Direct estimation of kinetic parametric images for dynamic PET.

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed

Kinetics of protein-based in vivo Imaging tracers for positron emission tomography

Within the framework of the “Sel-tag imaging project”, a novel method was used to rapidly label protein tracers and the in vivo targeting abilities of these tracers were studied in animal models of cancer using a preclinical positron emission tomography (PET) camera. To first evaluate and optimize preclinically the use of PET tracers can facilitate their translation to and implementation in human patient studies. The ultimate goal of the different projects within the Sel-tag imaging project was to find imaging biomarkers that could potentially be used for individualizing cancer treatment and thereby improve the therapeutic results. This thesis focuses on methods employed to describe the distribution of these protein-based tracers in human xenografts. Many of the techniques used had been developed for other imaging circumstances. Therefore verification for these imaging applications was an important aspect of these papers. Paper I examined the distribution in a tumour of a medium-sized AnnexinA5-based tracer that targeted phosphatidylserine externalised during cell death in tumours in two cases; first, with no pre-treatment (baseline) and, second, after pre-treatment with a chemotherapeutic agent. Small differences between tracer uptakes in the two cases required a macro parameter analysis method for quantifications. Evaluations of the influence of the enhanced permeability and retention effect by using a size-matched control were introduced. The AnnexinA5 results were compared to those of the metabolic tracer [18F]FDG and complemented with circulating serum markers to increase sensitivity. Paper II extended the analysis in paper I to incorporate more verifications that were also more thorough. The choice of input (blood or reference tissue) and the statistical significance of intergroup comparisons when using conventional uptake measurements and the more involved macro parameter analyses like in paper I were compared. We also proposed that distribution volume ratio was a more appropriate quantification parameter concept for these protein-based tracers with relatively large non-specific uptake. Paper III assessed the smaller Affibody™ tracer ZHER2:342 as an imaging biomarker for human epidermal growth factor 2 (HER2), whose overexpressions are associated with a poor prognosis for breast cancer patients. In order to demonstrate specific binding to HER2, pre-treatment of the tumour with unlabelled protein and uptake in xenografts with low HER2 expression was evaluated. Ex vivo immunohistochemistry of expression levels supported the imaging results. Paper IV examined a radiopharmaceutical that targeted the epidermal growth factor receptor (EGFR), whose overexposure in tumours is associated with a negative prognosis. Again an Affibody™ molecule, (ZEGFR:2377), was used and, as in in paper I, a size-matched control was also used to estimate the non-specific uptake. Uptakes, quantified by conventional uptake methods, varied in tumours with different EGFR expression levels. Ex vivo analyses of expression levels were also performed. Paper V addressed the non-uniform (heterogeneous) uptake of different tracers in a tumour tissue. An algorithm was written that aimed at incorporating all relevant aspects that will influence non-uniformity. Histograms were generated that visualized how the frequency and spread of deviations contributed to the heterogeneity. These aspects could not always be attended in a direct manner, but instead had to be handled in an indirect way. The effect of varying imaging parameters was examined as part of the validation procedure. The method developed is a robust, user-friendly tool for comparing heterogeneity in similar volume preclinical tumor tissues

지연가역 신경수용체 결합 파라메트릭 영상화를 위한 동적 뇌 PET 기반 비침습적 이중도표분석법

학위논문 (박사)-- 서울대학교 대학원 : 뇌인지과학과, 2016. 2. 이재성.Tracer kinetic modeling in dynamic positron emission tomography (PET) has been widely used to investigate characteristic distribution pattern or dysfunction of neuroreceptors in brain diseases, by offering a unique tool for generating images of quantitative parameters (or parametric imaging) of neuroreceptor binding. Graphical analysis (GA) is a major technique of parametric imaging, and is based on a simple linear regression model that is linearized and further simplified from a more complex general compartment model. Although each simple model of various GA methods enables very desirable parametric imaging, it depends on several assumptions that are commonly hard to satisfy simultaneously in parametric imaging for slow kinetic tracers, leading to error in parameter estimates. A combination of two GA methods, a bi-graphical analysis, may improve such intrinsic limitation of GA approaches by taking full advantage of spatiotemporal information captured in dynamic PET data and diverse strengths of individual GA methods. This thesis focuses on a bi-graphical analysis for parametric imaging of reversible neuroreceptor binding. Firstly, I provide an overview of GA-based parametric image generation with dynamic neuroreceptor PET data. The associated basic concepts in tracer kinetic modeling are presented, including commonly used compartment models and major parameters of interest. Then, technical details of GA approaches for reversible and irreversible radioligands are described considering both arterial-plasma-input-based (invasive) and reference-region-input-based (noninvasive) modelstheir underlying assumptions and statistical properties are described in view of parametric imaging. Next, I present a novel noninvasive bi-graphical analysis for the quantification of a reversible radiotracer binding that may be too slow to reach relative equilibrium (RE) state during PET scans. The proposed method indirectly implements the conventional noninvasive Logan plot, through arithmetic combination of the parameters of two other noninvasive GA methods and the apparent tissue-to-plasma efflux rate constant for the reference region (k_2^'). I investigate its validity and statistical properties, by performing a simulation study with various noise levels and k_2^' values, and also evaluate its feasibility for [18F]FP-CIT PET in human brain. The results reveal that the proposed approach provides a binding-parameter estimation comparable to the Logan plot at low noise levels while improving underestimation caused by non-RE state differently depending on k_2^'. Furthermore, the proposed method is able to avoid noise-induced bias of the Logan plot at high noise levels, and the variability of its results is less dependent on k_2^' than the Logan plot. In sum, this approach, without issues related to arterial blood sampling if a pre-estimated k_2^' is given, could be useful in parametric image generation for slow kinetic tracers staying in a non-RE state within a PET scan.Chapter 1 Introduction 1 1.1 Tracer Kinetic Modeling in PET 1 1.2 Regional versus Voxel-wise Quantification 2 1.3 Requirements for Parametric Imaging 3 1.4 Graphical Analysis 4 1.5 Thesis Statement and Contributions 5 1.6 Organization of the Thesis 6 Chapter 2 Basic Theory in Tracer Kinetic Modeling 8 2.1 Dynamic PET Acquisition 8 2.2 Compartmental Models 11 2.3 Parameters of Interest in Neuroreceptor Study 14 2.4 Limitations in Parametric Image Generation 18 Chapter 3 Overview of Graphical Analysis 20 3.1 General Characteristics 20 3.2 Reversible Radioligand Models 25 3.2.1 Logan Plot 25 3.2.2 Relative Equilibrium-based Graphical Plot 31 3.2.3 Ito Plot 36 3.3 Irreversible Radioligand Models 39 3.3.1 Invasive Gjedde-Patlak Plot 39 3.3.2 Noninvasive Gjedde-Patlak Approaches 40 Chapter 4 Noninvasive Bi-graphical Analysis for the Quantification of Slowly Reversible Radioligand Binding 43 4.1 Background 43 4.2 Materials and Methods 45 4.2.1 Invasive RE-GP Plots 45 4.2.2 Noninvasive GA Approaches 47 4.2.3 Noninvasive RE-GP Plots 49 4.2.4 Computer Simulations 51 4.2.5 Human [18F]FP-CIT PET Data 52 4.3 Results 54 4.3.1 Regional Time-activity Curves and Graphical Plots 54 4.3.2 Simulation Results 59 4.3.3 Application to Human Data 60 4.4 Discussion 66 4.4.1 Characteristics of [18F]FP-CIT PET Data 67 4.4.2 Kinetic Methods for [18F]FP-CIT PET 67 4.4.3 Correction for NRE Effects 68 4.4.4 Linearity Condition 69 4.4.5 Advantages over the Noninvasive Logan plot 69 4.4.6 Comparison with the SRTM 71 4.4.7 Simulation Settings 72 4.4.8 Noninvasiveness 74 Chapter 5 Summary and Conclusion 76 Bibliography 77 초 록 97Docto

Quantification methods for brain imaging with novel and repurposed PET tracers

The number of people suffering from brain disorders is annually increasing. Knowledge about the molecular processes in the healthy and diseased brain is essential for a better understanding of disease conditions, treatment selection, and drug development. Positron emission tomography (PET) is a noninvasive imaging technique that can be used to acquire information about processes that are essential for normal brain functioning, but are altered in neurodegenerative diseases. Quantitative information about specific targets inside the brain, such as the density, activity, or occupancy of particular enzymes, transporters, or receptors, can be obtained by pharmacokinetic modeling of PET data. In the present study, we assessed quantification methods for brain imaging with novel and repurposed PET tracers. A PET tracer for inflammation in the brain, called [11C]SC-560, was evaluated, but overexpression of the inflammatory marker COX-1, could not be detected in the inflamed rat brain. Thus, more efforts to find an appropriate tracer are required. Next, we determined the optimal method for quantification of histamine H3 receptors in the rat brain, using PET and the radiotracer [11C]GSK-189254. Blockade of these receptors may improve cognition in patients with dementia. [11C]GSK-189254 PET and [11C]raclopride PET were subsequently used to measure the dose-dependent occupancy of histamine H3 and dopamine D2 receptors in the brain of living rats by the investigational drug AG-0029. D2 receptors play an important role in motor control. Since AG-0029 blocks histamine H3 receptors and stimulates dopamine D2 receptors, AG-0029 is a candidate drug for treatment of Parkinson disease. Finally, we evaluated the feasibility of quantifying the expression of estrogen receptors in the brains of post-menopausal women with [18F]FES PET. We were able to detect estrogen receptors in brain regions with a high density of the receptor (i.e., the pituitary). The methods described in this study may be used to enhance knowledge about the brain, the treatment of brain diseases and the development of novel drugs

Kinetic Modelling in Human Brain Imaging

Geneeskunde en GesondheidswetenskappeKerngeneeskundePlease help us populate SUNScholar with the post print version of this article. It can be e-mailed to: [email protected]

Quantitative assessment of myelin density using [C-11]MeDAS PET in patients with multiple sclerosis:a first-in-human study

Purpose: Multiple sclerosis (MS) is a disease characterized by inflammatory demyelinated lesions. New treatment strategies are being developed to stimulate myelin repair. Quantitative myelin imaging could facilitate these developments. This first-in-man study aimed to evaluate [11C]MeDAS as a PET tracer for myelin imaging in humans. Methods: Six healthy controls and 11 MS patients underwent MRI and dynamic [11C]MeDAS PET scanning with arterial sampling. Lesion detection and classification were performed on MRI. [11C]MeDAS time-activity curves of brain regions and MS lesions were fitted with various compartment models for the identification of the best model to describe [11C]MeDAS kinetics. Several simplified methods were compared to the optimal compartment model. Results: Visual analysis of the fits of [11C]MeDAS time-activity curves showed no preference for irreversible (2T3k) or reversible (2T4k) two-tissue compartment model. Both volume of distribution and binding potential estimates showed a high degree of variability. As this was not the case for 2T3k-derived net influx rate (Ki), the 2T3k model was selected as the model of choice. Simplified methods, such as SUV and MLAIR2 correlated well with 2T3k-derived Ki, but SUV showed subject-dependent bias when compared to 2T3k. Both the 2T3k model and the simplified methods were able to differentiate not only between gray and white matter, but also between lesions with different myelin densities. Conclusion: [11C]MeDAS PET can be used for quantification of myelin density in MS patients and is able to distinguish differences in myelin density within MS lesions. The 2T3k model is the optimal compartment model and MLAIR2 is the best simplified method for quantification. Trial registration. NL7262. Registered 18 September 2018

Quantitative PET-CT Perfusion Imaging of Prostate Cancer

Functional imaging of 18F-Fluorocholine PET holds promise in the detection of dominant prostatic lesions. Quantitative parameters from PET-CT Perfusion may be capable of measuring choline kinase activity, which could assist in identification of the dominant prostatic lesion for more accurate targeting of biopsies and radiation dose escalation. The objectives of this thesis are: 1) investigate the feasibility of using venous TACs in quantitative graphical analysis, and 2) develop and test a quantitative PET-CT Perfusion imaging technique that shows promise for identifying dominant prostatic lesions. Chapter 2 describes the effect of venous dispersion on distribution volume measurements with the Logan Plot. The dispersion of venous PET curves was simulated based on the arterio-venous transit time spectrum measured in a perfusion CT study of the human forearm. The analysis showed good agreement between distribution volume measurements produced by the arterial and venous TACs. Chapter 3 details the mathematical implementation of a linearized solution of the 3-Compartment kinetic model for hybrid PET-CT Perfusion imaging. A noise simulation determined the effect of incorporating CT perfusion parameters into the PET model on the accuracy and variability of measurements of the choline kinase activity. Results indicated that inclusion of CT perfusion parameters known a priori can significantly improve the accuracy and variability of imaging parameters measured with PET. Chapter 4 presents the implementation of PET-CT Perfusion imaging in a xenograft mouse model of human prostate cancer. Image-derived arterial TACs from the left ventricle were corrected for partial volume and spillover effects and validated by comparing to blood sampled curves. The PET-CT Perfusion imaging technique produced parametric maps of the choline kinase activity, k3. The results showed that the partial volume and spillover corrected arterial TACs agreed well with the blood sampled curves, and that k3max was significantly correlated with tumor volume, while SUV was not. In summary, this thesis establishes a solid foundation for future clinical research into 18F-fluorocholine PET imaging for the identification of dominant prostatic lesions. Quantitative PET-CT Perfusion imaging shows promise for assisting targeting of biopsy and radiation dose escalation of prostate cancer

Improved Quantitative Methods for Multiple Neuropharmacological Non-Invasive Brain PET Studies.

Positron emission tomography (PET) is a medical imaging modality offering a powerful tool for brain research by mapping of in vivo neuropharmacological functions such as metabolism, enzyme activity, and neuroreceptor binding site density and occupancy. Quantification in brain-PET can be classified into: 1) accurate quantification of radiotracer distribution such that image values are proportional to the radiotracer concentration in tissue, and 2) accurate quantification of the pharmacological state of the system-of-interest. This thesis addresses both these aspects for functional neuroreceptor imaging studies of the living brain. Traditional brain PET studies have at least two primary limitations. First, they measure only a single neuropharmacological aspect in isolation, which is often insufficient for characterizing a neurological condition. Second, data acquisition is accompanied by the invasive arterial blood sampling for measuring the input function to the system-of-interest. The motivation for this thesis was to address both these limitations, which led to the development of quantitative methods for multiple neuropharmacological PET studies performed without blood sampling. One such experimental design investigated was a dual-measurement intervention study where the system-of-interest is perturbed with the intent of changing the subject’s pharmacological status and system parameters are estimated both pre- and post-intervention. Second was a dual-tracer study where two radiotracers targeting two different neuropharmacological systems were injected closely in time in the same study. A major challenge in analyzing multiple pharmacological PET studies is the statistical noise-induced bias and variance in the parameter estimates. Methods developed in this thesis reduced almost all the bias (>90%) in the intervention studies with a corresponding improvement in precision. Parameter estimates for dual-tracer studies were obtained with inter-subject regions-of-interest means within ±10% of those obtained from single-tracer scans without appreciable increase in variance. The thesis also addresses inter-scanner PET image variability, a major confound in multi-center studies used to investigate disease progression. Since various PET centers have scanners with different hardware and software, systematic differences exist in multi-center data. This thesis develops a framework to reduce the inter-scanner PET image variability before pooling multi-center data for analysis. The methods developed reduced variability in phantom scans from different sites by approximately 50%.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61729/1/adjoshi_1.pd

Parametric imaging of FET PET using nonlinear based fitting

Tese de mestrado integrado em Engenharia Biomédica e Biofísica , apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2016A importância do uso de aminoácidos marcados com isótopos radioativos em estudos de Tomografia de Emissão de Positrões (PET) tem sido amplamente demonstrada. Dentro deste grupo de traçadores, a metionina marcada com 11C tem sido o mais estudado. No entanto, a curta semi-vida do radioisótopo 11C tem levado ao desenvolvimento de marcadores análogos. Os marcadores com o radioisótopo 18F revelam-se os mais promissores para deteção de tumores no cérebro. Mais especificamente, o marcador O-(2-18F-Fluoroetil)-L-tirosina (FET) provou ser de grande importância na determinação da dimensão de tumores cerebrais e dos locais onde realizar a biopsia, no planeamento do tratamento a aplicar, e na deteção de recorrências. Foi também demonstrado que a forma como o FET é metabolizado ao longo do tempo depende do grau do tumor em estudo. Em gliomas de alto grau (HGG), a taxa de captação do FET é caracterizada por um pico inicial, seguido de uma diminuição da captação de FET, enquanto que em gliomas de baixo-grau (LGG) a taxa de captação do marcador tem um aumento contínuo ao longo do tempo. O presente estudo contou com 11 pacientes (3 mulheres, 8 homens, idade: 45 ± 15 anos) com tumores cerebrais primários não tratados confirmados por histologia. Seis pacientes foram diagnosticados com HGG, enquanto os restantes 5 foram diagnosticados com LGG. Os dados de PET foram adquiridos com o PET Insert do sistema híbrido Siemens 3T MR-BrainPET. As imagens foram segmentadas de forma a extrair apenas o volume correspondente ao tumor. Após a segmentação, calculou-se a média das curvas de tempo-atividade (TAC) dos volumes tumorais segmentados (STVs), e foram usados métodos de regressão linear e não linear para fazer o ajuste à TAC de cada volume. Para calcular os ajustes com o modelo linear, foram descartados os primeiros 5 minutos de aquisição. Os ajustes baseados na regressão não-linear foram aplicados à TAC correspondente à média entre os 2 e os 60 minutos de aquisição após a injeção. As imagens dos parâmetros foram calculadas a partir dos ajustes baseados na regressão não linear e aplicados a cada voxel. Foram testados três modelos não lineares diferentes: um modelo linear amortecido exponencialmente, um modelo linear amortecido exponencialmente e com um offset, e um modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada. Dos ajustes não lineares, foram extraídos dois parâmetros: a amplitude, A, e o parâmetro κ. De seguida, geraram-se as imagens dos parâmetros calculados sobre uma área tridimensional selecionada manualmente e contendo o tumor. Para tal, além dos três modelos não lineares, utilizou-se também o modelo linear, de modo a permitir uma comparação entre os diferentes métodos. No caso dos ajustes lineares, os parâmetros extraídos foram a ordenada na origem e o declive. Calcularam-se também as imagens dos parâmetros da regressão não linear usando o modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada para a cabeça inteira. Os modelos não-lineares foram mais precisos na reprodução das curvas de FET. Os modelos mais robustos foram os modelos lineares exponencialmente amortecidos sem offset. Nos ajustes aplicados à TAC média dos STVs, o modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada provou ser o que reproduz mais precisamente os dados, com valores de 2 entre 0,94 e 1,00. O parâmetro A do modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada foi o único que revelou uma diferença significativa entre HGG e LGG (p-value= 0.04, α=0.05). Ao gerar imagens paramétricas com base nos ajustes aplicados a cada voxel, os modelos de regressão não-linear com 2 parâmetros tiveram o melhor desempenho, com valores de 2 perto de 1. Combinando as imagens do parâmetro amplitude e as imagens da atividade total ao longo do tempo, foi possível distinguir entre graus tumorais. Os LGGs assumem valores de amplitude próximos dos valores do tecido saudável à sua volta, e por isso “desaparecem” da imagem paramétrica da amplitude. No caso dos HGGs, a imagem da amplitude reproduz a atividade no tumor. Os ajustes realizados com base na regressão linear devolveram valores de 2 próximos de zero, quer no caso dos STVs, quer no cálculo das imagens paramétricas. A distinção entre HGG e LGG é possível com base nas imagens paramétricas do declive, com os LGGs a assumirem valores de declive superiores aos do tecido saudável adjacente. Com os HGGs, a situação é a oposta: os valores do declive no tumor são inferiores aos do tecido saudável que o rodeia. Em geral, os modelos não lineares reproduzem melhor os dados provenientes de FET PET, mas a distinção entre HGG e LGG baseada num parâmetro apenas é melhor conseguida através de regressão linear. No entanto, a distinção entre HGG e LGG também é possível analisando simultaneamente as imagens dos parâmetros A e κ.The importance of radiolabeled amino acids in Positron Emission Tomography (PET) imaging of the brain has been demonstrated by several studies. The most well studied amino acid tracer is 11C-metionine, but because of the short half-life of 11C, 18F-labeled amino acid analogues have been developed for tumour imaging. A number of studies have proven the importance of O-(2-18F-Fluoroethyl)-L-tyrosine (FET) in determining the extent of cerebral gliomas, biopsy guidance, treatment planning, and detecting recurrence of brain tumours. It was also demonstrated that dynamic changes of FET accumulation in gliomas are variable. High-grade gliomas (HGG) are characterized by an early peak, followed by decrease of FET uptake, whereas the uptake in low-grade gliomas (LGG) steadily increases. Eleven patients (3 female, 8 male, age: 45±15 years) with untreated primary brain tumours and histopathologic confirmation were studied. Six patients had HGG, while the remaining 5 were diagnosed with LGG. PET acquisition was done with the PET Insert of a hybrid Siemens 3T MR-BrainPET system. For tumour volume fitting, a segmentation procedure was applied. After segmentation, the mean time-activity curve (TAC) of the segmented tumour volumes (STVs) was calculated. Linear and nonlinear regression were used to fit to the TAC of each volume. When performing the fits with the linear model, the first 5 minutes of acquisition were discarded. For the nonlinear regression, the fits were applied to the mean TAC from 2 to 60 minutes after injection. Parametric images were calculated based on nonlinear regression fitting of FET data in each voxel. Three different nonlinear models were tested: an exponentially damped linear model, an exponentially damped linear model with an offset, and an exponentially damped linear model with square-root time dependence. The considered nonlinear model parameters were amplitude, A, and κ. The parametric images of manually selected tridimensional volumes containing the tumour were generated. Linear regression based parametric images were also computed for comparison, and the assessed parameters were intercept and slope. Whole-head parametric images were calculated based on nonlinear regression fitting using the exponentially damped linear model with square-root time dependence. Nonlinear regression models were more accurate at reproducing FET TAC characteristics. The most robust models are the exponentially damped linear models without offset. For mean TAC fitting, a model with square-root time dependence reproduced FET activity curves more accurately, with coefficient of determination (2) values between 0.94 and 1.00. The A parameter from the exponentially damped linear model with square-root time dependence was the only one significantly different between HGG and LGG (p-value= 0.04, α=0.05). When generating parametric images based on voxel-wise fit, the nonlinear regression models with 2 parameters performed the best, with 2 close to 1. Visual distinction between tumour grades was possible by comparing the amplitude images with the images of the summed activity across time. In the amplitude, LGGs take values similar to the ones of the surrounding background, thus disappearing from the image. On the other hand, HGGs amplitude images reproduce tumour uptake. Linear regression model fits returned 2 values that were close to zero in both mean TAC fitting, and parametric image calculation. Grade distinction was possible based on the slope parameter alone, with LGGs showing higher slope values than the neighbouring tissue, and HGGs showing lower slope values than their surroundings. In general, though nonlinear models reproduce FET time activity curves more accurately, the distinction between low-grade and high-grade tumours based on one parameter only is better achieved by using linear regression model fitting. However, a reliable differentiation seems to be possible with joint analysis of A and κ parametric images