2,164 research outputs found
Computer assisted analysis of contrast enhanced ultrasound images for quantification in vascular diseases
Contrast enhanced ultrasound (CEUS) with microbubble contrast agents has shown great potential in imaging microvasculature, quantifying perfusion and hence detecting vascular diseases. However, most existing perfusion quantification methods based on image intensity, and are susceptible to confounding factors such as attenuation artefacts. Improving reproducibility is also a key challenge to clinical translation. Therefore, this thesis aims at developing attenuation correction and quantification techniques in CEUS with applications for detection and quantification of microvascular flow / perfusion.
Firstly, a technique for automatic correction of attenuation effects in vascular imaging was developed and validated on a tissue mimicking phantom. The application of this technique to studying contrast enhancement of carotid adventitial vasa vasorum as a biomarker of radiation-induced atherosclerosis was demonstrated. The results showed great potential in reducing attenuation artefact and improve quantification in CEUS of carotid arteries. Furthermore, contrast intensity was shown to significantly increase in irradiated carotid arteries and could be a useful imaging biomarker for radiation-induced atherosclerosis.
Secondly, a robust and automated tool for quantification of microbubble identification in CEUS image sequences using a temporal and spatial analysis was developed and validated on a flow phantom. The application of this technique to evaluate human musculoskeletal microcirculation with contrast enhanced ultrasound was demonstrated. The results showed an excellent accuracy and repeatability in quantifying active vascular density. It has great potential for clinical translation in the assessment of lower limb perfusion.
Finally, a new bubble activity identification and quantification technique based on differential intensity projection in CEUS was developed and demonstrated with an in-vivo study, and applied to the quantification of intraplaque neovascularisation in an irradiated carotid artery of patients who were previously treated for head and neck cancer. The results showed a significantly more specific identification of bubble signals and had good agreement between the differential intensity-based technique and clinical visual assessment. This technique has potential to assist clinicians to diagnose and monitor intraplque neovascularisation.Open Acces
CT PERFUSION INVESTIGATION OF HEPATIC HEMODYNAMICS IN A RODENT MODEL OF LIVER CIRRHOSIS
This thesis aims to evaluate the utility of dynamic contrast enhanced computed tomography (DCE-CT) imaging in conjunction with kinetic analysis (CT Perfusion) for the investigation of fibrotic liver disease. Monte Carlo simulations and sensitivity analysis of the kinetic model were used to characterize the bias, variance and covariance of perfusion parameters calculated with CT Perfusion. DCE-CT scans were performed on rats treated with carbon tetrachloride (CCI4) for 8 weeks to induce liver fibrosis, as well as sham injected control rats. Perfusion parameters were then derived from the DCE-CT scans using CT Perfusion. CCI4 treated rats showed significant changes in total hepatic blood flow, arterial hepatic blood flow, blood volume, and arterial fraction of blood flow. Histological samples were collected at various stages of treatment and stained with methyl blue. Digital image analysis was used to quantify fibrosis content of stained tissue. A strong correlation was found between fibrosis content and arterial fraction of blood flow (r=.82 p\u3c.00001)
Image based approach for early assessment of heart failure.
In diagnosing heart diseases, the estimation of cardiac performance indices requires accurate segmentation of the left ventricle (LV) wall from cine cardiac magnetic resonance (CMR) images. MR imaging is noninvasive and generates clear images; however, it is impractical to manually process the huge number of images generated to calculate the performance indices. In this dissertation, we introduce a novel, fast, robust, bi-directional coupled parametric deformable models that are capable of segmenting the LV wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of the LV wall to track the evolution of the parametric deformable models control points. We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926±0.022 and mean AD value of 2.16±0.60 mm compared to two other level set methods that achieve mean DSC values of 0.904±0.033 and 0.885±0.02; and mean AD values of 2.86±1.35 mm and 5.72±4.70 mm, respectively. Also, a novel framework for assessing both 3D functional strain and wall thickening from 4D cine cardiac magnetic resonance imaging (CCMR) is introduced. The introduced approach is primarily based on using geometrical features to track the LV wall during the cardiac cycle. The 4D tracking approach consists of the following two main steps: (i) Initially, the surface points on the LV wall are tracked by solving a 3D Laplace equation between two subsequent LV surfaces; and (ii) Secondly, the locations of the tracked LV surface points are iteratively adjusted through an energy minimization cost function using a generalized Gauss-Markov random field (GGMRF) image model in order to remove inconsistencies and preserve the anatomy of the heart wall during the tracking process. Then the circumferential strains are straight forward calculated from the location of the tracked LV surface points. In addition, myocardial wall thickening is estimated by co-allocation of the corresponding points, or matches between the endocardium and epicardium surfaces of the LV wall using the solution of the 3D laplace equation. Experimental results on in vivo data confirm the accuracy and robustness of our method. Moreover, the comparison results demonstrate that our approach outperforms 2D wall thickening estimation approaches
Unfocused ultrasound waves for manipulating and imaging microbubbles
With unfocused plane/diverging ultrasound waves, the capability of simultaneous sampling on each element of an array transducer has spawned a branch known as high-frame-rate (HFR) ultrasound imaging, whose frame rate can be two orders of magnitude faster than traditional imaging systems. Microbubbles are micron-sized spheres with a heavy gas core that is stabilized by a shell made of lipids, polymers, proteins, or surfactants. They are excellent ultrasound scatters and have been used as ultrasound contrast agents, and more recently researched as a mechanism for targeted drug delivery. With the Ultrasound Array Research Platform II (UARP II), the
objective of this thesis was to develop and advance several techniques for manipulating and imaging microbubbles using unfocused ultrasound waves. These techniques were achieved by combining custom transmit/receiving sequencing and advanced signal processing algorithms, holding promise for enhanced diagnostic and therapeutic applications of microbubbles. A method for locally accumulating microbubbles with fast image guidance was first presented. A linear array transducer performed trapping of microbubble populations interleaved with plane wave imaging, through the use of a composite ultrasound pulse sequence. This technique could enhance image-guided targeted drug delivery using microbubbles.
A key component of targeted drug delivery using liposome-loaded microbubbles and ultrasound is the ability to track these drug vehicles to guide payload release locally. As a uniquely identifiable emission from microbubbles, the subharmonic signal is of interest for this purpose. The feasibility of subharmonic plane wave imaging of liposome-loaded microbubbles was then proved. The improved subharmonic sensitivity especially at depth
compared to their counterpart of bare (unloaded) microbubbles was confirmed.
Following plane wave imaging, the combination of diverging ultrasound waves and microbubbles was investigated. The image formation techniques using coherent summation of diverging waves are susceptible to tissue and microbubble motion artefacts, resulting in poor image quality. A correlation-based 2-D motion estimation algorithm was then proposed to perform motion
compensation for HFR contrast-enhanced echocardiography (CEE). A triplex cardiac imaging technique, consisting of B mode, contrast mode and 2-D vector flow imaging with a frame rate of 250 Hz was presented.
It was shown that the efficacy of coherent diverging wave imaging of the heart is reliant on carefully designed motion compensation algorithms capable of correcting for incoherence between steered diverging-wave transmissions.
Finally, comparisons were made between the correlation-based method and one established image registration method for motion compensation. Results show that the proposed correlation-based method outperformed the image registration model for motion compensation in HFR CEE,
with the improved image contrast ratio and visibility of geometrical borders both in vitro and in vivo
Quantification of tumour heterogenity in MRI
Cancer is the leading cause of death that touches us all, either directly or indirectly.
It is estimated that the number of newly diagnosed cases in the Netherlands will increase
to 123,000 by the year 2020. General Dutch statistics are similar to those in
the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised
at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence
per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup
Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion
Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life.
In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging.
Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets.
Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging
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