Tracer Kinetic Modeling Research Articles

Dynamic contrast-enhanced MRI of malignant pleural mesothelioma: a comparative study of pharmacokinetic models and correlation with mRECIST criteria

BackgroundMalignant pleural mesothelioma (MPM) is a rare and aggressive thoracic malignancy that is difficult to cure. Dynamic contrast-enhanced (DCE) MRI is a functional imaging technique used to analyze tumor microvascular properties and to monitor therapy response. Purpose of this study was to compare two tracer kinetic models, the extended Tofts (ET) and the adiabatic approximation tissue homogeneity model (AATH) for analysis of DCE-MRI and examine the value of the DCE parameters to predict response to chemotherapy in patients with MPM.MethodThis prospective, longitudinal, single tertiary radiology center study was conducted between October 2013 and July 2015. Patient underwent DCE-MRI studies at three time points: prior to therapy, during and after cisplatin-based chemotherapy. The images were analyzed using ET and AATH models. In short-term follow-up, the patients were classified as having disease control or progressive disease according to modified response evaluation criteria in solid tumors (mRECIST) criteria.Receiver operating characteristic curve analysis was used to examine specificity and sensitivity of DCE parameters for predicting response to therapy. Comparison tests were used to analyze whether derived parameters are interchangeable between the two models.ResultsNineteen patients form the study population. The results indicate that the derived parameters are not interchangeable between the models.Significant correlation with response to therapy was found for AATH-calculated median pre-treatment efflux rate (kep) showing sensitivity of 83% and specificity of 100% (AUC 0.9). ET-calculated maximal pre-treatment kep showed 100% sensitivity and specificity for predicting treatment response during the early phase of the therapy and reached a favorable trend to significant prognostic value post-therapy.ConclusionBoth models show potential in predicting response to therapy in MPM. High pre-treatment kep values suggest MPM disease control post-chemotherapy.

Robust Non-Rigid Motion Compensation of Free-Breathing Myocardial Perfusion MRI Data.

Kinetic parameter values, such as myocardial perfusion, can be quantified from dynamic contrast-enhanced magnetic resonance imaging data using tracer-kinetic modeling. However, respiratory motion affects the accuracy of this process. Motion compensation of the image series is difficult due to the rapid local signal enhancement caused by the passing of the gadolinium-based contrast agent. This contrast enhancement invalidates the assumptions of the (global) cost functions traditionally used in intensity-based registrations. The algorithms are unable to distinguish whether the differences in signal intensity between frames are caused by the spatial motion artifacts or the local contrast enhancement. In order to address this problem, a fully automated motion compensation scheme is proposed, which consists of two stages. The first of which uses robust principal component analysis (PCA) to separate the local signal enhancement from the baseline signal, before a refinement stage which uses the traditional PCA to construct a synthetic reference series that is free from motion but preserves the signal enhancement. Validation is performed on 18 subjects acquired in free-breathing and 5 clinical subjects acquired with a breath-hold. The validation assesses the visual quality, the temporal smoothness of tissue curves, and the clinically relevant quantitative perfusion values. The expert observers score the visual quality increased by a mean of 1.58/5 after motion compensation and improvement over the previously published methods. The proposed motion compensation scheme also leads to the improved quantitative performance of motion compensated free-breathing image series [30% reduction in the coefficient of variation across quantitative perfusion maps and 53% reduction in temporal variations (p < 0.001)].

Pilot Study to Diagnose Nonalcoholic Steatohepatitis With Dynamic 18F-FDG PET.

Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are major causes of chronic liver disease characterized by steatosis, inflammation, and fibrosis. Diagnosis of inflammation is limited by the need for liver biopsy. Dynamic PET with the widely used radiotracer 18F-FDG provides a novel method for evaluating spatial and temporal changes in liver inflammation. Patients with NAFLD or NASH underwent dynamic FDG PET and MRI within 6 months of undergoing liver biopsy. Liver time-activity curves were extracted to estimate kinetic parameters representing various rate constants of FDG transport using tracer kinetic modeling. Liver biopsy specimens were scored on the basis of NASH Clinical Research Network criteria. This pilot study included 22 patients, 14 of whom were women. Patient age ranged from 18 to 70 years, and the mean body mass index (weight in kilograms divided by the square of height in meters) was 33.2 (range, 24-43.1). The K1 value, which represents the rate of FDG transport from blood to hepatic tissue, was significantly correlated with inflammation (r = -0.7284; p = 0.0001) and the overall NAFLD activity score (NAS; r = -0.6750; p = 0.0006). K1 values were inversely related to the hepatic inflammation score and NAS. Although heterogeneity in K1 values across eight liver segments was noted, distinct segregation existed among segmental K1 values dependent on the histologic inflammation score (p = 0.022) or NAS (p = 0.0091). K1 had a strong association with both inflammation (ROC AUC value, 0.88) and the NAS (ROC AUC value, 0.89), with K1 = 1.02 (mL/min/mL) corresponding to a sensitivity and specificity of 93% and 88%, respectively, for the NAS. Dynamic FDG PET with tracer kinetic modeling has the potential to determine liver inflammation in patients with NAFLD and NASH and can fill an essential gap in diagnosis.

Comparative assessment of linear least-squares, nonlinear least-squares, and Patlak graphical method for regional and local quantitative tracer kinetic modeling in cerebral dynamic 18 F-FDG PET.

Dynamic 18 F-FDG PET allows quantitative estimation of cerebral glucose metabolism both at the regional and local (voxel) level. Although sensitive to noise and highly computationally expensive, nonlinear least-squares (NLS) optimization stands as the reference approach for the estimation of the kinetic model parameters. Nevertheless, faster techniques, including linear least-squares (LLS) and Patlak graphical method, have been proposed to deal with high resolution noisy data, representing a more adaptable solution for routine clinical implementation. Former research investigating the relative performance of the available algorithms lack precise evaluation of kinetic parameter estimates under realistic acquisition conditions. The present study aims at the systematic comparison of the feasibility and pertinence of kinetic modeling of dynamic cerebral 18 F-FDG PET using NLS, LLS, and Patlak method, based on numerical simulations and patient data. Numerical simulations were used to study the bias and variance of K1 and Ki parameters estimation under representative noise levels. Patient data allowed to assess the concordance between the three methods at the regional and voxel scale, and to evaluate the robustness of the estimations with respect to patient head motion. Our findings indicate that at the regional level NLS and LLS provide kinetic parameter estimates (K1 and Ki ) with similar bias and variance characteristics (K1 bias±relative standard deviation [RSD] 0.0±5.1% and 0.1%±4.9% for NLS and LLS respectively, Ki bias±RSD 0.1%±4.5% and -0.7%±4.4% for NLS and LLS respectively). NLS estimates appear, however, to be slightly less sensitive to patient motion. At the voxel level, provided that patient motion is negligible or corrected, LLS offers an appealing alternative solution for local K1 mapping. It yields K1 estimates that are highly correlated, with high correlation with NLS values (Pearson's r=0.95 on actual data) within computations times less than two orders of magnitude lower. Last, Patlak method appears as the most robust and accurate technique for the estimation of Ki values at the regional and voxel scale, with or without head motion. It provides low bias/low variance Ki quantification (bias±RSD -1.5±9.5% and -4.1±19.7% for Patlak and NLS respectively) as well as smooth parametric images suitable for visual assessment.

Effect of Tumor Volume on Drug Delivery in Heterogeneous Vasculature of Human Brain Tumors

Drug distribution in tumors is strongly dependent on tumor biological properties such as tumor volume, vasculature, and porosity. An understanding of the drug distribution pattern in tumors can help in enhancing the effectiveness of anticancer treatment. A numerical model is employed to study the distribution of contrast agent in the heterogeneous vasculature of human brain tumors of different volumes. Dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) has been done for a number of patients with different tumor volumes. Leaky tracer kinetic model (LTKM) is employed to obtain perfusion parameters from the DCE-MRI data. These parameters are used as input in the computational fluid dynamics (CFD) model to predict interstitial fluid pressure (IFP), interstitial fluid velocity (IFV), and distribution of the contrast agent in different tumors. Numerical results demonstrate that the IFP is independent of tumor volume. On the other hand, the IFV increases as the tumor volume increases. Further, the concentration of contrast agent also increases with the tumor volume. The results obtained in this work are in line with the experimental DCE-MRI data. It is observed that large volume tumors tend to retain a higher concentration of contrast agent for a longer duration of time because of large extravasation flux and slow washout as compared to smaller tumors. These results may be qualitatively extrapolated to chemotherapeutic drug delivery, implying faster healing in large volume tumors. This study helps in understanding the effect of tumor volume on the treatment outcome for a wide range of human tumors.

Convolutional Neural Networks for Direct Inference of Pharmacokinetic Parameters: Application to Stroke Dynamic Contrast-Enhanced MRI.

Background and Purpose: The T1-weighted dynamic contrast enhanced (DCE)-MRI is an imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters characterizing microvasculature of tissues. For the present study, we propose a new machine learning (ML) based approach to directly estimate the PK parameters from the acquired DCE-MRI image-time series that is both more robust and faster than conventional model fitting.Materials and Methods: We specifically utilize deep convolutional neural networks (CNNs) to learn the mapping between the image-time series and corresponding PK parameters. DCE-MRI datasets acquired from 15 patients with clinically evident mild ischaemic stroke were used in the experiments. Training and testing were carried out based on leave-one-patient-out cross- validation. The parameter estimates obtained by the proposed CNN model were compared against the two tracer kinetic models: (1) Patlak model, (2) Extended Tofts model, where the estimation of model parameters is done via voxelwise linear and nonlinear least squares fitting respectively.Results: The trained CNN model is able to yield PK parameters which can better discriminate different brain tissues, including stroke regions. The results also demonstrate that the model generalizes well to new cases even if a subject specific arterial input function (AIF) is not available for the new data.Conclusion: A ML-based model can be used for direct inference of the PK parameters from DCE image series. This method may allow fast and robust parameter inference in population DCE studies. Parameter inference on a 3D volume-time series takes only a few seconds on a GPU machine, which is significantly faster compared to conventional non-linear least squares fitting.

Linear Time Invariant Model based Motion Correction (LiMo-MoCo) of Dynamic Radial Contrast Enhanced MRI.

Early identification of kidney function deterioration is essential to determine which newborn patients with dilation of the renal pelvis (hydronephrosis) should undergo surgery. Kidney function can be measured by fitting a tracer kinetic (TK) model onto a series of Dynamic Contrast Enhanced (DCE) MR images and deriving the glomerular filtration rate (GFR) from the TK model. Unfortunately, heavy breathing and large bulk motion events create outliers and misalignments that introduce large errors in the TK estimates. Moreover, aligning the series of DCE images is not trivial due to the contrast differences between them and the undersampling artifacts due to fast imaging. We present a bulk motion detection and a linear time invariant (LTI) model-based motion correction approach for DCE-MRI alignment that leverages the temporal dynamics of the DCE data at each voxel. We evaluate our approach on 10 newborn patients that underwent DCE imaging without sedation. For each patient, we reconstructed the sequence of DCE images, detected and removed the volumes corrupted by motion using a self navigation approach, aligned the sequence using our approach and fitted the TK model to compute GFR. The results show that our approach correctly aligned all volumes and improved the TK model fit and, on average, reducing the normalized root-mean-squared error by 0.17.

A Clinically Suitable Approach to Whole-Body Imaging for Quantification of Regional Perfusion: Validation of Positron Emission Tomography (PET) with 62Cu-ETS and Image-based Tracer Kinetic Modeling

Background &amp; Hypothesis: We hypothesize that whole-body PET imaging with 62Cu-ETS and readily implemented tracer kinetic models, can enable absolute quantification of regional perfusion (mL•min-1•g-1) in a fashion that is reproducible; readily standardized across institutions; and logistically suitable for clinical implementation. &#x0D; Experimental Design: Thirty-five paired 62Cu-ETS and H215O studies were performed in six Göttingen minipigs to validate the use of image-derived input functions. H215O estimates of tissue perfusion served as a reference standard for comparison with 62Cu-ETS. To demonstrate quantitative whole-body perfusion imaging in humans, paired 62Cu-ETS and H215O studies were performed in 14 renal cell carcinoma patients both prior to and following sunitinib therapy. &#x0D; Results: The pig studies showed a strong correlation between regional blood flow estimates made with 62Cu-ETS and H215O, using image-derived input functions with tracer kinetic model-based corrections for 62Cu-ETS decomposition in blood (slope=0.932, R2=0.746). High quality voxel-wise 62Cu-ETS perfusion and blood volume parametric images demonstrated a strong correlation with H215O across all tissues within the imaging field-of-view. Using a same-day test-retest design, which was then repeated across two weeks, the animal study demonstrated good test-retest variability (TRV) for 62Cu-ETS and H215O with TRV of 6.3% ± 5.40% and 5.0% ± 4.77%, respectively. These findings strongly support application of the modeling methods to the human data, which is currently in progress. &#x0D; Conclusion &amp; Potential Impact: Whole-body imaging to non-invasively quantify regional perfusion holds promising potential for clinical implementation, using 62Cu-ETS PET coupled with tracer kinetic models that rely solely on the acquired imaging data.

Intermodel disagreement of myocardial blood flow estimation from dynamic CT perfusion imaging

PurposeTo assess the intermodel agreement of different tracer kinetic models to determine myocardial blood flow (MBF) and their diagnostic accuracy in coronary artery disease (CAD) at dynamic CT myocardial perfusion imaging (CTMPI). MethodsThree porcine hearts perfused in Langendorff mode and 15 patients with suspected CAD and perfusion single photon emission CT (SPECT) were included. Dynamic CTMPI was performed in shuttle-mode (70 kVp, 350mAs/rot) on 3rd generation dual-source CT. In porcine hearts and patients, myocardial segments (AHA-16-segment model) were drawn. Tissue attenuation curves were constructed per segment and arterial input functions were derived from the aorta. True MBF was calculated with input flow and weight of the porcine hearts. In patients, ischemic segments were based on SPECT results. MBF quantification was performed using the VPCT-software, Upslope, Extended Toft (ET), Two-compartment (TC) and Fermi models. ResultsIn porcine hearts, true MBF was 1.88 (interquartile range [IQR]:1.80-2.80)mL/g/min. Diagnostic accuracy was similar for all models: 0.96, 0.99, 0.92, 0.93 and 0.96 for VPCT software, Upslope method, Fermi, ET and TC model. The VPCT software and Upslope method resulted in lower MBF (median 1.44 [1.29–1.58] and 1.39 [1.25–1.59]mL/g/min) compared to the Fermi, ET, and TC model (median values of 1.76 mL/g/min [1.36–2.44], 2.55 mL/g/min [2.20–2.92], and 1.98 mL/g/min [1.60–2.60], respectively [p < 0.001]). In patients, all models showed a significant difference in MBF between the 34 ischemic and 206 non-ischemic segments (p-value<0.001). ConclusionAbsolute MBF values are significantly different between the models and a uniform threshold could not be determined; however, diagnostic accuracy for detecting ischemia is similar.

Evaluation of Uterine Tumor Angiogenesis with Quantitative Magnetic Resonance Imaging Perfusion Parameters

The development and metastasis of uterine tumors depend highly on tumor angiogenesis. Multiphase dynamic contrast-enhanced magnetic resonance imaging can quantitatively describe the hemodynamic changes of uterine tumors based on a variety of tracer kinetic models and time-signal curves and by simulating the distribution of contrast inside and outside the blood vessels. Functional parameters can accurately and noninvasively assess tumor angiogenesis. It provides a non-invasive functional evaluation method for the differential diagnosis,staging,response evaluation,and prognostic prediction of uterine tumors.

Feasibility and robustness of dynamic 18F-FET PET based tracer kinetic models applied to patients with recurrent high-grade glioma prior to carbon ion irradiation

The aim of this study was to analyze the robustness and diagnostic value of different compartment models for dynamic 18F-FET PET in recurrent high-grade glioma (HGG). Dynamic 18F-FET PET data of patients with recurrent WHO grade III (n:7) and WHO grade IV (n: 9) tumors undergoing re-irradiation with carbon ions were analyzed by voxelwise fitting of the time-activity curves with a simplified and an extended one-tissue compartment model (1TCM) and a two-tissue compartment model (2TCM), respectively. A simulation study was conducted to assess robustness and precision of the 2TCM. Parameter maps showed enhanced detail on tumor substructure. Neglecting the blood volume VB in the 1TCM yields insufficient results. Parameter K1 from both 1TCM and 2TCM showed correlation with overall patient survival after carbon ion irradiation (p = 0.043 and 0.036, respectively). The 2TCM yields realistic estimates for tumor blood volume, which was found to be significantly higher in WHO IV compared to WHO III (p = 0.031). Simulations on the 2TCM showed that K1 yields good accuracy and robustness while k2 showed lowest stability of all parameters. The 1TCM provides the best compromise between parameter stability and model accuracy; however application of the 2TCM is still feasible and provides a more accurate representation of tracer-kinetics at the cost of reduced robustness. Detailed tracer kinetic analysis of 18F-FET PET with compartment models holds valuable information on tumor substructures and provides additional diagnostic and prognostic value.

APPIAN: Automated Pipeline for PET Image Analysis.

APPIAN is an automated pipeline for user-friendly and reproducible analysis of positron emission tomography (PET) images with the aim of automating all processing steps up to the statistical analysis of measures derived from the final output images. The three primary processing steps are coregistration of PET images to T1-weighted magnetic resonance (MR) images, partial-volume correction (PVC), and quantification with tracer kinetic modeling. While there are alternate open-source PET pipelines, none offers all of the features necessary for making automated PET analysis as reliably, flexibly and easily extendible as possible. To this end, a novel method for automated quality control (QC) has been designed to facilitate reliable, reproducible research by helping users verify that each processing stage has been performed as expected. Additionally, a web browser-based GUI has been implemented to allow both the 3D visualization of the output images, as well as plots describing the quantitative results of the analyses performed by the pipeline. APPIAN also uses flexible region of interest (ROI) definition—with both volumetric and, optionally, surface-based ROI—to allow users to analyze data from a wide variety of experimental paradigms, e.g., longitudinal lesion studies, large cross-sectional population studies, multi-factorial experimental designs, etc. Finally, APPIAN is designed to be modular so that users can easily test new algorithms for PVC or quantification or add entirely new analyses to the basic pipeline. We validate the accuracy of APPIAN against the Monte-Carlo simulated SORTEO database and show that, after PVC, APPIAN recovers radiotracer concentrations within 93–100% accuracy.

Shutter-speed dynamic contrast-enhanced MRI: Is it fit for purpose?

To test the ability of shutter-speed dynamic contrast-enhanced (DCE) MRI to estimate water exchange (WX) using simulations and assess its performance in clinical case studies of malignant and benign breast tumors. Data were simulated using a 1-compartment tracer kinetic (TK) model combined with a 2-pool WX model (2PX) and with a 2-compartment TK model. Typical DCE-MRI acquisition parameters were used with both WX-sensitive (8°) and -insensitive (25°) flip angles. Clinical data were obtained from patients with malignant and benign breast tumors. Data were fitted using a 2-compartment TK model and a 1-compartment TK model combined with 4 WX models: fast exchange limit (FXL), no exchange, 2PX, and shutter-speed. Fits to the 1-compartment simulated data were excellent, but estimates of WX obtained using the 2PX and shutter-speed models were poor. One-compartment TK model fits to the clinical malignant tumor data were bad, except for the shutter-speed model. However, that overestimated TK parameters compared to the best-fit 2-compartment TK model, which predicted a significant blood volume and leaky capillaries (1 tracer compartment is insufficient, 2 are necessary). All models produced excellent fits to the clinical benign tumor data with little variation between parameter estimates (1 tracer compartment is sufficient). The 2PX and shutter-speed models were unable to estimate WX from the DCE-MRI data. A good fit to malignant tumor data using the shutter-speed model was not explained by WX, but the choice of an inappropriate TK model leading to distorted parameter estimates.

High Temporal-Resolution Dynamic PET Image Reconstruction Using a New Spatiotemporal Kernel Method

Current clinical dynamic PET has an effective temporal resolution of 5-10 seconds, which can be adequate for traditional compartmental modeling but is inadequate for exploiting the benefit of more advanced tracer kinetic modeling for characterization of diseases (e.g., cancer and heart disease). There is a need to improve dynamic PET to allow fine temporal sampling of 1-2 seconds. However, the reconstruction of these short-time frames from tomographic data is extremely challenging as the count level of each frame is very low and high noise presents in both spatial and temporal domains. Previously, the kernel framework has been developed and demonstrated as a statistically efficient approach to utilizing image prior for low-count PET image reconstruction. Nevertheless, the existing kernel methods mainly explore spatial correlations in the data and only have a limited ability in suppressing temporal noise. In this paper, we propose a new kernel method which extends the previous spatial kernel method to the general spatiotemporal domain. The new kernelized model encodes both spatial and temporal correlations obtained from image prior information and are incorporated into the PET forward projection model to improve themaximumlikelihood(ML) image reconstruction. Computer simulations and an application to real patient scan have shown that the proposed approach can achieve effective noise reduction in both spatial and temporal domains and outperform the spatial kernel method and conventional ML reconstruction method for improving the high temporal-resolution dynamic PET imaging.

Improved sensitivity and temporal resolution in perfusion FMRI using velocity selective inversion ASL.

This work aims to investigate the utility of velocity selective inversion pulses for perfusion weighted functional MRI. Tracer kinetic properties of velocity selective inversion (VSI) pulses as an input function for an arterial spin labeling (ASL) experiment were characterized in a group of healthy participants. Numerical simulations were conducted to search for a robust set of timing parameters for FMRI time series acquisition with maximal signal to noise ratio efficiency. The performance of three VSI pulse sequences with different timing parameters was compared with a pseudocontinuous ASL sequence in a simple FMRI experiment conducted on healthy participants. The fit to the tracer kinetic model yielded arterial CBV of 1.24% ± 0.52% and 0.45 ± 0.11% and perfusion rates of 60.8 ± 32.3 and 34.4 ± 5.4 mL/min/100 g for gray and white matter, respectively. Bolus arrival times were estimated as 75.7 ± 21 ms and 349 ± 78 ms for gray and white matter, respectively. The FMRI experiments showed that VSI pulses yield comparable sensitivity to PCASL with similar timing parameters (TR = 4 s). However, VSI pulses could be used at a faster acquisition speed (TR = 3 s) and were more sensitive to neuronal activity than PCASL pulses, as evidenced by the 31% higher Z scores obtained on average in the active regions. VSI pulses can be very beneficial for perfusion weighted functional MRI because of their tracer kinetic characteristics, which allow a faster acquisition rate while maintaining an efficient labeling input function.

Modeling Gadoxetate Liver Uptake and Efflux Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Enables Preclinical Quantification of Transporter Drug-Drug Interactions.

The aim of this study was to model the in vivo transporter-mediated uptake and efflux of the hepatobiliary contrast agent gadoxetate in the liver. The efficacy of the proposed technique was assessed for its ability to provide quantitative insights into drug-drug interactions (DDIs), using rifampicin as inhibitor. Three groups of C57 mice were scanned twice with a dynamic gadoxetate-enhanced magnetic resonance imaging protocol, using a 3-dimensional spoiled gradient-echo sequence for approximately 72 minutes. Before the second magnetic resonance imaging session, 2 of the groups received a rifampicin dose of 20 (n = 7) or 40 (n = 7) mg/kg, respectively. Data from regions of interest in the liver were analyzed using 2 simplifications of a 2-compartment uptake and efflux model to provide estimates for the gadoxetate uptake rate (ki) into the hepatocytes and its efflux rate (kef) into the bile. Both models were assessed for goodness-of-fit in the group without rifampicin (n = 9), and the appropriate model was selected for assessing the ability to monitor DDIs in vivo. Seven of 9 mice from the group without rifampicin were assessed for model implementation and reproducibility. A simple 3 parameter model (ki, kef, and extracellular space, vecs) adequately described the observed liver concentration time series with mean ki = 0.47 ± 0.11 min and mean kef = 0.039 ± 0.016 min. Visually, the area under the liver concentration time profile was reduced for the groups receiving rifampicin. Furthermore, tracer kinetic modeling demonstrated a significant dose-dependent decrease in the uptake (5.9- and 17.3-fold decrease for 20 mg/kg and 40 mg/kg, respectively) and efflux rates (2.2- and 7.9-fold decrease) compared with the first scan for each group. This study presents the first in vivo implementation of a 2-compartment uptake and efflux model to monitor DDIs at the transporter-protein level, using the clinically relevant organic anion transporting polypeptide inhibitor rifampicin. The technique has the potential to be a novel alternative to other methods, allowing real-time changes in transporter DDIs to be measured directly in vivo.

Regional times to equilibria and their impact on semi-quantification of [18F]AV-1451 uptake

The semi-quantitative estimate standardised uptake value ratios (SUVR) correlate well with specific binding of the tracer expressed as distribution volume ratios (DVR) for the tau positron emission tomography tracer [18F]AV-1451 uptake and are therefore widely used as proxy for tracer binding. With regard to tracer kinetic modelling, there exists a time point when SUVR deviates minimally from DVR, occurring when the specific binding reaches a transient equilibrium. Here, we have investigated whether the time to equilibrium affects the agreement between SUVR and DVR across different brain regions. We show that the time required to reach equilibrium differs across brain regions, resulting in region-specific biases. However, even though the 80–100 min post-injection time window did not show the smallest bias numerically, the disagreement between SUVR and DVR varied least between regions during this time. In conclusion, our findings suggest a regional component to the bias of SUVR related to the time to transient equilibrium of the specific binding. [18F]AV-1451 uptake should consequently be interpreted with some caution when compared across brain regions using this method of quantification. The commonly used time window 80–100 min post-injection shows the most consistent bias across regions and is recommended for semi-quantification of [18F]AV-1451.

Variations of quantitative perfusion measurement on dynamic contrast enhanced CT for colorectal cancer: implication of standardized image protocol

Tumor angiogenesis is considered an important prognostic factor. With an increasing emphasis on imaging evaluation of the tumor microenvironment, dynamic contrast enhanced-computed tomography (DCE-CT) has evolved as an important functional technique in this setting. Yet many questions remain as to how and when these functional measurements should be performed for each agent and tumor type, and what quantitative models should be used in the fitting process. In this study, we evaluated the variations of perfusion measurement on DCE-CT for rectal cancer patients from (1) different tracer kinetic models, (2) different scan acquisition lengths, and (3) different scan intervals. A total of seven commonly used models were studied: the adiabatic approximation to the tissue homogeneity (AATH) model, adiabatic approximation to the homogeneity tissue with fixed transit time (AATHFT) model, the Tofts model (TM), the extended Tofts model (ETM), Patlak model, Logan model, and the model-free deconvolution method. Akaike’s information criterion was used to identify the best fitting model. The interchangeability of different models was further evaluated using Bland–Altman analysis. All models gave comparable blood volume (BV) measurements except the Patlak method. While for the volume transfer constant (Ktrans) estimation, AATHFT, AATH, and ETM generated reasonable agreement among each other but not for the other models. Regarding the blood flow (BF) measurement, no two models were interchangeable. In addition, the perfusion parameters were compared with four acquisition times (45, 65, 85, and 105 s) and four temporal intervals (1, 2, 3, and 4 s). No significant difference was observed in the volume transfer constant (Ktrans), BV, and BF measurements when comparing data acquired over 65 s with data acquired over 105 s using any of the DCE models in this study. Yet increasing the temporal interval led to a significant overestimation of BF in the deconvolution method. In conclusion, the perfusion measurement is indeed model dependent and the image acquisition/processing technique is dependent. The radiation dose of DCE-CT was an average of 1.5–2 times an abdomen/pelvic CT, which is not insubstantial. To take the DCE-CT forward as a biomarker in oncology, prospective studies should be carefully designed with the optimal image acquisition and analysis technique.

I103] The case for quantitative imaging in nuclear medicine

Nuclear medicine provides the opportunity to image molecular processes in the human body. Positron emission tomography (PET) is quantitatively the most accurate nuclear medicine modality. It was developed in the 1970s as an in vivo method to measure regional pathophysiological processes. In the 1990s the focus moved to the detection of local increases in uptake, first in the brain (activation studies) and later in oncology (finding metastases), where [18F]FDG emerged as a highly sensitive staging technique. This focus on sensitivity has overshadowed the other main characteristic of PET, its quantitative nature. In recent years there has been a new shift. PET is now seen as a promising tool for precision medicine, i.e. a method to monitor or even predict response to therapy. For precision medicine quantification is essential, which can be achieved by tracer kinetic modelling in combination with a dynamic scanning protocol. In routine nuclear medicine practice, however, usually static scans are performed at some time after tracer injection. Unfortunately, based on this practice, nowadays many studies use simplified semi-quantitative methods without first validating those methods. In this presentation several examples are provided to illustrate that simplified methods may lead to less accurate or even misleading results. This is due to the fact that a static scan contains multiple signals, such as free, non-specifically bound and specifically bound tracer, and uptake may be affected by confounding physiological processes, such as changes in perfusion. Simplification is important for routine clinical practice, but it requires careful studies to find the optimal balance between accuracy and simplicity. It is argued that the use of simplified approaches without proper validation is indeed unethical.

Quantification of O-(2-[18F]fluoroethyl)-L-tyrosine kinetics in glioma

BackgroundThis study identified the optimal tracer kinetic model for quantification of dynamic O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) positron emission tomography (PET) studies in seven patients with diffuse glioma (four glioblastoma, three lower grade glioma). The performance of more simplified approaches was evaluated by comparison with the optimal compartment model. Additionally, the relationship with cerebral blood flow—determined by [15O]H2O PET—was investigated.ResultsThe optimal tracer kinetic model was the reversible two-tissue compartment model. Agreement analysis of binding potential estimates derived from reference tissue input models with the distribution volume ratio (DVR)-1 derived from the plasma input model showed no significant average difference and limits of agreement of − 0.39 and 0.37. Given the range of DVR-1 (− 0.25 to 1.5), these limits are wide. For the simplified methods, the 60–90 min tumour-to-blood ratio to parent plasma concentration yielded the highest correlation with volume of distribution VT as calculated by the plasma input model (r = 0.97). The 60–90 min standardized uptake value (SUV) showed better correlation with VT (r = 0.77) than SUV based on earlier intervals. The 60–90 min SUV ratio to contralateral healthy brain tissue showed moderate agreement with DVR with no significant average difference and limits of agreement of − 0.24 and 0.30. A significant but low correlation was found between VT and CBF in the tumour regions (r = 0.61, p = 0.007).ConclusionUptake of [18F]FET was best modelled by a reversible two-tissue compartment model. Reference tissue input models yielded estimates of binding potential which did not correspond well with plasma input-derived DVR-1. In comparison, SUV ratio to contralateral healthy brain tissue showed slightly better performance, if measured at the 60–90 min interval. SUV showed only moderate correlation with VT. VT shows correlation with CBF in tumour.