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.

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.

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.

The valuable role of dynamic (18)F FDG PET/CT-derived kinetic parameter K(i) in patients with nasopharyngeal carcinoma prior to radiotherapy: A prospective study

BACKGROUND AND PURPOSE

Dynamic positron emission tomography/computed tomography (PET/CT) served the potential role of characterizing malignant foci. The main objective of this prospective study was to explore the advantage of dynamic PET/CT imaging in characterizing nasopharyngeal carcinoma (NPC).

METHODS AND MATERIALS

Patients with probable head and neck disease underwent a local dynamic PET/CT scan followed by a whole-body static scan. Patlak analysis was used to generate parametric influx rate constant (K_i) images from 48 frames obtained from a dynamic PET/CT scan. By delineating the volumes-of-interest (VOIs) of: primary tumor (PT), lymph node (LN), and normal nasopharyngeal tissues (N), we acquired the corresponding K_i mean and SUVmean of each site respectively to perform the quantitative statistical analysis.

RESULTS

Qualified images of 71 patients with newly diagnosed NPC and 8 without nasopharyngeal malignant lesions were finally included. We found the correlations between K_i mean-PT and critical clinical features, including clinical stage (r = 0.368), T category (r = 0.643) and EBV-DNA copy status (r = 0.351), and K_i mean-PT differed within the group. SUVmean-PT showed correlations with clinical stage (r = 0.280) and T category (r = 0.472), but could hardly differ systematically within group of clinical features except T category. K_i mean-LN offered the positive correlations with N category (r = 0.294), M category (r = 0.238) and EBV-DNA copy status (r = 0.446), and differed within the group. In addition, K_i mean represented a sensitivity of 94.4 % and a specificity of 100 %, in distinguishing NPC from the non-NPC, when the cut-off was defined as 0.0106. When the cut-off of SUV being defined as 2.03, the sensitivity and specificity were both 100 %.

CONCLUSION

Our research confirmed K_i compared favorably to SUV in characterizing NPC and found that K_i can serve as an effective imaging marker of NPC.

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

Purpose

This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (K_i) from standardized uptake value (SUV) and/or dynamic PET image series.

Methods

Whole-body ¹⁸F-FDG dynamic PET images of 19 subjects consisting of 13 frames or passes were employed for training a residual deep learning model with SUV and/or dynamic series as input and K_i-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme. Owing to the availability of SUV images acquired 60 min post-injection (20 min total acquisition time), the data sets used for the training of the models were split into two groups: “With SUV” and “Without SUV.” For “With SUV” group, the model was first trained using only SUV images and then the passes (starting from pass 13, the last pass, to pass 9) were added to the training of the model (one pass each time). For this group, 6 models were developed with input data consisting of SUV, SUV plus pass 13, SUV plus passes 13 and 12, SUV plus passes 13 to 11, SUV plus passes 13 to 10, and SUV plus passes 13 to 9. For the “Without SUV” group, the same trend was followed, but without using the SUV images (5 models were developed with input data of passes 13 to 9). For model performance evaluation, the mean absolute error (MAE), mean error (ME), mean relative absolute error (MRAE%), relative error (RE%), mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between the predicted K_i-Patlak images by the two groups and the reference K_i-Patlak images generated through Patlak analysis using the whole acquired data sets. For specific evaluation of the method, regions of interest (ROIs) were drawn on representative organs, including the lung, liver, brain, and heart and around the identified malignant lesions.

Results

The MRAE%, RE%, PSNR, and SSIM indices across all patients were estimated as 7.45 ± 0.94%, 4.54 ± 2.93%, 46.89 ± 2.93, and 1.00 ± 6.7 × 10⁻⁷, respectively, for models predicted using SUV plus passes 13 to 9 as input. The predicted parameters using passes 13 to 11 as input exhibited almost similar results compared to the predicted models using SUV plus passes 13 to 9 as input. Yet, the bias was continuously reduced by adding passes until pass 11, after which the magnitude of error reduction was negligible. Hence, the predicted model with SUV plus passes 13 to 9 had the lowest quantification bias. Lesions invisible in one or both of SUV and K_i-Patlak images appeared similarly through visual inspection in the predicted images with tolerable bias.

Conclusion

This study concluded the feasibility of direct deep learning-based approach to estimate K_i-Patlak parametric maps without requiring the input function and with a fewer number of passes. This would lead to shorter acquisition times for WB dynamic imaging with acceptable bias and comparable lesion detectability performance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05867-w.

Subcutaneous adipose tissue free fatty acid uptake measured using positron emission tomography and adipose biopsies in humans

Positron emission tomography (PET) radiopharmaceuticals can noninvasively measure free fatty acid (FFA) uptake into adipose tissue. We studied 29 volunteers to test whether abdominal and femoral subcutaneous adipose tissue FFA uptake measured using [1-¹¹C]palmitate PET agrees with FFA storage rates measured using an intravenous bolus of [1-¹⁴C]palmitate and adipose biopsies. The dynamic left ventricular cavity PET images combined with blood sample radioactivity corrected for the ¹¹CO₂ content were used to create the blood time activity curve (TAC), and the constant (K_i) was determined using Patlak analysis of the TACs generated for regions of interest in abdominal subcutaneous fat. These data were used to calculate palmitate uptake rates in abdominal subcutaneous adipose tissue (µmol·kg^-1·min^-1). Immediately after the dynamic imaging, a static image of the thigh was taken to measure the standardized uptake value (SUV) in thigh adipose tissue, which was scaled to each participant's abdominal adipose tissue SUV to calculate thigh adipose palmitate uptake rates. Abdominal adipose palmitate uptake using PET [1-¹¹C]palmitate was correlated with, but significantly (P < 0.001) greater than, FFA storage measured using [1-¹⁴C]palmitate and adipose biopsy. Thigh adipose palmitate measured using PET calculation was positively correlated (R² = 0.44, P < 0.0001) with and not different from the biopsy approach. The relative differences between PET measured abdominal subcutaneous adipose tissue palmitate uptake and biopsy-measured palmitate storage were positively correlated (P = 0.03) with abdominal subcutaneous fat. We conclude that abdominal adipose tissue FFA uptake measured using PET does not equate to adipose FFA storage measured using biopsy techniques.

Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI

PURPOSE

The aim of this work is to investigate the impact of tissue classification in magnetic resonance imaging (MRI)-guided positron emission tomography (PET) attenuation correction (AC) for whole-body (WB) Patlak net uptake rate constant (K_i) imaging in PET/MRI studies.

PROCEDURES

WB dynamic PET/CT data were acquired for 14 patients. The CT images were utilized to generate attenuation maps (μ-map_CTAC) of continuous attenuation coefficient values (A_coeff). The μ-map_CTAC were then segmented into four tissue classes (μ-map_4-classes), namely background (air), lung, fat, and soft tissue, where a predefined A_coeff was assigned to each class. To assess the impact of bone for AC, the bones in the μ-map_CTAC were then assigned a predefined soft tissue A_coeff (0.1 cm^-1) to produce an AC μ-map without bones (μ-map_no-bones). Thereafter, both WB static SUV and dynamic PET images were reconstructed using μ-map_CTAC, μ-map_4-classes, and μ-map_no-bones (PET_CTAC, PET_4-classes, and PET_no-bones), respectively. WB indirect and direct parametric K_i images were generated using Patlak graphical analysis. Malignant lesions were delineated on PET images with an automatic segmentation method that uses an active contour model (MASAC). Then, the quantitative metrics of the metabolically active tumor volume (MATV), target-to-background (TBR), contrast-to-noise ratio (CNR), peak region-of-interest (ROI_peak), maximum region-of-interest (ROI_max), mean region-of-interest (ROI_mean), and metabolic volume product (MVP) were analyzed. The Wilcoxon test was conducted to assess the difference between PET_4-classes and PET_no-bones against PET_CTAC for all images. The same test was also adopted to compare the differences between SUV, indirect K_i, and direct K_i images for each evaluated AC method.

RESULTS

No significant differences in MATV, TBR, and CNR were observed between PET_4-classes and PET_CTAC for either SUV or K_i images. PET_4-classes significantly overestimated ROI_peak, ROI_max, ROI_mean, as well as MVP scores compared with PET_CTAC in both SUV and K_i images. SUV images exhibited the highest median relative errors for PET_4-classes with respect to PET_CTAC (RE_4-classes): 6.91 %, 6.55 %, 5.90 %, and 6.56 % for ROI_peak, ROI_max, ROI_mean, and MVP, respectively. On the contrary, K_i images showed slightly reduced RE_4-classes (indirect 5.52 %, 5.95 %, 4.43 %, and 5.70 %, direct 6.61 %, 6.33 %, 5.53 %, and 4.96 %) for ROI_peak, ROI_max, ROI_mean, and MVP, respectively. A higher TBR was observed on indirect and direct K_i images relative to SUV, while direct K_i images demonstrated the highest CNR.

CONCLUSIONS

Four-tissue class AC may impact SUV and K_i parameter estimation but only to a limited extent, thereby suggesting that WB Patlak K_i imaging for dynamic WB PET/MRI studies is feasible. Patlak K_i imaging can enhance TBR, thereby facilitating lesion segmentation and quantification. However, patient-specific A_coeff for each tissue class should be used when possible to address the high inter-patient variability of A_coeff distributions.

68Ga-PSMA-11 PET/CT in prostate cancer local recurrence: impact of early images and parametric analysis

⁶⁸Ga-PSMA-11 PET/CT performed 60 min post tracer injection (p.i.) can underestimate prostate cancer (PC) local recurrence, due to high ⁶⁸Ga-PSMA-11 urinary bladder accumulation. Aim of this analysis is to evaluate the complementary role of early dynamic and parametric PET imaging in patients with PC local recurrence. Sixteen patients with PC biochemical relapse attributed to local recurrence underwent dynamic ⁶⁸Ga-PSMA-11 PET/CT scanning of the pelvis and whole-body PET/CT. Data analysis was based on visual analysis of the PET/CT scans, SUV calculations, quantitative analysis based on two-tissue compartment and Patlak models as well as parametric imaging based on Patlak analysis. 12/16 patients were PSMA-positive in the static ⁶⁸Ga-PSMA-11 PET/CT scans (60 min p.i.). All 12 lesions corresponding to PC local recurrence were detected in the early dynamic images at a median time of 4.5 min p.i. (range = 1.5-11.5 min). Moreover, early dynamic PET imaging could detect local recurrence in 1/4 static PET/CT-negative patients. Tracer accumulation in the urinary bladder began at a median time of 10 min (range = 6.0-17.5 min). All PC local recurrences visible on late static PET/CT and the local recurrence, which was positive only in early dynamic but not in late PET images, could be delineated on Patlak images. The present findings indicate that early dynamic ⁶⁸Ga-PSMA-11 PET/CT scan of the pelvis up to 12 min p.i. as well as Patlak analysis, performed in addition to the conventional PET/CT acquired at 60 min p.i., seem a practical approach to increase the detection rate of PC local recurrence.

An open tool for input function estimation and quantification of dynamic PET FDG brain scans

PURPOSE

Positron emission tomography (PET) analysis of clinical studies is mostly restricted to qualitative evaluation. Quantitative analysis of PET studies is highly desirable to be able to compute an objective measurement of the process of interest in order to evaluate treatment response and/or compare patient data. But implementation of quantitative analysis generally requires the determination of the input function: the arterial blood or plasma activity which indicates how much tracer is available for uptake in the brain. The purpose of our work was to share with the community an open software tool that can assist in the estimation of this input function, and the derivation of a quantitative map from the dynamic PET study.

METHODS

Arterial blood sampling during the PET study is the gold standard method to get the input function, but is uncomfortable and risky for the patient so it is rarely used in routine studies. To overcome the lack of a direct input function, different alternatives have been devised and are available in the literature. These alternatives derive the input function from the PET image itself (image-derived input function) or from data gathered from previous similar studies (population-based input function). In this article, we present ongoing work that includes the development of a software tool that integrates several methods with novel strategies for the segmentation of blood pools and parameter estimation.

RESULTS

The tool is available as an extension to the 3D Slicer software. Tests on phantoms were conducted in order to validate the implemented methods. We evaluated the segmentation algorithms over a range of acquisition conditions and vasculature size. Input function estimation algorithms were evaluated against ground truth of the phantoms, as well as on their impact over the final quantification map. End-to-end use of the tool yields quantification maps with [Formula: see text] relative error in the estimated influx versus ground truth on phantoms.

CONCLUSIONS

The main contribution of this article is the development of an open-source, free to use tool that encapsulates several well-known methods for the estimation of the input function and the quantification of dynamic PET FDG studies. Some alternative strategies are also proposed and implemented in the tool for the segmentation of blood pools and parameter estimation. The tool was tested on phantoms with encouraging results that suggest that even bloodless estimators could provide a viable alternative to blood sampling for quantification using graphical analysis. The open tool is a promising opportunity for collaboration among investigators and further validation on real studies.

Reproducibility of VPCT parameters in the normal pancreas: comparison of two different kinetic calculation models

RATIONALE AND OBJECTIVES

To assess the reproducibility of volume computed tomographic perfusion (VPCT) measurements in normal pancreatic tissue using two different kinetic perfusion calculation models at three different time points.

MATERIALS AND METHODS

Institutional ethical board approval was obtained for retrospective analysis of pancreas perfusion data sets generated by our prospective study for liver response monitoring to local therapy in patients experiencing unresectable hepatocellular carcinoma, which was approved by the institutional review board. VPCT of the entire pancreas was performed in 41 patients (mean age, 64.8 years) using 26 consecutive volume measurements and intravenous injection of 50 mL of iodinated contrast at a flow rate of 5 mL/s. Blood volume(BV) and blood flow (BF) were calculated using two mathematical methods: maximum slope + Patlak analysis versus deconvolution method. Pancreas perfusion was calculated using two volume of interests. Median interval between the first and the second VPCT was 2 days and between the second and the third VPCT 82 days. Variability was assessed with within-patient coefficients of variation (CVs) and Bland-Altman analyses. Interobserver agreement for all perfusion parameters was calculated using intraclass correlation coefficients (ICCs).

RESULTS

BF and BV values varied widely by method of analysis as did within-patient CVs for BF and BV at the second versus the first VPCT by 22.4%/50.4% (method 1) and 24.6%/24.0% (method 2) measured in the pancreatic head and 18.4%/62.6% (method 1) and 23.8%/28.1% (method 2) measured in the pancreatic corpus and at the third versus the first VPCT by 21.7%/61.8% (method 1) and 25.7%/34.5% (method 2) measured also in the pancreatic head and 19.1%/66.1% (method 1) and 22.0%/31.8% (method 2) measured in the pancreatic corpus, respectively. Interobserver agreement measured with ICC shows fair-to-good reproducibility.

CONCLUSIONS

VPCT performed with the presented examinational protocol is reproducible and can be used for monitoring purposes. Best reproducibility was obtained with both methods for BF and with method 2 also for BV data for both follow-up studies.

Measuring Bone Metabolism with Fluoride PET: Methodological Considerations

In recent years the more widespread availability of PET systems and the development of hybrid PET/computed tomography (CT) imaging, allowing improved morphologic characterization of sites with increased tracer uptake, have improved the accuracy of diagnosis and strengthened the role of 18F-fluoride PET for quantitative assessment of bone pathology. This article reviews the role of 18F-fluoride PET in the skeleton, with a focus on (1) the underlying physiologic and pathophysiological processes of different conditions of bone metabolism and (2) methodological aspects of quantitative measurement of 18F-fluoride kinetics. Recent comparative studies have demonstrated that 18F-fluoride PET and, to an even greater extent, PET/CT are more accurate than 99mTc-bisphosphonate single-photon emission CT for the identification of malignant and benign lesions of the skeleton. Quantitative 18F-flouride PET has been shown valuable for direct non-invasive assessment of bone metabolism and monitoring response to therapy.