Nicolas A. Karakatsanis

Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application

Static whole-body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single-bed-coverage limiting the axial field-of-view to ~15-20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole-body PET acquisition protocol of ~45 min total length is presented, composed of (i) an initial 6 min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (six passes × seven bed positions, each scanned for 45 s). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of ten different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole body. In addition, the total acquisition length can be reduced from 45 to ~35 min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error and the CNR metrics, resulting in enhanced task-based imaging.

Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observers task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical (18)F-deoxyglucose patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30 min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole-body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.

Dynamic Multi-Bed FDG PET imaging: Feasibility and optimization

Multi-Bed FDG PET/CT as applied to oncologic imaging is currently widely and routinely used for assessment of localized and metastatic disease involvement. In the past, based on conventional (single-bed) dynamic PET imaging, standard tracer kinetic modeling techniques have been developed to estimate the FDG uptake rate Ki. However, routine clinical multi-bed FDG PET imaging commonly involves a single time frame per bed, i.e. static imaging, and the standardized uptake value (SUV), a surrogate of metabolic activity, is employed to estimate the uptake rate Ki. The accuracy depends on two conditions: (i) in the voxel or region of interest, contribution of non-phosphorylated FDG is negligible relative to phosphorylated FDG, and (ii) time integral of plasma FDG concentration is proportional to injected dose divided by lean body mass, which can fail in clinical FDG PET imaging and pose problems in differentiating malignant from benign tumors. The objective of the proposed work is to facilitate, for the fist time, a transition from static to dynamic multi-bed FDG PET/CT imaging in clinically feasible times where, given the challenge of sparse temporal sampling at each bed, novel dynamic acquisition schemes should be employed to yield quantitative whole-body imaging of FDG uptake. Thus, a set of novel dynamic multi-bed PET image acquisition schemes have been modeled, using Monte Carlo simulations, to quantitatively evaluate the clinical feasibility of the method and optimize the number of passes per bed and the total study duration. It has been determined that a data acquisition scheme consisting of 6 whole-body passes and constant time frames of 45sec produces parametric images with the optimal noise vs. bias performance. Finally, clinical whole-body patient data have been acquired dynamically and results demonstrate the potential of the proposed method in enhancing treatment response monitoring capabilities of clinical PET studies.

GATE simulations for small animal SPECT/PET using voxelized phantoms and rotating-head detectors

GATE (Geant4 Application for Tomographic Emission) simulation toolkit has become a well validated toolkit for the simulation of SPECT and PET systems. A very important feature of GATE is that it allows modelling of time-dependent phenomena. In addition, complex voxelized object such as realistic anthropomorphic or small animal phantoms can be used as emission sources. In this work two small field of view scanners have been evaluated experimentally, modelled in GATE and mice studies have been simulated using MOBY mouse phantom. Two scanners have been simulated: The first one is a mouse sized gamma camera (field of view is 5 times 10cm) that is based on two Hamamatsu H8500 PSPMTs, a NaI pixelized scintillator and a tungsten collimator with hexagonal parallel holes. The system has been modelled in GATE and good agreement has been found between simulation and experimental results. MOBY mouse has been introduced as a voxelized source and planar and tomography simulations were carried out. The second small animal PET scanner has four heads which are equipped with a H8500 PSPMTs and a pixelated LYSO scintillator. Systems geometry has been modelled in GATE. The results of both systems simulation and comparison between simulation and experimental data are presented. In addition, mouse bone scans were simulated both for SPECT and PET and tomographic image are derived. The presented methodology is aimed to provide all necessary tools in order to perform optimized simulations of small animal emission tomography scans.

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET

We recently developed a dynamic multi-bed PET data acquisition framework to translate the quantitative benefits of Patlak voxel-wise analysis to the domain of routine clinical whole-body (WB) imaging. The standard Patlak (sPatlak) linear graphical analysis assumes irreversible PET tracer uptake, ignoring the effect of FDG dephosphorylation, which has been suggested by a number of PET studies. In this work: (i) a non-linear generalized Patlak (gPatlak) model is utilized, including a net efflux rate constant kloss, and (ii) a hybrid (s/g)Patlak (hPatlak) imaging technique is introduced to enhance contrast to noise ratios (CNRs) of uptake rate Ki images. Representative set of kinetic parameter values and the XCAT phantom were employed to generate realistic 4D simulation PET data, and the proposed methods were additionally evaluated on 11 WB dynamic PET patient studies. Quantitative analysis on the simulated Ki images over 2 groups of regions-of-interest (ROIs), with low (ROI A) or high (ROI B) true kloss relative to Ki, suggested superior accuracy for gPatlak. Bias of sPatlak was found to be 16-18% and 20-40% poorer than gPatlak for ROIs A and B, respectively. By contrast, gPatlak exhibited, on average, 10% higher noise than sPatlak. Meanwhile, the bias and noise levels for hPatlak always ranged between the other two methods. In general, hPatlak was seen to outperform all methods in terms of target-to-background ratio (TBR) and CNR for all ROIs. Validation on patient datasets demonstrated clinical feasibility for all Patlak methods, while TBR and CNR evaluations confirmed our simulation findings, and suggested presence of non-negligible kloss reversibility in clinical data. As such, we recommend gPatlak for highly quantitative imaging tasks, while, for tasks emphasizing lesion detectability (e.g. TBR, CNR) over quantification, or for high levels of noise, hPatlak is instead preferred. Finally, gPatlak and hPatlak CNR was systematically higher compared to routine SUV values.

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation–maximization reconstruction

Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate K i as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting K i images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit K i bias of sPatlak analysis at regions with non-negligible (18)F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source software for tomographic image reconstruction platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation-maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published (18)F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced K i target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D versus the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10-20 sub-iterations. Moreover, systematic reduction in K i % bias and improved TBR were observed for gPatlak versus sPatlak. Finally, validation on clinical WB dynamic data demonstrated the clinical feasibility and superior K i CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging.

Whole-body PET parametric imaging employing direct 4D nested reconstruction and a generalized non-linear Patlak model

Graphical analysis is employed in the research setting to provide quantitative estimation of PET tracer kinetics from dynamic images at a single bed. Recently, we proposed a multi-bed dynamic acquisition framework enabling clinically feasible whole-body parametric PET imaging by employing post-reconstruction parameter estimation. In addition, by incorporating linear Patlak modeling within the system matrix, we enabled direct 4D reconstruction in order to effectively circumvent noise amplification in dynamic whole-body imaging. However, direct 4D Patlak reconstruction exhibits a relatively slow convergence due to the presence of non-sparse spatial correlations in temporal kinetic analysis. In addition, the standard Patlak model does not account for reversible uptake, thus underestimating the influx rate Ki. We have developed a novel whole-body PET parametric reconstruction framework in the STIR platform, a widely employed open-source reconstruction toolkit, a) enabling accelerated convergence of direct 4D multi-bed reconstruction, by employing a nested algorithm to decouple the temporal parameter estimation from the spatial image update process, and b) enhancing the quantitative performance particularly in regions with reversible uptake, by pursuing a non-linear generalized Patlak 4D nested reconstruction algorithm. A set of published kinetic parameters and the XCAT phantom were employed for the simulation of dynamic multi-bed acquisitions. Quantitative analysis on the Ki images demonstrated considerable acceleration in the convergence of the nested 4D whole-body Patlak algorithm. In addition, our simulated and patient whole-body data in the postreconstruction domain indicated the quantitative benefits of our extended generalized Patlak 4D nested reconstruction for tumor diagnosis and treatment response monitoring.

Quantitative whole-body parametric PET imaging incorporating a generalized Patlak model

Recently, we proposed a dynamic multi-bed PET imaging and analysis framework enabling clinically feasible whole-body parametric imaging. The standard Patlak linear graphical analysis allows for efficient modeling of whole-body tracer kinetics by directly estimating the uptake rate constant Ki and blood distribution volume V, based on a common two-compartment kinetic model. However, this model does not account for reversible uptake (e.g. dephosphorylation in FDG), thus underestimating Ki in this case, a finding observed in a number of published FDG or similar tracer studies. We propose a novel generalized PET parametric imaging framework enabling truly quantitative whole-body Patlak imaging including in regions exhibiting reversibility. For this purpose: a) an extended non-linear Patlak model has been utilized, enriched with the net efflux rate constant kloss, (b) a basis function method has been applied to linearize the estimation process through a computationally efficient algorithm, and (c) a hybrid Ki imaging technique is introduced based on the Patlak correlation-coefficient to enhance robustness to noise. Our evaluation included both simulated and real subject clinical studies. A set of published kinetic parameter values and the XCAT phantom were employed to generate realistic simulation data for 2 dynamic 7-bed acquisition protocols (0-45min and 30-90min post-injection). Quantitative analysis on the Ki images suggests superior quantitative performance of the generalized Patlak in comparison to the standard Patlak imaging in both acquisitions, even when kloss is comparable to Ki. In addition, validation on three dynamic whole-body patient datasets demonstrated clinical feasibility and increased focal uptake with potential for enhanced diagnosis and treatment response monitoring.

Introducing time-of-flight and resolution recovery image reconstruction to clinical whole-body PET parametric imaging

We recently proposed a dynamic multi-bed acquisition scheme enabling whole-body FDG PET parametric imaging from limited axial field-of-view PET/CT scanners in clinically feasible scan times. However, the proposed framework was only evaluated for standard ordered subsets expectation maximization (OSEM) reconstruction. Currently, state-of-the-art commercial PET/CT scanners are equipped with advanced detection systems, capable of measuring the time-of-flight (TOF) of each annihilated photon enabling to confine the location of the annihilation position to a small segment within the line of response. As such, noise propagation is reduced and TOF reconstruction may provide superior contrast to noise ratio (CNR). Furthermore, image reconstruction is enriched with the feature of scanner resolution point spread function (PSF) modeling within the system response matrix of OSEM algorithm, similarly allowing for higher CNR. In this study, we extended TOF and PSF modeling to the dynamic multi-bed domain and systematically investigated their impact on the quality of wholebody PET parametric images. The state-of-the-art Siemens Biograph mCT scanner and its reconstruction suite were utilized. An extensive set of realistic 4D phantom simulations for the mCT scanner with and without TOF features were performed. Resolution degradation was applied to match a spatial resolution of 4.5mm. Then, TOF and non-TOF reconstructed images with and without resolution modeling were produced. Subsequently, the impact of TOF and PSF was assessed for standard and generalized Patlak models. Our results demonstrate the potential benefit of introducing TOF and PSF in parametric imaging, with both features providing superior noise vs. bias trade-off. Tumor-to-background ratio is enhanced by 30% when utilizing TOF, while CNR is improved by 40% and 60% when either TOF or PSF capabilities are introduced, respectively. Finally, total CNR enhancement approaches 100% if the two features are combined.

Impact of acquisition time-window on clinical whole-body PET parametric imaging

Whole-body PET parametric imaging can combine the benefit of extended axial field-of-view (FOV) in multi-bed scans with that of generating time activity curves (TACs) in dynamic scans. We have recently proposed such a framework capable of delivering whole-body FDG Patlak images in clinically feasible scan times. The design of the acquisition protocol was limited to a single time-window and the standard Patlak graphical analysis method. However, the relatively long FDG half-life and uptake, compared to clinically acceptable acquisition time-windows, render the choice of this window critical. The major FDG kinetic components can be estimated from the early and intermediate TAC segments. On the contrary, at later time-windows, tumor contrast may be overall higher. In addition, the standard Patlak method does not account for tracer uptake reversibility, a property that becomes more apparent at later acquisition time-windows for certain tumors, thus increasing the probability for larger bias at later times. Consequently, the choice of the optimal time-window can be critical and should constitute an important design aspect of multi-bed dynamic protocols. In the present work we assessed the impact of a sliding acquisition time-window on whole-body FDG PET parametric images. This included incremental shift of a 6-pass acquisition time-window (~35min) along an extended scan period of 0-90min post injection, using both real patient kinetic data as well as realistic 4D simulations of the state-of-the-art Siemens Biograph mCT scanner. We also propose the selective application of a generalized Patlak method accounting for uptake reversibility. Our simulated and clinical results demonstrate that both Patlak methods (standard and generalized) result in enhanced tumor-to-background contrast as well as contrast-to-noise ratios with minimal bias at an early acquisition time window (10-45min post injection) with the generalized method exhibiting systematically superior performance.