Towards Image-Guided Pediatric Atrial Septal Defect Repair

Congenital heart disease occurs in 107.6 out of 10,000 live births, with Atrial Septal Defects (ASD) accounting for 10\% of these conditions. Historically, ASDs were treated with open heart surgery using cardiopulmonary bypass, allowing a patch to be sewn over the defect. In 1976, King et al. demonstrated use of a transcatheter occlusion procedure, thus reducing the invasiveness of ASD repair. Localization during these catheter based procedures traditionally has relied on bi-plane fluoroscopy; more recently trans-esophageal echocardiography (TEE) and intra-cardiac echocardiography (ICE) have been used to navigate these procedures. Although there is a high success rate using the transcatheter occlusion procedure, fluoroscopy poses radiation dose risk to both patient and clinician. The impact of this dose to the patients is important as many of those undergoing this procedure are children, who have an increased risk associated with radiation exposure. Their longer life expectancy than adults provides a larger window of opportunity for expressing the damaging effects of ionizing radiation. In addition, epidemiologic studies of exposed populations have demonstrated that children are considerably more sensitive to the carcinogenic effects radiation. Image-guided surgery (IGS) uses pre-operative and intra-operative images to guide surgery or an interventional procedure. Central to every IGS system is a software application capable of processing and displaying patient images, registration between multiple coordinate systems, and interfacing with a tool tracking system. We have developed a novel image-guided surgery framework called Kit for Navigation by Image Focused Exploration (KNIFE). This software system serves as the core technology by which a system for reduction of radiation exposure to pediatric patients was developed. The bulk of the initial work in this research endevaour was the development of KNIFE which itself went through countless iterations before arriving at its current state as per the feature requirements established. Secondly, since this work involved the use of captured medical images and their use in an IGS software suite, a brief analysis of the physics behind the images was conducted. Through this aspect of the work, intrinsic parameters (principal point and focal point) of the fluoroscope were quantified using a 3D grid calibration phantom. A second grid phantom was traversed through the fluoroscopic imaging volume of II and flat panel based systems at 2 cm intervals building a scatter field of the volume to demonstrate pincushion and \u27S\u27 distortion in the images. Effects of projection distortion on the images was assessed by measuring the fiducial registration error (FRE) of each point used in two different registration techniques, where both methods utilized ordinary procrustes analysis but the second used a projection matrix built from the fluoroscopes calculated intrinsic parameters. A case study was performed to test whether the projection registration outperforms the rigid transform only. Using the knowledge generated were able to successfully design and complete mock clinical procedures using cardiac phantom models. These mock trials at the beginning of this work used a single point to represent catheter location but this was eventually replaced with a full shape model that offered numerous advantages. At the conclusion of this work a novel protocol for conducting IG ASD procedures was developed. Future work would involve the construction of novel EM tracked tools, phantom models for other vascular diseases and finally clinical integration and use

Investigation of Neonatal Pulmonary Structure and Function via Proton and Hyperpolarized Gas Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a modality that utilizes the phenomenon of nuclear magnetic resonance (NMR) to yield tomographic images of the body. Proton (1H) MRI has historically been successful in soft tissues but has suffered in the lung due to a variety of technical challenges, such as the low proton-density, rapid T2* relaxation time of the lung parenchymal tissue, and inherent physiological motion in the chest. Recent developments in radial ultrashort echo time (UTE) MRI have in part overcome these issues. In addition, there has been much progress in techniques for hyperpolarization of noble gases (3He and 129Xe) out of thermal equilibrium via spin exchange optical pumping, which can greatly enhance the gas NMR signal such that it is detectable within the airspaces of the lung on MRI. The lung is a unique organ due to its complex structural and functional dynamics, and its early development through the neonatal (newborn) period is not yet well understood in normal or abnormal conditions. Pulmonary morbidities are relatively common in infants and are present in a majority of patients admitted to the neonatal intensive care unit, often stemming from preterm birth and/or congenital defects. Current clinical lung imaging in these patients is typically limited to chest x-ray radiography, which does not provide tomographic information and so has lowered sensitivity. More rarely, x-ray computed tomography (CT) is used but exposes infants to ionizing radiation and typically requires sedation, both of which pose increased risks to pediatric patients. Thus the opportunity is ripe for application of novel pulmonary MRI techniques to the infant population. However, MR imaging of very small pulmonary structure and microstructure requires fundamental changes in the imaging theory of both 1H UTE MRI and hyperpolarized gas diffusion MRI. Furthermore, such young patients are often non-compliant, yielding a need for new and innovative techniques for monitoring respiratory and bulk motion. This dissertation describes methodology development and provides experimental results in both 1H UTE MRI and hyperpolarized 3He and 129Xe gas diffusion MRI, with investigation into the structure and function of infant lungs at both the macrostructural and microstructural level. In particular, anisotropically restricted gas diffusion within infant alveolar microstructure is investigated as a measurement of airspace size and geometry. Additionally, the phenomenon of respiratory and bulk motion-tracking via modulation of the k-space center\u27s magnitude and phase is explored and applied via UTE MRI in various neonatal pulmonary conditions to extract imaging-based metrics of diagnostic value. Further, the proton-density regime of pulmonary UTE MRI is validated in translational applications. These techniques are applied in infants with various pulmonary conditions, including patients diagnosed with bronchopulmonary dysplasia, congenital diaphragmatic hernia, esophageal atresia/tracheoesophageal fistula, tracheomalacia, and no suspected lung disease. In addition, explanted lung specimens from both infants with and without lung disease are examined. Development and implementation of these techniques involves a strong understanding of the physics-based theory of NMR, hyperpolarization, and MR imaging, in addition to foundations in hardware, software, and image analysis techniques. This thesis first outlines the theory and background of NMR, MRI, and pulmonary physiology and development (Part I), then proceeds into the theory, equipment, and imaging experiments for hyperpolarized gas diffusion MRI in infant lung airspaces (Part II), and finally details the theory, data processing methods, and applications of pulmonary UTE MRI in infant patients (Part III). The potential for clinical translation of the neonatal pulmonary MRI methods presented in this dissertation is very high, with the foundations of these techniques firmly rooted in the laws of physics

Focal Spot, Winter 2008/2009

https://digitalcommons.wustl.edu/focal_spot_archives/1110/thumbnail.jp

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world