Adaption in Dynamic Contrast-Enhanced MRI

In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps

Adaption in Dynamic Contrast-Enhanced MRI

In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps

Integrative multimodal image analysis using physical models for characterization of brain tumors in radiotherapy

Therapy failure with subsequent tumor progress is a common problem in radiotherapy of high grade glioma. Definition of treatment volumes with CT and MRI is limited due to uncertainties concerning tumor outlines. The goal of the presented work was to enable assessment of tumor physiology and prediction of progression patterns using multi-modal image analysis and thus, improve target delineation. Physiological imaging modalities, such as 18F-FET PET, diffusion and perfusion MRI were used to predict recurrence patterns. The Medical Imaging Interaction ToolKit together with own software implementation enabled side-by-side evaluation of all image modalities. These included tools for PET analysis and a module for voxel wise fitting of dynamic data with pharmacokinetic models. Robustness and accuracy of parameter estimates were studied on synthetic perfusion data. Parameter feasibility for progression prediction was investigated on DCE MRI and 18F-FET PET data. Using the developed software tools, a pipeline for prediction of tumor progression patterns based on multi-modal image classification with a random forest machine learning algorithm was established. Exemplary prediction analysis was applied on a small patient set for illustration of workflow functionality and classification results

Understanding quantitative DCE-MRI of the breast : towards meaningful clinical application

In most industrialized countries breast cancer will affect one out of eight women during her lifetime. In the USA, after continuously increasing for more than two decades, incidence rates are slowly decreasing since 2001. Since 1990, death rates from breast cancer have steadily decreased in women, which is attributed to both earlier detection and improved treatment. Still, it is second only to lung cancer as a cause of cancer death in women. In this work we set out to improve early detection of breast cancer via quantitative analysis of magnetic resonance images (MRI). Screening and diagnosis of breast cancer are generally performed using X-ray mammography, possibly in conjunction with ultrasonography. However, MRI is becoming an important modality for screening of women at high-risk due to for instance hereditary gene mutations, as a problem-solving tool in case of indecisive mammographic and / or ultrasonic imaging, and for anti-cancer therapy assessment. In this work, we focused on MR imaging of the breast. More specifically, the dynamic contrast-enhanced (DCE) part of the protocol was highlighted, as well as radiological assessment of DCE-MRI data. The T_1-weighted (T_1: longitudinal relaxation time, a tissue property) signal-versus-time curve that can be extracted from the DCE-MRI series that is acquired at the time of and after injection of a T_1-shortening (shorter T_1 results in higher signal) contrast agent, is usually visually assessed by the radiologist. For example, a fast initial rise to the peak (1-2 minutes post injection) followed by loss of signal within a time frame of about 5-6 minutes is a sign for malignancy, whereas a curve showing persistent (slow) uptake within the same time frame is a sign for benignity. This difference in contrast agent uptake pattern is related to physiological changes in tumorous tissue that for instance result in a stronger uptake of the contrast agent. However, this descriptive way of curve type classification is based on clinical statistics, not on knowledge about tumor physiology. We investigated pharmacokinetic modeling as a quantitative image analysis tool. Pharmacokinetics describes what happens to a substance (e.g. drug or contrast agent) after it has been administered to a living organism. This includes the mechanisms of absorption and distribution. The terms in which these mechanisms are described are physiological and can therefore provide parameters describing the functioning of the tissue. This physiological aspect makes it an attractive approach to investigate (aberrant) tissue functioning. In addition, this type of analysis excludes confounding factors due to inter- and intra-patient differences in the systemic blood circulation, as well as differences in the injection protocol. In this work, we discussed the physiological basis and details of different types of pharmacokinetic models, with the focus on compartmental models. Practical implications such as obtaining an arterial input function and model parameter estimation were taken into account as well. A simulation study of the data-imposed limitations – in terms of temporal resolution and noise properties – on the complexity of pharmacokinetic models led to the insight that only one of the tested models, the basic Tofts model, is applicable to DCE-MRI data of the breast. For the basic Tofts model we further investigated the aspect of temporal resolution, because a typical diagnostic DCE-MRI scan of the breast is acquired at a rate of about 1 image volume every minute; whereas pharmacokinetic modeling usually requires a sampling time of less than 10 s. For this experiment we developed a new downsampling method using high-temporal-resolution raw k-space data to simulate what uptake curves would have looked like if they were acquired at lower temporal resolutions. We made use of preclinical animal data. With this data we demonstrated that the limit of 10 s can be stretched to about 1 min if the arterial input function (AIF, the input to the pharmacokinetic model) is inversely derived from a healthy reference tissue, instead of measured in an artery or taken from the literature. An important precondition for the application of pharmacokinetic modeling is knowledge of the relationship between the acquired DCE-MRI signal and the actual concentration of the contrast agent in the tissue. This relationship is not trivial because with MRI we measure the indirect effect of the contrast agent on water protons. To establish this relationship via calculation of T_1 (t), we investigated both a theoretical and an empirical approach, making use of an in-house (University of Chicago) developed reference object that is scanned concurrently with the patient. The use of the calibration object can shorten the scan duration (an empirical approach requires less additional scans than an approach using a model of the acquisition technique), and can demonstrate if theoretical approaches are valid. Moreover we produced concentration images and estimated tissue proton density, also making use of the calibration object. Finally, via pharmacokinetic modeling and other MRI-derived measures we partly revealed the actions of a novel therapeutic in a preclinical study. In particular, the anti-tumor activity of a single dose of liposomal prednisolone phosphate was investigated, which is an anti-inflammatory drug that has demonstrated tumor growth inhibition. The work presented in this thesis contributes to a meaningful clinical application and interpretation of quantitative DCE-MRI of the breast

One dimensional photonic crystal for label-free and fluorescence sensing application

The development of more sensitive and more reliable sensors aids medical applications in many fields as diseases detection or therapy progress. This thesis threats the development of an optical biosensor based on electromagnetic modes propagating at the interface between a finite one-dimensional photonic crystal (1DPC) and a homogeneous external medium, also named Bloch Surface Waves (BSW). BSW have emerged as an attractive approach for label-free sensing in plasmon-like sensor configurations. Besides label-free operation, the large field enhancement and the absence of quenching allow the use of BSW to excite fluorescent labels that are in proximity of the 1DPC surface. This approach was adapted to the case of angularly resolved resonance detection, thus giving rise to a combined label-free/labelled biosensor platform. BSW present many degrees of design freedomthat enable tuning of resonance properties. In order to obtain a figure of merit for an optimization, I investigated the measurement uncertainty depending on resonance width and depth with different numericalmodels. This has led to a limit of detection that can assist the choice of the best design to use. Two tumor biomarkers, such as vascular endothelial growth factor (VEGF) and Angiopoietin-2 (Ang2), have been considered to be detected with the BSW biosensing platform. For this purpose the specific antibodies for the two tumor biomarkers were immobilized on the 1DPC biochip surface. The conclusive experiments reported in this work demonstrated the successful detection of the VEGF biomarker in complex matrices, such as cell culture supernatants and human plasma samples. Moreover, the platformwas used to determinate Ang2 concentration in untreated human plasma samples using low volumes, 300 μL, and with short turnaround times, 30 minutes. This is the first BSW based biosensor assay for the determination of tumor biomarker in human plasma samples at clinically relevant concentrations

Characterisation of the Haemodynamic Response Function (HRF) in the neonatal brain using functional MRI

Background: Preterm birth is associated with a marked increase in the risk of later neurodevelopmental impairment. With the incidence rising, novel tools are needed to provide an improved understanding of the underlying pathology and better prognostic information. Functional Magnetic Resonance Imaging (fMRI) with Blood Oxygen Level Dependent (BOLD) contrast has the potential to add greatly to the knowledge gained through traditional MRI techniques. However, it has been rarely used with neonatal subjects due to difficulties in application and inconsistent results. Central to this is uncertainity regarding the effects of early brain development on the Haemodynamic Response Function (HRF), knowledge of which is fundamental to fMRI methodology and analysis. Hypotheses: (1) Well localised and positive BOLD functional responses can be identified in the neonatal brain. (2) The morphology of the neonatal HRF differs significantly during early human development. (3) The application of an age-appropriate HRF will improve the identification of functional responses in neonatal fMRI studies. Methods: To test these hypotheses, a systematic fMRI study of neonatal subjects was carried out using a custom made somatosensory stimulus, and an adapted study design and analysis pipeline. The neonatal HRF was then characterised using an event related study design. The potential future application of the findings was then tested in a series of small experiments. Results: Well localised and positive BOLD functional responses were identified in neonatal subjects, with a maturational tendency towards an increasingly complex pattern of activation. A positive amplitude HRF was identified in neonatal subjects, with a maturational trend of a decreasing time-to-peak and increasing positive peak amplitude. Application of the empirical HRF significantly improved the precision of analysis in further fMRI studies. Conclusions: fMRI can be used to study functional activity in the neonatal brain, and may provide vital new information about both development and pathology