Characterizing Single Ventricle Patient-Specific Anatomy Using Segmentation of MRI and 3D Reconstruction to Aid Surgical Planning

Single ventricle congenital heart defects occur 2 per every 1000 live births in the USA. In these cases, cyanosis occurs due to the mixing of venous deoxygenated blood and oxygenated blood from the lungs. These defects are surgically treated by the total cavo-pulmonary connection (TCPC), where the superior and inferior vena cavae are connected to the pulmonary arteries routing the systemic venous return directly to the lungs. However, this Fontan repair results in high energy losses and therefore the optimization of this connection prior to the surgery could significantly improve post-operative performance. In this paper, the in-house segmentation and 3D reconstruction scheme is used in the following studies. First, 3D geometrical analysis of the TCPCs is used to determine the advantages and disadvantages of two commonly performed TCPC palliations intra-atrial and extra-cardiac configurations. Then, a surgical planning outline is proposed with segmentation of pre and post surgical Magnetic Resonance Imaging (MRI) data followed by the 3D reconstruction with emphasis on extracting surrounding vessels and structures. A pediatric surgeon performs a virtual surgery on the reconstruction of the patient s pre-Fontan anatomy prior to the actual surgery. A segmentation of the heart, aorta and surrounding vessels superimposed with the Glenn, when used with the SURGEM® tool, simulates the actual Fontan operation. This outline allows the surgeon to envision numerous scenarios of possible surgical options, and accordingly to predict the post operative procedures. The segmentation tool is improved upon to increase the accuracy and efficiency of the process and enhance the quality of the anatomical reconstructions.Committee Member/Second Reader: Kartik S. Sundareswaran; Faculty Mentor: Dr. Ajit P. Yoganatha

A Non-Rigid Registration Method for Analyzing Myocardial Wall Motion for Cardiac CT Images

Cardiac resynchronization therapy (CRT) has a high percentage of non-responders. Successfully locating the optimal location for CRT lead placement on a priori images can increase efficiency in procedural preparation and execution and could potentially increase the rate of CRT responders. Registration has been used in the past to assess the motion of medical images. Specifically, one method of non-rigid registration has been utilized to assess the motion of left ventricular MR cardiac images. As CT imaging is often performed as part of resynchronization treatment planning and is a fast and accessible means of imaging, extending this registration method to assessing left ventricular motion of CT images could provide another means of reproducible contractility assessment. This thesis investigates the use of non-rigid registration to evaluate the myocardium motion in multi-phase multi-slice computed tomography (MSCT) cardiac imaging for the evaluation of mechanical contraction of the left ventricle

Statistical Shape Modelling and Segmentation of the Respiratory Airway

The human respiratory airway consists of the upper (nasal cavity, pharynx) and the lower (trachea, bronchi) respiratory tracts. Accurate segmentation of these two airway tracts can lead to better diagnosis and interpretation of airway-specific diseases, and lead to improvement in the localization of abnormal metabolic or pathological sites found within and/or surrounding the respiratory regions. Due to the complexity and the variability displayed in the anatomical structure of the upper respiratory airway along with the challenges in distinguishing the nasal cavity from non-respiratory regions such as the paranasal sinuses, it is difficult for existing algorithms to accurately segment the upper airway without manual intervention. This thesis presents an implicit non-parametric framework for constructing a statistical shape model (SSM) of the upper and lower respiratory tract, capable of distinct shape generation and be adapted for segmentation. An SSM of the nasal cavity was successfully constructed using 50 nasal CT scans. The performance of the SSM was evaluated for compactness, specificity and generality. An averaged distance error of 1.47 mm was measured for the generality assessment. The constructed SSM was further adapted with a modified locally constrained random walk algorithm to segment the nasal cavity. The proposed algorithm was evaluated on 30 CT images and outperformed comparative state-of-the-art and conventional algorithms. For the lower airway, a separate algorithm was proposed to automatically segment the trachea and bronchi, and was designed to tolerate the image characteristics inherent in low-contrast CT images. The algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance (87.1±2.8 DSC and distance error of 0.37±0.08 mm) in segmentation results against comparative state-of-the-art algorithms

Doctor of Philosophy

dissertationImage-based biomechanics, particularly numerical modeling using subject-specific data obtained via imaging, has proven useful for elucidating several biomechanical processes, such as prediction of deformation due to external loads, applicable to both normal function and pathophysiology of various organs. As the field evolves towards applications that stretch the limits of imaging hardware and acquisition time, the information traditionally expected as input for numerical routines often becomes incomplete or ambiguous, and requires specific acquisition and processing strategies to ensure physical accuracy and compatibility with predictive mathematical modeling. These strategies, often derivatives or specializations of traditional mechanics, effectively extend the nominal capability of medical imaging hardware providing subject-specific information coupled with the option of using the results for predictive numerical simulations. This research deals with the development of tools for extracting mechanical measurements from a finite set of imaging data and finite element analysis in the context of constructing structural atlases of the heart, understanding the biomechanics of the venous vasculature, and right ventricular failure. The tools include: (1) application of Hyperelastic Warping image registration to displacement-encoded MRI for reconstructing absolute displacement fields, (2) combination of imaging and a material parameter identification approach to measure morphology, deformation, and mechanical properties of vascular tissue, and (3) extrapolation of diffusion tensor MRI acquired at a single time point for the prediction the structural changes across the cardiac cycle with mechanical simulations. Selected tools were then applied to evaluate structural changes in a reversible animal model for right ventricular failure due to pressure overload

Measuring aortic annulus size using a soft robotic balloon catheter

Transcatheter aortic valve implantation (TAVI) is a minimally invasive surgical technique to treat aortic heart valve diseases. According to current clinical guidelines, the implanted prosthetic valve replacing the native one is selected based on pre-operative size assessment of the aortic annulus through different imaging techniques. That very often leads to suboptimal device selection resulting in major complications, such as aortic regurgitation and atrioventricular blocks. In this work, we propose a new, intra-operative approach to determine the diameter of the aortic annulus exploiting intra-balloon pressure and volume (p-v) data, acquired from a robotised valvuloplasty balloon catheter. This strategy, combined with current imaging-based sizing methods, would allow to obtain more accurate measurements and check whether the implantation region has changed as a consequence of the valvuloplasty procedure. That would improve TAVI device selection, potentially reducing the occurrence of the aforementioned complications. Two robotic inflation devices, capable of collecting real-time intra-balloon p-v data, were designed and interfaced with a commercially available valvuloplasty balloon catheter. A sizing algorithm that can precisely estimate the annular diameter from acquired p-v data was also implemented. The algorithm relies on a mathematical model of the balloon free inflation and an iterative method based on linear regression. Two different mathematical models of the balloon free inflation, one analytical and one numerical, were developed and compared in terms of sizing accuracy. In vitro tests were performed on idealised aortic phantoms. Experimental results show that pressure-volume data can be used to determine annular diameters bigger than the unstretched diameter of the balloon catheter. This conclusion applies to both rigid and compliant phantoms characterised by a rigidity greater than 100 kPa/%. For these cases, the proposed approach exhibited good precision (maximum average error 1.972%) and good repeatability (maximum standard deviation ±0.263 mm)

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

Improved quantification of perfusion in patients with cerebrovascular disease.

In recent years measurements of cerebral perfusion using bolus-tracking MRI have become common clinical practice in the diagnosis and management of patients with stroke and cerebrovascular disease. An active area of research is the development of methods to identify brain tissue that is at risk of irreversible damage, but amenable to salvage using reperfusion treatments, such as thrombolysis. However, the specificity and sensitivity of these methods are limited by the inaccuracies in the perfusion data. Accurate measurements of perfusion are difficult to obtain, especially in patients with cerebrovascular diseases. In particular, if the bolus of MR contrast is delayed and/or dispersed due to cerebral arterial abnormalities, perfusion is likely to be underestimated using the standard analysis techniques. The potential for such underestimation is often overlooked when using the perfusion maps to assess stroke patients. Since thrombolysis can increase the risk of haemorrhage, a misidentification of 'at-risk' tissue has potentially dangerous clinical implications. This thesis presents several methodologies which aim to improve the accuracy and interpretation of the analysed bolus-tracking data. Two novel data analysis techniques are proposed, which enable the identification of brain regions where delay and dispersion of the bolus are likely to bias the perfusion measurements. In this way true hypoperfusion can be distinguished from erroneously low perfusion estimates. The size of the perfusion measurement errors are investigated in vivo, and a parameterised characterisation of the bolus delay and dispersion is obtained. Such information is valuable for the interpretation of in vivo data, and for further investigation into the effects of abnormal vasculature on perfusion estimates. Finally, methodology is presented to minimise the perfusion measurement errors prevalent in patients with cerebrovascular diseases. The in vivo application of this method highlights the dangers of interpreting perfusion values independently of the bolus delay and dispersion

Microscopy Conference 2021 (MC 2021) - Proceedings

Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2021"

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus