Application of Dynamic [18F]FDG PET/CT Multiparametric Imaging Leads to an Improved Differentiation of Benign and Malignant Lung Lesions.

PURPOSE: To evaluate the potential of whole-body dynamic (WBD) 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) multiparametric imaging in the differential diagnosis between benign and malignant lung lesions. PROCEDURES: We retrospectively analyzed WBD PET/CT scans from patients with lung lesions performed between April 2020 and March 2023. Multiparametric images including standardized uptake value (SUV), metabolic rate (MRFDG) and distribution volume (DVFDG) were visually interpreted and compared. We adopted SUVmax, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) for semi-quantitative analysis, MRmax and DVmax values for quantitative analysis. We also collected the patients' clinical characteristics. The variables above with P-value < 0.05 in the univariate analysis were entered into a multivariate logistic regression. The statistically significant metrics were plotted on receiver-operating characteristic (ROC) curves. RESULTS: A total of 60 patients were included for data evaluation. We found that most malignant lesions showed high uptake on MRFDG and SUV images, and low or absent uptake on DVFDG images, while benign lesions showed low uptake on MRFDG images and high uptake on DVFDG images. Most malignant lesions showed a characteristic pattern of gradually increasing FDG uptake, whereas benign lesions presented an initial rise with rapid fall, then kept stable at a low level. The AUC values of MRmax and SUVmax are 0.874 (95% CI: 0.763-0.946) and 0.792 (95% CI: 0.667-0.886), respectively. DeLong's test showed the difference between the areas is statistically significant (P < 0.001). CONCLUSIONS: Our study demonstrated that dynamic [18F]FDG PET/CT imaging based on the Patlak analysis was a more accurate method of distinguishing malignancies from benign lesions than conventional static PET/CT scans.

Evaluation of a novel PET tracer [18F]-Florbetazine for Alzheimer's disease diagnosis and ß-amyloid deposition quantification.

[18F]-Florbetazine ([18F]-92) is a selective PET tracer for ß-amyloid (Aß) depositions with a novel diaryl-azine scaffold to reduce lipophilicity and to achieve higher gray-to-white matter contrast. We aimed to assess its diagnostic value in Alzheimer's disease (AD) and pharmacokinetics characteristics in human subjects. METHODS: Six healthy controls (HCs) and nine AD patients underwent dynamic PET examination with [18F]-Florbetazine and a structural MRI scan. The time-activity-curves (TACs) for volumes of interest (VOIs) in cerebral cortex, cerebellar cortex and cerebral white matter was depicted and their standardized uptake value ratios (SUVRs) with cerebellar cortex as reference were compared between HCs and AD patients. The cerebral gray-to-white matter SUV ratio (GWR) was also calculated. RESULTS: In HCs, radioactivities in the cerebral cortex VOIs were homogeneously low and at the same level as in cerebellar cortex, while in AD patients, cortical VOIs expected to contain Aß exhibited high radioactivity. Cerebral cortex SUVRs remain relatively low in HCs while keep increasing along with time in AD patients. After 15 min, the cerebral cortex SUVRs became significant higher in AD patients compared to HCs with 100 % discrimination accuracy. In AD patients, GWR remained over 1.3 for all time intervals and visual inspection showed lower uptake in cerebral white matter compared to cerebral cortex. CONCLUSION: [18F]-Florbetazine PET showed high uptake on Aß plaques and high gray-to-white contrast in AD patients that are favorable in visual read. [18F]-Florbetazine can be potentially used for detection and quantification of Aß depositions in the living human brain.

Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components.

Objective.Respiratory motion correction is beneficial in positron emission tomography (PET), as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the real time position management system) or a data driven method, such as those based on dimensionality reduction techniques (for instance principal component analysis (PCA)). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging.Approach.The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyeret al(2014)), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic [18F]-FDG study on patients with idiopathic pulmonary fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a real time position management system.Main results.The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods.Significance.This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).

Whole-body parametric mapping of tumour perfusion in metastatic prostate cancer using long axial field-of-view [15O]H2O PET.

PURPOSE: Tumour perfusion is a nutrient-agnostic biomarker for cancer metabolic rate. Use of tumour perfusion for cancer growth assessment has been limited by complicated image acquisition, image analysis and limited field-of-view scanners. Long axial field-of-view (LAFOV) PET scan using [15O]H2O, allows quantitative assessment of whole-body tumour perfusion. We created a tool for automated creation of quantitative parametric whole-body tumour perfusion images in metastatic cancer. METHODS: Ten metastatic prostate cancer patients underwent dynamic LAFOV [15O]H2O PET (Siemens, Quadra) followed by [18F]PSMA-1007 PET. Perfusion was measured as [15O]H2O K1 (mL/min/mL) with a single-tissue compartment model and an automatically captured cardiac image-derived input function. Parametric perfusion images were automatically calculated using the basis-function method with initial voxel-wise delay estimation and a leading-edge approach. Subsequently, perfusion of volumes-of-interest (VOI) can be directly extracted from the parametric images. We used a [18F]PSMA-1007 SUV 4 fixed threshold for tumour delineation and transferred these VOIs to the perfusion map. RESULTS: For 8 primary tumours, 64 lymph node metastases, and 85 bone metastases, median tumour perfusion were 0.19 (0.15-0.27) mL/min/mL, 0.16 (0.13-0.27) mL/min/mL, and 0.26 (0.21-0.39), respectively. The correlation between calculated perfusion from time-activity-curves and parametric images was excellent (r = 0.99, p < 0.0001). CONCLUSION: LAFOV PET imaging using [15O]H2O enables truly quantitative parametric images of whole-body tumour perfusion, a potential biomarker for guiding personalized treatment and monitoring treatment response.

Proof of concept of a new plasma complement Factor H from waste plasma fraction.

Introduction: Complement factor H (FH) is a major regulator of the complement alternative pathway, its mutations predispose to an uncontrolled activation in the kidney and on blood cells and to secondary C3 deficiency. Plasma exchange has been used to correct for FH deficiency and although the therapeutic potential of purified FH has been suggested by in vivo experiments in animal models, a clinical approved FH concentrate is not yet available. We aimed to develop a purification process of FH from a waste fraction rather than whole plasma allowing a more efficient and ethical use of blood and plasma donations. Methods: Waste fractions from industrial plasma fractionation (pooled human plasma) were analyzed for FH content by ELISA. FH was purified from unused fraction III and its decay acceleration, cofactor, and C3 binding capacity were characterized in vitro. Biodistribution was assessed by high-resolution dynamic PET imaging. Finally, the efficacy of the purified FH preparation was tested in the mouse model of C3 glomerulopathy (Cfh-/- mice). Results: Our purification method resulted in a high yield of highly purified (92,07%), pathogen-safe FH. FH concentrate is intact and fully functional as demonstrated by in vitro functional assays. The biodistribution revealed lower renal and liver clearance of human FH in Cfh-/- mice than in wt mice. Treatment of Cfh-/- mice documented its efficacy in limiting C3 activation and promoting the clearance of C3 glomerular deposits. Conclusion: We developed an efficient and economical system for purifying intact and functional FH, starting from waste material of industrial plasma fractionation. The FH concentrate could therefore constitute possible treatments options of patients with C3 glomerulopathy, particularly for those with FH deficiency, but also for patients with other diseases associated with alternative pathway activation.

Challenges in Pharmacokinetic Modelling of [18F]fluoro-PEG-folate PET/CT Imaging in Epithelial Ovarian Cancer Patients.

PURPOSE: To describe the pharmacokinetic properties of the [18F]fluoro-polyethylene glycol(PEG)-folate radiotracer in PET/CT imaging of patients with advanced stage epithelial ovarian cancer (EOC). PROCEDURES: In five patients with advanced EOC (FIGO stage IIIB/IIIC, Fédération Internationale de Gynécologie et d'Obstétrique), a 90-min dynamic PET acquisition of the pelvis was performed directly after i.v. administration of 185 MBq [18F]fluoro-PEG6-folate. Arterial blood samples collected at nineteen timepoints were used to determine the plasma input function. A static volume of interest (VOI) for included tumor lesions was drawn manually on the PET images. Modelling was performed using PMOD software. Three different models (a 1-tissue compartment model (1T2k) and two 2-tissue compartment models, irreversible (2T3k) and reversible (2T4k)) were compared in goodness of fit with the time activity curves by means of the Akaike information criterion. RESULTS: The pharmacokinetic analysis in the pelvic area has proven to be much more challenging than expected. Only four out of 22 tumor lesions in five patients were considered suitable to perform modelling on. The remaining tumor lesions were inapt due to either low tracer uptake, small size, proximity to other [18F]fluoro-PEG6-folate -avid structures and/or displacement by abdominal organ motion in the dynamic scan. Data from the four analyzed tumor lesions suggest that the irreversible 2T3k may best describe the pharmacokinetics. All 22 lesions were immunohistochemically stained positive for the folate receptor alpha (FRα) after resection. CONCLUSION: Performing pharmacokinetic analysis in the abdominal pelvic region is very challenging. This brief article describes the challenges and pitfalls in pharmacokinetic analysis of a tracer with high physiological accumulation in the intestines, in case of lesions of limited size in the abdominal pelvic area.

Dual time-point [18F]FDG PET imaging for quantification of metabolic uptake rate: Evaluation of a simple, clinically feasible method.

PURPOSE: We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (Ki) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy. METHODS: We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.). We determined Ki for a total of 81 lesions. Ki quantification from full dynamic PET data (Patlak-Ki) and Ki from DTP (DTP-Ki) were compared. In addition, we scaled a population-based input function (PBIFscl) with the image-derived blood pool activity sampled at different time points to assess the best scaling time-point for Ki quantifications in the simulation data. RESULTS: In the simulation study, Ki estimated using DTP via (30,60-min), (30,90-min), (60,90-min), and (60,120-min) samples showed strong correlations (r ≥ 0.944, P < 0.0001) with the true value of Ki. The DTP results with the PBIFscl at 60-min time-point in (30,60-min), (60,90-min), and (60,120-min) were linearly related to the true Ki with a slope of 1.037, 1.008, 1.013 and intercept of -6 × 10-4, 2 × 10-5, 5 × 10-5, respectively. In a clinical study, strong correlations (r ≥ 0.833, P < 0.0001) were observed between Patlak-Ki and DTP-Ki. The Patlak-derived mean values of Ki, tumor-to-background-ratio, signal-to-noise-ratio, and contrast-to-noise-ratio were linearly correlated with the DTP method. CONCLUSIONS: Besides calculating the retention index as a commonly used quantification parameter inDTP imaging,our DTP method can accurately estimate Ki.

Mapping 18F-FDG Kinetics Together with Patient-Specific Bootstrap Assessment of Uncertainties: An Illustration with Data from a PET/CT Scanner with a Long Axial Field of View.

The purpose of this study was to examine a nonparametric approach to mapping kinetic parameters and their uncertainties with data from the emerging generation of dynamic whole-body PET/CT scanners. Methods: Dynamic PET 18F-FDG data from a set of 24 cancer patients studied on a long-axial-field-of-view PET/CT scanner were considered. Kinetics were mapped using a nonparametric residue mapping (NPRM) technique. Uncertainties were evaluated using an image-based bootstrapping methodology. Kinetics and bootstrap-derived uncertainties are reported for voxels, maximum-intensity projections, and volumes of interest (VOIs) corresponding to several key organs and lesions. Comparisons between NPRM and standard 2-compartment (2C) modeling of VOI kinetics are carefully examined. Results: NPRM-generated kinetic maps were of good quality and well aligned with vascular and metabolic 18F-FDG patterns, reasonable for the range of VOIs considered. On a single 3.2-GHz processor, the specification of the bootstrapping model took 140 min; individual bootstrap replicates required 80 min each. VOI time-course data were much more accurately represented, particularly in the early time course, by NPRM than by 2C modeling constructs, and improvements in fit were statistically highly significant. Although 18F-FDG flux values evaluated by NPRM and 2C modeling were generally similar, significant deviations between vascular blood and distribution volume estimates were found. The bootstrap enables the assessment of quite complex summaries of mapped kinetics. This is illustrated with maximum-intensity maps of kinetics and their uncertainties. Conclusion: NPRM kinetics combined with image-domain bootstrapping is practical with large whole-body dynamic 18F-FDG datasets. The information provided by bootstrapping could support more sophisticated uses of PET biomarkers used in clinical decision-making for the individual patient.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification.

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.

[1-11C]-Butanol Positron Emission Tomography reveals an impaired brain to nasal turbinates pathway in aging amyloid positive subjects.

BACKGROUND: Reduced clearance of cerebrospinal fluid (CSF) has been suggested as a pathological feature of Alzheimer's disease (AD). With extensive documentation in non-human mammals and contradictory human neuroimaging data it remains unknown whether the nasal mucosa is a CSF drainage site in humans. Here, we used dynamic PET with [1-11C]-Butanol, a highly permeable radiotracer with no appreciable brain binding, to test the hypothesis that tracer drainage from the nasal pathway reflects CSF drainage from brain. As a test of the hypothesis, we examined whether brain and nasal fluid drainage times were correlated and affected by brain amyloid. METHODS: 24 cognitively normal subjects (≥ 65 years) were dynamically PET imaged for 60 min. using [1-11C]-Butanol. Imaging with either [11C]-PiB or [18F]-FBB identified 8 amyloid PET positive (Aß+) and 16 Aß- subjects. MRI-determined regions of interest (ROI) included: the carotid artery, the lateral orbitofrontal (LOF) brain, the cribriform plate, and an All-turbinate region comprised of the superior, middle, and inferior turbinates. The bilateral temporalis muscle and jugular veins served as control regions. Regional time-activity were used to model tracer influx, egress, and AUC. RESULTS: LOF and All-turbinate 60 min AUC were positively associated, thus suggesting a connection between the brain and the nose. Further, the Aß+ subgroup demonstrated impaired tracer kinetics, marked by reduced tracer influx and slower egress. CONCLUSION: The data show that tracer kinetics for brain and nasal turbinates are related to each other and both reflect the amyloid status of the brain. As such, these data add to evidence that the nasal pathway is a potential CSF drainage site in humans. These data warrant further investigation of brain and nasal contributions to protein clearance in neurodegenerative disease.

Automated extraction of the arterial input function from brain images for parametric PET studies.

BACKGROUND: Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images. RESULTS: Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07. CONCLUSIONS: Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.

Improvement in the estimation of perfusable tissue fraction and myocardial flow reserve in the ischemic myocardial lesions using ECG-gated dynamic myocardial PET with 15O-water.

OBJECTIVE: Perfusable tissue fraction (PTF) and myocardial flow reserve (MFR) from 15O-water dynamic positron emission tomography (PET) are parameters of myocardial viability. However, myocardial motion causes errors in these values. We aimed to develop accurate estimation of PTF and MFR in ischemic lesions using an electro-cardiogram (ECG)-gated dynamic myocardial PET with 15O-water. METHODS: Twenty-seven patients with ischemic heart disease were enrolled. All patients underwent percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). List mode 3D PET data and ECG signals were acquired using Philips Gemini TF64 instrument. For each scan, 500 MBq of 15O-water was infused slowly for 2 min, and the dynamic data were scanned for 6 min. Both non-gated dynamic images and ECG-gated diastolic dynamic images were reconstructed. On the myocardial PET images of each patient, the entire myocardial region of interest (ROI) was set and divided into 17 segments. Myocardial blood flow in the resting state (rest MBF), hyperemic state (stress MBF), PTF, and MFR in each segment were estimated from both non-gated and ECG-gated dynamic PET images. Coronary arteriograms were obtained for all patients. In total, 128 normal segments without stenosis and 50 ischemic segments with > 90% stenosis were evaluated. RESULTS: In the ischemic myocardial segments, the PTF with ECG-gated PET was estimated as significantly lower than that with non-gated PET (0.63 ± 0.09 vs. 0.72 ± 0.08 [mL/mL], p < 0.001). The ECG-gated PET estimated a significantly lower PTF in the ischemic segments than in the normal segments (0.63 ± 0.09 vs. 0.67 ± 0.07 [mL/mL], p < 0.01). In the normal segments, the ECG-gated PET detected no significant difference in MFR compared with those from the non-gated PET (2.15 ± 0.76 vs. 2.24 ± 0.79, p = 0.28). However, in the ischemic myocardial segments, the MFR with ECG-gated PET was estimated as significantly lower than that with the non-gated PET (1.23 ± 0.29 vs. 1.69 ± 0.71, p < 0.001). The ECG-gated PET presented a significantly higher inter-observer reproducibility of PTF and rest MBF than the non-gated PET (p < 0.01). Neither stress MBF nor MFR yielded significant differences in inter-observer reproducibility between the ECG-gated and non-gated PET. CONCLUSIONS: The ECG-gated dynamic 15O-water PET suppressed the myocardial motion effect and resulted in a lower PTF and MFR in ischemic myocardial lesions than the non-gated PET. The ECG-gated PET seemed to be better than the conventional non-gated dynamic PET for the detection of ischemic myocardial lesion.

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.

PET KinetiX-A Software Solution for PET Parametric Imaging at the Whole Field of View Level.

Kinetic modeling represents the ultimate foundations of PET quantitative imaging, a unique opportunity to better characterize the diseases or prevent the reduction of drugs development. Primarily designed for research, parametric imaging based on PET kinetic modeling may become a reality in future clinical practice, enhanced by the technical abilities of the latest generation of commercially available PET systems. In the era of precision medicine, such paradigm shift should be promoted, regardless of the PET system. In order to anticipate and stimulate this emerging clinical paradigm shift, we developed a constructor-independent software package, called PET KinetiX, allowing a faster and easier computation of parametric images from any 4D PET DICOM series, at the whole field of view level. The PET KinetiX package is currently a plug-in for Osirix DICOM viewer. The package provides a suite of five PET kinetic models: Patlak, Logan, 1-tissue compartment model, 2-tissue compartment model, and first pass blood flow. After uploading the 4D-PET DICOM series into Osirix, the image processing requires very few steps: the choice of the kinetic model and the definition of an input function. After a 2-min process, the PET parametric and error maps of the chosen model are automatically estimated voxel-wise and written in DICOM format. The software benefits from the graphical user interface of Osirix, making it user-friendly. Compared to PMOD-PKIN (version 4.4) on twelve 18F-FDG PET dynamic datasets, PET KinetiX provided an absolute bias of 0.1% (0.05-0.25) and 5.8% (3.3-12.3) for KiPatlak and Ki2TCM, respectively. Several clinical research illustrative cases acquired on different hybrid PET systems (standard or extended axial fields of view, PET/CT, and PET/MRI), with different acquisition schemes (single-bed single-pass or multi-bed multipass), are also provided. PET KinetiX is a very fast and efficient independent research software that helps molecular imaging users easily and quickly produce 3D PET parametric images from any reconstructed 4D-PET data acquired on standard or large PET systems.

Combined whole-body dynamic and static PET/CT with low-dose [18F]PSMA-1007 in prostate cancer patients.

AIM: In addition to significant improvements in sensitivity and image quality, the recent introduction of long axial field-of-view (LAFOV) PET/CT scanners has enabled dynamic whole-body imaging for the first time. We aim herein to determine an appropriate acquisition time range for static low-dose [18F]PSMA-1007 PET imaging and to investigate the whole-body pharmacokinetics of [18F]PSMA-1007 by dynamic PET with the LAFOV Biograph Vision Quadra PET/CT in a group of prostate cancer patients. METHODOLOGY: In total, 38 prostate cancer patients were enrolled in the analysis for staging or re-staging purposes. Thirty-four patients underwent dynamic whole-body PET/CT (60 min) followed by static whole-body PET/CT and four patients underwent static whole-body PET/CT only. The activity applied was 2 MBq/kg [18F]PSMA-1007. The static PET images of 10-min duration (PET-10) were reconstructed and further split into 8-min (PET-8), 6-min (PET-6), 5-min (PET-5), 4-min (PET-4), and 2-min (PET-2) duration groups. Comparisons were made between the different reconstructed scan times in terms of lesion detection rate and image quality based on SUV calculations of tumor lesions and the spleen, which served as background. Analysis of the dynamic PET/CT data was based on a two-tissue compartment model using an image-derived input function obtained from the descending aorta. RESULTS: Analysis of lesion detection rate showed no significant differences when reducing PET acquisitions from 10 up to 5 min. In particular, a total of 169 lesions were counted with PET-10, and the corresponding lesion detection rates (95% CI for the 90% quantile of the differences in tumor lesions) for shorter acquisitions were 100% (169/169) for PET-8 (95% CI: 0-0), 98.8% (167/169) for PET-6 (95% CI: 0-1), 95.9% (162/169) for PET-5 (95% CI: 0-3), 91.7% (155/169) for PET-4 (95% CI: 1-2), and 85.2% (144/169) for PET-2 (95% CI: 1-6). With the exception of PET-2, the differences observed between PET-10 and the other shorter acquisition protocols would have no impact on any patient in terms of clinical management. Objective evaluation of PET/CT image quality showed no significant decrease in tumor-to-background ratio (TBR) with shorter acquisition times, despite a gradual decrease in signal-to-noise ratio (SNR) in the spleen. Whole-body quantitative [18F]PSMA-1007 pharmacokinetic analysis acquired with full dynamic PET scanning was feasible in all patients. Two-tissue compartment modeling revealed significantly higher values for the parameter k3 in tumor lesions and parotid gland compared to liver and spleen, reflecting a higher specific tracer binding to the PSMA molecule and internalization rate in these tissues, a finding also supported by the respective time-activity curves. Furthermore, correlation analysis demonstrated a significantly strong positive correlation (r = 0.72) between SUV and k3 in tumor lesions. CONCLUSIONS: In prostate cancer, low-dose (2 MBq/kg) [18F]PSMA-1007 LAFOV PET/CT can reduce static scan time by 50% without significantly compromising lesion detection rate and objective image quality. In addition, dynamic PET can elucidate molecular pathways related to the physiology of [18F]PSMA-1007 in both tumor lesions and normal organs at the whole-body level. These findings unfold many of the potentials of the new LAFOV PET/CT technology in the field of PSMA-based diagnosis and theranostics of prostate cancer.

Determining the optimal pharmacokinetic modelling and simplified quantification method of [18F]AlF-P16-093 for patients with primary prostate cancer (PPCa).

PURPOSE: This paper discusses the optimization of pharmacokinetic modelling and alternate simplified quantification method for [18F]AlF-P16-093, a novel tracer for in vivo imaging of prostate cancer. METHODS: Dynamic PET/CT scans were conducted on eight primary prostate cancer patients, followed by a whole-body scan at 60 min post-injection. Time-activity curves (TACs) were obtained by drawing volumes of interest for primary prostatic and metastatic lesions. Optimal kinetic modelling involved evaluating three compartmental models (1T2K, 2T3K, and 2T4K) accounting for fractional blood volume (Vb). The simplified quantification method was then determined based on the correlation between the static uptake measure and total distribution volume (Vt) obtained from the optimal pharmacokinetic analysis. RESULTS: In total, 17 intraprostatic lesions, 10 lymph nodes, and 36 osseous metastases were evaluated. Visually, the contrast of the tumor increased and showed the steepest incline within the first few minutes, whereas background activity decreased over time. Full pharmacokinetic analysis revealed that a reversible two-compartmental (2T4K) model is the preferred kinetic model for the given tracer. The kinetic parameters K1, k3, Vb, and Vt were all significantly higher in lesions when compared with normal tissue (P < 0.01). Several simplified protocols were tested for approximating comprehensive dynamic quantification in tumors, with image-based SURmean (the ratio of tumor SUVmean to blood SUVmean) within the 28-34 min window found to be sufficient for approximating the total distribution Vt values (R2 = 0.949, P < 0.01). Both Vt and SURmean correlated significantly with the total serum prostate-specific antigen (tPSA) levels (P < 0.01). CONCLUSIONS: This study introduced an optimized pharmacokinetic modelling approach and a simplified acquisition method for [18F]AlF-P16-093, a novel PSMA-targeted radioligand, highlighting the feasibility of utilizing one static PET imaging (between 30 and 60 min) for the diagnosis of prostate cancer. Note that the image-derived input function in this study may not reflect the true corrected plasma input function, therefore the interpretation of the associated kinetic parameter estimates should be done with caution.

A short 18F-FDG imaging window triple injection neuroimaging protocol for parametric mapping in PET.

BACKGROUND: In parametric PET, kinetic parameters are extracted from dynamic PET images. It is not commonly used in clinical practice because of long scan times and the requirement for an arterial input function (AIF). To address these limitations, we designed an 18F-fluorodeoxyglucose (18F-FDG) triple injection dynamic PET protocol for brain imaging with a standard field of view PET scanner using a 24-min imaging window and an input function modeled using measurements from a region of interest placed over the left ventricle. METHODS: To test the protocol in 6 healthy participants, we examined the quality of voxel-based maps of kinetic parameters in the brain generated using the two-tissue compartment model and compared estimated parameter values with previously published values. We also utilized data from a 36-min validation imaging window to compare (1) the modeled AIF against the input function measured in the validation window; and (2) the net influx rate ([Formula: see text]) computed using parameter estimates from the short imaging window against the net influx rate obtained using Patlak analysis in the validation window. RESULTS: Compared to the AIF measured in the validation window, the input function estimated from the short imaging window achieved a mean area under the curve error of 9%. The voxel-wise Pearson's correlation between [Formula: see text] estimates from the short imaging window and the validation imaging window exceeded 0.95. CONCLUSION: The proposed 24-min triple injection protocol enables parametric 18F-FDG neuroimaging with noninvasive estimation of the AIF from cardiac images using a standard field of view PET scanner.

Characteristics of whole-body dynamic 18F-FDG PET/CT Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters.

Background: Dynamic course of flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) Patlak muti-parametric imaging spatial distribution in the targeted tissues may reveal highly useful clinical information about the tissue's metabolic properties. The characteristics of the Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters may provide reference for the clinical application of this new technology. Methods: A total of 27 patients with pathologically diagnosed lung cancer underwent whole-body dynamic 18F-FDG PET/CT examination before treatment. Parametric images of metabolic rate of FDG (MRFDG) and Patlak intercept (or distribution volume; DV) were generated using Patlak reconstruction. The values of primary lung cancer lesions, target-to-background ratio (TBR), and contrast-to-noise ratio (CNR) were investigated using contour delineation and boundary delineation. Statistical analysis was performed to analyze the relationship between multi-parametric images and clinicopathological features, and to compare the effects of contour delineation and boundary delineation on quantitative parameters. Results: MRFDG images showed higher TBR and CNR than did standardized uptake value (SUV) images. There were significant differences in MRFDG-max, MRFDG-mean, and MRFDG-peak among groups with different tumor diameters and pathology types (P<0.05). Moreover, the metabolic parameters of MRFDG were higher in patients with tumor diameters ≥3 cm and squamous carcinoma. The differences of the maximum and peak values of MRFDG and DV were not statistically significant in the different outlining method subgroups (all P>0.05). However, the difference of the mean values of MRFDG and DV were statistically significant in the different outline method groupings (all P<0.05). Conclusions: Dynamic 18F-FDG PET/CT Patlak multi-parametric imaging can obtain quantitative values for lung cancer with high TBR and CNR. Moreover, the multi-parameters are various from different pathology types to tumor size. Different delineation methods have a greater influence on the mean value of quantitative parameters.

Comparison of parametric imaging and SUV imaging with [68 Ga]Ga-PSMA-11 using dynamic total-body PET/CT in prostate cancer.

PURPOSE: Standardized uptake value (SUV) has been prevalently used to measure [68 Ga]Ga-PSMA-11 activity in prostate cancer, but it is susceptible to multiple factors. Parametric imaging allows for absolute quantification of tracer uptake and provides a better diagnostic accuracy that is crucial for lesion detection. However, the clinical significance of total-body parametric imaging of [68 Ga]Ga-PSMA-11 remains to be fully assessed. Therefore, the aim of our study is to delve into the diagnostic implications of total-body parametric imaging of [68 Ga]Ga-PSMA-11 PET/CT for patients with prostate cancer. METHODS: Twenty prostate cancer patients were included and underwent a dynamic total-body [68 Ga]Ga-PSMA-11 PET/CT scan. An irreversible two-tissue compartment model (2T3k) was fitted for each tissue time-to-activity curve, and the net influx rate (Ki) was obtained. The image quality and semi-quantitative analysis of lesion-to-background ratio (LBR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between parametric images and SUV images. RESULTS: Kinetic modeling using 2T3k demonstrated favorable model fitting in both normal organs and lesions. All of the lesions detected on SUV images (55-60 min) could be detected on Ki images. The correlation between Ki, SUVmean, and SUVmax in both normal organs and pathological lesions was found to be positive and statistically significant. Conversely, a moderate positive correlations were found between Ki and K1 (R = 0.69, P < 0.001; R = 0.61, P < 0.001) and Ki and k3 (R = 0.69, P < 0.001; R = 0.62, P < 0.001), in normal organs and pathological lesions, respectively. Visual assessment in Ki images showed less image noise and higher lesions conspicuity compared to SUV images. Ki image-derived LBR, SNR, and CBR of pathological lesions including primary tumors (PTs), lymph node metastases (LNMs) and bone metastases (BMs), exhibited remarkably higher folds (1.4-3.6 folds) compared to those derived from SUV of corresponding lesions. CONCLUSIONS: Total-body parametric imaging of [68 Ga]Ga-PSMA-11 enhanced lesion contrast and improved lesion detectability compared to SUV images. This may potentially serve as an imaging biomarker and theranostic tool for precise diagnosis and treatment evaluation in prostate cancer patients.

Inter-pass motion correction for whole-body dynamic PET and parametric imaging.

Whole-body dynamic FDG-PET imaging through continuous-bed-motion (CBM) mode multi-pass acquisition protocol is a promising metabolism measurement. However, inter-pass misalignment originating from body movement could degrade parametric quantification. We aim to apply a non-rigid registration method for inter-pass motion correction in whole-body dynamic PET. 27 subjects underwent a 90-min whole-body FDG CBM PET scan on a Biograph mCT (Siemens Healthineers), acquiring 9 over-the-heart single-bed passes and subsequently 19 CBM passes (frames). The inter-pass motion correction was executed using non-rigid image registration with multi-resolution, B-spline free-form deformations. The parametric images were then generated by Patlak analysis. The overlaid Patlak slope Ki and y-intercept Vb images were visualized to qualitatively evaluate motion impact and correction effect. The normalized weighted mean squared Patlak fitting errors (NFE) were compared in the whole body, head, and hypermetabolic regions of interest (ROI). In Ki images, ROI statistics were collected and malignancy discrimination capacity was estimated by the area under the receiver operating characteristic curve (AUC). After the inter-pass motion correction was applied, the spatial misalignment appearance between Ki and Vb images was successfully reduced. Voxel-wise normalized fitting error maps showed global error reduction after motion correction. The NFE in the whole body (p = 0.0013), head (p = 0.0021), and ROIs (p = 0.0377) significantly decreased. The visual performance of each hypermetabolic ROI in Ki images was enhanced, while 3.59% and 3.67% average absolute percentage changes were observed in mean and maximum Ki values, respectively, across all evaluated ROIs. The estimated mean Ki values had substantial changes with motion correction (p = 0.0021). The AUC of both mean Ki and maximum Ki after motion correction increased, possibly suggesting the potential of enhancing oncological discrimination capacity through inter-pass motion correction.