Short-duration dynamic FDG PET imaging: Optimization and clinical application

Hepatocellular carcinoma 18F-FDG PET/CT kinetic parameter estimation based on the advantage actor-critic algorithm

BACKGROUND

Kinetic parameters estimated with dynamic 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) help characterize hepatocellular carcinoma (HCC), and deep reinforcement learning (DRL) can improve kinetic parameter estimation.

PURPOSE

The advantage actor-critic (A2C) algorithm is a DRL algorithm with neural networks that seek the optimal parameters. The aim of this study was to preliminarily assess the role of the A2C algorithm in estimating the kinetic parameters of 18F-FDG PET/CT in patients with HCC.

MATERIALS AND METHODS

18F-FDG PET data from 14 liver tissues and 17 HCC tumors obtained via a previously developed, abbreviated acquisition protocol (5-min dynamic PET/CT imaging supplemented with 1-min static imaging at 60 min) were prospectively collected. The A2C algorithm was used to estimate kinetic parameters with a reversible double-input, three-compartment model, and the results were compared with those of the conventional nonlinear least squares (NLLS) algorithm. Fitting errors were compared via the root-mean-square errors (RMSEs) of the time activity curves (TACs).

RESULTS

Significant differences in K1, k2, k3, k4, fa, and vb according to the A2C algorithm and k3, fa, and vb according to the NLLS algorithm were detected between HCC and normal liver tissues (all p < 0.05). Furthermore, A2C demonstrated superior diagnostic performance over NLLS in terms of k3 and vb (both p < 0.05 in the Delong test). Notably, A2C yielded a smaller fitting error for normal liver tissue (0.62 ± 0.24 vs. 1.04 ± 1.00) and HCC tissue (1.40 ± 0.42 vs. 1.51 ± 0.97) than did NLLS.

CONCLUSIONS

Compared with the conventional postreconstruction NLLS method, the A2C algorithm can more precisely estimate 18F-FDG kinetic parameters with a reversible double-input, three-compartment model for HCC tumors, attaining better TAC fitting with a lower RMSE.

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).

Validating image-derived input functions of dynamic 18F-FDG long axial field-of-view PET/CT studies

Aim/background

Dynamic PET imaging requires an input function typically obtained through blood sampling. Image-derived input functions (IDIFs) of the ascending aorta (AA), aortic arch, descending aorta (DA), or left ventricle (LV) offer non-invasive alternatives, especially with long-axial field-of-view (LAFOV) PET/CT systems enabling whole-body dynamic 1⁸F-FDG imaging. This study aimed to validate uncorrected IDIFs derived from the AA, DA, aortic arch, and LV by comparing them to (late) venous whole-blood in patients undergoing LAFOV PET/CT.

Methods

Eleven oncology patients who underwent 70-min dynamic 18F-FDG PET/CT scans on a LAFOV PET/CT system after receiving an intravenous bolus injection of 3.0 MBq/kg were included. Seven venous blood samples were collected manually at approximately 5, 10, 15, 25, 35, 45, and 60 min post-injection (pi) and compared to IDIFs derived from the AA, aortic arch, DA, and LV. Bias between IDIFs and venous blood samples was assessed at each time point.

Results

IDIF accuracy relative to venous blood samples improved over time, with a median percentage bias <10% after 25 min pi. At 60 min pi, the aortic arch showed the smallest bias (median -1.1%, IQR 5.9%), followed by the AA (2.5%, IQR 7.0%), DA (5.1%, IQR 8.6%), and LV (7.4%, IQR 7.6%).

Conclusion

The high precision of aorta-derived IDIFs suggests that IDIFs are a reliable alternative to manual blood sampling for dynamic 18F-FDG PET imaging on a LAFOV PET/CT system. Using IDIFs reduces variability, simplifies protocols, minimizes radiation exposure, and enhances patient safety with a non-invasive approach.

A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma

BACKGROUND

18F fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) pharmacokinetics is an approach for efficiently quantifying perfusion and metabolic processes in the liver, but the conventional single-individual optimization algorithms and single-population optimization algorithms have difficulty obtaining reasonable physiological characteristics from estimated parameters. A prior-based multi-population multi-objective optimization (p-MPMOO) approach using two sub-populations based on two categories of prior information was preliminarily proposed for estimating the 18F-FDG PET/CT pharmacokinetics of patients with hepatocellular carcinoma.

METHODS

PET data from 24 hepatocellular carcinoma (HCC) tumors of 5-min dynamic PET/CT supplemented with 1-min static PET at 60 min were prospectively collected. A reversible double-input three-compartment model and kinetic parameters (K1, k2, k3, k4, fa, and [Formula: see text]) were used to quantify the metabolic information. The single-individual Levenberg-Marquardt (LM) algorithm, single-population algorithms (Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA)) and p-MPMO optimization algorithms (p-MPMOPSO, p-MPMODE, and p-MPMOGA) were used to estimate the parameters.

RESULTS

The areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in K1 and k4 (P < 0.05 in the DeLong test) and the single population optimization in k2 and k3 (P < 0.05), and did not differ from other methods in fa and vb (P > 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between K1, k2, k3, and k4. The p-MPMOPSO showed significant differences (P < 0.05) in the parameter estimation of k2, k3, k4, and fa. The p-MPMODE is implemented on K1, k2, k3, k4, and fa; The p-MPMOGA does it on all six parameters.

CONCLUSIONS

The p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.

Total Body Positron Emission Tomography/Computed Tomography: Current Status in Oncology

Positron Emission Tomography (PET) is a crucial imaging modality in oncology, providing functional insights by detecting metabolic activity in tissues. Total-body (TB) PET and large field-of-view PET have emerged as advanced techniques, offering whole-body imaging in a single acquisition. TB PET enables simultaneous imaging from head to toe, providing comprehensive information on tumor distribution, metastasis, and treatment response. This is particularly valuable in oncology, where metastatic spread often requires evaluation of multiple body areas. By covering the entire body, TB PET improves diagnostic accuracy, reduces scan time, and increases patient comfort. Furthermore, these new tomographs offer a marked increase in sensitivity, thanks to their ability to capture a larger volume of data simultaneously. This heightened sensitivity enables the detection of smaller lesions and more subtle metabolic changes, improving diagnostic accuracy in the early stages of cancer or in the evaluation of minimal residual disease. Moreover, the increased sensitivity allows for lower radiotracer doses without compromising image quality, reducing patient exposure to radiation or very quick acquisitions. Another significant advantage is the possibility of dynamic acquisitions, which allow for continuous monitoring of tracer kinetics over time. This provides critical information about tissue perfusion, metabolism, and receptor binding in real time. Dynamic imaging is particularly useful for assessing treatment response in oncology, as it enables the evaluation of tumor behavior over a period rather than a single static snapshot, offering insights into tumor aggressiveness and potential therapeutic targets. This review is focused on the current applications of TB and large field-of-view PET scanners in oncology.

Assessment of dual time point protocols to produce parametric Ki images in FDG PET/CT: A virtual clinical study

PURPOSE

This simulation study investigated the feasibility of generating Patlak Ki images using a dual time point (DTP-Ki) scan protocol involving two 3-min/bed routine static PET scans and, subsequently, assessed DTP-Ki performance for an optimal DTP scan time frame combination, against conventional Patlak Ki estimated from complete 0-93 min dynamic PET data.

METHODS

Six realistic heterogeneous tumors of different characteristic spatiotemporal [18F]FDG uptake distributions for three noise levels commonly found in clinical studies and 20 noise realizations (N = 360 samples) were produced by analytic simulations of the XCAT phantom. Subsequently, DTP-Ki images were generated by performing standard linear indirect Patlak analysis with t*≥ 12 $ \ge 12$ -min (Patlakt* = 12) using a scaled population-based input function (sPBIF) model on 66 combinations of early and late 3-min/bed static whole-body PET reconstructed images. All DTP-Ki images were evaluated against respective DTP-Ki images estimated with Patlakt* = 12 and 0-93 min individual input functions (iIFs) and against gold standard Ki images estimated with Patlakt* = 12, 0-93 min iIFs and tissue time activity curves from all reconstructed WB passes 12-93 min post injection. The optimal combination of early and late frames, in terms of attaining the highest correlation between DTP-Ki with sPBIF and gold standard Ki was also determined from a set of 66 different combinations of 2-min early and late frames. Moreover, the performance of DTP-Ki with sPBIF was compared against that of the retention index (RI) in terms of their correlation to the gold standard Ki. Finally, the feasibility and practicality of DTP protocol in the clinic were assessed through the analysis of nine patients.

RESULTS

High correlations (>0.9) were observed between DTP-Ki values from sPBIF and those from iIFs for all evaluated DTP protocols while the mean AUC difference between sPBIF and iIFs was less than 10%. The percentage difference of mean values between DTP-Ki from sPBIF and from iIFs was less than 1%. DTP Ki from sPBIF exhibited significantly higher correlation with gold standard Ki, in contrast to RI, across all 66 DTP protocols (p < 0.05 using the two-tailed t-test by Williams) with the highest correlation attained for the 50-53-min early + 90-93-min late scan time frames (optimal DTP protocol).

CONCLUSION

Feasibility of generating Patlak Ki [18F] FDG images from an early and a late post injection 3-min/bed routine static scan using a population-based input function model was demonstrated and an optimal DTP scan protocol was determined. The results indicated high correlations between DTP-Ki and gold-standard Ki images that are significantly larger than those between RI and gold-standard Ki.

FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool

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

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

PURPOSE

We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (Ki) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy.

METHODS

We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.). We determined Ki for a total of 81 lesions. Ki quantification from full dynamic PET data (Patlak-Ki) and Ki from DTP (DTP-Ki) were compared. In addition, we scaled a population-based input function (PBIFscl) with the image-derived blood pool activity sampled at different time points to assess the best scaling time-point for Ki quantifications in the simulation data.

RESULTS

In the simulation study, Ki estimated using DTP via (30,60-min), (30,90-min), (60,90-min), and (60,120-min) samples showed strong correlations (r ≥ 0.944, P < 0.0001) with the true value of Ki. The DTP results with the PBIFscl at 60-min time-point in (30,60-min), (60,90-min), and (60,120-min) were linearly related to the true Ki with a slope of 1.037, 1.008, 1.013 and intercept of -6 × 10-4, 2 × 10-5, 5 × 10-5, respectively. In a clinical study, strong correlations (r ≥ 0.833, P < 0.0001) were observed between Patlak-Ki and DTP-Ki. The Patlak-derived mean values of Ki, tumor-to-background-ratio, signal-to-noise-ratio, and contrast-to-noise-ratio were linearly correlated with the DTP method.

CONCLUSIONS

Besides calculating the retention index as a commonly used quantification parameter inDTP imaging,our DTP method can accurately estimate Ki.

Estimation of kinetic parameters in dynamic FDG PET imaging based on shortened protocols: a virtual clinical study

This study investigated the estimation of kinetic parameters and production of related parametric Ki images in FDG PET imaging using the proposed shortened protocol (three 3-min/bed routine static images) by means of the simulated annealing (SA) algorithm. Six realistic heterogeneous tumors and various levels of [18F] FDG uptake were simulated by the XCAT phantom. An irreversible two-tissue compartment model (2TCM) using population-based input function was employed. By keeping two routine clinical scans fixed (60-min and 90-min post injection), the effect of the early scan time on optimizing the estimation of the pharmacokinetic parameters was investigated. The SA optimization algorithm was applied to estimate micro- and macro-parameters (K1, k2, k3, Ki). The minimum bias for most parameters was observed at a scan time of 20-min, which was < 10%. A highly significant correlation (> 0.9) as well as limited bias (< 10%) were observed between kinetic parameters generated from two methods [two-tissue compartment full dynamic scan (2TCM-full) and two-tissue compartment by SA algorithm (2TCM-SA)]. The analysis showed a strong correlation (> 0.8) between (2TCM-SA) Ki and SUV images. In addition, the tumor-to-background ratio (TBR) metric in the parametric (2TCM-SA) Ki images was significantly higher than SUV, although the SUV images provide better Contrast-to-noise ratio relative to parametric (2TCM-SA) Ki images. The proposed shortened protocol by the SA algorithm can estimate the kinetic parameters in FDG PET scan with high accuracy and robustness. It was also concluded that the parametric Ki images obtained from the 2TCM-SA as a complementary image of the SUV possess more quantification information than SUV images and can be used by the nuclear medicine specialist. This method has the potential to be an alternative to a full dynamic PET scan.

Kinetic 18F-FDG PET/CT imaging of hepatocellular carcinoma: a dual input four-compartment model

BACKGROUND

The endoplasmic reticulum plays an important role in glucose metabolism and has not been explored in the kinetic estimation of hepatocellular carcinoma (HCC) via 18F-fluoro-2-deoxy-D-glucose PET/CT.

METHODS

A dual-input four-compartment (4C) model, regarding endoplasmic reticulum was preliminarily used for kinetic estimation to differentiate 28 tumours from background liver tissue from 24 patients with HCC. Moreover, parameter images of the 4C model were generated from one patient with negative findings on conventional metabolic PET/CT.

RESULTS

Compared to the dual-input three-compartment (3C) model, the 4C model has better fitting quality, a close transport rate constant (K1) and a dephosphorylation rate constant (k6/k4), and a different removal rate constant (k2) and phosphorylation rate constant (k3) in HCC and background liver tissue. The K1, k2, k3, and hepatic arterial perfusion index (HPI) from the 4C model and k3, HPI, and volume fraction of blood (Vb) from the 3C model were significantly different between HCC and background liver tissues (all P < 0.05). Meanwhile, the 4C model yielded additional kinetic parameters for differentiating HCC. The diagnostic performance of the top ten genes from the most to least common was HPI(4C), Vb(3C), HPI(3C), SUVmax, k5(4C), k3(3C), k2(4C), v(4C), K1(4C) and Vb(4C). Moreover, a patient who showed negative findings on conventional metabolic PET/CT had positive parameter images in the 4C model.

CONCLUSIONS

The 4C model with the endoplasmic reticulum performed better than the 3C model and produced additional useful parameters in kinetic estimation for differentiating HCC from background liver tissue.

Kinetic modeling and parametric imaging of 18 F-PSMA-11: An evaluation based on total-body dynamic positron emission tomography scans

BACKGROUND

The prostate-specific membrane antigen (PSMA) targeted positron-emitting tomography (PET) tracers are increasingly used in clinical practice, with novel tracers constantly being developed. Recently, 18 F-PSMA-11 has been gaining growing interest for several merits; however, direct in vivo visualization of its kinetic features in humans remains lacking.

PURPOSE

To visualize the kinetic features of 18 F-PSMA-11 in healthy subjects and patients with prostate cancer derived from the total-body dynamic PET scans.

METHODS

A total of 8 healthy volunteers (7 males; 1 female) and 3 patients with prostate cancer underwent total-body PET/CT imaging at 1 and 2 h post injection (p.i.) of 18 F-PSMA-11, of which 7 healthy subjects and 3 patients underwent total-body dynamic PET scans lasting 30 min. Reversible two-tissue compartments (2TC) and Patlak models were fitted based on the voxel-based time activity curves (TACs), with the parametric images generated subsequently. Additionally, semi-automated segmentation of multiple organs was performed in the dynamic images to measure the SUVmean at different time points and in the parametric images to estimate the mean value of the kinetic parameters of these organs.

RESULTS

18 F-PSMA-11 showed quick accumulation within prostate cancer, as early as 45 s after tracer injection. It was rapidly cleared from blood circulation and predominantly excreted through the urinary system. High and rapid radiotracer accumulation was observed in the liver, spleen, lacrimal glands, and salivary glands, whereas gradual accumulation was observed in the skeleton. Prostate cancer tissue is visualized in all parametric images, and best seen in DV and Patlak Ki images. Patlak Ki showed a good correlation with 2TC Ki values (r = 0.858, p < 0.05) but less noise than 2TC images. A scanning time point of 30-35 min p.i. was then suggested for satisfactory tumor to background ratio.

CONCLUSION

Prostate cancer tissue is visible in most parametric images, and is better shown by Patlak Ki and 2TC DV images. Patlak Ki is consistent with, and thus is preferred over, 2TC Ki images for substantially quicker calculation. Based on the dynamic imaging analysis, a shorter uptake time (30-35 min) might be preferred for a better balance of tumor to background ratio.

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model

BACKGROUND

Kinetic estimation provides fitted parameters related to blood flow perfusion and fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) transport and intracellular metabolism to characterize hepatocellular carcinoma (HCC) but usually requires 60 min or more for dynamic PET, which is time-consuming and impractical in a busy clinical setting and has poor patient tolerance.

METHODS

This study preliminarily evaluated the equivalence of liver kinetic estimation between short-term (5-min dynamic data supplemented with 1-min static data at 60 min postinjection) and fully 60-min dynamic protocols and whether short-term ¹⁸F-FDG PET-derived kinetic parameters using a three-compartment model can be used to discriminate HCC from the background liver tissue. Then, we proposed a combined model, a combination of the maximum-slope method and a three-compartment model, to improve kinetic estimation.

RESULTS

There is a strong correlation between the kinetic parameters K₁ ~ k₃, HPI and [Formula: see text] in the short-term and fully dynamic protocols. With the three-compartment model, HCCs were found to have higher k₂, HPI and k₃ values than background liver tissues, while K₁, k₄ and [Formula: see text] values were not significantly different between HCCs and background liver tissues. With the combined model, HCCs were found to have higher HPI, K₁ and k₂, k₃ and [Formula: see text] values than background liver tissues; however, the k₄ value was not significantly different between HCCs and the background liver tissues.

CONCLUSIONS

Short-term PET is closely equivalent to fully dynamic PET for liver kinetic estimation. Short-term PET-derived kinetic parameters can be used to distinguish HCC from background liver tissue, and the combined model improves the kinetic estimation.

CLINICAL RELEVANCE STATEMENT

Short-term PET could be used for hepatic kinetic parameter estimation. The combined model could improve the estimation of liver kinetic parameters.

FDG PET Scan Durations via Effective Data Processing

BACKGROUND

Total-body dynamic PET (dPET) imaging using ¹⁸ F-fluorodeoxyglucose (¹⁸ F-FDG) has received widespread attention in clinical oncology. However, the conventionally required scan duration of approximately 1 hour seriously limits the application and promotion of this imaging technique. In this study, we investigated the possibility and feasibility of shortening the total-body dynamic scan duration to 30 min post-injection (PI) with the help of a novel Patlak data processing algorithm for accurate K_i estimations of tumor lesions.

METHODS

Total-body dPET images acquired by uEXPLORER (United Imaging Healthcare Inc.) using ¹⁸ F-FDG of 15 patients with different tumor types were analyzed in this study. Dynamic images were reconstructed into 25 frames with a specific temporal dividing protocol for the scan data acquired 1 hour PI. Patlak analysis-based K_i parametric imaging was conducted based on the imaging data corresponding to the first 30 min PI, during which a Patlak data processing method based on cubit Hermite interpolation (THI) was applied. The resultant K_i images acquired by 30-min dynamic PET data and the standard 1-hour K_i images were compared in terms of visual imaging effect, region signal-to-noise ratio (SNR), and K_i estimation accuracy to evaluate the performance of the proposed K_i imaging method with a shortened scan duration.

RESULTS

With the help of Patlak data processing, acceptable K_i parametric images were obtained from dynamic PET data acquired with a scan duration of 30 min PI. Compared with K_i images obtained from unprocessed Patlak data, the resulting images from the proposed method performed better in terms of noise reduction. Moreover, Bland-Altman (BA) plot and Person correlation coefficient (PPC) analysis showed that that 30-min K_i images obtained from the processed Patlak data had higher accuracy for tumor lesions.

CONCLUSION

Satisfactory K_i parametric images with high tumor accuracy can be acquired from dynamic imaging data corresponding to the first 30 min PI. Patlak data processing can help achieve higher K_i imaging quality and higher accuracy regarding tumor lesion K_i values. Clinically, it is possible to shorten the dynamic scan duration of ¹⁸ F-FDG PET to 30 min to acquire an accurate tumor K_i and further effective tumor detection with uEXPLORER scanners. This article is protected by copyright. All rights reserved.

Dynamic chaotic gravitational search algorithm-based kinetic parameter estimation of hepatocellular carcinoma on 18F-FDG PET/CT

Background

Kinetic parameters estimated with dynamic ¹⁸F-FDG PET/CT can help to characterize hepatocellular carcinoma (HCC). We aim to evaluate the feasibility of the gravitational search algorithm (GSA) for kinetic parameter estimation and to propose a dynamic chaotic gravitational search algorithm (DCGSA) to enhance parameter estimation.

Methods

Five-minute dynamic PET/CT data of 20 HCCs were prospectively enrolled, and the kinetic parameters k₁ ~ k₄ and the hepatic arterial perfusion index (HPI) were estimated with a dual-input three-compartment model based on nonlinear least squares (NLLS), GSA and DCGSA.

Results

The results showed that there were significant differences between the HCCs and background liver tissues for k₁, k₄ and the HPI of NLLS; k₁, k₃, k₄ and the HPI of GSA; and k₁, k₂, k₃, k₄ and the HPI of DCGSA. DCGSA had a higher diagnostic performance for k₃ than NLLS and GSA.

Conclusions

GSA enables accurate estimation of the kinetic parameters of dynamic PET/CT in the diagnosis of HCC, and DCGSA can enhance the diagnostic performance.

Computational modeling of PET tracer distribution in solid tumors integrating microvasculature

BACKGROUND

We present computational modeling of positron emission tomography radiotracer uptake with consideration of blood flow and interstitial fluid flow, performing spatiotemporally-coupled modeling of uptake and integrating the microvasculature. In our mathematical modeling, the uptake of fluorodeoxyglucose F-18 (FDG) was simulated based on the Convection-Diffusion-Reaction equation given its high accuracy and reliability in modeling of transport phenomena. In the proposed model, blood flow and interstitial flow are solved simultaneously to calculate interstitial pressure and velocity distribution inside cancer and normal tissues. As a result, the spatiotemporal distribution of the FDG tracer is calculated based on velocity and pressure distributions in both kinds of tissues.

RESULTS

Interstitial pressure has maximum value in the tumor region compared to surrounding tissue. In addition, interstitial fluid velocity is extremely low in the entire computational domain indicating that convection can be neglected without effecting results noticeably. Furthermore, our results illustrate that the total concentration of FDG in the tumor region is an order of magnitude larger than in surrounding normal tissue, due to lack of functional lymphatic drainage system and also highly-permeable microvessels in tumors. The magnitude of the free tracer and metabolized (phosphorylated) radiotracer concentrations followed very different trends over the entire time period, regardless of tissue type (tumor vs. normal).

CONCLUSION

Our spatiotemporally-coupled modeling provides helpful tools towards improved understanding and quantification of in vivo preclinical and clinical studies.

Fast dynamic brain PET imaging using stochastic variational prediction for recurrent frame generation

Purpose

We assess the performance of a recurrent frame generation algorithm for prediction of late frames from initial frames in dynamic brain PET imaging.

Methods

Clinical dynamic ¹⁸F‐DOPA brain PET/CT studies of 46 subjects with ten folds cross‐validation were retrospectively employed. A novel stochastic adversarial video prediction model was implemented to predict the last 13 frames (25–90 minutes) from the initial 13 frames (0–25 minutes). The quantitative analysis of the predicted dynamic PET frames was performed for the test and validation dataset using established metrics.

Results

The predicted dynamic images demonstrated that the model is capable of predicting the trend of change in time‐varying tracer biodistribution. The Bland‐Altman plots reported the lowest tracer uptake bias (−0.04) for the putamen region and the smallest variance (95% CI: −0.38, +0.14) for the cerebellum. The region‐wise Patlak graphical analysis in the caudate and putamen regions for eight subjects from the test and validation dataset showed that the average bias for Ki and distribution volume was 4.3%, 5.1% and 4.4%, 4.2%, (P‐value <0.05), respectively.

Conclusion

We have developed a novel deep learning approach for fast dynamic brain PET imaging capable of generating the last 65 minutes time frames from the initial 25 minutes frames, thus enabling significant reduction in scanning time.

Feasibility of perfusion and early-uptake 18F-FDG PET/CT in primary hepatocellular carcinoma: a dual-input dual-compartment uptake model

PURPOSE

PET enables a concurrent evaluation of perfusion status and metabolic activity. We aimed to evaluate the feasibility of perfusion and early-uptake ¹⁸F-FDG PET/CT in hepatocellular carcinoma (HCC) using a dual-input dual-compartment uptake model.

MATERIALS AND METHODS

Data from 5 min dynamic PET/CT and conventional PET/CT scans were retrospectively collected from 17 pathologically diagnosed HCCs. Parameters such as hepatic arterial blood flow (Fa), portal vein blood flow (Fv), total blood flow (F), hepatic arterial perfusion index (HPI), portal vein perfusion index (PPI), blood volume (BV), extracellular mean transit time (MTT) and intracellular uptake rate (Ki) were calculated. Fa, HPI, MTT and Ki images were generated and used to identify HCC.

RESULTS

Compared with the surrounding liver tissue, HCCs showed significant increases in Fa, HPI, Ki and the maximum standard uptake value (SUVmax) (all P < 0.001) and significant reductions in Fv (P < 0.05) and PPI (P < 0.001). F, BV and MTT (all P > 0.05) did not differ significantly between HCCs and the surrounding liver tissue. Perfusion and early-uptake PET/CT increased the positivity rate of HCCs from 52.9% with conventional PET/CT alone to 88.2% with the combined method (P < 0.05).

CONCLUSIONS

Perfusion and early-uptake PET/CT are feasible for diagnosing HCC and provide added functional information to enhance diagnostic performance.