Estimation of myocardial glucose utilisation with PET using the left ventricular time-activity curve as a non-invasive input function

Multiparametric Cardiac 18F-FDG PET in Humans: Kinetic Model Selection and Identifiability Analysis

Cardiac ¹⁸F-FDG PET has been used in clinics to assess myocardial glucose metabolism. Its ability for imaging myocardial glucose transport, however, has rarely been exploited in clinics. Using the dynamic FDG-PET scans of ten patients with coronary artery disease, we investigate in this paper appropriate dynamic scan and kinetic modeling protocols for efficient quantification of myocardial glucose transport. Three kinetic models and the effect of scan duration were evaluated by using statistical fit quality, assessing the impact on kinetic quantification, and analyzing the practical identifiability. The results show that the kinetic model selection depends on the scan duration. The reversible two-tissue model was needed for a one-hour dynamic scan. The irreversible two-tissue model was optimal for a scan duration of around 10-15 minutes. If the scan duration was shortened to 2-3 minutes, a one-tissue model was the most appropriate. For global quantification of myocardial glucose transport, we demonstrated that an early dynamic scan with a duration of 10-15 minutes and irreversible kinetic modeling was comparable to the full one-hour scan with reversible kinetic modeling. Myocardial glucose transport quantification provides an additional physiological parameter on top of the existing assessment of glucose metabolism and has the potential to enable single tracer multiparametric imaging in the myocardium.

Determination of the unmetabolised (18)F-FDG fraction by using an extension of simplified kinetic analysis method: clinical evaluation in paragangliomas

Tumours with high (18)F-FDG uptake values on static late PET images do not always exhibit high proliferation indices. These discrepancies might be related to high proportion of unmetabolised (18)F-FDG components in the tissues. We propose a method that enables to calculate different (18)F-FDG kinetic parameters based on a new mathematical approach that integrates a measurement error model. Six patients with diagnosed non-metastatic paragangliomas (PGLs) and six control patients with different types of lesions were investigated in this pilot study using (18)F-FDG PET/CT. In all cases, a whole-body acquisition was followed by four static acquisitions centred over the target lesions, associated with venous blood samplings. We used an extension of the Hunter's method to calculate the net influx rate constant (K H). The exact net influx rate constant and vascular volume fraction (K i and V, respectively) were subsequently obtained by the method of least squares. Next, we calculated the mean percentages of metabolised (PM) and unmetabolised (PUM) (18)F-FDG components, and the times required to reach 80 % of the amount of metabolised (18)F-FDG (T80%). A test-retest evaluation indicated that the repeatability of our approach was accurate; the coefficients of variation were below 2 % regardless of the kinetic parameters considered. We observed that the PGLs were characterised by high dispersions of the maximum standardised uptake value SUVmax (9.7 ± 11, coefficient of variation CV = 114 %), K i (0.0137 ± 0.0119, CV = 87 %), and V (0.292 ± 0.306, CV = 105 %) values. The PGLs were associated with higher PUM (p = 0.02) and T80% (p = 0.02) values and lower k 3 (p = 0.02) values compared to the malignant lesions despite the similar SUVmax values (p = 0.55). The estimations of these new kinetic parameters are more accurate than SUVmax or K i for in vivo metabolic assessment of PGLs at the molecular level.

A study of the metabolism of transplanted tumor in the lung by micro PET/CT in mice

The difference of tumor metabolism from that of normal tissue is an important factor for diagnosis through functional imaging such as positron emission tomography (PET). A quantitative description of the metabolic process will help to improve the diagnosis methods. In this study, the metabolism of tumor in lung was quantitatively described in mice. The melanoma was transplanted into the lung of mice, and the metabolism of the transplanted tumor was detected by micro PET/CT with [(18)F]fluoro-2-deoxy-D-glucose (FDG). Nine mice were transplanted with B16 melanoma cells through their tail vein. Lung tumor was detected by pathological method. The lesions smaller than 1mm could hardly be directly detected directly by micro PET/CT, while the tumor with a 1-4mm diameter could be detected by micro PET/CT. A metabolic model with three compartments was separately established for lung tumors and normal lung tissues. In this model, the lung cancer had a significantly higher metabolic rate constant as compared to that of the normal lung tissue (p=0.01). The outputs of the model fit well with the original curve from the dynamic images. It is also found that difference of tissue activity between tumors and normal lung tissues varied along scan time. Through this comparison, it was suggested that the difference in metabolism between the lung tissue and the tumor might contribute to the tumor diagnosis.

Extraction of an input function from dynamic micro-PET images using wavelet packet based sub-band decomposition independent component analysis

Positron emission tomography (PET) can be used to quantify physiological parameters. However to perform quantification requires that an input function is measured, namely a plasma time activity curve (TAC). Image-derived input functions (IDIFs) are attractive because they are noninvasive and nearly no blood loss is involved. However, the spatial resolution and the signal to noise ratio (SNR) of PET images are low, which degrades the accuracy of IDIFs. The objective of this study was to extract accurate input functions from microPET images with zero or one plasma sample using wavelet packet based sub-band decomposition independent component analysis (WP SDICA). Two approaches were used in this study. The first was the use of simulated dynamic rat images with different spatial resolutions and SNRs, and the second was the use of dynamic images of eight Sprague-Dawley rats. We also used a population-based input function and a fuzzy c-means clustering approach and compared their results with those obtained by our method using normalized root mean square errors, area under curve errors, and correlation coefficients. Our results showed that the accuracy of the one-sample WP SDICA approach was better than the other approaches using both simulated and realistic comparisons. The errors in the metabolic rate, as estimated by one-sample WP SDICA, were also the smallest using our approach.

A robust state-space kinetics-guided framework for dynamic PET image reconstruction

Dynamic PET image reconstruction is a challenging issue due to the low SNR and the large quantity of spatio-temporal data. We propose a robust state-space image reconstruction (SSIR) framework for activity reconstruction in dynamic PET. Unlike statistically-based frame-by-frame methods, tracer kinetic modeling is incorporated to provide physiological guidance for the reconstruction, harnessing the temporal information of the dynamic data. Dynamic reconstruction is formulated in a state-space representation, where a compartmental model describes the kinetic processes in a continuous-time system equation, and the imaging data are expressed in a discrete measurement equation. Tracer activity concentrations are treated as the state variables, and are estimated from the dynamic data. Sampled-data H(∞) filtering is adopted for robust estimation. H(∞) filtering makes no assumptions on the system and measurement statistics, and guarantees bounded estimation error for finite-energy disturbances, leading to robust performance for dynamic data with low SNR and/or errors. This alternative reconstruction approach could help us to deal with unpredictable situations in imaging (e.g. data corruption from failed detector blocks) or inaccurate noise models. Experiments on synthetic phantom and patient PET data are performed to demonstrate feasibility of the SSIR framework, and to explore its potential advantages over frame-by-frame statistical reconstruction approaches.

A hybrid clustering method for ROI delineation in small-animal dynamic PET images: application to the automatic estimation of FDG input functions

Tracer kinetic modeling with dynamic positron emission tomography (PET) requires a plasma time-activity curve (PTAC) as an input function. Several image-derived input function (IDIF) methods that rely on drawing the region of interest (ROI) in large vascular structures have been proposed to overcome the problems caused by the invasive approach for obtaining the PTAC, especially for small-animal studies. However, the manual placement of ROIs for estimating IDIF is subjective and labor-intensive, making it an undesirable and unreliable process. In this paper, we propose a novel hybrid clustering method (HCM) that objectively delineates ROIs in dynamic PET images for the estimation of IDIFs, and demonstrate its application to the mouse PET studies acquired with [ (18)F]Fluoro-2-deoxy-2-D-glucose (FDG). We begin our HCM using k-means clustering for background removal. We then model the time-activity curves using polynomial regression mixture models in curve clustering for heart structure detection. The hierarchical clustering is finally applied for ROI refinements. The HCM achieved accurate ROI delineation in both computer simulations and experimental mouse studies. In the mouse studies, the predicted IDIF had a high correlation with the gold standard, the PTAC derived from the invasive blood samples. The results indicate that the proposed HCM has a great potential in ROI delineation for automatic estimation of IDIF in dynamic FDG-PET studies.

Tracer kinetics guided dynamic PET reconstruction

Dynamic PET reconstruction is a challenging issue due to the spatio-temporal nature and the complexity of the data. Conventional frame-by-frame approaches fail to explore the temporal information of dynamic PET data, and may lead to inaccurate results due to the low SNR of data. Due to the ill-conditioning of image reconstruction, proper prior knowledge should be incorporated to constrain the reconstruction. In this paper, we propose a tracer kinetics guided reconstruction framework for dynamic PET imaging. The dynamic reconstruction problem is formulated in a state-space representation, where compartment model serves as a continuous-time system equation to describe the tracer kinetic processes, and the imaging data is expressed as discrete sampling of the system states in a measurement equation. The reconstruction problem has therefore become a state estimation problem in a continuous-discrete hybrid paradigm, and sampled-data Hinfinity filtering is applied to for the estimation. As Hinfinity filtering makes no assumptions on the system and measurement statistics, robust reconstruction results can be obtained for dynamic PET imaging where the statistical properties of measurement data and system uncertainty are not available a priori.

Measurement of input functions in rodents: challenges and solutions

INTRODUCTION

Tracer kinetic modeling used in conjunction with positron emission tomography (PET) is an excellent tool for the noninvasive quantification of physiological, biological and molecular processes and their alterations due to disease. Currently, complex multi-compartment modeling approaches are being applied in a variety of clinical studies to determine myocardial perfusion, viability and glucose utilization as well as fatty acid metabolism and oxidation in the normal and diseased heart. These kinetic models require two key measurements of tracer activity over time, tracer activity in arterial blood (input function) and its corresponding activity in the organ of interest. The alteration in the time course of tracer activity as it travels from blood to the organ of interest describes the kinetics of the tracer. To be able to implement these approaches in rodent models of disease using small-animal PET (microPET), it is imperative that the input function is measured accurately.

METHODS

The blood input functions in rodent experiments were obtained by (1) direct blood sampling, (2) direct measurement of blood activity by a beta-detecting probe that counts the activity in the blood, (3) an arterial-venous bypass (A/V shunt), (4) factor analysis of dynamic structures from dynamic PET images and (5) measurement from region-of-interest (ROI) analysis of dynamic PET images. Direct blood sampling was used as the reference standard to which the results of the other techniques were compared.

RESULTS

Beta probes are difficult to operate and may not provide accurate blood input functions unless they are used intravenously, which requires complicated microsurgery. A similar limitation applies to the A/V shunt. Factor analysis successfully extracts the blood input function for mice and rats. The ROI-based method is less accurate due to limited image resolution of the PET system, which results in severe partial volume effect and spillover from myocardium.

CONCLUSION

The current reference standard, direct blood sampling, is more invasive and has limited temporal resolution. With current imaging technology, image-based extraction of blood input functions is possible by factor analysis, while forthcoming technological developments are likely to allow extraction of input function directly from the images. These techniques will reduce the level of complexity and invasiveness for animal experiments and are likely to be used more widely in the future.

Tracer kinetic modeling of 11C-acetate applied in the liver with positron emission tomography

It is well known that 40%-50% of hepatocellular carcinoma (HCC) do not show increased 18F-fluorodeoxyglucose (FDG) uptake. Recent research studies have demonstrated that 11C-acetate may be a complementary tracer to FDG in positron emission tomography (PET) imaging of HCC in the liver. Quantitative dynamic modeling is, therefore, conducted to evaluate the kinetic characteristics of this tracer in HCC and nontumor liver tissue. A three-compartment model consisting of four parameters with dual inputs is proposed and compared with that of five parameters. Twelve regions of dynamic datasets of the liver extracted from six patients are used to test the models. Estimation of the adequacy of these models is based on Akaike Information Criteria (AIC) and Schwarz Criteria (SC) by statistical study. The forward clearance K = K1 * k3/(k2 + k3) is estimated and defined as a new parameter called the local hepatic metabolic rate-constant of acetate (LHMRAct) using both the weighted nonlinear least squares (NLS) and the linear Patlak methods. Preliminary results show that the LHMRAct of the HCC is significantly higher than that of the nontumor liver tissue. These model parameters provide quantitative evidence and understanding on the kinetic basis of C-acetate for its potential role in the imaging of HCC using PET.

Optimal image sampling schedule for both image-derived input and output functions in PET cardiac studies

Optimal sampling schedule (OSS) design for both image-derived input and output functions in tracer kinetic modeling with positron emission tomography (PET) is investigated. This problem is very important in noninvasive PET dynamic cardiac studies where both the input function, i.e., the plasma time-activity curve (PTAC), and the output function, i.e., the tissue time-activity curve (TTAC), are obtained simultaneously from the same sequence of PET images. The integral PET measurement is used in this study. The spillover correction for the cross contaminations in cardiac studies is incorporated into the OSS design procedure. A new target function based on the D-optimal criterion involving both the input and output sensitivity functions is proposed. The fluorodeoxyglucose (FDG) model and a six-parameter PTAC model are used to illustrate the simultaneous OSS design for both the PTAC and TTAC. An OSS design consisting of six different scanning intervals is derived. Computer simulations are performed based on the estimated parameters from real studies to evaluate the effectiveness of the OSS. The double modeling approach is used in parameter estimation to simultaneously estimate the parameters involved. The results have shown that, for a wide range of parameter variations, the OSS is as effective as a conventional sampling schedule (CSS) and comparable parameter estimates can be obtained. Compared with the use of the CSS, the use of the OSS leads to an approximately 70% reduction in the storage space and data processing time.

Information technology applications in biomedical functional imaging

In parallel with rapid advances in computer technology, biomedical functional imaging is having an ever-increasing impact on healthcare. Functional imaging allows us to see dynamic processes quantitatively in the living human body. However, as we need to deal with four-dimensional time-varying images, space requirements and computational complexity are extremely high. This makes information management, processing, and communication difficult. Using the minimum amount of data to represent the required information, developing fast algorithms to process the data, organizing the data in such a way as to facilitate information management, and extracting the maximum amount of useful information from the recorded data have become important research tasks in biomedical information technology. For the last ten years, the Biomedical and Multimedia Information Technology (BMIT) Group and, recently, the Center for Multimedia Signal Processing have conducted systematic studies on these topics. Some of the results relating to functional imaging data acquisition, compression, storage, management, processing, modeling, and simulation are briefly reported in this paper.