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

A pilot study of [18F]F-fluciclovine positron emission tomography/computed tomography for staging muscle invasive bladder cancer preceding radical cystectomy

AIM

To assess the ability of [18F]F-fluciclovine-PET/CT to stage muscle invasive bladder cancer (MIBC) before radical cystectomy.

METHODS

This single-site prospective pilot study enrolled patients with MIBC and T2-T4, N0 disease on CT/MRI slated to undergo radical cystectomy (RC). Dynamic and static [18F]F-fluciclovine-PET/CT images were acquired. Clinical readers assessed for confirmation of the primary bladder lesion on imaging and the presence of pelvic nodal metastases. Findings were compared to pathology at RC. Kinetic parameters from dynamic PET/CT were compared across bladder lesions of different clinical stages.

RESULTS

The study enrolled sixteen patients (median age: 73 years, range: 57-88 years, 11 males, 5 females), twelve receiving neoadjuvant chemotherapy before RC. There was high specificity amongst all three readers for detecting lymph node metastases (overall specificity: 0.91, 95%CI: 0.81-1.00) with good overall agreement rate with pathology (0.67, 95%CI: 0.44-0.83). The overall PPV for all readers for identifying node-positive disease was 0.4 (95%CI: 0-1.00), and the overall sensitivity was 0.13 (95%CI: 0-0.44). The overall PPV for detecting the primary tumor was 0.69 (95%CI: 0.47-0.88), and the sensitivity was 0.89 (95%CI: 0.78-1.00), with NPV and specificity being 0.70 (95%CI: 0.33, 1.00) and 0.39 (95%CI: 0.33, 0.50), respectively. Compartmental analysis of the primary bladder tumor revealed that k1 and vb parameters significantly differentiated between low (pT0-pT1) and high (pT2-pT4) risk disease (p < 0.05). Immunohistochemical assessment showed no significant correlation of tumor [18F]F-fluciclovine uptake nor kinetic parameter with amino acid transporter expression.

CONCLUSIONS

[18F]F-fluciclovine demonstrates good specificity and agreement rate for MIBC staging, with sensitivity like CT/MRI. Kinetic parameters such as k1 was able to delineate higher-stage ( ≥ = pT2) primary lesions. Heterogeneous amino acid transporter expression can be seen across lesions. Further studies are warranted to understand [18F]F-fluciclovine PET/CT use in the context of other imaging modalities in this disease.

CLINICAL TRIAL REGISTRATION

NCT04018053 Registered 2/26/2020.

Dynamic FDG PET/CT imaging: quantitative assessment, advantages and application in the diagnosis of malignant solid tumors

Dynamic imaging has obtained remarkable achievements among a variety of malignant tumors due to the development of multiple simplified scanning protocols and the emergence of whole-body PET/CT scanners, which promote wider application of dynamic PET/CT. In this paper, we mainly review the acquisition protocols of dynamic imaging, related kinetic parameters, advantages and the application of dynamic PET/CT imaging in malignant tumors, including lung cancer, hepatocellular carcinoma, breast cancer, pancreatic carcinoma, prostate neoplasm, and cancer of head and neck. Dynamic PET/CT imaging is increasingly being applied the diagnosis, staging, efficacy monitoring, and prognosis evaluation of malignant tumors. Although standardized uptake value is the most frequently employed semi-quantitative assessment index for static imaging, it is susceptible to several factors, thus cannot be used to evaluate the tracer kinetic information of the lesion. Dynamic PET/CT imaging can be used to achieve continuous assessment of the metabolic activity of a lesion over a certain time frame through quantitative measurement of kinetic parameters, such as the net uptake rate constant. Compared with conventional static imaging, dynamic scanning can be used for the early estimation of minute metabolic changes in tumors. Besides, dynamic scanning can directly and effectively reflect tracer uptake. Nevertheless, the intricacy of parameter analysis and the lengthy scanning time related to dynamic scanning limits its clinical application. Dynamic imaging has obtained remarkable achievements among a variety of malignant tumors due to the development of multiple simplified scanning protocols and the emergence of whole-body PET/CT scanners, which promote wider application of dynamic PET/CT. In this paper, we mainly review the acquisition protocols of dynamic imaging, related kinetic parameters, advantages and the application of dynamic PET/CT imaging in malignant tumors, including lung cancer, hepatocellular carcinoma, breast cancer, pancreatic carcinoma, prostate neoplasm, and cancer of head and neck.

Leveraging machine learning with dynamic 18F-FDG PET/CT: integrating metabolic and flow features for lung cancer differential diagnosis

BACKGROUND

Dynamic 18F-fluorodeoxyglucose (18F-FDG) PET/CT imaging has been shown to provide additional information for diagnosing lung cancer. The aim of this study was to investigate whether metabolic and flow features directly extracted from time activity curves (TACs) help differentiate between benign and malignant conditions of lung lesions.

METHODS

TACs at the primary lesion were extracted from each dynamic 18F-FDG PET/CT scan. The TAC signal was then decomposed into metabolism and blood flow components through kinetic modeling. Dynamic features including area under the curve (AUC), time-to-peak, and slopes were then extracted from each component. The extracted features from 187 patients (mean age, 60.41 ± 11.01 years; 117 males) were used to train a classification model based on bagging, a machine-learning method built with decision trees. The performance of the trained model on differentiating benign and malignant was tested using receiver operating characteristic analysis with cross-validation. External testing was then performed for an independent dataset that consisted of 42 dynamic scans. For the results, SHapley Additive exPlanations (SHAP) were used to assess the relative importance of the contributed features for individuals. Waterfall charts were also plotted, together with assessment of Cohen's effect size to demonstrate the superiority of the proposed model over SUVmax and the net FDG influx rate Ki.

RESULTS

The combination of the multiple dynamic features was able to separate benign and malignant lesions. For cross-validation, the trained model had an AUC of 0.89, sensitivity of 0.80, and specificity of 0.88, which was significantly higher than that of either SUVmax (AUC = 0.79, DeLong p < 0.001) or Ki (AUC = 0.76, DeLong test p < 0.001). For the testing dataset, the model had an AUC of 0.86, which again was better than either SUVmax (AUC of 0.72) or Ki (AUC of 0.71). The most important features that contributed to the diagnosis identified by SHAP included the slope and maximum metabolism TAC at the lesion, the AUC, and the peak time of blood TAC at the lesion. The waterfall chart illustrated that the model had significantly different prediction scores between the benign and malignant groups (p < 0.001) with a Cohen's effect size of 1.71, which was higher than that of the values for SUV and Ki (Cohen's effect size 0.96 and 0.81, respectively).

CONCLUSION

An explainable machine learning model that combines dynamic FDG metabolic and flow features can predict benign or malignant lung cancer patients more accurately than conventional parameters such as SUVmax or net influx rate Ki.

Novel PET-CT-MR Imaging Based Quantitative Technique for Accurate Assessment of Radiation Induced Injuries

Radiation-induced injuries (RIIs) are significant complications of radiation therapy used in cancer treatments and affect organs in a systemic fashion such as the heart, lungs, liver, and bone marrow. Such ionizing radiation leads to inflammation, fibrosis, and/or irreparable DNA damage, each of which can significantly impact patient's quality of life, underscoring the need for advanced diagnostic and imaging techniques. A novel combination of PET/Computed Tomography (CT) with Quantitative MR Imaging has emerged as a crucial tool for early diagnosis and timely evaluation of RIIs. This review focuses on the important role of quantitative PET-CT-MR imaging in diagnosing and monitoring RIIs.

[18F]F-FAPI-42 PET dynamic imaging characteristics and multiparametric quantification of lung cancer: an exploratory study using uEXPLORER PET/CT

PURPOSE

To explore the dynamic and parametric characteristics of [18F]F-FAPI-42 PET/CT in lung cancers.

METHODS

Nineteen participants with newly diagnosed lung cancer underwent 60-min dynamic [18F]F-FAPI-42 PET/CT. Time-activity curves (TAC) were generated for tumors and normal organs, with kinetic parameters (K1, K2, K3, K4, Ki) calculated. A new parameter, the K ratio (K1 + K3)/(K2 + K4), was introduced to measure net uptake efficiency.

RESULTS

In primary tumor (PT), [18F]F-FAPI-42 uptake showed a gradual increase followed by a plateau, contrasting with organs like the thyroid and pancreas, which showed rapid uptake and continuous washout. Compared to non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) lesions reached the plateau earlier (11 min vs. 14 min) but had a lower uptake. During the plateau phase, [18F]F-FAPI-42 demonstrated slight washout in SCLC, whereas its uptake increased slightly in NSCLC. Lymph node and distant metastases exhibited similar TAC profiles to primary tumors. Kinetic modeling revealed that an irreversible two-compartment model (irre-2TCM) best represented the pharmacokinetics of [18F]F-FAPI-42 in lung cancer, whereas re-2TCM was better suited for the pancreas and thyroid. Lower K1, K2, K3 and K4 were observed in PT compared to those in the pancreas and thyroid (P < 0.05), however, the K ratio in PT was found to be 2-3 times higher. SCLC had lower Ki and SUVmean than NSCLC (P < 0.05). Kinetic parameter differences were also observed between PT and metastatic lesions. Larger metastatic lymph nodes exhibited higher K1, Ki, and K ratio than smaller ones.

CONCLUSION

Lung cancers exhibit distinct [18F]F-FAPI-42 dynamic and kinetic characteristics compared to the thyroid gland and pancreas. Differences were also observed between SCLC and NSCLC, primary and metastatic lesions, as well as larger versus smaller lesions. These findings provide valuable insights into the in vivo pharmacokinetics of [18F]F-FAPI-42, potentially improving the diagnosis of lung cancer.

TRIAL REGISTRATION

ChiCTR2100045757. Registered April 24, 2021 retrospectively registered, http//www.chictr.org.cn.

New compartment model for hepatic blood flow quantification in humans from 15O-water PET images

BACKGROUND

Different compartment models are commonly used to derive crucial information about blood flow, metabolism, and oxygenation from the results of a dynamic positron emission tomography (PET) scan. However, compared to blood flow in many other organs of interest, the hepatic blood flow (HBF) quantification is challenging due to the dual blood supply of liver from both the hepatic artery and the portal vein (PV). Here, we introduce a new model that can be used to estimate the HBF in combination with an automatic volume of interest selection method.

MATERIALS AND METHODS

By using the15 O-water PET data of 57 patients, we extract the mean time-activity curves (TACs) from aorta, hepatic PV, liver, and spleen with help of an automated computer tomography-based segmentation tool and systematically fit our new compartment model and three earlier compartment models from literature to the TACs. After this, we compare the model performance with mean relative error (MRE), mean squared error, and Akaike's information criteria with one-sided Wilcoxon signed-rank tests. After determining the best model, we study possible HBF differences caused by age, sex, and weight with Mann-Whitney U test and Pearson's correlations coefficient.

RESULTS

We obtained the mean arterial HBF of 0.299±0.168 mL/min/mL, the mean portal HBF of 0.930±0.520 mL/min/mL, and the total HBF of 1.229±0.612 mL/min/mL with our new model. Based on earlier research, both these estimates and also the results of two earlier versions of the original dual-input model are realistic. Out of these three models, our proposed model performed the best in terms of MRE (p-values ≤ 0.001). According to our results, there are no significant sex- or age-based differences but there is moderate positive correlation between the arterial and portal HBFs and negative correlation between the total HBF and the weight of patients.

CONCLUSION

The HBF can effectively be estimated from15 O-water PET data with our new model in combination with robust segmentation by TotalSegmentator. Due to the fact that potential underestimation of the PV concentration caused by the small size of this vessel might lead to overestimation of the HBF, more research would be beneficial to validate these methods further. Our results suggests that there is a negative trend between the HBF and the weight, though this might be related to the underlying conditions of the patients.

Pulmonary blood flow quantification in humans from 15O-water PET

PURPOSE

Dynamic positron emission tomography (PET) imaging has commonly been applied to study blood perfusion in the human brain and heart, but there is a very limited amount of existing research about the suitability of this method for many other organs of interest. Here, we focus on the quantification of pulmonary blood flow (PBF) in human lungs. We evaluate both the potential of the15 O-water PET imaging via compartmental modeling with automatic volume of interest (VOI) selection for PBF quantification and study the possible differences in PBF caused by different patient characteristics such as age or sex.

PROCEDURES

We systematically fit the one-tissue compartment model to the mean time-activity curves derived from the15 O-water PET data of 103 patients. The machine learning-based segmentation tool TotalSegmentator is utilized to find segmentation masks for different lung lobes and right ventricle of the heart. Additionally, we automatically remove the majority of the air inside the lung lobe VOIs and the areas surrounding subclavian arteries and brachiocephalic veins with the help of binary erosion and dilatation operations. After the model fitting, we evaluate possible differences in the results caused by age, sex, weight, and body mass index (BMI) by performing Mann-Whitney U tests between different patient subgroups and computing Spearman's correlations coefficients.

RESULTS

The estimated PBF within all the lung lobes had a mean of1.21±0.825 mL/min/cm3 and a median of 1.03 mL/min/cm3 , but this value was notably lower in right lower lung lobe and much higher in the upper lung lobes. The PBF was higher in both the female patients and in the patients under 65 years but not statistically significantly so. The individual variation was very high.

CONCLUSIONS

The PBF quantification based on15 O-water PET imaging combined with our automatic VOI selection method is an effective method to produce relatively realistic results. In case of upper lung lobes, the results are likely overestimated if pulmonary vessels are not removed from the VOI. The accurate estimation of the air volume within the lung lobe VOIs is also a non-trivial problem. More research on this topic is warranted to find whether there is a decreasing trend between PBF and age or significant differences between the sexes.

A deep learning method for total-body dynamic PET imaging with dual-time-window protocols

PURPOSE

Prolonged scanning durations are one of the primary barriers to the widespread clinical adoption of dynamic Positron Emission Tomography (PET). In this paper, we developed a deep learning algorithm that capable of predicting dynamic images from dual-time-window protocols, thereby shortening the scanning time.

METHODS

This study includes 70 patients (mean age ± standard deviation, 53.61 ± 13.53 years; 32 males) diagnosed with pulmonary nodules or breast nodules between 2022 to 2024. Each patient underwent a 65-min dynamic total-body [18F]FDG PET/CT scan. Acquisitions using early-stop protocols and dual-time-window protocols were simulated to reduce the scanning time. To predict the missing frames, we developed a bidirectional sequence-to-sequence model with attention mechanism (Bi-AT-Seq2Seq); and then compared the model with unidirectional or non-attentional models in terms of Mean Absolute Error (MAE), Bias, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) of predicted frames. Furthermore, we reported the comparison of concordance correlation coefficient (CCC) of the kinetic parameters between the proposed method and traditional methods.

RESULTS

The Bi-AT-Seq2Seq significantly outperform unidirectional or non-attentional models in terms of MAE, Bias, PSNR, and SSIM. Using a dual-time-window protocol, which includes a 10-min early scan followed by a 5-min late scan, improves the four metrics of predicted dynamic images by 37.31%, 36.24%, 7.10%, and 0.014% respectively, compared to the early-stop protocol with a 15-min acquisition. The CCCs of tumor' kinetic parameters estimated with recovered full time-activity-curves (TACs) is higher than those with abbreviated TACs.

CONCLUSION

The proposed algorithm can accurately generate a complete dynamic acquisition (65 min) from dual-time-window protocols (10 + 5 min).

Distinguishing benign from malignant lesions with high [68 Ga]Ga-FAPI-04 uptake in oncology patients: Insights from dynamic total-body [68 Ga]Ga-FAPI-04 PET/CT

BACKGROUND AND PURPOSE

While [68 Ga]Ga-FAPI-04 PET/CT is widely used in various malignant tumors diagnosis, its specificity is challenged by high uptake in benign lesions such as inflammatory lymphadenopathy, bone fractures, and degenerative changes. This study aimed to quantitatively assess and characterize the metabolic heterogeneity of [68 Ga]Ga-FAPI-04 uptake in benign and malignant lesions using dynamic total-body PET/CT.

METHODS

Dynamic total-body [68 Ga]Ga-FAPI-04 PET/CT scans (0-60 min post-injection) were performed on 17 oncology patients. Time-activity curves (TACs) were generated for benign and malignant lesions with high [68 Ga]Ga-FAPI-04 uptake. The reversible two-tissue compartment model (2T4k) was used to derive kinetic metrics, including K1, k2, k3, k4, vB and VT. Receiver operating characteristic (ROC) curve analysis was used to determine the cut-off values for differentiating benign and malignant lesions.

RESULTS

The study included 58 malignant and 55 inflammatory lesions with high [68 Ga]Ga-FAPI-04 uptake. Malignant lesions exhibited higher K1 (0.277 ± 0.217 ml/ccm/min vs. 0.221 ± 0.216 ml/ccm/min, P = 0.011), vB (0.042 ± 0.007 vs. 0.013 ± 0.004, P < 0.001), and lower k3 (0.267 ± 0.041 1/min vs. 0.481 ± 0.085 1/min, P = 0.008) compared to benign lesions. Lesions were classified into low, medium, and high-probability groups for being malignant based on K1, k3 and vB values, with probabilities of 0%, 50.7%, and 92.0%, respectively (P < 0.001).

CONCLUSIONS

Kinetic metrics, particularly K1, k3 and vB values, show promise as imaging biomarkers for distinguishing between benign and malignant lesions with high [68 Ga]Ga-FAPI-04 uptake in oncology patients. These metrics reflect the metabolic heterogeneity of the lesions and may improve diagnostic accuracy in oncological imaging.

Expert consensus on workflow of PET/CT with long axial field-of-view

PURPOSE

Positron emission tomography/computed tomography (PET/CT) imaging has been widely used in clinical practice. Long axial field-of-view (LAFOV) systems have enhanced clinical practice by leveraging their technological advantages and have emerged as the new state-of-the-art PET imaging modalities. A consensus was conducted to explore expert views in this emerging field to comprehensively elucidate the proposed workflow in LAFOV PET/CT examinations and highlight the potential challenges inherent in the workflow.

METHODS

A multidisciplinary task group formed by 28 experts from six countries over the world discussed and approved the consensus based on the published guidelines, peer-reviewed articles of LAFOV PET/CT, and the collective experience from clinical practice. This consensus focuses on the workflow that allows for a broader range of imaging protocols of LAFOV PET/CT, catering to diverse patient needs and in line with precision medicine principles.

RESULTS

This consensus describes the workflows and imaging protocols of LAFOV PET/CT for various imaging scenarios including routine static imaging, dynamic imaging, low-activity imaging, fast imaging, prolonged imaging, delayed imaging, and dual-tracer imaging. In addition, imaging reconstruction and reviewing specific to LAFOV PET/CT imaging, as well as the main challenges facing installation and application of LAFOV PET/CT scanner were also summarized.

CONCLUSION

This consensus summarized the various imaging workflow, imaging protocol, and challenges of LAFOV PET/CT imaging, aiming to enhance the clinical and research applications of these scanners.

Self-supervised parametric map estimation for multiplexed PET with a deep image prior

Multiplexed positron emission tomography (mPET) imaging allows simultaneous observation of physiological and pathological information from multiple tracers in a single PET scan. Although supervised deep learning has demonstrated superior performance in mPET image separation compared to purely model-based methods, acquiring large amounts of paired single-tracer data and multi-tracer data for training poses a practical challenge and needs extended scan durations for patients. In addition, the generalisation ability of the supervised learning framework is a concern, as the patient being scanned and their tracer kinetics may potentially fall outside the training distribution. In this work, we propose a self-supervised learning framework based on the deep image prior (DIP) for mPET image separation using just one dataset. In particular, we integrate the multi-tracer compartmental model into the DIP framework to estimate the parametric maps of each tracer from the measured dynamic dual-tracer activity images. Consequently, the separated dynamic single-tracer activity images can be recovered from the estimated tracer-specific parametric maps. In the proposed method, dynamic dual-tracer activity images are used as the training label, and the static dual-tracer image (reconstructed from the same patient data from the start to the end of acquisition) is used as the network input. The performance of the proposed method was evaluated on a simulated brain phantom for dynamic dual-tracer [18F]FDG+[11C]MET activity image separation and parametric map estimation. The results demonstrate that the proposed method outperforms the conventional voxel-wise multi-tracer compartmental modeling method (vMTCM) and the two-step method DIP-Dn+vMTCM (where dynamic dual-tracer activity images are first denoised using a U-net within the DIP framework, followed by vMTCM separation) in terms of lower bias and standard deviation in the separated single-tracer images and also for the estimated parametric maps for each tracer, at both voxel and ROI levels.

Realistic total-body J-PET geometry optimization: Monte Carlo study

BACKGROUND

Total-body (TB) Positron Emission Tomography (PET) is one of the most promising medical diagnostics modalities, opening new perspectives for personalized medicine, low-dose imaging, multi-organ dynamic imaging or kinetic modeling. The high sensitivity provided by total-body technology can be advantageous for novel tomography methods like positronium imaging, demanding the registration of triple coincidences. Currently, state-of-the-art PET scanners use inorganic scintillators. However, the high acquisition cost reduces the accessibility of TB PET technology. Several efforts are ongoing to mitigate this problem. Among the alternatives, the Jagiellonian PET (J-PET) technology, based on axially arranged plastic scintillator strips, offers a low-cost alternative solution for TB PET.

PURPOSE

The work aimed to compare five total-body J-PET geometries with plastic scintillators suitable for multi-organ and positronium tomography as a possible next-generation J-PET scanner design.

METHODS

We present comparative studies of performance characteristics of the cost-effective total-body PET scanners using J-PET technology. We investigated in silico five TB scanner geometries, varying the number of rings, scanner radii, and other parameters. Monte Carlo simulations of the anthropomorphic XCAT phantom, the extended 2-m sensitivity line source and positronium sensitivity phantoms were used to assess the performance of the geometries. Two hot spheres were placed in the lungs and in the liver of the XCAT phantom to mimic the pathological changes. We compared the sensitivity profiles and performed quantitative analysis of the reconstructed images by using quality metrics such as contrast recovery coefficient, background variability and root mean squared error. The studies are complemented by the determination of sensitivity for the positronium lifetime tomography and the relative cost analysis of the studied setups.

RESULTS

The analysis of the reconstructed XCAT images reveals the superiority of the seven-ring scanners over the three-ring setups. However, the three-ring scanners would be approximately 2-3 times cheaper. The peak sensitivity values for two-gamma vary from 20 to 34 cps/kBq and are dominated by the differences in geometrical acceptance of the scanners. The sensitivity curves for the positronium tomography have a similar shape to the two-gamma sensitivity profiles. The peak values are lower compared to the two-gamma cases, from about 20-28 times, with a maximum value of 1.66 cps/kBq. This can be contrasted with the 50-cm one-layer J-PET modular scanner used to perform the first in-vivo positronium imaging with a sensitivity of 0.06 cps/kBq.

CONCLUSIONS

The results show the feasibility of multi-organ imaging of all the systems to be considered for the next generation of TB J-PET designs. Among the scanner parameters, the most important ones are related to the axial field-of-view coverage. The two-gamma sensitivity and XCAT image reconstruction analyzes show the advantage of seven-ring scanners. However, the cost of the scintillator materials and SiPMs is more than two times higher for the longer modalities compared to the three-ring solutions. Nevertheless, the relative cost for all the scanners is about 10-4 times lower compared to the cost of the uExplorer. These properties coupled together with J-PET cost-effectiveness and triggerless acquisition mode enabling three-gamma positronium imaging, make the J-PET technology an attractive solution for broad application in clinics.

Impact of different parametric Patlak imaging approaches and comparison with a 2-tissue compartment pharmacokinetic model with a long axial field-of-view (LAFOV) PET/CT in oncological patients

AIM

The recently introduced Long-Axial-Field-of-View (LAFOV) PET-CT scanners allow for the first-time whole-body dynamic- and parametric imaging. Primary aim of this study was the comparison of direct and indirect Patlak imaging as well as the comparison of different time frames for Patlak calculation with the LAFOV PET-CT in oncological patients. Secondary aims of the study were lesion detectability and comparison of Patlak analysis with a two-tissue-compartment model (2TCM).

METHODOLOGY

50 oncological patients with 346 tumor lesions were enrolled in the study. All patients underwent [18F]FDG PET/CT (skull to upper thigh). Here, the Image-Derived-Input-Function) (IDIF) from the descending aorta was used as the exclusive input function. Four sets of images have been reviewed visually and evaluated quantitatively using the target-to-background (TBR) and contrast-to-noise ratio (CNR): short-time (30 min)-direct (STD) Patlak Ki, short-time (30 min)-indirect (STI) Patlak Ki, long-time (59.25 min)-indirect (LTI) Patlak Ki, and 50-60 min SUV (sumSUV). VOI-based 2TCM was used for the evaluation of tumor lesions and normal tissues and compared with the results of Patlak model.

RESULTS

No significant differences were observed between the four approaches regarding the number of tumor lesions. However, we found three discordant results: a true positive liver lesion in all Patlak Ki images, a false positive liver lesion delineated only in LTI Ki which was a hemangioma according to MRI and a true negative example in a patient with an atelectasis next to a lung tumor. STD, STI and LTI Ki images had superior TBR in comparison with sumSUV images (2.9-, 3.3- and 4.3-fold higher respectively). TBR of LTI Ki were significantly higher than STD Ki. VOI-based k3 showed a 21-fold higher TBR than sumSUV. Parameters of different models vary in their differential capability between tumor lesions and normal tissue like Patlak Ki which was better in normal lung and 2TCM k3 which was better in normal liver. 2TCM Ki revealed the highest correlation (r = 0.95) with the LTI Patlak Ki in tumor lesions group and demonstrated the highest correlation with the STD Patlak Ki in all tissues group and normal tissues group (r = 0.93 and r = 0.74 respectively).

CONCLUSIONS

Dynamic [18F]-FDG with the new LAFOV PET/CT scanner produces Patlak Ki images with better lesion contrast than SUV images, but does not increase the lesion detection rate. The time window used for Patlak imaging plays a more important role than the direct or indirect method. A combination of different models, like Patlak and 2TCM may be helpful in parametric imaging to obtain the best TBR in the whole body in future.

Optimized FDG-PET/MRI protocol reveals metabolic predictors of long-term survival in pancreatic cancer patients

PURPOSE

To optimize and assess an abbreviated dual time-point 18-Fluor-Deoxyglucose (FDG)-Positron Emission Tomography (PET)/Magnetic Resonance Imaging (MRI) protocol for predicting patient outcomes in pancreatic cancer.

METHODS

70 patients (47 pancreatic cancer, 23 chronic pancreatitis) underwent hybrid PET/MRI with dual time-point PET/CT at 60 and 84 minutes post-injection. Metabolic indices (MI) were calculated from Standardized Uptake Value (SUV) changes (SUVmin, SUVmean and SUVmax). Multivariate analysis was performed on PET, MRI, laboratory, and histologic data. Top predictors were used for survival analysis.

RESULTS

MI SUVmax, thresholded at 11%, was the best outcome predictor, distinguishing high-risk (2year (2y)-Overall Survival (OAS) 32%, 5y-OAS 14%, 10y-OAS 8%) and low-risk groups (2y-OAS 76%, 5y-OAS 32%, 10y-OAS 23%). Tumor size, CBD obstruction, and infiltrative disease had lower predictive value.

CONCLUSIONS

Metabolic indices from abbreviated dual time-point FDG-PET/MRI can differentiate pancreatic malignancy from pancreatitis and predict outcomes, outperforming other indices. This protocol offers a valuable diagnostic tool for characterizing pancreatic lesions and predicting outcomes based on imaging criteria.

Bidirectional dynamic frame prediction network for total-body [68Ga]Ga-PSMA-11 and [68Ga]Ga-FAPI-04 PET images

PURPOSE

Total-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations ([Formula: see text]60 minutes), potentially causing patient discomfort and resulting in artifacts in the final images. Therefore, we propose a dynamic frame prediction method for total-body PET imaging via deep learning technology to reduce the required scanning time.

METHODS

On the basis of total-body dynamic PET data acquired from 13 subjects who received [68Ga]Ga-FAPI-04 (68Ga-FAPI) and 24 subjects who received [68Ga]Ga-PSMA-11 (68Ga-PSMA), we propose a bidirectional dynamic frame prediction network that uses the initial and final 10 min of PET imaging data (frames 1-6 and frames 25-30, respectively) as inputs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed as evaluation metrics for an image quality assessment. Moreover, we calculated parametric images (68Ga-FAPI: [Formula: see text], 68Ga-PSMA: [Formula: see text]) based on the supplemented sequence data to observe the quantitative accuracy of our approach. Regions of interest (ROIs) and statistical analyses were utilized to evaluate the performance of the model.

RESULTS

Both the visual and quantitative results illustrate the effectiveness of our approach. The generated dynamic PET images yielded PSNRs of 36.056 ± 0.709 dB for the 68Ga-PSMA group and 33.779 ± 0.760 dB for the 68Ga-FAPI group. Additionally, the SSIM reached 0.935 ± 0.006 for the 68Ga-FAPI group and 0.922 ± 0.009 for the 68Ga-PSMA group. By conducting a quantitative analysis on the parametric images, we obtained PSNRs of 36.155 ± 4.813 dB (68Ga-PSMA, [Formula: see text]) and 43.150 ± 4.102 dB (68Ga-FAPI, [Formula: see text]). The obtained SSIM values were 0.932 ± 0.041 (68Ga-PSMA) and 0.980 ± 0.011 (68Ga-FAPI). The ROI analysis conducted on our generated dynamic PET sequences also revealed that our method can accurately predict temporal voxel intensity changes, maintaining overall visual consistency with the ground truth.

CONCLUSION

In this work, we propose a bidirectional dynamic frame prediction network for total-body 68Ga-PSMA and 68Ga-FAPI PET imaging with a reduced scan duration. Visual and quantitative analyses demonstrated that our approach performed well when it was used to predict one-hour dynamic PET images. https://github.com/OPMZZZ/BDF-NET .

Non-FDG hypoxia tracers

Hypoxia plays a critical role in tumor biology, influencing cancer progression, treatment resistance, and patient prognosis. While 18-Fluorine fluoredeoxyglucose ([18F]F-FDG) PET imaging has been the standard for metabolic assessment, its limitations in accurately depicting hypoxic tumor regions necessitate the exploration of non-FDG hypoxia tracers. This review aims to evaluate emerging non-FDG radiotracers, such as nitroimidazole derivatives, copper-based agents, gallium-based agents and other innovative compounds, highlighting their mechanisms of action, biodistribution, and clinical applications. We will discuss the advantages and challenges associated with hypoxia imaging, as well as recent advancements in imaging techniques that enhance the assessment of tumor hypoxia. By synthesizing current research, this review seeks to provide insights into the potential of non-FDG hypoxia tracers for improving cancer diagnosis, treatment planning, and monitoring, ultimately contributing to more personalized and effective cancer care.

Whole-Body Multiparametric PET in Clinical Oncology: Current Status, Challenges, and Opportunities

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

Advances in Molecular Imaging of Kidney Diseases

BACKGROUND

Diagnosing and monitoring kidney diseases traditionally rely on blood and urine analyses and invasive procedures such as kidney biopsies, the latter offering limited possibilities for longitudinal monitoring and a comprehensive understanding of disease dynamics. Current noninvasive methods lack specificity in capturing intrarenal molecular processes, hindering patient stratification and patient monitoring in clinical practice and clinical trials.

SUMMARY

Molecular imaging enables noninvasive and quantitative assessment of physiological and pathological molecular processes. By using specific molecular probes and imaging technologies, e.g., magnetic resonance imaging, positron emission tomography, single-photon emission computed tomography, or ultrasound, molecular imaging allows the detection and longitudinal monitoring of disease activity with spatial and temporal resolution of different kidney diseases and disease-specific pathways. Several approaches have already shown promising results in kidneys and exploratory clinical studies, and validation is needed before implementation in clinical practice.

KEY MESSAGES

Molecular imaging offers a noninvasive assessment of intrarenal molecular processes, overcoming the limitations of current diagnostic methods. It has the potential to serve as companion diagnostics, not only in clinical trials, aiding in patient stratification and treatment response assessment. By guiding therapeutic interventions, molecular imaging might contribute to the development of targeted therapies for kidney diseases.

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging

INTRODUCTION

We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.

METHODS

Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to incorporatea priori knowledge about the AIF's functional form and shape, enabling precise predictions of the concentrations of [11C]PBR28 in whole blood and the free tracer in metabolite-corrected plasma.

RESULTS

We found a robust linear correlation between our model's predicted AIF curves and those obtained through traditional, invasive measurements. We achieved an average cross-validated Pearson correlation of 0.86 for whole blood and 0.89 for parent plasma curves. Moreover, our method's ability to estimate the volumes of distribution across several key brain regions - without significant differences between the use of predicted versus actual AIFs in a two-tissue compartmental model - successfully captures the intrinsic variability related to sex, the binding affinity of the translocator protein (18 kDa), and age.

CONCLUSIONS

These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.

Current trends in the characterization and monitoring of vascular response to cancer therapy

Tumor vascular physiology is an important determinant of disease progression as well as the therapeutic outcome of cancer treatment. Angiogenesis or the lack of it provides crucial information about the tumor's blood supply and therefore can be used as an index for cancer growth and progression. While standalone anti-angiogenic therapy demonstrated limited therapeutic benefits, its combination with chemotherapeutic agents improved the overall survival of cancer patients. This could be attributed to the effect of vascular normalization, a dynamic process that temporarily reverts abnormal vasculature to the normal phenotype maximizing the delivery and intratumor distribution of chemotherapeutic agents. Longitudinal monitoring of vascular changes following antiangiogenic therapy can indicate an optimal window for drug administration and estimate the potential outcome of treatment. This review primarily focuses on the status of various imaging modalities used for the longitudinal characterization of vascular changes before and after anti-angiogenic therapies and their clinical prospects.

Mathematical modeling of18F-Fluoromisonidazole (18F-FMISO) radiopharmaceutical transport in vascularized solid tumors

18F-Fluoromisonidazole (18F-FMISO) is a highly promising positron emission tomography radiopharmaceutical for identifying hypoxic regions in solid tumors. This research employs spatiotemporal multi-scale mathematical modeling to explore how different levels of angiogenesis influence the transport of radiopharmaceuticals within tumors. In this study, two tumor geometries with heterogeneous and uniform distributions of capillary networks were employed to incorporate varying degrees of microvascular density. The synthetic image of the heterogeneous and vascularized tumor was generated by simulating the angiogenesis process. The proposed multi-scale spatiotemporal model accounts for intricate physiological and biochemical factors within the tumor microenvironment, such as the transvascular transport of the radiopharmaceutical agent, its movement into the interstitial space by diffusion and convection mechanisms, and ultimately its uptake by tumor cells. Results showed that both quantitative and semi-quantitative metrics of18F-FMISO uptake differ spatially and temporally at different stages during tumor growth. The presence of a high microvascular density in uniformly vascularized tumor increases cellular uptake, as it allows for more efficient release and rapid distribution of radiopharmaceutical molecules. This results in enhanced uptake compared to the heterogeneous vascularized tumor. In both heterogeneous and uniform distribution of microvessels in tumors, the diffusion transport mechanism has a more pronounced than convection. The findings of this study shed light on the transport phenomena behind18F-FMISO radiopharmaceutical distribution and its delivery in the tumor microenvironment, aiding oncologists in their routine decision-making processes.

Optimization and impact of sensitivity mode on abbreviated scan protocols with population-based input function for parametric imaging of [18F]-FDG for a long axial FOV PET scanner

BACKGROUND

The long axial field of view, combined with the high sensitivity of the Biograph Vision Quadra PET/CT scanner enables the precise deviation of an image derived input function (IDIF) required for parametric imaging. Traditionally, this requires an hour-long dynamic PET scan for [18F]-FDG, which can be significantly reduced by using a population-based input function (PBIF). In this study, we expand these examinations and include the scanner's ultra-high sensitivity (UHS) mode in comparison to the high sensitivity (HS) mode and evaluate the potential for further shortening of the scan time.

METHODS

Patlak Ki and DV estimates were determined by the indirect and direct Patlak methods using dynamic [18F]-FDG data of 6 oncological patients with 26 lesions (0-65 min p.i.). Both sensitivity modes for different number/duration of PET data frames were compared, together with the potential of using abbreviated scan durations of 20, 15 and 10 min by using a PBIF. The differences in parametric images and tumour-to-background ratio (TBR) due to the shorter scans using the PBIF method and between the sensitivity modes were assessed.

RESULTS

A difference of 3.4 ± 7.0% (Ki) and 1.2 ± 2.6% (DV) was found between both sensitivity modes using indirect Patlak and the full IDIF (0-65 min). For the abbreviated protocols and indirect Patlak, the UHS mode resulted in a lower bias and higher precision, e.g., 45-65 min p.i. 3.8 ± 4.4% (UHS) and 6.4 ± 8.9% (HS), allowing shorter scan protocols, e.g. 50-65 min p.i. 4.4 ± 11.2% (UHS) instead of 7.3 ± 20.0% (HS). The variation of Ki and DV estimates for both Patlak methods was comparable, e.g., UHS mode 3.8 ± 4.4% and 2.7 ± 3.4% (Ki) and 14.4 ± 2.7% and 18.1 ± 7.5% (DV) for indirect and direct Patlak, respectively. Only a minor impact of the number of Patlak frames was observed for both sensitivity modes and Patlak methods. The TBR obtained with direct Patlak and PBIF was not affected by the sensitivity mode, was higher than that derived from the SUV image (6.2 ± 3.1) and degraded from 20.2 ± 12.0 (20 min) to 10.6 ± 5.4 (15 min). Ki and DV estimate images showed good agreement (UHS mode, RC: 6.9 ± 2.3% (Ki), 0.1 ± 3.1% (DV), peak signal-to-noise ratio (PSNR): 64.5 ± 3.3 dB (Ki), 61.2 ± 10.6 dB (DV)) even for abbreviated scan protocols of 50-65 min p.i.

CONCLUSIONS

Both sensitivity modes provide comparable results for the full 65 min dynamic scans and abbreviated scans using the direct Patlak reconstruction method, with good Ki and DV estimates for 15 min short scans. For the indirect Patlak approach the UHS mode improved the Ki estimates for the abbreviated scans.

Dual-time-point dynamic 68Ga-PSMA-11 PET/CT for parametric imaging generation in prostate cancer

PURPOSE

To investigate the optimal dual-time-point (DTP) approaches using dynamic 68Ga-PSMA-11 PET/CT imaging to generate parametric images for prostate cancer patients.

METHODS

Fifteen patients with prostate cancer were intravenously administered 68Ga-PSMA-11 of 181.9 ± 47.2 MBq, followed by an immediate 60 min dynamic PET/CT scan. List-mode data were reconstructed into 25 timeframes (6 × 10 s, 8 × 30 s, and 11 × 300 s) and corrected for motion and partial volume effect. DTP parametric images were generated using different interval time points of 5 min and 10 min, with a minimum of 30 min time interval. Net influx rates (Ki) were calculated through the fitting of a single irreversible two-tissue compartmental model. Intraclass correlation coefficient (ICC) values between DTP protocols and 60 min Ki were obtained. Lesion-to-background ratios (LBRs) of Ki and standardized uptake value (SUV) images in each DTP protocol were determined.

RESULTS

The DTP protocol of 5-10 min with a 40-45 min interval showed the highest ICC of 0.988 compared with the 60 min Ki, whereas the ICC values for the intervals of 0-5 min with 55-60 min and 0-10 min with 50-60 min were 0.941. The LBRs of the 60 min Ki, 5-10 min with 40-45 min Ki, 0-5 min with 55-60 min Ki, 0-10 min with 50-60 min Ki, SUVmean, and SUVmax images were 29.53 ± 27.33, 13.05 ± 15.28, 45.15 ± 53.11, 45.52 ± 70.31, 19.77 ± 23.43, and 25.06 ± 30.07, respectively.

CONCLUSION

The 0-5 min with 55-60 min DTP parametric imaging exhibits a comparable Ki to 60 min parametric imaging and remarkable image quality and contrast than SUV imaging, enhancing prostate cancer diagnosis while maintaining time efficiency.

Accurate brain pharmacokinetic parametric imaging using the blood input function extracted from the cavernous sinus

Brain pharmacokinetic parametric imaging based on dynamic positron emission tomography (PET) scan is valuable in the diagnosis of brain tumor and neurodegenerative diseases. For short-axis PET system, standard blood input function (BIF) of the descending aorta is not acquirable during the dynamic brain scan. BIF extracted from the intracerebral vascular is inaccurate, making the brain parametric imaging task challenging. This study introduces a novel technique tailored for brain pharmacokinetic parameter imaging in short-axis PET in which the head BIF (hBIF) is acquired from the cavernous sinus. The proposed method optimizes the hBIF within the Patlak model via data fitting, curve correction and Patlak graphical model rewriting. The proposed method was built and evaluated using dynamic PET datasets of 67 patients acquired by uEXPLORER PET/CT, among which 64 datasets were used for data fitting and model construction, and 3 were used for method testing; using cross-validation, a total of 15 patient datasets were finally used to test the model. The performance of the new method was evaluated via visual inspection, root-mean-square error (RMSE) measurements and VOI-based accuracy analysis using linear regression and Person's correlation coefficients (PCC). Compared to directly using the cavernous sinus BIF directly for parameter imaging, the new method achieves higher accuracy in parametric analysis, including the generation of Patlak plots closer to the standard plots, better visual effects and lower RMSE values in the Ki (P = 0.0012) and V (P = 0.0042) images. VOI-based analysis shows regression lines with slopes closer to 1 (P = 0.0019 for Ki ) and smaller intercepts (P = 0.0085 for V). The proposed method is capable of achieving accurate brain pharmacokinetic parametric imaging using cavernous sinus BIF with short-axis PET scan. This may facilitate the application of this imaging technology in the clinical diagnosis of brain diseases.

The relationship between gadolinium enhancement and [18 F]fluorothymidine uptake in brain lesions with the use of hybrid PET/MRI

BACKGROUND

To evaluate and compare the diagnostic power of [18F]FLT-PET with ceMRI in patients with brain tumours or other focal lesions.

METHODS

121 patients with suspected brain tumour or those after brain tumour surgery were enroled in this retrospective study (61 females, 60 males, mean age 37.3 years, range 1-80 years). All patients underwent [18F]FLT-PET/MRI with gadolinium contrast agent application. In 118 of these patients, a final diagnosis was made, verified by histopathology or by follow-up. Agreement between ceMRI and [18F]FLT-PET of the whole study group was established. Further, sensitivity and specificity of ceMRI and [18F]FLT-PET were calculated for differentiation of high-grade vs. low-grade tumours, high-grade vs. low-grade tumours together with non-tumour lesions and for differentiation of high-grade tumours from all other verified lesions.

RESULTS

[18F]FLT-PET and ceMRI findings were concordant in 119 cases (98%). On closer analysis of a subset of 64 patients with verified gliomas, the sensitivity and specificity of both PET and ceMRI were identical (90% and 84%, respectively) for differentiating low-grade from high-grade tumours, if the contrast enhancement and [18F]FLT uptake were considered as hallmarks of high-grade tumour. For differentiation of high-grade tumours from low-grade tumours and lesions of nontumorous aetiology (e.g., inflammatory lesions or post-therapeutic changes) in a subgroup of 93 patients by visual evaluation, the sensitivity of both PET and ceMRI was 90%, whereas the specificity of PET was slightly higher (61%) compared to ceMRI (57%). By receiver operating characteristic analysis, the sensitivity and specificity were 82% and 74%, respectively, when the threshold of SUVmax in the tumour was set to 0.9 g/ml.

CONCLUSION

We demonstrated a generally very high correlation of [18F]FLT accumulation with contrast enhancement visible on ceMRI and a comparable diagnostic yield in both modalities for differentiating high-grade tumours from low-grade tumours and lesions of other aetiology.

A novel relative-equilibrium graphical plot for rapid reversible tracer studies in dynamic PET imaging

Objective.This study aims to address the issue of long scan durations required by traditional graphical analysis methods, such as the Logan plot and its variant, the reversible equilibrium (RE) Logan plot, for dynamic PET imaging of tracer kinetics.Approach.We propose a relative RE Logan model that builds on the principles of the Logan plot and its variant to significantly reduce scan time without compromising the accuracy of tracer kinetics analysis. The model is supported by theoretical evidence and experimental validations, including two computer simulations and one clinical data analysis.Main results.The proposed model demonstrates a significant linear relationship between the variablexand the slopeDVTof the RE Logan plot, and the variablex' and the slopeDVT'of the relative RE Logan plot. The Pearson correlation coefficients (r) of the linear fitting of thex' to thexequal 0.9849 in the simulated data and 0.9912 in the clinical data. Similarly, thervalues for the linear fitting ofDVT'toDVTequal 0.9989 and 0.9988 in the simulated data, and 0.9954 in the clinical data.Significance.These results demonstrate the model's capability to maintain strong linear relationships and produce parametric images comparable to the traditional RE Logan plot, but with the considerable advantage of shorter scan durations. This innovation holds significant potential for enhancing the efficiency and feasibility of PET imaging in clinical settings.

Exact parameter identification in PET pharmacokinetic modeling using the irreversible two tissue compartment model. Phys Med Biol 2024;69:165008. [PMID: 38830366 PMCID: PMC11288174 DOI: 10.1088/1361-6560/ad539e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Objective.In quantitative dynamic positron emission tomography (PET), time series of images, reflecting the tissue response to the arterial tracer supply, are reconstructed. This response is described by kinetic parameters, which are commonly determined on basis of the tracer concentration in tissue and the arterial input function. In clinical routine the latter is estimated by arterial blood sampling and analysis, which is a challenging process and thus, attempted to be derived directly from reconstructed PET images. However, a mathematical analysis about the necessity of measurements of the common arterial whole blood activity concentration, and the concentration of free non-metabolized tracer in the arterial plasma, for a successful kinetic parameter identification does not exist. Here we aim to address this problem mathematically.Approach.We consider the identification problem in simultaneous pharmacokinetic modeling of multiple regions of interests of dynamic PET data using the irreversible two-tissue compartment model analytically. In addition to this consideration, the situation of noisy measurements is addressed using Tikhonov regularization. Furthermore, numerical simulations with a regularization approach are carried out to illustrate the analytical results in a synthetic application example.Main results.We provide mathematical proofs showing that, under reasonable assumptions, all metabolic tissue parameters can be uniquely identified without requiring additional blood samples to measure the arterial input function. A connection to noisy measurement data is made via a consistency result, showing that exact reconstruction of the ground-truth tissue parameters is stably maintained in the vanishing noise limit. Furthermore, our numerical experiments suggest that an approximate reconstruction of kinetic parameters according to our analytic results is also possible in practice for moderate noise levels.Significance.The analytical result, which holds in the idealized, noiseless scenario, suggests that for irreversible tracers, fully quantitative dynamic PET imaging is in principle possible without costly arterial blood sampling and metabolite analysis.

Noninvasive Quantification of Glucose Metabolism in Mice Myocardium Using the Spline Reconstruction Technique

The spline reconstruction technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study was to compare the SRT, filtered back-projection (FBP), and the Tera-Tomo 3D algorithm for various iteration numbers, using small-animal dynamic PET data obtained from a Mediso nanoScan® PET/CT scanner. For this purpose, Patlak graphical kinetic analysis was employed to noninvasively quantify the myocardial metabolic rate of glucose (MRGlu) in seven male C57BL/6 mice (n=7). All analytic reconstructions were performed via software for tomographic image reconstruction. The analysis of all PET-reconstructed images was conducted with PMOD software (version 3.506, PMOD Technologies LLC, Fällanden, Switzerland) using the inferior vena cava as the image-derived input function. Statistical significance was determined by employing the one-way analysis of variance test. The results revealed that the differences between the values of MRGlu obtained via SRT versus FBP, and the variants of he Tera-Tomo 3D algorithm were not statistically significant (p > 0.05). Overall, the SRT appears to perform similarly to the other algorithms investigated, providing a valid alternative analytic method for preclinical dynamic PET studies.

The earliest optimal timing for total-body 68Ga-fibroblast activation protein inhibitor-04 PET scans: an evidence-based single-centre study

OBJECTIVES

To investigate the earliest optimal timing for positron emission tomography (PET) scans after 68Ga-fibroblast activation protein inhibitor-04 ([68Ga]Ga-FAPI-04) injection.

METHODS

This prospective study enrolled patients who underwent 60-min dynamic 68Ga-FAPI-04 total-body PET/CT scans; the images were reconstructed at 10-min intervals (G0-10, G10-20, G20-30, G30-40, G40-50, and G50-60), and the [68Ga]Ga-FAPI-04 uptake patterns were evaluated. The standardised uptake value (SUV), liver signal-to-noise ratio (SNR), and lesion-to-background ratios (LBRs) for different time windows were calculated to evaluate image quality and lesion detectability. The period from 30 to 40 min was then split into overlapping 5-min intervals starting 1 min apart for further evaluation. G50-60 was considered the reference.

RESULTS

A total of 30 patients with suspected malignant tumours were analysed. In the images reconstructed over 10-min intervals, longer acquisition times were associated with lower background uptake and better image quality. Some lesions could not be detected until G30-40. The lesion detection rate, uptake, and LBRs did not differ significantly among G30-40, G40-50, and G50-60 (all p > 0.05). The SUVmean and LBRs of primary tumours in the reconstructed images did not differ significantly among the 5-min intervals between 30 and 40 min; for metastatic and benign lesions, G34-39 and G35-40 showed significantly better SUVmean and LBR values than the other images. The G34-39 and G50-60 scans showed no significant differences in uptake, LBRs, or detection rates (all p > 0.05).

CONCLUSION

The earliest optimal time to start acquisition was 34 min after injection of half-dose [68Ga]Ga-FAPI-04.

CLINICAL RELEVANCE STATEMENT

This study evaluated 68Ga-fibroblast activation protein inhibitor-04 ([68Ga]Ga-FAPI-04) uptake patterns by comparing the image quality and lesion detection rate with 60-min dynamic [68Ga]Ga-FAPI-04 total-body PET/CT scans and identified the earliest optimal scan time after [68Ga]Ga-FAPI-04 injection.

KEY POINTS

• A prospective single-centre study showed that the earliest optimal time point to start acquisition was 34 min after injection of half-dose [68Ga-fibroblast activation protein inhibitor-04 (68Ga]Ga-FAPI-04). • There were statistically significant differences in standardised uptake value, lesion-to-background ratios, and lesion detectability between scans before and after 34 min from the injection of [68Ga]Ga-FAPI-04, but these values did not change further from 34 to 60 min after injection. • With a reasonable acquisition time, the image quality could still meet diagnostic requirements.

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.

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.

The Role of Total-Body PET in Drug Development and Evaluation: Status and Outlook

Total-body PET, an emerging technique, enables high-quality simultaneous total-body dynamic PET acquisition and accurate kinetic analysis. It has the potential to facilitate the study of multiple tracers while minimizing radiation dose and improving tracer-specific imaging. This advancement holds promise for enhancing the development and clinical evaluation of drugs, particularly radiopharmaceuticals. Multiple clinical trials are using a total-body PET scanner to explore existing and innovative radiopharmaceuticals. However, challenges persist, along with the opportunities, with regard to the use of total-body PET in drug development and evaluation. Specifically, considerations relate to the role of total-body PET in clinical pharmacologic evaluations and its integration into the theranostic paradigm. In this review, state-of-the-art total-body PET and its potential roles in pharmaceutical research are explored.

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.

Empowering PET: harnessing deep learning for improved clinical insight

This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the "black-box" problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.Relevance statementAI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.Key points• Applying AI has the potential to enhance the entire PET imaging pipeline.• AI may support several clinical tasks in both PET diagnosis and prognosis.• Interpreting the relationships between imaging and multiomics data will heavily rely on AI.

Assessing dynamic metabolic heterogeneity in prostate cancer patients via total-body [68Ga]Ga-PSMA-11 PET/CT imaging: quantitative analysis of [68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs

PURPOSE

This study aimed to quantitatively assess [68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs in prostate cancer using the total-body [68Ga]Ga-PSMA-11 PET/CT and to characterize the dynamic metabolic heterogeneity of prostate cancer.

METHODS

Dynamic total-body [68Ga]Ga-PSMA-11 PET/CT scans were performed on ten prostate cancer patients. Manual delineation of volume-of-interests (VOIs) was performed on multiple normal organs displaying high [68Ga]Ga-PSMA-11 uptake, as well as pathological lesions. Time-to-activity curves (TACs) were generated, and the four compartment models including one-tissue compartmental model (1T1k), reversible one-tissue compartmental model (1T2k), irreversible two-tissue compartment model (2T3k) and reversible two-tissue compartmental model (2T4k) were fitted to each tissue TAC. Various rate constants, including K1 (forward transport rate from plasma to the reversible compartment), k2 (reverse transport rate from the reversible compartment to plasma), k3 (tracer binding on the PSMA-receptor and its internalization), k4 (the externalization rate of the tracer) and Ki (net influx rate), were obtained. The selection of the optimal model for describing the uptake of both lesions and normal organs was determined using the Akaike information criteria (AIC). Receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off values for differentiating physiological and pathological [68Ga]Ga-PSMA-11 uptake.

RESULTS

Both 1T1k and 1T2k models showed relatively high AIC values compared to the 2T3k and 2T4k models in both pathological lesions and normal organs. The kinetic behavior of pathological lesions was better described by the 2T3k model compared to the 2T4k model, while the normal organs were better described by the 2T4k model. Significant variations in kinetic metrics, such as K1, k2, and k3, and Ki, were observed among normal organs with high [68Ga]Ga-PSMA-11 uptake and pathological lesions. The high Ki value in normal organs was primarily determined by elevated K1 and low k3, rather than k2. Conversely, the high Ki value in pathological lesions, ranking second to the kidney and similar to salivary glands and spleen, was predominantly determined by the highest k3 value. Notably, k3 exhibited the highest performance in distinguishing between physiological and pathological [68Ga]Ga-PSMA-11 uptake, with an area under the curve (AUC) of 0.844 (95% CI, 0.773-0.915), sensitivity of 82.9%, and specificity of 74.1%. The k3 values showed better performance than SUVmean (AUC, 0.659), SUVmax (AUC, 0.637), and other kinetic parameter including K1 (AUC, 0.604), k2 (AUC, 0.634), and Ki (AUC, 0.651).

CONCLUSIONS

Significant discrepancies in kinetic metrics were detected between pathological lesions and normal organs, despite their shared high uptake of [68Ga]Ga-PSMA-11. Notably, the k3 value exhibits a noteworthy capability to distinguish between pathological lesions and normal organs with elevated [68Ga]Ga-PSMA-11 uptake. This discovery implies that k3 holds promise as a prospective imaging biomarker for distinguishing between pathologic and non-specific [68Ga]Ga-PSMA-11 uptake in patients with prostate cancer.

Feasibility study of a SiPM-fiber detector for non-invasive measurement of arterial input function for preclinical and clinical positron emission tomography

Pharmacokinetic positron emission tomography (PET) studies rely on the measurement of the arterial input function (AIF), which represents the time-activity curve of the radiotracer concentration in the blood plasma. Traditionally, obtaining the AIF requires invasive procedures, such as arterial catheterization, which can be challenging, time-consuming, and associated with potential risks. Therefore, the development of non-invasive techniques for AIF measurement is highly desirable. This study presents a detector for the non-invasive measurement of the AIF in PET studies. The detector is based on the combination of scintillation fibers and silicon photomultipliers (SiPMs) which leads to a very compact and rugged device. The feasibility of the detector was assessed through Monte Carlo simulations conducted on mouse tail and human wrist anatomies studying relevant parameters such as energy spectrum, detector efficiency and minimum detectable activity (MDA). The simulations involved the use of 18F and 68Ga isotopes, which exhibit significantly different positron ranges. In addition, several prototypes were built in order to study the different components of the detector including the scintillation fiber, the coating of the fiber, the SiPMs, and the operating configuration. Finally, the simulations were compared with experimental measurements conducted using a tube filled with both 18F and 68Ga to validate the obtained results. The MDA achieved for both anatomies (approximately 1000 kBq/mL for mice and 1 kBq/mL for humans) falls below the peak radiotracer concentrations typically found in PET studies, affirming the feasibility of conducting non-invasive AIF measurements with the fiber detector. The sensitivity for measurements with a tube filled with 18F (68Ga) was 1.2 (2.07) cps/(kBq/mL), while for simulations, it was 2.81 (6.23) cps/(kBq/mL). Further studies are needed to validate these results in pharmacokinetic PET studies.

ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages

BACKGROUND

The indirect method for generating parametric images in positron emission tomography (PET) involves the acquisition and reconstruction of dynamic images and temporal modelling of tissue activity given a measured arterial input function. This approach is not robust, as noise in each dynamic image leads to a degradation in parameter estimation. Direct methods incorporate into the image reconstruction step both the kinetic and noise models, leading to improved parametric images. These methods require extensive computational time and large computing resources. Machine learning methods have demonstrated significant potential in overcoming these challenges. But they are limited by the requirement of a paired training dataset. A further challenge within the existing framework is the use of state-of-the-art arterial input function estimation via temporal arterial blood sampling, which is an invasive procedure, or an additional magnetic resonance imaging (MRI) scan for selecting a region where arterial blood signal can be measured from the PET image. We propose a novel machine learning approach for reconstructing high-quality parametric brain images from histoimages produced from time-of-flight PET data without requiring invasive arterial sampling, an MRI scan, or paired training data from standard field-of-view scanners.

RESULT

The proposed is tested on a simulated phantom and five oncological subjects undergoing an 18F-FDG-PET scan of the brain using Siemens Biograph Vision Quadra. Kinetic parameters set in the brain phantom correlated strongly with the estimated parameters (K1, k2 and k3, Pearson correlation coefficient of 0.91, 0.92 and 0.93) and a mean squared error of less than 0.0004. In addition, our method significantly outperforms (p < 0.05, paired t-test) the conventional nonlinear least squares method in terms of contrast-to-noise ratio. At last, the proposed method was found to be 37% faster than the conventional method.

CONCLUSION

We proposed a direct non-invasive DL-based reconstruction method and produced high-quality parametric maps of the brain. The use of histoimages holds promising potential for enhancing the estimation of parametric images, an area that has not been extensively explored thus far. The proposed method can be applied to subject-specific dynamic PET data alone.

Comparison of tracer kinetic models for 68Ga-PSMA-11 PET in intermediate-risk primary prostate cancer patients

BACKGROUND

68Ga-PSMA-11 positron emission tomography enables the detection of primary, recurrent, and metastatic prostate cancer. Regional radiopharmaceutical uptake is generally evaluated in static images and quantified as standard uptake values (SUVs) for clinical decision-making. However, analysis of dynamic images characterizing both tracer uptake and pharmacokinetics may offer added insights into the underlying tissue pathophysiology. This study was undertaken to evaluate the suitability of various kinetic models for 68Ga-PSMA-11 PET analysis. Twenty-three lesions in 18 patients were included in a retrospective kinetic evaluation of 55-min dynamic 68Ga-PSMA-11 pre-prostatectomy PET scans from patients with biopsy-demonstrated intermediate- to high-risk prostate cancer. Three kinetic models-a reversible one-tissue compartment model, an irreversible two-tissue compartment model, and a reversible two-tissue compartment model, were evaluated for their goodness of fit to lesion and normal reference prostate time-activity curves. Kinetic parameters obtained through graphical analysis and tracer kinetic modeling techniques were compared for reference prostate tissue and lesion regions of interest.

RESULTS

Supported by goodness of fit and information loss criteria, the irreversible two-tissue compartment model optimally fit the time-activity curves. Lesions exhibited significant differences in kinetic rate constants (K1, k2, k3, Ki) and semiquantitative measures (SUV and %ID/kg) when compared with reference prostatic tissue. The two-tissue irreversible tracer kinetic model was consistently appropriate across prostatic zones.

CONCLUSIONS

An irreversible tracer kinetic model is appropriate for dynamic analysis of 68Ga-PSMA-11 PET images. Kinetic parameters estimated by Patlak graphical analysis or full compartmental analysis can distinguish tumor from normal prostate tissue.

The role of dynamic, static, and delayed total-body PET imaging in the detection and differential diagnosis of oncological lesions

OBJECTIVES

Commercialized total-body PET scanners can provide high-quality images due to its ultra-high sensitivity. We compared the dynamic, regular static, and delayed 18F-fluorodeoxyglucose (FDG) scans to detect lesions in oncologic patients on a total-body PET/CT scanner.

MATERIALS & METHODS

In all, 45 patients were scanned continuously for the first 60 min, followed by a delayed acquisition. FDG metabolic rate was calculated from dynamic data using full compartmental modeling, whereas regular static and delayed SUV images were obtained approximately 60- and 145-min post-injection, respectively. The retention index was computed from static and delayed measures for all lesions. Pearson's correlation and Kruskal-Wallis tests were used to compare parameters.

RESULTS

The number of lesions was largely identical between the three protocols, except MRFDG and delayed images on total-body PET only detected 4 and 2 more lesions, respectively (85 total). FDG metabolic rate (MRFDG) image-derived contrast-to-noise ratio and target-to-background ratio were significantly higher than those from static standardized uptake value (SUV) images (P < 0.01), but this is not the case for the delayed images (P > 0.05). Dynamic protocol did not significantly differentiate between benign and malignant lesions just like regular SUV, delayed SUV, and retention index.

CONCLUSION

The potential quantitative advantages of dynamic imaging may not improve lesion detection and differential diagnosis significantly on a total-body PET/CT scanner. The same conclusion applied to delayed imaging. This suggested the added benefits of complex imaging protocols must be weighed against the complex implementation in the future.

CLINICAL RELEVANCE

Total-body PET/CT was known to significantly improve the PET image quality due to its ultra-high sensitivity. However, whether the dynamic and delay imaging on total-body scanner could show additional clinical benefits is largely unknown. Head-to-head comparison between two protocols is relevant to oncological management.

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.

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.

Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks

Objective. Reducing dose in positron emission tomography (PET) imaging increases noise in reconstructed dynamic frames, which inevitably results in higher noise and possible bias in subsequently estimated images of kinetic parameters than those estimated in the standard dose case. We report the development of a spatiotemporal denoising technique for reduced-count dynamic frames through integrating a cascade artificial neural network (ANN) with the highly constrained back-projection (HYPR) scheme to improve low-dose parametric imaging.Approach. We implemented and assessed the proposed method using imaging data acquired with11C-UCB-J, a PET radioligand bound to synaptic vesicle glycoprotein 2A (SV2A) in the human brain. The patch-based ANN was trained with a reduced-count frame and its full-count correspondence of a subject and was used in cascade to process dynamic frames of other subjects to further take advantage of its denoising capability. The HYPR strategy was then applied to the spatial ANN processed image frames to make use of the temporal information from the entire dynamic scan.Main results. In all the testing subjects including healthy volunteers and Parkinson's disease patients, the proposed method reduced more noise while introducing minimal bias in dynamic frames and the resulting parametric images, as compared with conventional denoising methods.Significance. Achieving 80% noise reduction with a bias of -2% in dynamic frames, which translates into 75% and 70% of noise reduction in the tracer uptake (bias, -2%) and distribution volume (bias, -5%) images, the proposed ANN+HYPR technique demonstrates the denoising capability equivalent to a 11-fold dose increase for dynamic SV2A PET imaging with11C-UCB-J.

The feasibility of quantitative assessment of dynamic 18F-fluorodeoxyglucose PET in Takayasu's arteritis: a pilot study

PURPOSE

PET has been demonstrated to be sensitive for detecting active inflammation in Takayasu's arteritis (TAK) patients, but semi-quantitative-based assessment may be susceptible to various biological and technical factors. Absolute quantification via dynamic PET (dPET) may provide a more reliable and quantitative assessment of TAK-active arteries. The purpose of this study was to investigate the feasibility and efficacy of dPET in quantifying TAK-active arteries compared to static PET.

MATERIALS AND METHODS

This prospective study enrolled 10 TAK-active patients (fulfilled the NIH criteria) and 5 control participants from March to October 2022. One-hour dPET scan (all TAK and control participants) and delayed static PET scan at 2-h (all TAK patients) were acquired. For 1-h static PET, summed images from 50 to 60 min of the dPET were extracted. PET parameters derived from 1- and 2-h static PET including SUV (SUV1H and SUV2H), target-to-background ratio (TBR) (TBR1H and TBR2H), net influx rate (Ki), and TBRKi extracted from dPET were obtained. The detectability of TAK-active arteries was compared among different scanning methods using the generalized estimating equation (GEE) with a logistic regression with repeated measures, and the GEE with gamma distribution and log link function was used to evaluate the different study groups or scanning methods.

RESULTS

Based on the disease states, 5 cases of TAK were classified as untreated and relapsed, respectively. The SUVmax on 2-h PET was higher than that on 1-h PET in the untreated patients (P < 0.05). However, no significant differences were observed in the median SUVmax between 1-h PET and 2-h PET in the relapsed patients (P > 0.05). The TBRKi was significantly higher than both TBR1H and TBR2H (all P < 0.001). Moreover, the detectability of TAK-active arteries by dPET-derived Ki was significantly higher than 1-h and 2-h PET (all P < 0.001). Significant differences were observed in Kimax, SUVmax-1H, TBR1H, and TBRKi among untreated, relapsed, and control groups (all P < 0.05).

CONCLUSIONS

Absolute quantitative assessment by dPET provides an improved sensitivity and detectability in both visualization and quantification of TAK-active arteries. This elucidates the clinical significance of dPET in the early detection of active inflammation and monitoring recurrence.

An update on the use of image-derived input functions for human PET studies: new hopes or old illusions?

BACKGROUND

The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome.

MAIN BODY

This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the field's opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF.

CONCLUSION

Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.

Comparison of Tracer Kinetic Models for 68Ga-PSMA-11 PET in Intermediate Risk Primary Prostate Cancer Patients

BACKGROUND

68Ga-PSMA-11 positron emission tomography enables the detection of primary, recurrent, and metastatic prostate cancer. Regional radiopharmaceutical uptake is generally evaluated in static images and quantified as standard uptake values (SUV) for clinical decision-making. However, analysis of dynamic images characterizing both tracer uptake and pharmacokinetics may offer added insights into the underlying tissue pathophysiology. This study was undertaken to evaluate the suitability of various kinetic models for 68Ga-PSMA-11 PET analysis. Twenty-three lesions in 18 patients were included in a retrospective kinetic evaluation of 55-minute dynamic 68Ga-PSMA-11 pre-prostatectomy PET scans from patients with biopsy-demonstrated intermediate to high-risk prostate cancer. A reversible one-tissue compartment model, irreversible two-tissue compartment model, and a reversible two-tissue compartment model were evaluated for their goodness-of-fit to lesion and normal reference prostate time-activity curves. Kinetic parameters obtained through graphical analysis and tracer kinetic modeling techniques were compared for reference prostate tissue and lesion regions of interest.

RESULTS

Supported by goodness-of-fit and information loss criteria, the irreversible two-tissue compartment model was selected as optimally fitting the time-activity curves. Lesions exhibited significant differences in kinetic rate constants (K1, k2, k3, Ki) and semiquantitative measures (SUV) when compared with reference prostatic tissue. The two-tissue irreversible tracer kinetic model was consistently appropriate across prostatic zones.

CONCLUSIONS

An irreversible tracer kinetic model is appropriate for dynamic analysis of 68Ga-PSMA-11 PET images. Kinetic parameters estimated by Patlak graphical analysis or full compartmental analysis can distinguish tumor from normal prostate tissue.

Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images

BACKGROUND

Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (Ki) of tissues using graphical methods (such as Patlak plots). Ki is more stable than the standard uptake value and has a good reference value for clinical diagnosis. However, the long scanning time required for obtaining dynamic PET images, usually an hour, makes this method less useful in some ways. There is a tradeoff between the scan durations and the signal-to-noise ratios (SNRs) of Ki images. The purpose of our study is to obtain approximately the same image as that produced by scanning for one hour in just half an hour, improving the SNRs of images obtained by scanning for 30 min and reducing the necessary 1-h scanning time for acquiring dynamic PET images.

METHODS

In this paper, we use U-Net as a feature extractor to obtain feature vectors with a priori knowledge about the image structure of interest and then utilize a parameter generator to obtain five parameters for a two-tissue, three-compartment model and generate a time activity curve (TAC), which will become close to the original 1-h TAC through training. The above-generated dynamic PET image finally obtains the Ki parameter image.

RESULTS

A quantitative analysis showed that the network-generated Ki parameter maps improved the structural similarity index measure and peak SNR by averages of 2.27% and 7.04%, respectively, and decreased the root mean square error (RMSE) by 16.3% compared to those generated with a scan time of 30 min.

CONCLUSIONS

The proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.

Quantitation of dynamic total-body PET imaging: recent developments and future perspectives

BACKGROUND

Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades.

CHALLENGES

The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results.

CONCLUSIONS

In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.