-color flow cytometry. Results: A 150-fold mass excess of the desired imaging dose was tolerated well in male and female CD-1 mice (no observed adverse effect level). Time-activity curves from human imaging data showed rapid tracer clearance from blood via the kidneys and bladder. The effective dose of 64 Cu-LLP2A in humans was 0.036 ± 0.006 mSv/MBq, and the spleen had the highest organ uptake, 0.142 ± 0.034 mSv/MBq. Among all tissues, the red marrow demonstrated the highest residence time. Image quality analysis supports an early imaging time (4-5 h after injection of the radiotracer) as optimal. Cell studies showed statistically significant blocking for the tracer produced for all human studies (82.42% ± 13.47%). Blood metabolism studies confirmed a stable product peak (>90%) up to 1 h after injection of the radiopharmaceutical. No clinical or laboratory adverse events related to 64 Cu-LLP2A were observed in the human participants. Conclusion: 64 Cu-LLP2A exhibited a favorable dosimetry and safety profile for use in humans., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Single-photon emission-computed tomography (SPECT) provides a mechanism to estimate regional isotope uptake in lesions and at-risk organs after administration of α -particle-emitting radiopharmaceutical therapies ( α -RPTs). However, this estimation task is challenging due to the complex emission spectra, the very low number of detected counts (~20 times lower than in conventional SPECT), the impact of stray-radiation-related noise at these low counts, and the multiple image-degrading processes in SPECT. The conventional reconstruction-based quantification methods are observed to be erroneous for α -RPT SPECT. To address these challenges, we developed a low-count quantitative SPECT (LC-QSPECT) method that directly estimates the regional activity uptake from the projection data (obviating the reconstruction step), compensates for stray-radiation-related noise, and accounts for the radioisotope and SPECT physics, including the isotope spectra, scatter, attenuation, and collimator-detector response, using a Monte Carlo-based approach. The method was validated in the context of 3-D SPECT with 223 Ra, a commonly used radionuclide for α -RPT. Validation was performed using both realistic simulation studies, including a virtual clinical trial, and synthetic and 3-D-printed anthropomorphic physical-phantom studies. Across all studies, the LC-QSPECT method yielded reliable regional-uptake estimates and outperformed the conventional ordered subset expectation-maximization (OSEM)-based reconstruction and geometric transfer matrix (GTM)-based post-reconstruction partial-volume compensation methods. Furthermore, the method yielded reliable uptake across different lesion sizes, contrasts, and different levels of intralesion heterogeneity. Additionally, the variance of the estimated uptake approached the Cramér-Rao bound-defined theoretical limit. In conclusion, the proposed LC-QSPECT method demonstrated the ability to perform reliable quantification for α -RPT SPECT.

This study evaluated the safety, dosimetry, and characteristics of 3-((2-fluoro-4-(5-(2'-methyl-2-(trifluoromethyl)-[1,1'-biphenyl]-4-yl)-1,2,4-oxadiazol-3-yl)benzyl)(methyl- 11 C)amino)propanoic acid ( 11 C-CS1P1), a radiotracer targeting sphingosine-1-phosphate receptor (S1PR) 1 (S1PR1). S1PR1 is of clinical interest because of its role in multiple sclerosis (and other conditions), with an expanding class of S1PR modulators approved for relapsing multiple sclerosis. 11 C-CS1P1 binds S1PR1 with high specificity and has shown promise in animal models of inflammatory diseases. Methods: 11 C-CS1P1 was injected into 5 male and 6 female healthy participants. Ten participants were imaged with PET using a multipass whole-body continuous-bed-motion acquisition, and one had dedicated head and neck PET and MRI. Participants were continuously monitored for safety events. Organ time-activity curve data were collected, integrated, and normalized to the injected activity. Organ radiation doses and effective dose were computed using the adult male and female models in OLINDA, version 2.2. SUV images were evaluated for qualitative biodistribution. Results: No adverse events were observed after the dose, including no bradycardia. The liver was the critical organ from dosimetry analysis (mean ± SD: female, 23.12 ± 5.19 μSv/MBq; male, 21.06 ± 1.63 μSv/MBq). The whole-body effective dose (as defined by International Commission on Radiological Protection publication 103) was 4.18 ± 0.30 μSv/MBq in women and 3.54 ± 0.14 μSv/MBq in men. Using a maximum delivered dose of 740 MBq (20 mCi), the effective dose for women would be 3.1 mSv (0.31 rem), with a liver dose of 17.1 mSv (1.7 rem); the effective dose for men would be 2.6 mSv (0.26 rem), with a liver dose of 15.6 mSv (1.56 rem). Brain uptake was seen predominantly in gray matter and correlated with regional S1PR1 RNA expression ( r = 0.84). Conclusion: These results support the safety of 11 C-CS1P1 for evaluation of inflammation in human clinical populations. Dosimetry permits repeated measures in the same participants. Brain uptake correlates well with known target topography., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus (GP) derived from dopamine transporter-single-photon emission computed tomography (DaT-SPECT) images have potential as biomarkers for measuring the severity of Parkinson's disease. Reliable quantification of this uptake requires accurate segmentation of the considered regions. However, segmentation of these regions from DaT-SPECT images is challenging, a major reason being partial-volume effects (PVEs) in SPECT. The PVEs arise from two sources, namely the limited system resolution and reconstruction of images over finite-sized voxel grids. The limited system resolution results in blurred boundaries of the different regions. The finite voxel size leads to TFEs, that is, voxels contain a mixture of regions. Thus, there is an important need for methods that can account for the PVEs, including the TFEs, and accurately segment the caudate, putamen, and GP, from DaT-SPECT images., Purpose: Design and objectively evaluate a fully automated tissue-fraction estimation-based segmentation method that segments the caudate, putamen, and GP from DaT-SPECT images., Methods: The proposed method estimates the posterior mean of the fractional volumes occupied by the caudate, putamen, and GP within each voxel of a three-dimensional DaT-SPECT image. The estimate is obtained by minimizing a cost function based on the binary cross-entropy loss between the true and estimated fractional volumes over a population of SPECT images, where the distribution of true fractional volumes is obtained from existing populations of clinical magnetic resonance images. The method is implemented using a supervised deep-learning-based approach., Results: Evaluations using clinically guided highly realistic simulation studies show that the proposed method accurately segmented the caudate, putamen, and GP with high mean Dice similarity coefficients of ∼ 0.80 and significantly outperformed ( p < 0.01 $p &lt; 0.01$ ) all other considered segmentation methods. Further, an objective evaluation of the proposed method on the task of quantifying regional uptake shows that the method yielded reliable quantification with low ensemble normalized root mean square error (NRMSE) < 20% for all the considered regions. In particular, the method yielded an even lower ensemble NRMSE of ∼ 10% for the caudate and putamen., Conclusions: The proposed tissue-fraction estimation-based segmentation method for DaT-SPECT images demonstrated the ability to accurately segment the caudate, putamen, and GP, and reliably quantify the uptake within these regions. The results motivate further evaluation of the method with physical-phantom and patient studies., (© 2022 American Association of Physicists in Medicine.)

Purpose: We evaluated the impact of PET respiratory motion correction (MoCo) in a phantom and patients. Moreover, we proposed and examined a PET MoCo approach using motion vector fields (MVFs) from a deep-learning reconstructed short MRI scan., Methods: The evaluation of PET MoCo was performed in a respiratory motion phantom study with varying lesion sizes and tumor to background ratios (TBRs) using a static scan as the ground truth. MRI-based MVFs were derived from either 2000 spokes (MoCo 2000 , 5-6 min acquisition time) using a Fourier transform reconstruction or 200 spokes (MoCo P2P200 , 30-40 s acquisition time) using a deep-learning Phase2Phase (P2P) reconstruction and then incorporated into PET MoCo reconstruction. For six patients with hepatic lesions, the performance of PET MoCo was evaluated using quantitative metrics (SUV max , SUV peak , SUV mean , lesion volume) and a blinded radiological review on lesion conspicuity., Results: MRI-assisted PET MoCo methods provided similar results to static scans across most lesions with varying TBRs in the phantom. Both MoCo 2000 and MoCo P2P200 PET images had significantly higher SUV max , SUV peak , SUV mean and significantly lower lesion volume than non-motion-corrected (non-MoCo) PET images. There was no statistical difference between MoCo 2000 and MoCo P2P200 PET images for SUV max , SUV peak , SUV mean or lesion volume. Both radiological reviewers found that MoCo 2000 and MoCo P2P200 PET significantly improved lesion conspicuity., Conclusion: An MRI-assisted PET MoCo method was evaluated using the ground truth in a phantom study. In patients with hepatic lesions, PET MoCo images improved quantitative and qualitative metrics based on only 30-40 s of MRI motion modeling data., (© 2022 International Society for Magnetic Resonance in Medicine.)

Background: Although it is well-accepted that increased plasma free fatty acid (FFA) concentration causes lipid overload and muscle insulin resistance in people with obesity, plasma FFA concentration poorly predicts insulin-resistant glucose metabolism. It has been proposed that hyperinsulinemia in people with obesity sufficiently inhibits adipose tissue triglyceride lipolysis to prevent FFA-induced insulin resistance. However, we hypothesized enhanced FFA clearance in people with obesity, compared with lean people, prevents a marked increase in plasma FFA even when FFA appearance is high., Methods: We assessed FFA kinetics during basal conditions and during a hyperinsulinemic-euglycemic clamp procedure in 14 lean people and 46 people with obesity by using [ 13 C]palmitate tracer infusion. Insulin-stimulated muscle glucose uptake rate was evaluated by dynamic PET-imaging of skeletal muscles after [ 18 F]fluorodeoxyglucose injection., Results: Plasma FFA clearance was accelerated in participants with obesity and correlated negatively with muscle insulin sensitivity without a difference between lean and obese participants. Furthermore, insulin infusion increased FFA clearance and the increase was greater in obese than lean participants., Conclusions: Our findings suggest plasma FFA extraction efficiency, not just plasma FFA concentration, is an important determinant of the cellular fatty acid load and the stimulatory effect of insulin on FFA clearance counteracts some of its antilipolytic effect., (Copyright © 2022. Published by Elsevier Inc.)

We assessed image quality using a practical and time-efficient protocol for intravenous glucose loading and insulin injection before administration of 18 F-FDG for PET myocardial viability evaluation in patients with ischemic cardiomyopathy (ICM), with and without type 2 diabetes mellitus. Methods: The metabolic preparation period (MPP) or optimal cardiac 18 F-FDG uptake was determined from the time of intravenous infusion of 12.5 or 25 g of 50% dextrose to the time of 18 F-FDG injection. Cardiac 18 F-FDG image quality was evaluated according to a 5-point scoring system (from 5, excellent, to 1, nondiagnostic) by 2 independent observers. In cases of disagreement, consensus was achieved in a joint reading. Fifteen patients with ICM who underwent oral glucose loading and intravenous insulin administration served as a reference for MPP comparisons. Results: Fifty-nine consecutive patients (age, 63 ± 10 y; 48 men and 11 women) underwent rest 99m Tc-tetrofosmin SPECT/CT and 18 F-FDG PET/CT for the evaluation of myocardial viability. 18 F-FDG image quality was scored as excellent in 42%, very good in 36%, good in 17%, fair in 3%, and nondiagnostic in 2%. When diabetic and nondiabetic patients were compared, the quality scores were excellent in 29% versus 76%, very good in 41% versus 18%, good in 24% versus 6%, fair in 4% versus 0%, and nondiagnostic in 2% versus 0%. The mean (±SD) quality score was 4.12 ± 0.95, and overall it was better in nondiabetic than in diabetic patients (4.71 ± 0.59 vs. 3.88 ± 0.96; P < 0.0001). Notably, the average MPP was significantly less with intravenous glucose loading than with oral glucose loading (51 ± 15 min vs. 132 ± 29 min; P < 0.0001), paralleled by higher insulin doses (6.3 ± 2.2 U vs. 2.0 ± 1.69 U; P < 0.001). Conclusion: Using a practical and time-efficient protocol for intravenous glucose loading and insulin administration before 18 F-FDG injection reduces the MPP by 61% as compared with an oral glucose challenge and affords good-to-excellent image quality in 95% of ICM patients., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)

PET/MRI scanners cannot be qualified in the manner adopted for hybrid PET/CT devices. The main hurdle with qualification in PET/MRI is that attenuation correction (AC) cannot be adequately measured in conventional PET phantoms because of the difficulty in converting the MR images of the physical structures (e.g., plastic) into electron density maps. Over the last decade, a plethora of novel MRI-based algorithms has been developed to more accurately derive the attenuation properties of the human head, including the skull. Although promising, none of these techniques has yet emerged as an optimal and universally adopted strategy for AC in PET/MRI. In this work, we propose a path for PET/MRI qualification for multicenter brain imaging studies. Specifically, our solution is to separate the head AC from the other factors that affect PET data quantification and use a patient as a phantom to assess the former. The emission data collected on the integrated PET/MRI scanner to be qualified should be reconstructed using both MRI- and CT-based AC methods, and whole-brain qualitative and quantitative (both voxelwise and regional) analyses should be performed. The MRI-based approach will be considered satisfactory if the PET quantification bias is within the acceptance criteria specified here. We have implemented this approach successfully across 2 PET/MRI scanner manufacturers at 2 sites., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: Obstructive sleep apnea (OSA) is prevalent in people with obesity and is a major risk factor for type 2 diabetes (T2D). The effect of OSA on metabolic function and the precise mechanisms (insulin resistance, β-cell dysfunction, or both) responsible for the increased T2D risk in people with OSA are unknown., Design and Methods: We used a two-stage hyperinsulinemic-euglycemic clamp procedure in conjunction with stable isotopically labeled glucose and palmitate tracer infusions and 18F-fluorodeoxyglucose injection and positron emission tomography to quantify multi-organ insulin action and oral and intravenous tolerance tests to evaluate glucose-stimulated insulin secretion in fifteen people with obesity and OSA and thirteen people with obesity without OSA., Results: OSA was associated with marked insulin resistance of adipose tissue triglyceride lipolysis and glucose uptake into both skeletal muscles and adipose tissue, whereas there was no significant difference between the OSA and control groups in insulin action on endogenous glucose production, basal insulin secretion, and glucose-stimulated insulin secretion during both intravenous and oral glucose tolerance tests., Conclusions: These data demonstrate that OSA is a key determinant of insulin sensitivity in people with obesity and underscore the importance of taking OSA status into account when evaluating metabolic function in people with obesity. These findings may also have important clinical implications because disease progression and the risk of diabetes-related complications vary by T2D subtype (i.e. severe insulin resistance vs insulin deficiency). People with OSA may benefit most from the targeted treatment of peripheral insulin resistance and early screening for complications associated with peripheral insulin resistance.