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

Inverse Problems in data-driven multi-scale Systems Medicine: application to cancer physiology

Systems Medicine is an interdisciplinary framework involving reciprocal feedback between clinical investigation and mathematical modeling/analysis. Its aim is to improve the understanding of complex diseases by integrating knowledge and data across multiple levels of biological organization. This Thesis focuses on three inverse problems, arising from three kinds of data and related to cancer physiology, at different scales: tissues, cells, molecules. The general assumption of this piece of research is that cancer is associated toa path ological glucose consumption and, in fact, its functional behavior can be assessed by nuclear medicine experiments using [18F]-fluorodeoxyglucose (FDG) as a radioactive tracer mimicking the glucose properties. At tissue-scale, this Thesis considers the Positron Emission Tomography (PET) imaging technique, and deals with two distinct issues within compartmental analysis. First, this Thesis presents a compartmental approach, referred to as reference tissue model, for the estimation of FDG kinetics inside cancer tissues when the arterial blood input of the system is unknown. Then, this Thesis proposes an efficient and reliable method for recovering the compartmental kinetic parameters for each PET image pixel in the context of parametric imaging, exploiting information on the tissue physiology. Standard models in compartmental analysis assume that phosphorylation and dephosphorylation of FDG occur in the same intracellular cytosolic volume. Advances in cell biochemistry have shown that the appropriate location of dephosphorylation is the endoplasmic reticulum (ER). Therefore, at cell-scale, this Thesis formalizes a biochemically-driven compartmental model accounting for the specific role played by the ER, and applies it to the analysis of in vitro experiments on FDG uptake by cancer cell cultures obtained with a LigandTracer (LT) device. Finally, at molecule-scale, this Thesis provides a preliminary mathematical investigation of a chemical reaction network (CRN), represented by a huge Molecular Interaction Map (MIM), describing the biochemical interactions occurring between signaling proteins in specific pathways within a cancer cell. The main issue addressed in this case is the network parameterization problem, i.e. how to determine the reaction rate coefficients from protein concentration data

In Vivo Measurement of Hippocampal GABAA/cBZR Density with [18F]-Flumazenil PET for the Study of Disease Progression in an Animal Model of Temporal Lobe Epilepsy

PURPOSE: Imbalance of inhibitory GABAergic neurotransmission has been proposed to play a role in the pathogenesis of temporal lobe epilepsy (TLE). This study aimed to investigate whether [(18)F]-flumazenil ([(18)F]-FMZ) PET could be used to non-invasively characterise GABA(A)/central benzodiazepine receptor (GABA(A)/cBZR) density and affinity in vivo in the post-kainic acid status epilepticus (SE) model of TLE. METHODS: Dynamic [(18)F]-FMZ -PET scans using a multi-injection protocol were acquired in four male wistar rats for validation of the partial saturation model (PSM). SE was induced in eight male Wistar rats (10 weeks of age) by i.p. injection of kainic acid (7.5–25 mg/kg), while control rats (n = 7) received saline injections. Five weeks post-SE, an anatomic MRI scan was acquired and the following week an [(18)F]-FMZ PET scan (3.6–4.6 nmol). The PET data was co-registered to the MRI and regions of interest drawn on the MRI for selected structures. A PSM was used to derive receptor density and apparent affinity from the [(18)F]-FMZ PET data. KEY FINDINGS: The PSM was found to adequately model [(18)F]-FMZ binding in vivo. There was a significant decrease in hippocampal receptor density in the SE group (p<0.01), accompanied by an increase in apparent affinity (p<0.05) compared to controls. No change in cortical receptor binding was observed. Hippocampal volume reduction and cell loss was only seen in a subset of animals. Histological assessment of hippocampal cell loss was significantly correlated with hippocampal volume measured by MRI (p<0.05), but did not correlate with [(18)F]-FMZ binding. SIGNIFICANCE: Alterations to hippocampal GABA(A)/cBZR density and affinity in the post-kainic acid SE model of TLE are detectable in vivo with [(18)F]-FMZ PET and a PSM. These changes are independent from hippocampal cell and volume loss. [(18)F]-FMZ PET is useful for investigating the role that changes GABA(A)/cBZR density and binding affinity play in the pathogenesis of TLE

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

Advanced perfusion quantification methods for dynamic PET and MRI data modelling

The functionality of tissues is guaranteed by the capillaries, which supply the microvascular network providing a considerable surface area for exchanges between blood and tissues. Microcirculation is affected by any pathological condition and any change in the blood supply can be used as a biomarker for the diagnosis of lesions and the optimization of the treatment. Nowadays, a number of techniques for the study of perfusion in vivo and in vitro are available. Among the several imaging modalities developed for the study of microcirculation, the analysis of the tissue kinetics of intravenously injected contrast agents or tracers is the most widely used technique. Tissue kinetics can be studied using different modalities: the positive enhancement of the signal in the computed tomography and in the ultrasound dynamic contrast enhancement imaging; T1-weighted MRI or the negative enhancement of T2* weighted MRI signal for the dynamic susceptibility contrast imaging or, finally, the uptake of radiolabelled tracers in dynamic PET imaging. Here we will focus on the perfusion quantification of dynamic PET and MRI data. The kinetics of the contrast agent (or the tracer) can be analysed visually, to define qualitative criteria but, traditionally, quantitative physiological parameters are extracted with the implementation of mathematical models. Serial measurements of the concentration of the tracer (or of the contrast agent) in the tissue of interest, together with the knowledge of an arterial input function, are necessary for the calculation of blood flow or perfusion rates from the wash-in and/or wash-out kinetic rate constants. The results depend on the acquisition conditions (type of imaging device, imaging mode, frequency and total duration of the acquisition), the type of contrast agent or tracer used, the data pre-processing (motion correction, attenuation correction, correction of the signal into concentration) and the data analysis method. As for the MRI, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a non-invasive imaging technique that can be used to measure properties of tissue microvasculature. It is sensitive to differences in blood volume and vascular permeability that can be associated with tumour angiogenesis. DCE-MRI has been investigated for a range of clinical oncologic applications (breast, prostate, cervix, liver, lung, and rectum) including cancer detection, diagnosis, staging, and assessment of treatment response. Tumour microvascular measurements by DCE-MRI have been found to correlate with prognostic factors (such as tumour grade, microvessel density, and vascular endothelial growth factor expression) and with recurrence and survival outcomes. Furthermore, DCE-MRI changes measured during treatment have been shown to correlate with outcome, suggesting a role as a predictive marker. The accuracy of DCE-MRI relies on the ability to model the pharmacokinetics of an injected contrast agent using the signal intensity changes on sequential magnetic resonance images. DCE-MRI data are usually quantified with the application of the pharmacokinetic two-compartment Tofts model (also known as the standard model), which represents the system with the plasma and tissue (extravascular extracellular space) compartments and with the contrast reagent exchange rates between them. This model assumes a negligible contribution from the vascular space and considers the system in, what-is-known as, the fast exchange limit, assuming infinitely fast transcytolemmal water exchange kinetics. In general, the number, as well as any assumption about the compartments, depends on the properties of the contrast agent used (mainly gadolinium) together with the tissue physiology or pathology studied. For this reason, the choice of the model is crucial in the analysis of DCE-MRI data. The value of PET in clinical oncology has been demonstrated with studies in a variety of cancers including colorectal carcinomas, lung tumours, head and neck tumours, primary and metastatic brain tumours, breast carcinoma, lymphoma, melanoma, bone cancers, and other soft-tissue cancers. PET studies of tumours can be performed for several reasons including the quantification of tumour perfusion, the evaluation of tumour metabolism, the tracing of radiolabelled cytostatic agents. In particular, the kinetic analysis of PET imaging has showed, in the past few years, an increasing value in tumour diagnosis, as well as in tumour therapy, through providing additional indicative parameters. Many authors have showed the benefit of kinetic analysis of anticancer drugs after labelling with radionuclide in measuring the specific therapeutic effect bringing to light the feasibility of applying the kinetic analysis to the dynamic acquisition. Quantification methods can involve visual analysis together with compartmental modelling and can be applied to a wide range of different tracers. The increased glycolysis in the most malignancies makes 18F-FDG-PET the most common diagnostic method used in tumour imaging. But, PET metabolic alteration in the target tissue can depend by many other factors. For example, most types of cancer are characterized by increased choline transport and by the overexpression of choline kinase in highly proliferating cells in response to enhanced demand of phosphatidylcholine (prostate, breast, lung, ovarian and colon cancers). This effect can be diagnosed with choline-based tracers as the 18Ffluoromethylcholine (18F-FCH), or the even more stable 18F-D4-Choline. Cellular proliferation is also imaged with 18F-fluorothymidine (FLT), which is trapped within the cytosol after being mono phosphorylated by thymidine kinase-1 (TK1), a principal enzyme in the salvage pathway of DNA synthesis. 18F-FLT has been found to be useful for noninvasive assessment of the proliferation rate of several types of cancer and showed high reproducibility and accuracy in breast and lung cancer tumours. The aim of this thesis is the perfusion quantification of dynamic PET and MRI data of patients with lung, brain, liver, prostate and breast lesions with the application of advanced models. This study covers a wide range of imaging methods and applications, presenting a novel combination of MRI-based perfusion measures with PET kinetic modelling parameters in oncology. It assesses the applicability and stability of perfusion quantification methods, which are not currently used in the routine clinical practice. The main achievements of this work include: 1) the assessment of the stability of perfusion quantification of D4-Choline and 18F-FLT dynamic PET data in lung and liver lesions, respectively (first applications in the literature); 2) the development of a model selection in the analysis of DCE-MRI data of primary brain tumours (first application of the extended shutter speed model); 3) the multiparametric analysis of PET and MRI derived perfusion measurements of primary brain tumour and breast cancer together with the integration of immuohistochemical markers in the prediction of breast cancer subtype (analysis of data acquired on the hybrid PET/MRI scanner). The thesis is structured as follows: - Chapter 1 is an introductive chapter on cancer biology. Basic concepts, including the causes of cancer, cancer hallmarks, available cancer treatments, are described in this first chapter. Furthermore, there are basic concepts of brain, breast, prostate and lung cancers (which are the lesions that have been analysed in this work). - Chapter 2 is about Positron Emission Tomography. After a brief introduction on the basics of PET imaging, together with data acquisition and reconstruction methods, the chapter focuses on PET in the clinical settings. In particular, it shows the quantification techniques of static and dynamic PET data and my results of the application of graphical methods, spectral analysis and compartmental models on dynamic 18F-FDG, 18F-FLT and 18F-D4- Choline PET data of patients with breast, lung cancer and hepatocellular carcinoma. - Chapter 3 is about Magnetic Resonance Imaging. After a brief introduction on the basics of MRI, the chapter focuses on the quantification of perfusion weighted MRI data. In particular, it shows the pharmacokinetic models for the quantification of dynamic contrast enhanced MRI data and my results of the application of the Tofts, the extended Tofts, the shutter speed and the extended shutter speed models on a dataset of patients with brain glioma. - Chapter 4 introduces the multiparametric imaging techniques, in particular the combined PET/CT and the hybrid PET/MRI systems. The last part of the chapter shows the applications of perfusion quantification techniques on a multiparametric study of breast tumour patients, who simultaneously underwent DCE-MRI and 18F-FDG PET on a hybrid PET/MRI scanner. Then the results of a predictive study on the same dataset of breast tumour patients integrated with immunohistochemical markers. Furthermore, the results of a multiparametric study on DCE-MRI and 18F-FCM brain data acquired both on a PET/CT scanner and on an MR scanner, separately. Finally, it will show the application of kinetic analysis in a radiomic study of patients with prostate cancer

Analysis and interpretation of dynamic FDG PET oncological studies using data reduction techniques

<p>Abstract</p> <p>Background</p> <p>Dynamic positron emission tomography studies produce a large amount of image data, from which clinically useful parametric information can be extracted using tracer kinetic methods. Data reduction methods can facilitate the initial interpretation and visual analysis of these large image sequences and at the same time can preserve important information and allow for basic feature characterization.</p> <p>Methods</p> <p>We have applied principal component analysis to provide high-contrast parametric image sets of lower dimensions than the original data set separating structures based on their kinetic characteristics. Our method has the potential to constitute an alternative quantification method, independent of any kinetic model, and is particularly useful when the retrieval of the arterial input function is complicated. In independent component analysis images, structures that have different kinetic characteristics are assigned opposite values, and are readily discriminated. Furthermore, novel similarity mapping techniques are proposed, which can summarize in a single image the temporal properties of the entire image sequence according to a reference region.</p> <p>Results</p> <p>Using our new cubed sum coefficient similarity measure, we have shown that structures with similar time activity curves can be identified, thus facilitating the detection of lesions that are not easily discriminated using the conventional method employing standardized uptake values.</p

Kinetic modelling of [(11)C]PBR28 for 18 kDa translocator protein PET data:A validation study of vascular modelling in the brain using XBD173 and tissue analysis

The 18 kDa translocator protein (TSPO) is a marker of microglia activation in the central nervous system and represents the main target of radiotracers for the in vivo quantification of neuroinflammation with positron emission tomography (PET). TSPO PET is methodologically challenging given the heterogeneous distribution of TSPO in blood and brain. Our previous studies with the TSPO tracers [11C]PBR28 and [11C]PK11195 demonstrated that a model accounting for TSPO binding to the endothelium improves the quantification of PET data. Here, we performed a validation of the kinetic model with the additional endothelial compartment through a displacement study. Seven subjects with schizophrenia, all high-affinity binders, underwent two [11C]PBR28 PET scans before and after oral administration of 90 mg of the TSPO ligand XBD173. The addition of the endothelial component provided a signal compartmentalization much more consistent with the underlying biology, as only in this model, the blocking study produced the expected reduction in the tracer concentration of the specific tissue compartment, whereas the non-displaceable compartment remained unchanged. In addition, we also studied TSPO expression in vessels using 3D reconstructions of histological data of frontal lobe and cerebellum, demonstrating that TSPO positive vessels account for 30% of the vascular volume in cortical and white matter