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.

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.

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.

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.

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.

Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks.

PURPOSE: This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (Ki) from standardized uptake value (SUV) and/or dynamic PET image series. METHODS: Whole-body 18F-FDG dynamic PET images of 19 subjects consisting of 13 frames or passes were employed for training a residual deep learning model with SUV and/or dynamic series as input and Ki-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme. Owing to the availability of SUV images acquired 60 min post-injection (20 min total acquisition time), the data sets used for the training of the models were split into two groups: "With SUV" and "Without SUV." For "With SUV" group, the model was first trained using only SUV images and then the passes (starting from pass 13, the last pass, to pass 9) were added to the training of the model (one pass each time). For this group, 6 models were developed with input data consisting of SUV, SUV plus pass 13, SUV plus passes 13 and 12, SUV plus passes 13 to 11, SUV plus passes 13 to 10, and SUV plus passes 13 to 9. For the "Without SUV" group, the same trend was followed, but without using the SUV images (5 models were developed with input data of passes 13 to 9). For model performance evaluation, the mean absolute error (MAE), mean error (ME), mean relative absolute error (MRAE%), relative error (RE%), mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between the predicted Ki-Patlak images by the two groups and the reference Ki-Patlak images generated through Patlak analysis using the whole acquired data sets. For specific evaluation of the method, regions of interest (ROIs) were drawn on representative organs, including the lung, liver, brain, and heart and around the identified malignant lesions. RESULTS: The MRAE%, RE%, PSNR, and SSIM indices across all patients were estimated as 7.45 ± 0.94%, 4.54 ± 2.93%, 46.89 ± 2.93, and 1.00 ± 6.7 × 10-7, respectively, for models predicted using SUV plus passes 13 to 9 as input. The predicted parameters using passes 13 to 11 as input exhibited almost similar results compared to the predicted models using SUV plus passes 13 to 9 as input. Yet, the bias was continuously reduced by adding passes until pass 11, after which the magnitude of error reduction was negligible. Hence, the predicted model with SUV plus passes 13 to 9 had the lowest quantification bias. Lesions invisible in one or both of SUV and Ki-Patlak images appeared similarly through visual inspection in the predicted images with tolerable bias. CONCLUSION: This study concluded the feasibility of direct deep learning-based approach to estimate Ki-Patlak parametric maps without requiring the input function and with a fewer number of passes. This would lead to shorter acquisition times for WB dynamic imaging with acceptable bias and comparable lesion detectability performance.

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.

First experiences with dynamic renal [68Ga]Ga-DOTA PET/CT: a comparison to renal scintigraphy and compartmental modelling to non-invasively estimate the glomerular filtration rate.

PURPOSE: The determination of the glomerular filtration rate (GFR) is decisive for a variety of clinical issues, for example, to monitor the renal function in radionuclide therapy patients. Renal scintigraphy using glomerularly filtered tracers allows combined acquisition of renograms and GFR estimation but requires repeated blood sampling for several hours. In contrast, dynamic PET imaging using the glomerularly filtered tracer [68Ga]Ga-DOTA bears the potential to non-invasively estimate the GFR by compartmental kinetic modelling. Here, we report the, to our knowledge, first comparison of human renal dynamic [68Ga]Ga-DOTA PET imaging in comparison to renal scintigraphy and compare PET-derived to serum creatinine-derived GFR measurements. METHODS: Dynamic [68Ga]Ga-DOTA PET data were acquired for 30 min immediately after tracer injection in 12 patients. PET and renal scintigraphy images were visually interpreted in a consensus read by three nuclear medicine physicians. The functional renal cortex was segmented to obtain time-activity curves. The arterial input function was estimated from the PET signal in the abdominal aorta. Single-compartmental tracer kinetic modelling was performed to calculate the GFR using complete 30-min (GFRPET-30) and reduced 15-min PET data sets (GFRPET-15) to evaluate whether a shorter acquisition time is sufficient for an accurate GFR estimation. A modified approach excluding minutes 2 to 10 was applied to reduce urinary spill-over effects. Serum creatinine-derived GFRCKD (CKD-EPI-formula) was used as reference standard. RESULTS: PET image interpretation revealed the same findings as conventional scintigraphy (2/12 patients with both- and 1/12 patients with right-sided urinary obstruction). Model fit functions were substantially improved for the modified approach to exclude spill-over. Depending on the modelling approach, GFRCKD and both GFRPET-30 and GFRPET-15 were well correlated with interclass correlation coefficients (ICCs) from 0.74 to 0.80 and Pearson's correlation coefficients (PCCs) from 0.74 to 0.81. For a subgroup of patients with undisturbed urinary efflux (n = 9), correlations were good to excellent (ICCs from 0.82 to 0.95 and PCCs from 0.83 to 0.95). Overall, GFRPET-30 and GFRPET-15 were excellently correlated (ICCs from 0.96 to 0.99 and PCCs from 0.96 to 0.99). CONCLUSION: Renal [68Ga]Ga-DOTA PET can be a suitable alternative to conventional scintigraphy. Visual assessment of PET images and conventional renograms revealed comparable results. GFR values derived by non-invasive single-compartmental-modelling of PET data show a good correlation to serum creatinine-derived GFR values. In patients with undisturbed urinary efflux, the correlation was excellent. Dynamic PET data acquisition for 15 min is sufficient for visual evaluation and GFR derivation.

Head-to-head comparison of [68 Ga]Ga-P16-093 and [68 Ga]Ga-PSMA-617 in dynamic PET/CT evaluation of the same group of recurrent prostate cancer patients.

PURPOSE: This study was prospectively designed to evaluate the early dynamic organ distribution and tumor detection capability of [68 Ga]Ga-P16-093, which was compared with [68 Ga]Ga-PSMA-617 in the same group of recurrent prostate cancer patients. METHODS: Twenty patients with recurrent prostate cancer were enrolled. In 2 consecutive days, each patient underwent a 60-min dynamic PET/CT scan after intravenous administration of 148-185 MBq (4-5 mCi) [68 Ga]Ga-P16-093 and [68 Ga]Ga-PSMA-617, respectively. Following a low-dose CT scan, serial dynamic PET scans were performed from head to proximal thigh at 9 time points (30 s/bed at 4, 7, 10, 13, and 16 min; 1 min/bed at 20, 30, and 45 min; and 2 min/bed at 60 min). Standardized uptake values were measured for semi-quantitative comparison. RESULTS: [68 Ga]Ga-P16-093 PET/CT revealed a significantly higher tumor uptake at 4 min (SUVmax 7.88 ± 5.26 vs. 6.01 ± 3.88, P < 0.001), less blood pool retention at 4 min (SUVmean 5.12 ± 1.16 vs. 6.14 ± 0.98, P < 0.001), and lower bladder accumulation at 60 min (SUVmean 31.33 ± 27.47 vs. 48.74 ± 34.01, P = 0.042) than [68 Ga]Ga-PSMA-617 scan. Significantly higher [68 Ga]Ga-P16-093 uptakes were also observed in the parotid gland, liver, spleen, and kidney. Besides, [68 Ga]Ga-P16-093 exhibited a better detectability of tumor than [68 Ga]Ga-PSMA-617 (366 vs. 321, P = 0.009). CONCLUSIONS: [68 Ga]Ga-P16-093 showed advantages over [68 Ga]Ga-PSMA-617 with higher tumor uptakes, tumor-to-blood pool ratio and detection capability, less blood pool, and bladder accumulation in recurrent prostate cancer patients. TRIAL REGISTRATION: [68 Ga]Ga-P16-093 and [68 Ga]Ga-PSMA-617 PET/CT Imaging in the Same Group of Prostate Cancer Patients (NCT04796467, Registered 12 March 2021, retrospectively registered) URL of registry: https://clinicaltrials.gov/ct2/show/NCT04796467.

F-18 meta-fluorobenzylguanidine PET imaging of myocardial sympathetic innervation.

BACKGROUND: I-123 meta-iodobenzylguanidine (MIBG) imaging has long been employed to noninvasively assess the integrity of human norepinephrine transporter-1 and, hence, myocardial sympathetic innervation. Positron-emitting F-18 meta-fluorobenzylguanidine (MFBG) has recently been developed for potentially superior quantitative characterization. We assessed the feasibility of MFBG imaging of myocardial sympathetic innervation. METHODS: 16 patients were imaged with MFBG PET (30-minute dynamic imaging of chest, followed by 3 whole-body acquisitions between 30 minutes and 4-hour post-injection). Blood kinetics were assessed from multiple samples. Pharmacokinetic modeling with reversible 1- and 2-compartment models was performed. Kinetic rate constants were re-calculated from truncated datasets. All patients underwent concurrent MIBG SPECT. RESULTS: MFBG myocardial uptake was rapid and sustained; the mean standardized uptake value (SUV (mean ± standard deviation)) was 5.1 ± 2.2 and 3.4 ± 1.9 at 1 hour and 3-4-hour post-injection, respectively. The mean K1 and distribution volume (VT) were 1.1 ± 0.6 mL/min/g and 34 ± 22 mL/cm3, respectively. Both were reproducible when re-calculated from truncated 1-hour datasets (Intraclass Correlation Coefficient of 0.99 and 0.91, respectively). Spearman's ϱ = 0.86 between MFBG SUV and VT and 0.80 between MFBG PET-derived VT and MIBG SPECT-derived heart-to-mediastinum activity concentration ratio. CONCLUSION: MFBG is a promising PET radiotracer for the assessment of myocardial sympathetic innervation.

Assessment of left and right ventricular functional parameters using dynamic dual-tracer [13N]NH3 and [18F]FDG PET/MRI.

BACKGROUND: Cardiac positron emission tomography/magnetic resonance imaging (PET/MRI) can assess various cardiovascular diseases. In this study, we intra-individually compared right (RV) and left ventricular (LV) parameters obtained from dual-tracer PET/MRI scan. METHODS: In 22 patients with coronary heart disease (69 ± 9 years) dynamic [13N]NH3 (NH3) and [18F]FDG (FDG) PET scans were acquired. The first 2 minutes were used to calculate LV and RV first-pass ejection fraction (FPEF). Additionally, LV end-systolic (LVESV) and end-diastolic (LVEDV) volume and ejection fraction (LVEF) were calculated from the early (EP) and late-myocardial phases (LP). MRI served as a reference. RESULTS: RVFPEF and LVFPEF from FDG and NH3 as well as RVEF and LVEF from MRI were (28 ± 11%, 32 ± 15%), (32 ± 11%, 41 ± 14%) and (42 ± 16%, 45 ± 19%), respectively. LVESV, LVEDV and LVEF from EP FDG and NH3 in 8 and 16 gates were [71 (15 to 213 mL), 98 (16 to 241 mL), 32 ± 17%] and [50 (17 to 206 mL), 93 (13 to 219 mL), 36 ± 17%] as well as [60 (19 to 360 mL), 109 (56 to 384 mL), 41 ± 22%] and [54 (16 to 371 mL), 116 (57 to 431 mL), 46 ± 24%], respectively. Moreover, LVESV, LVEDV and LVEF acquired from LP FDG and NH3 were (85 ± 63 mL, 138 ± 63 mL, 47 ± 19%) and (79 ± 56 mL, 137 ± 63 mL, 47 ± 20%), respectively. The LVESV, LVEDV from MRI were 93 ± 66 mL and 153 ± 71 mL, respectively. Significant correlations were observed for RVFPEF and LVFPEF between FDG and MRI (R = .51, P = .01; R = .64, P = .001), respectively. LVESV, LVEDV, and LVEF revealed moderate to strong correlations to MRI when they acquired from EP FDG and NH3 in 16 gates (all R > .7, P = .000). Similarly, all LV parameters from LP FDG and NH3 correlated good to strongly positive with MRI (all R > .7, and P < .001), except EDV from NH3 weakly correlated to EDV of MRI (R = .54, P < .05). Generally, Bland-Altman plots showed good agreements between PET and MRI. CONCLUSIONS: Deriving LV and RV functional values from various phases of dynamic NH3 and FDG PET is feasible. These results could open a new perspective for further clinical applications of the PET examinations.

The imaging quantification of multiple organs by dynamic 18F-FDG PET/CT in discharged COVID-19 patients: A prospective pilot study.

Purpose: To early identify abnormal lesions by applying the 18F-FDG PET dynamic modeling approach for discharged patients recovering from COVID-19. Methods: Seven discharged COVID-19 patients (COVID-19 group), twelve healthy volunteers (control group 1), and eight cancer patients with normal pulmonary function (control group 2) were prospectively enrolled. Control group 1 completed static 18F-FDG PET/CT only; COVID-19 group and control group 2 completed 60-min dynamic 18F-FDG PET/CT. Among COVID-19 group and control group 2, the uptake of FDG on the last frame (at 55-60 min) of dynamic scans was used for static analysis. Prior to performing scans, COVID-19 patients provided negative real-time Reverse Transcription-Polymerase Chain Reaction (rRT-PCR) of SARS-CoV-2, normal lung functions test, and normal laboratory test. Organ-to-liver standard uptake ratio (OLR, i.e. SUVmax evaluated organ/ SUVmax liver) from conventional static data and Patlak analysis based on the dynamic modeling to calculate the 18F-FDG net uptake rate constant (Ki) were performed. Results: Compared to the control groups, COVID-19 patients at two to three months after discharge still maintained significantly higher Ki values in multiple organs (including lung, bone marrow, lymph nodes, myocardium and liver), although results for regular OLR measurements were normal for all discharged COVID-19 patients. Taking the image of lung as an example, the differences of SUVmax images between COVID-19 group and control group were hard to distinguish. In contrast, a high 18F-FDG signal of the lung among the COVID-19 group was observed for Ki images. Conclusion: The Ki from 18F-FDG PET/CT dynamic imaging quantification might contribute to identifying residual lesions for COVID-19 survivors. Trial registration: The trial is registered with ClinicalTrials.gov, number NCT04519255 (IRB-approved number, K52-1).

Simultaneous Denoising of Dynamic PET Images Based on Deep Image Prior.

Parametric imaging obtained from kinetic modeling analysis of dynamic positron emission tomography (PET) data is a useful tool for quantifying tracer kinetics. However, pixel-wise time-activity curves have high noise levels which lead to poor quality of parametric images. To solve this limitation, we proposed a new image denoising method based on deep image prior (DIP). Like the original DIP method, the proposed DIP method is an unsupervised method, in which no training dataset is required. However, the difference is that our method can simultaneously denoise all dynamic PET images. Moreover, we propose a modified version of the DIP method called double DIP (DDIP), which has two DIP architectures. The additional DIP model is used to generate high-quality input data for the second DIP model. Computer simulations were performed to evaluate the performance of the proposed DIP-based methods. Our simulation results showed that the DDIP method outperformed the single DIP method. In addition, the DDIP method combined with data augmentation could generate PET parametric images with superior image quality compared to the spatiotemporal-based non-local means filtering and high constrained backprojection. Our preliminary results show that our proposed DDIP method is a novel and effective unsupervised method for simultaneously denoising dynamic PET images.

Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network.

Parametric imaging based on dynamic positron emission tomography (PET) has wide applications in neurology. Compared to indirect methods, direct reconstruction methods, which reconstruct parametric images directly from the raw PET data, have superior image quality due to better noise modeling and richer information extracted from the PET raw data. For low-dose scenarios, the advantages of direct methods are more obvious. However, the wide adoption of direct reconstruction is inevitably impeded by the excessive computational demand and deficiency of the accessible raw data. In addition, motion modeling inside dynamic PET image reconstruction raises more computational challenges for direct reconstruction methods. In this work, we focused on the 18F-FDG Patlak model, and proposed a data-driven approach which can estimate the motion corrected full-dose direct Patlak images from the dynamic PET reconstruction series, based on a proposed novel temporal non-local convolutional neural network. During network training, direct reconstruction with motion correction based on full-dose dynamic PET sinograms was performed to obtain the training labels. The reconstructed full-dose /low-dose dynamic PET images were supplied as the network input. In addition, a temporal non-local block based on the dynamic PET images was proposed to better recover the structural information and reduce the image noise. During testing, the proposed network can directly output high-quality Patlak parametric images from the full-dose /low-dose dynamic PET images in seconds. Experiments based on 15 full-dose and 15 low-dose 18F-FDG brain datasets were conducted and analyzed to validate the feasibility of the proposed framework. Results show that the proposed framework can generate better image quality than reference methods.

Feasibility and utility of MRI and dynamic 18F-FDG-PET in an orthotopic organoid-based patient-derived mouse model of endometrial cancer.

BACKGROUND: Pelvic magnetic resonance imaging (MRI) and whole-body positron emission tomography-computed tomography (PET-CT) play an important role at primary diagnostic work-up and in detecting recurrent disease in endometrial cancer (EC) patients, however the preclinical use of these imaging methods is currently limited. We demonstrate the feasibility and utility of MRI and dynamic 18F-fluorodeoxyglucose (FDG)-PET imaging for monitoring tumor progression and assessing chemotherapy response in an orthotopic organoid-based patient-derived xenograft (O-PDX) mouse model of EC. METHODS: 18 O-PDX mice (grade 3 endometrioid EC, stage IIIC1), selectively underwent weekly T2-weighted MRI (total scans = 32), diffusion-weighted MRI (DWI) (total scans = 9) and dynamic 18F-FDG-PET (total scans = 26) during tumor progression. MRI tumor volumes (vMRI), tumor apparent diffusion coefficient values (ADCmean) and metabolic tumor parameters from 18F-FDG-PET including maximum and mean standard uptake values (SUVmax/SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG) and metabolic rate of 18F-FDG (MRFDG) were calculated. Further, nine mice were included in a chemotherapy treatment study (treatment; n = 5, controls; n = 4) and tumor ADCmean-values were compared to changes in vMRI and cellular density from histology at endpoint. A Mann-Whitney test was used to evaluate differences between groups. RESULTS: Tumors with large tumor volumes (vMRI) had higher metabolic activity (MTV and TLG) in a clear linear relationship (r2 = 0.92 and 0.89, respectively). Non-invasive calculation of MRFDG from dynamic 18F-FDG-PET (mean MRFDG = 0.39 µmol/min) was feasible using an image-derived input function. Treated mice had higher tumor ADCmean (p = 0.03), lower vMRI (p = 0.03) and tumor cellular density (p = 0.02) than non-treated mice, all indicating treatment response. CONCLUSION: Preclinical imaging mirroring clinical imaging methods in EC is highly feasible for monitoring tumor progression and treatment response in the present orthotopic organoid mouse model.

Functional 4-D clustering for characterizing intratumor heterogeneity in dynamic imaging: evaluation in FDG PET as a prognostic biomarker for breast cancer.

PURPOSE: Probe-based dynamic (4-D) imaging modalities capture breast intratumor heterogeneity both spatially and kinetically. Characterizing heterogeneity through tumor sub-populations with distinct functional behavior may elucidate tumor biology to improve targeted therapy specificity and enable precision clinical decision making. METHODS: We propose an unsupervised clustering algorithm for 4-D imaging that integrates Markov-Random Field (MRF) image segmentation with time-series analysis to characterize kinetic intratumor heterogeneity. We applied this to dynamic FDG PET scans by identifying distinct time-activity curve (TAC) profiles with spatial proximity constraints. We first evaluated algorithm performance using simulated dynamic data. We then applied our algorithm to a dataset of 50 women with locally advanced breast cancer imaged by dynamic FDG PET prior to treatment and followed to monitor for disease recurrence. A functional tumor heterogeneity (FTH) signature was then extracted from functionally distinct sub-regions within each tumor. Cross-validated time-to-event analysis was performed to assess the prognostic value of FTH signatures compared to established histopathological and kinetic prognostic markers. RESULTS: Adding FTH signatures to a baseline model of known predictors of disease recurrence and established FDG PET uptake and kinetic markers improved the concordance statistic (C-statistic) from 0.59 to 0.74 (p = 0.005). Unsupervised hierarchical clustering of the FTH signatures identified two significant (p < 0.001) phenotypes of tumor heterogeneity corresponding to high and low FTH. Distributions of FDG flux, or Ki, were significantly different (p = 0.04) across the two phenotypes. CONCLUSIONS: Our findings suggest that imaging markers of FTH add independent value beyond standard PET imaging metrics in predicting recurrence-free survival in breast cancer and thus merit further study.

Kinetic modeling and parametric imaging with dynamic PET for oncological applications: general considerations, current clinical applications, and future perspectives.

Dynamic PET (dPET) studies have been used until now primarily within research purposes. Although it is generally accepted that the information provided by dPET is superior to that of conventional static PET acquisitions acquired usually 60 min post injection of the radiotracer, the duration of dynamic protocols, the limited axial field of view (FOV) of current generation clinical PET systems covering a relatively small axial extent of the human body for a dynamic measurement, and the complexity of data evaluation have hampered its implementation into clinical routine. However, the development of new-generation PET/CT scanners with an extended FOV as well as of more sophisticated evaluation software packages that offer better segmentation algorithms, automatic retrieval of the arterial input function, and automatic calculation of parametric imaging, in combination with dedicated shorter dynamic protocols, will facilitate the wider use of dPET. This is expected to aid in oncological diagnostics and therapy assessment. The aim of this review is to present some general considerations about dPET analysis in oncology by means of kinetic modeling, based on compartmental and noncompartmental approaches, and parametric imaging. Moreover, the current clinical applications and future perspectives of the modality are outlined.

SAFIR-I: Design and Performance of a High-Rate Preclinical PET Insert for MRI.

(1) Background: Small Animal Fast Insert for MRI detector I (SAFIR-I) is a preclinical Positron Emission Tomography (PET) insert for the Bruker BioSpec 70/30 Ultra Shield Refrigerated (USR) preclinical 7T Magnetic Resonance Imaging (MRI) system. It is designed explicitly for high-rate kinetic studies in mice and rats with injected activities reaching 500MBq, enabling truly simultaneous quantitative PET and Magnetic Resonance (MR) imaging with time frames of a few seconds in length. (2) Methods: SAFIR-I has an axial field of view of 54.2mm and an inner diameter of 114mm. It employs Lutetium Yttrium OxyorthoSilicate (LYSO) crystals and Multi Pixel Photon Counter (MPPC) arrays. The Position-Energy-Timing Application Specific Integrated Circuit, version 6, Single Ended (PETA6SE) digitizes the MPPC signals and provides time stamps and energy information. (3) Results: SAFIR-I is MR-compatible. The system's Coincidence Resolving Time (CRT) and energy resolution are between separate-uncertainty 209.0(3)ps and separate-uncertainty 12.41(02) Full Width at Half Maximum (FWHM) at low activity and separate-uncertainty 326.89(12)ps and separate-uncertainty 20.630(011) FWHM at 550MBq, respectively. The peak sensitivity is ∼1.6. The excellent performance facilitated the successful execution of first in vivo rat studies beyond 300MBq. Based on features visible in the acquired images, we estimate the spatial resolution to be ∼2mm in the center of the Field Of View (FOV). (4) Conclusion: The SAFIR-I PET insert provides excellent performance, permitting simultaneous in vivo small animal PET/MR image acquisitions with time frames of a few seconds in length at activities of up to 500MBq.

[Dynamic11C-methionine PET/CT in differential diagnosis of brain gliomas]. / Dinamicheskaya PET/KT s 11S-metioninom v differentsial'noi diagnostike glial'nykh opukholei golovnogo mozga.

OBJECTIVE: To evaluate the possibilities of dynamic preoperative 11C-methionine (MET) PET/CT in differential diagnosis of various types of brain gliomas in adults. MATERIAL AND METHODS: The study included 74 patients aged 48±14 years with supratentorial gliomas: Grade IV - glioblastoma (GB, n=33), Grade III - anaplastic oligodendroglioma (AOD, n=10) and anaplastic astrocytoma (AA, n=12), Grade II - diffuse astrocytoma (DA, n=13) and oligodendroglioma (OD, n=6). All patients underwent standard MRI and dynamic MET PET/CT within 20 minutes after intravenous injection of radiopharmaceutical. Then, we compared MRI and PET/CT data and comprehensively analyzed the early stages of time-activity curve using 2 parameters: the first pass peak (FPP) and the first peak of maximum uptake (Pmax). RESULTS: We have significantly distinguished high-grade tumors (GB and AA+AOD) and certain benign gliomas (DA and OD) (p<0.05). AUC was over 0.7 and 0.8 for FPP and Pmax in differential diagnosis of various gliomas, respectively. We found that difficulties in differential diagnosis of gliomas arise mainly if oligodendrogliomas are included in the control group. CONCLUSION: Dynamic PET/CT with analysis of FPP and Pmax increases specificity of differential diagnosis of various gliomas compared to standard static imaging. These data are valuable for choice of optimal treatment strategy, as well as fundamental research of metabolic processes and vascularization of various tumors.

18F-PSMA-1007 multiparametric, dynamic PET/CT in biochemical relapse and progression of prostate cancer.

OBJECTIVES: Aim of the present analysis is to investigate the biodistribution and pharmacokinetics of the recently clinically introduced radioligand 18F-PSMA-1007 in patients with biochemical recurrence or progression of prostate cancer (PC) by means of multiparametric (dynamic and whole-body) PET/CT. METHODS: Twenty-five (25) patients with PC biochemical relapse or progression (median age = 66.0 years) were enrolled in the analysis. The median PSA value was 1.2 ng/mL (range = 0.1-237.3 ng/mL) and the median Gleason score was 7 (range = 6-10). All patients underwent dynamic PET/CT (dPET/CT) scanning (60 min) of the pelvis and lower abdomen as well as whole-body PET/CT with 18F-PSMA-1007. PET/CT assessment was based on qualitative evaluation, SUV calculation, and quantitative analysis based on a two-tissue compartment model and fractal analysis. RESULTS: 15/25 patients were PET-positive. Plasma PSA values in the 18F-PSMA-1007 positive group were higher (median = 3.6 ng/mL; range = 0.2-237.3 ng/mL) than in the 18F-PSMA-1007 negative group (median value = 0.7 ng/mL; range = 0.1-3.0 ng/mL). Semi-quantitative analysis in the PC lesions demonstrated a mean SUVaverage = 25.1 (median = 15.4; range = 3.5-119.2) and a mean SUVmax = 41.5 (median = 25.7; range = 3.8-213.2). Time-activity curves derived from dPET/CT revealed an increasing tracer accumulation during the 60 min of dynamic PET acquisition into the PC lesions, higher than in the urinary bladder and the colon. Significant correlations were observed between 18F-PSMA-1007 uptake (SUV), influx, and fractal dimension (FD). CONCLUSIONS: 18F-PSMA-1007 PET/CT could detect PC lesions in 60% of the patients of a mixed population, including also patients with very low PSA values. Higher PSA values were associated with a higher detection rate. Dynamic PET analysis revealed an increasing tracer uptake during the dynamic PET acquisition as well as high binding and internalization of the radiofluorinated PSMA ligand in the PC lesions.