Total-Body Parametric Imaging Using Relative Patlak Plot.

Standard Patlak plot is widely used to describe FDG kinetics for dynamic PET imaging. Whole-body Patlak parametric imaging remains constrained due to the need for a full-time input function. Here, we demonstrate the Relative Patlak (RP) plot, which eliminates the need for the early-time input function, for total-body parametric imaging and its application to clinical 20-min scan acquired in list-mode. We demonstrated that the RP intercept b' is equivalent to a ratio of standardized uptake value relative to the blood, while the RP slope Ki' is equal to the standard Patlak Ki multiplied by a global scaling factor for each subject. One challenge in applying RP to a short scan duration (20 min) is the high noise in parametric images. We applied a deep kernel method for noise reduction. Using the standard Patlak plot as the reference, the RP method was evaluated for lesion quantification, lesion-to-background contrast, and myocardial visualization in total-body parametric imaging with uEXPLORER in 22 human subjects who underwent a 1-h dynamic 18F-FDG scan. The RP method was also applied to the dynamic data regenerated from a clinical standard 20-min scan either at 1-h or 2-h post-injection for two cancer patients. We demonstrated that it is feasible to obtain high-quality parametric images from 20-min dynamic scans using the RP plot with a self-supervised deep-kernel noise reduction strategy. The RP Ki' highly correlated with Ki in lesions and major organs, demonstrating its quantitative potential across subjects. Compared to conventional SUVs, the Ki' images significantly improved lesion contrast and enabled visualization of the myocardium for potential cardiac assessment. The application of RP parametric imaging to two clinical scans also showed similar benefits. Total-body PET with the RP plot is feasible to generate parametric images from the dynamic data of a 20-min clinical scan.

Quantitative PET imaging and modeling of molecular blood-brain barrier permeability.

Blood-brain barrier (BBB) disruption is involved in the pathogenesis and progression of many neurological and systemic diseases. Non-invasive assessment of BBB permeability in humans has mainly been performed with dynamic contrast-enhanced magnetic resonance imaging, evaluating the BBB as a structural barrier. Here, we developed a novel non-invasive positron emission tomography (PET) method in humans to measure the BBB permeability of molecular radiotracers that cross the BBB through different transport mechanisms. Our method uses high-temporal resolution dynamic imaging and kinetic modeling to jointly estimate cerebral blood flow and tracer-specific BBB transport rate from a single dynamic PET scan and measure the molecular permeability-surface area (PS) product of the radiotracer. We show our method can resolve BBB PS across three PET radiotracers with greatly differing permeabilities, measure reductions in BBB PS of 18 F-fluorodeoxyglucose (FDG) in healthy aging, and demonstrate a possible brain-body association between decreased FDG BBB PS in patients with metabolic dysfunction-associated steatotic liver inflammation. Our method opens new directions to efficiently study the molecular permeability of the human BBB in vivo using the large catalogue of available molecular PET tracers.

Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging.

Objective.This study investigates the potential of cloud-based serverless computing to accelerate Monte Carlo (MC) simulations for nuclear medicine imaging tasks. MC simulations can pose a high computational burden-even when executed on modern multi-core computing servers. Cloud computing allows simulation tasks to be highly parallelized and considerably accelerated.Approach.We investigate the computational performance of a cloud-based serverless MC simulation of radioactive decays for positron emission tomography imaging using Amazon Web Service (AWS) Lambda serverless computing platform for the first time in scientific literature. We provide a comparison of the computational performance of AWS to a modern on-premises multi-thread reconstruction server by measuring the execution times of the processes using between105and2·1010simulated decays. We deployed two popular MC simulation frameworks-SimSET and GATE-within the AWS computing environment. Containerized application images were used as a basis for an AWS Lambda function, and local (non-cloud) scripts were used to orchestrate the deployment of simulations. The task was broken down into smaller parallel runs, and launched on concurrently running AWS Lambda instances, and the results were postprocessed and downloaded via the Simple Storage Service.Main results.Our implementation of cloud-based MC simulations with SimSET outperforms local server-based computations by more than an order of magnitude. However, the GATE implementation creates more and larger output file sizes and reveals that the internet connection speed can become the primary bottleneck for data transfers. Simulating 109decays using SimSET is possible within 5 min and accrues computation costs of about $10 on AWS, whereas GATE would have to run in batches for more than 100 min at considerably higher costs.Significance.Adopting cloud-based serverless computing architecture in medical imaging research facilities can considerably improve processing times and overall workflow efficiency, with future research exploring additional enhancements through optimized configurations and computational methods.

Performance Characteristics of the NeuroEXPLORER, a Next-Generation Human Brain PET/CT Imager.

The collaboration of Yale, the University of California, Davis, and United Imaging Healthcare has successfully developed the NeuroEXPLORER, a dedicated human brain PET imager with high spatial resolution, high sensitivity, and a built-in 3-dimensional camera for markerless continuous motion tracking. It has high depth-of-interaction and time-of-flight resolutions, along with a 52.4-cm transverse field of view (FOV) and an extended axial FOV (49.5 cm) to enhance sensitivity. Here, we present the physical characterization, performance evaluation, and first human images of the NeuroEXPLORER. Methods: Measurements of spatial resolution, sensitivity, count rate performance, energy and timing resolution, and image quality were performed adhering to the National Electrical Manufacturers Association (NEMA) NU 2-2018 standard. The system's performance was demonstrated through imaging studies of the Hoffman 3-dimensional brain phantom and the mini-Derenzo phantom. Initial 18F-FDG images from a healthy volunteer are presented. Results: With filtered backprojection reconstruction, the radial and tangential spatial resolutions (full width at half maximum) averaged 1.64, 2.06, and 2.51 mm, with axial resolutions of 2.73, 2.89, and 2.93 mm for radial offsets of 1, 10, and 20 cm, respectively. The average time-of-flight resolution was 236 ps, and the energy resolution was 10.5%. NEMA sensitivities were 46.0 and 47.6 kcps/MBq at the center and 10-cm offset, respectively. A sensitivity of 11.8% was achieved at the FOV center. The peak noise-equivalent count rate was 1.31 Mcps at 58.0 kBq/mL, and the scatter fraction at 5.3 kBq/mL was 36.5%. The maximum count rate error at the peak noise-equivalent count rate was less than 5%. At 3 iterations, the NEMA image-quality contrast recovery coefficients varied from 74.5% (10-mm sphere) to 92.6% (37-mm sphere), and background variability ranged from 3.1% to 1.4% at a contrast of 4.0:1. An example human brain 18F-FDG image exhibited very high resolution, capturing intricate details in the cortex and subcortical structures. Conclusion: The NeuroEXPLORER offers high sensitivity and high spatial resolution. With its long axial length, it also enables high-quality spinal cord imaging and image-derived input functions from the carotid arteries. These performance enhancements will substantially broaden the range of human brain PET paradigms, protocols, and thereby clinical research applications.

Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging.

Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation. METHODS: The conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored. RESULTS: The estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method. CONCLUSION: The proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.

Dose Reduction in Pediatric Oncology Patients with Delayed Total-Body [18F]FDG PET/CT.

Our aim was to define a lower limit of reduced injected activity in delayed [18F]FDG total-body (TB) PET/CT in pediatric oncology patients. Methods: In this single-center prospective study, children were scanned for 20 min with TB PET/CT, 120 min after intravenous administration of a 4.07 ± 0.49 MBq/kg dose of [18F]FDG. Five randomly subsampled low-count reconstructions were generated using », ⅛, [Formula: see text], and [Formula: see text] of the counts in the full-dose list-mode reference standard acquisition (20 min), to simulate dose reduction. For the 2 lowest-count reconstructions, smoothing was applied. Background uptake was measured with volumes of interest placed on the ascending aorta, right liver lobe, and third lumbar vertebra body (L3). Tumor lesions were segmented using a 40% isocontour volume-of-interest approach. Signal-to-noise ratio, tumor-to-background ratio, and contrast-to-noise ratio were calculated. Three physicians identified malignant lesions independently and assessed the image quality using a 5-point Likert scale. Results: In total, 113 malignant lesions were identified in 18 patients, who met the inclusion criteria. Of these lesions, 87.6% were quantifiable. Liver SUVmean did not change significantly, whereas a lower signal-to-noise ratio was observed in all low-count reconstructions compared with the reference standard (P < 0.0001) because of higher noise rates. Tumor uptake (SUVmax), tumor-to-background ratio, and total lesion count were significantly lower in the reconstructions with [Formula: see text] and [Formula: see text] of the counts of the reference standard (P < 0.001). Contrast-to-noise ratio and clinical image quality were significantly lower in all low-count reconstructions than with the reference standard. Conclusion: Dose reduction for delayed [18F]FDG TB PET/CT imaging in children is possible without loss of image quality or lesion conspicuity. However, our results indicate that to maintain comparable tumor uptake and lesion conspicuity, PET centers should not reduce the injected [18F]FDG activity below 0.5 MBq/kg when using TB PET/CT in pediatric imaging at 120 min after injection.

High-Temporal-Resolution Kinetic Modeling of Lung Tumors with Dual-Blood Input Function Using Total-Body Dynamic PET.

The lungs are supplied by both the pulmonary arteries carrying deoxygenated blood originating from the right ventricle and the bronchial arteries carrying oxygenated blood downstream from the left ventricle. However, this effect of dual blood supply has never been investigated using PET, partially because the temporal resolution of conventional dynamic PET scans is limited. The advent of PET scanners with a long axial field of view, such as the uEXPLORER total-body PET/CT system, permits dynamic imaging with high temporal resolution (HTR). In this work, we modeled the dual-blood input function (DBIF) and studied its impact on the kinetic quantification of normal lung tissue and lung tumors using HTR dynamic PET imaging. Methods: Thirteen healthy subjects and 6 cancer subjects with lung tumors underwent a dynamic 18F-FDG scan with the uEXPLORER for 1 h. Data were reconstructed into dynamic frames of 1 s in the early phase. Regional time-activity curves of lung tissue and tumors were analyzed using a 2-tissue compartmental model with 3 different input functions: the right ventricle input function, left ventricle input function, and proposed DBIF, all with time delay and dispersion corrections. These models were compared for time-activity curve fitting quality using the corrected Akaike information criterion and for differentiating lung tumors from lung tissue using the Mann-Whitney U test. Voxelwise multiparametric images by the DBIF model were further generated to verify the regional kinetic analysis. Results: The effect of dual blood supply was pronounced in the high-temporal-resolution time-activity curves of lung tumors. The DBIF model achieved better time-activity curve fitting than the other 2 single-input models according to the corrected Akaike information criterion. The estimated fraction of left ventricle input was low in normal lung tissue of healthy subjects but much higher in lung tumors (∼0.04 vs. ∼0.3, P < 0.0003). The DBIF model also showed better robustness in the difference in 18F-FDG net influx rate [Formula: see text] and delivery rate [Formula: see text] between lung tumors and normal lung tissue. Multiparametric imaging with the DBIF model further confirmed the differences in tracer kinetics between normal lung tissue and lung tumors. Conclusion: The effect of dual blood supply in the lungs was demonstrated using HTR dynamic imaging and compartmental modeling with the proposed DBIF model. The effect was small in lung tissue but nonnegligible in lung tumors. HTR dynamic imaging with total-body PET can offer a sensitive tool for investigating lung diseases.

Super-resolution reconstruction of γ-ray CT images for PET-enabled dual-energy CT imaging.

Dual-energy computed tomography (DECT) enables material decomposition for tissues and produces additional information for PET/CT imaging to potentially improve the characterization of diseases. PET-enabled DECT (PDECT) allows the generation of PET and DECT images simultaneously with a conventional PET/CT scanner without the need for a second x-ray CT scan. In PDECT, high-energy γ-ray CT (GCT) images at 511 keV are obtained from time-of-flight (TOF) PET data and are combined with the existing x-ray CT images to form DECT imaging. We have developed a kernel-based maximum-likelihood attenuation and activity (MLAA) method that uses x-ray CT images as a priori information for noise suppression. However, our previous studies focused on GCT image reconstruction at the PET image resolution which is coarser than the image resolution of the x-ray CT. In this work, we explored the feasibility of generating super-resolution GCT images at the corresponding CT resolution. The study was conducted using both phantom and patient scans acquired with the uEXPLORER total-body PET/CT system. GCT images at the PET resolution with a pixel size of 4.0 mm × 4.0 mm and at the CT resolution with a pixel size of 1.2 mm × 1.2 mm were reconstructed using both the standard MLAA and kernel MLAA methods. The results indicated that the GCT images at the CT resolution had sharper edges and revealed more structural details compared to the images reconstructed at the PET resolution. Furthermore, images from the kernel MLAA method showed substantially improved image quality compared to those obtained with the standard MLAA method.

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and patient results.

X-ray computed tomography (CT) in PET/CT is commonly operated with a single energy, resulting in a limitation of lacking tissue composition information. Dual-energy (DE) spectral CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging, but would either require hardware upgrade or increase radiation dose due to the added second x-ray CT scan. Recently proposed PET-enabled DECT method allows dual-energy spectral imaging using a conventional PET/CT scanner without the need for a second x-ray CT scan. A gamma-ray CT (gCT) image at 511 keV can be generated from the existing time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and is then combined with the low-energy x-ray CT image to form dual-energy spectral imaging. To improve the image quality of gCT, a kernel MLAA method was further proposed by incorporating x-ray CT as a priori information. The concept of this PET-enabled DECT has been validated using simulation studies, but not yet with 3D real data. In this work, we developed a general open-source implementation for gCT reconstruction from PET data and use this implementation for the first real data validation with both a physical phantom study and a human subject study on a uEXPLORER total-body PET/CT system. These results have demonstrated the feasibility of this method for spectral imaging and material decomposition.

Development of a Monte Carlo-based scatter correction method for total-body PET using the uEXPLORER PET/CT scanner.

Objective.This study presents and evaluates a robust Monte Carlo-based scatter correction (SC) method for long axial field of view (FOV) and total-body positron emission tomography (PET) using the uEXPLORER total-body PET/CT scanner.Approach.Our algorithm utilizes the Monte Carlo (MC) tool SimSET to compute SC factors in between individual image reconstruction iterations within our in-house list-mode and time-of-flight-based image reconstruction framework. We also introduced a unique scatter scaling technique at the detector block-level for optimal estimation of the scatter contribution in each line of response. First image evaluations were derived from phantom data spanning the entire axial FOV along with image data from a human subject with a large body mass index. Data was evaluated based on qualitative inspections, and contrast recovery, background variability, residual scatter removal from cold regions, biases and axial uniformity were quantified and compared to non-scatter-corrected images.Main results.All reconstructed images demonstrated qualitative and quantitative improvements compared to non-scatter-corrected images: contrast recovery coefficients improved by up to 17.2% and background variability was reduced by up to 34.3%, and the residual lung error was between 1.26% and 2.08%. Low biases throughout the axial FOV indicate high quantitative accuracy and axial uniformity of the corrections. Up to 99% of residual activity in cold areas in the human subject was removed, and the reliability of the method was demonstrated in challenging body regions like in the proximity of a highly attenuating knee prosthesis.Significance.The MC SC method employed was demonstrated to be accurate and robust in TB-PET. The results of this study can serve as a benchmark for optimizing the quantitative performance of future SC techniques.

Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET.

Quantitative PET attenuation correction (AC) for cardiac PET/CT and PET/MR is a challenging problem. We propose and evaluate an AC approach that uses coincidences from a relatively weak and physically fixed sparse external source, in combination with that from the patient, to reconstruct µ -maps based on physics principles alone. The low 30 cm3 volume of the source makes it easy to fill and place, and the method does not use prior image data or attenuation map assumptions. Our supplemental transmission aided maximum likelihood reconstruction of attenuation and activity (sTX-MLAA) algorithm contains an attenuation map update that maximizes the likelihood of terms representing coincidences originating from tracer in the patient and a weighted expression of counts segmented from the external source alone. Both external source and patient scatter and randoms are fully corrected. We evaluated performance of sTX-MLAA compared to reference standard CT-based AC with FDG PET/CT phantom studies; including modeling a patient with myocardial inflammation. Through an ROI analysis we measured ≤ 5 % bias in activity concentrations for PET images generated with sTX-MLAA and a TX source strength ≥ 12.7 MBq, relative to CT-AC. PET background variability (from noise and sparse sampling) was substantially reduced with sTX-MLAA compared to using counts segmented from the transmission source alone for AC. Results suggest that sTX-MLAA will enable quantitative PET during cardiac PET/CT and PET/MR of human patients.

Numerical investigation reveals challenges in measuring the contrast recovery coefficients in PET.

Objective.Contrast recovery coefficient (CRC) is essential for image quality (IQ) assessment in positron emission tomography (PET), typically measured according to the National Electrical Manufacturers Association (NEMA) NU 2 standard. This study quantifies systematic uncertainties of the CRC measurement by a numerical investigation of the effects from scanner-independent parameters like voxel size, region-of-interest (ROI) misplacement, and sphere position on the underlying image grid.Approach.CRC measurements with 2D and 3D ROIs were performed on computer-generated images of a NEMA IQ-like phantom, using voxel sizes of 1-4 mm for sphere diameters of 5-40 mm-first in absence of noise and blurring, then with simulated spatial resolution and image noise with varying noise levels. The systematic uncertainties of the CRC measurement were quantified from above variations of scanner-independent parameters. Subsampled experimental images of a NEMA IQ phantom were additionally used to investigate the impact of ROI misplacement at different noise levels.Main results.In absence of noise and blurring, systematic uncertainties were up to 28.8% and 31.0% with 2D and 3D ROIs, respectively, for the 10 mm sphere, with the highest impact from ROI misplacement. In all cases, smaller spheres showed higher uncertainties with larger voxels. Contrary to prior assumptions, the use of 3D ROIs did not exhibit less susceptibility for parameter changes. Experimental and computer-generated images both demonstrated considerable variations on individual CRC measurements when background coefficient-of-variation exceeded 20%, despite negligible effects on mean CRC.Significance.This study underscores the effect of scanner-independent parameters on reliability, reproducibility, and comparability of CRC measurements. Our findings highlight the trade-off between the benefits of smaller voxel sizes and noise-induced CRC fluctuations, which is not considered in the current version of the NEMA IQ standards. The results furthermore warrant adjustments to the standard to accommodate the advances in sensitivity and spatial resolution of current-generation PET scanners.

First-in-human immunoPET imaging of COVID-19 convalescent patients using dynamic total-body PET and a CD8-targeted minibody.

With most of the T cells residing in the tissue, not the blood, developing noninvasive methods for in vivo quantification of their biodistribution and kinetics is important for studying their role in immune response and memory. This study presents the first use of dynamic positron emission tomography (PET) and kinetic modeling for in vivo measurement of CD8+ T cell biodistribution in humans. A 89Zr-labeled CD8-targeted minibody (89Zr-Df-Crefmirlimab) was used with total-body PET in healthy individuals (N = 3) and coronavirus disease 2019 (COVID-19) convalescent patients (N = 5). Kinetic modeling results aligned with T cell-trafficking effects expected in lymphoid organs. Tissue-to-blood ratios from the first 7 hours of imaging were higher in bone marrow of COVID-19 convalescent patients compared to controls, with an increasing trend between 2 and 6 months after infection, consistent with modeled net influx rates and peripheral blood flow cytometry analysis. These results provide a promising platform for using dynamic PET to study the total-body immune response and memory.

Total-Body Multiparametric PET Quantification of 18F-FDG Delivery and Metabolism in the Study of Coronavirus Disease 2019 Recovery.

Conventional whole-body static 18F-FDG PET imaging provides a semiquantitative evaluation of overall glucose metabolism without insight into the specific transport and metabolic steps. Here we demonstrate the ability of total-body multiparametric 18F-FDG PET to quantitatively evaluate glucose metabolism using macroparametric quantification and assess specific glucose delivery and phosphorylation processes using microparametric quantification for studying recovery from coronavirus disease 2019 (COVID-19). Methods: The study included 13 healthy subjects and 12 recovering COVID-19 subjects within 8 wk of confirmed diagnosis. Each subject had a 1-h dynamic 18F-FDG scan on the uEXPLORER total-body PET/CT system. Semiquantitative SUV and the SUV ratio relative to blood (SUVR) were calculated for different organs to measure glucose utilization. Tracer kinetic modeling was performed to quantify the microparametric blood-to-tissue 18F-FDG delivery rate [Formula: see text] and the phosphorylation rate k 3, as well as the macroparametric 18F-FDG net influx rate ([Formula: see text]). Statistical tests were performed to examine differences between healthy subjects and recovering COVID-19 subjects. The effect of COVID-19 vaccination was also investigated. Results: We detected no significant difference in lung SUV but significantly higher lung SUVR and [Formula: see text] in COVID-19 recovery, indicating improved sensitivity of kinetic quantification for detecting the difference in glucose metabolism. A significant difference was also observed in the lungs with the phosphorylation rate k 3 but not with [Formula: see text], which suggests that glucose phosphorylation, rather than glucose delivery, drives the observed difference of glucose metabolism. Meanwhile, there was no or little difference in bone marrow 18F-FDG metabolism measured with SUV, SUVR, and [Formula: see text] but a significantly higher bone marrow [Formula: see text] in the COVID-19 group, suggesting a difference in glucose delivery. Vaccinated COVID-19 subjects had a lower lung [Formula: see text] and a higher spleen [Formula: see text] than unvaccinated COVID-19 subjects. Conclusion: Higher lung glucose metabolism and bone marrow glucose delivery were observed with total-body multiparametric 18F-FDG PET in recovering COVID-19 subjects than in healthy subjects, implying continued inflammation during recovery. Vaccination demonstrated potential protection effects. Total-body multiparametric PET of 18F-FDG can provide a more sensitive tool and more insights than conventional whole-body static 18F-FDG imaging to evaluate metabolic changes in systemic diseases such as COVID-19.

Total-Body Perfusion Imaging with [11C]-Butanol.

Tissue perfusion can be affected by physiology or disease. With the advent of total-body PET, quantitative measurement of perfusion across the entire body is possible. [11C]-butanol is a perfusion tracer with a superior extraction fraction compared with [15O]-water and [13N]-ammonia. To develop the methodology for total-body perfusion imaging, a pilot study using [11C]-butanol on the uEXPLORER total-body PET/CT scanner was conducted. Methods: Eight participants (6 healthy volunteers and 2 patients with peripheral vascular disease [PVD]) were injected with a bolus of [11C]-butanol and underwent 30-min dynamic acquisitions. Three healthy volunteers underwent repeat studies at rest (baseline) to assess test-retest reproducibility; 1 volunteer underwent paired rest and cold pressor test (CPT) studies. Changes in perfusion were measured in the paired rest-CPT study. For PVD patients, local changes in perfusion were investigated and correlated with patient medical history. Regional and parametric kinetic analysis methods were developed using a 1-tissue compartment model and leading-edge delay correction. Results: Estimated baseline perfusion values ranged from 0.02 to 1.95 mL·min-1·cm-3 across organs. Test-retest analysis showed that repeat baseline perfusion measurements were highly correlated (slope, 0.99; Pearson r = 0.96, P < 0.001). For the CPT subject, the largest regional increases were in skeletal muscle (psoas, 142%) and the myocardium (64%). One of the PVD patients showed increased collateral vessel growth in the calf because of a peripheral stenosis. Comorbidities including myocardial infarction, hypothyroidism, and renal failure were correlated with variations in organ-specific perfusion. Conclusion: This pilot study demonstrates the ability to obtain reproducible measurements of total-body perfusion using [11C]-butanol. The methods are sensitive to local perturbations in flow because of physiologic stressors and disease.

Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies.

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTATATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.

Facial Anonymization and Privacy Concerns in Total-Body PET/CT.

Total-body PET/CT images can be rendered to produce images of a subject's face and body. In response to privacy and identifiability concerns when sharing data, we have developed and validated a workflow that obscures (defaces) a subject's face in 3-dimensional volumetric data. Methods: To validate our method, we measured facial identifiability before and after defacing images from 30 healthy subjects who were imaged with both [18F]FDG PET and CT at either 3 or 6 time points. Briefly, facial embeddings were calculated using Google's FaceNet, and an analysis of clustering was used to estimate identifiability. Results: Faces rendered from CT images were correctly matched to CT scans at other time points at a rate of 93%, which decreased to 6% after defacing. Faces rendered from PET images were correctly matched to PET images at other time points at a maximum rate of 64% and to CT images at a maximum rate of 50%, both of which decreased to 7% after defacing. We further demonstrated that defaced CT images can be used for attenuation correction during PET reconstruction, introducing a maximum bias of -3.3% in regions of the cerebral cortex nearest the face. Conclusion: We believe that the proposed method provides a baseline of anonymity and discretion when sharing image data online or between institutions and will help to facilitate collaboration and future regulatory compliance.

Long Axial Field-of-View PET/CT Could Answer Unmet Needs in Gynecological Cancers.

Gynecological malignancies currently affect about 3.5 million women all over the world. Imaging of uterine, cervical, vaginal, ovarian, and vulvar cancer still presents several unmet needs when using conventional modalities such as ultrasound, computed tomography (CT), magnetic resonance, and standard positron emission tomography (PET)/CT. Some of the current diagnostic limitations are represented by differential diagnosis between inflammatory and cancerous findings, detection of peritoneal carcinomatosis and metastases <1 cm, detection of cancer-associated vascular complications, effective assessment of post-therapy changes, as well as bone metabolism and osteoporosis assessment. As a result of recent advances in PET/CT instrumentation, new systems now offer a long-axial field-of-view (LAFOV) to image between 106 cm and 194 cm (i.e., total-body PET) of the patient's body simultaneously and feature higher physical sensitivity and spatial resolution compared to standard PET/CT systems. LAFOV PET could overcome the forementioned limitations of conventional imaging and provide valuable global disease assessment, allowing for improved patient-tailored care. This article provides a comprehensive overview of these and other potential applications of LAFOV PET/CT imaging for patients with gynecological malignancies.

Total-Body Multiparametric PET Quantification of 18 F-FDG Delivery and Metabolism in the Study of COVID-19 Recovery.

Conventional whole-body 18 F-FDG PET imaging provides a semi-quantitative evaluation of overall glucose metabolism without gaining insight into the specific transport and metabolic steps. Here we demonstrate the ability of total-body multiparametric 18 F-FDG PET to quantitatively evaluate glucose metabolism using macroparametric quantification and assess specific glucose delivery and phosphorylation processes using microparametric quantification for studying recovery from coronavirus disease 2019 (COVID-19). Methods: The study included thirteen healthy subjects and twelve recovering COVID-19 subjects within eight weeks of confirmed diagnosis. Each subject had a dynamic 18 F-FDG scan on the uEXPLORER total-body PET/CT system for one hour. Semiquantitative standardized uptake value (SUV) and SUV ratio relative to blood (SUVR) were calculated for regions of interest (ROIs) in different organs to measure glucose utilization. Tracer kinetic modeling was performed to quantify microparametric rate constants K 1 and k 3 that characterize 18 F-FDG blood-to-tissue delivery and intracellular phosphorylation, respectively, and a macroparameter K i that represents 18 F-FDG net influx rate. Statistical tests were performed to examine differences between the healthy controls and recovering COVID-19 subjects. Impact of COVID-19 vaccination was investigated. We further generated parametric images to confirm the ROI-based analysis. Results: We detected no significant difference in lung SUV but significantly higher lung SUVR and K i in the recovering COVID-19 subjects, indicating an improved sensitivity of kinetic quantification for detecting the difference in glucose metabolism. A significant difference was also observed in the lungs with the phosphorylation rate k 3 , but not with the delivery rate K 1 , which suggests it is glucose phosphorylation, not glucose delivery, that drives the observed difference of glucose metabolism in the lungs. Meanwhile, there was no or little difference in bone marrow metabolism measured with SUV, SUVR and K i , but a significant increase in bone-marrow 18 F-FDG delivery rate K 1 in the COVID-19 group ( p < 0.05), revealing a difference of glucose delivery in this immune-related organ. The observed differences were lower or similar in vaccinated COVID-19 subjects as compared to unvaccinated ones. The organ ROI-based findings were further supported by parametric images. Conclusions: Higher lung glucose metabolism and bone-marrow glucose delivery were observed with total-body multiparametric 18 F-FDG PET in recovering COVID-19 subjects as compared to healthy subjects, which suggests continued inflammation due to COVID-19 during the early stages of recovery. Total-body multiparametric PET of 18 F-FDG delivery and metabolism can provide a more sensitive tool and more insights than conventional static whole-body 18 F-FDG imaging to evaluate metabolic changes in systemic diseases such as COVID-19.

High-Temporal-Resolution Lung Kinetic Modeling Using Total-Body Dynamic PET with Time-Delay and Dispersion Corrections.

Tracer kinetic modeling in dynamic PET has the potential to improve the diagnosis, prognosis, and research of lung diseases. The advent of total-body PET systems with much greater detection sensitivity enables high-temporal-resolution (HTR) dynamic PET imaging of the lungs. However, existing models may become insufficient for modeling the HTR data. In this paper, we investigate the necessity of additional corrections to the input function for HTR lung kinetic modeling. Methods: Dynamic scans with HTR frames of as short as 1 s were performed on 13 healthy subjects with a bolus injection of about [Formula: see text] of 18F-FDG using the uEXPLORER total-body PET/CT system. Three kinetic models with and without time-delay and dispersion corrections were compared for the quality of lung time-activity curve fitting using the Akaike information criterion. The impact on quantification of 18F-FDG delivery rate [Formula: see text], net influx rate [Formula: see text] and fractional blood volume [Formula: see text] was assessed. Parameter identifiability analysis was also performed to evaluate the reliability of kinetic quantification with respect to noise. Correlation of kinetic parameters with age was investigated. Results: HTR dynamic imaging clearly revealed the rapid change in tracer concentration in the lungs and blood supply (i.e., the right ventricle). The uncorrected input function led to poor time-activity curve fitting and biased quantification in HTR kinetic modeling. The fitting was improved by time-delay and dispersion corrections. The proposed model resulted in an approximately 85% decrease in [Formula: see text], an approximately 75% increase in [Formula: see text], and a more reasonable [Formula: see text] (∼0.14) than the uncorrected model (∼0.04). The identifiability analysis showed that the proposed models had good quantification stability for [Formula: see text], [Formula: see text], and [Formula: see text] The [Formula: see text] estimated by the proposed model with simultaneous time-delay and dispersion corrections correlated inversely with age, as would be expected. Conclusion: Corrections to the input function are important for accurate lung kinetic analysis of HTR dynamic PET data. The modeling of both delay and dispersion can improve model fitting and significantly impact quantification of [Formula: see text], [Formula: see text], and [Formula: see text].