A novel MRA-based framework for the detection of changes in cerebrovascular blood pressure.

Background: High blood pressure (HBP) affects 75 million adults and is the primary or contributing cause of mortality in 410,000 adults each year in the United States. Chronic HBP leads to cerebrovascular changes and is a significant contributor for strokes, dementia, and cognitive impairment. Non-invasive measurement of changes in cerebral vasculature and blood pressure (BP) may enable physicians to optimally treat HBP patients. This manuscript describes a method to non-invasively quantify changes in cerebral vasculature and BP using Magnetic Resonance Angiography (MRA) imaging. Methods: MRA images and BP measurements were obtained from patients (n=15, M=8, F=7, Age= 49.2 ± 7.3 years) over a span of 700 days. A novel segmentation algorithm was developed to identify brain vasculature from surrounding tissue. The data was processed to calculate the vascular probability distribution function (PDF); a measure of the vascular diameters in the brain. The initial (day 0) PDF and final (day 700) PDF were used to correlate the changes in cerebral vasculature and BP. Correlation was determined by a mixed effects linear model analysis. Results: The segmentation algorithm had a 99.9% specificity and 99.7% sensitivity in identifying and delineating cerebral vasculature. The PDFs had a statistically significant correlation to BP changes below the circle of Willis (p-value = 0.0007), but not significant (p-value = 0.53) above the circle of Willis, due to smaller blood vessels. Conclusion: Changes in cerebral vasculature and pressure can be non-invasively obtained through MRA image analysis, which may be a useful tool for clinicians to optimize medical management of HBP

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

Neuroimage

ObjectivesThe lenticulostriate arteries (LSAs) with small diameters of a few hundred microns take origin directly from the high flow middle cerebral artery (MCA), making them especially susceptible to damage (e.g. by hypertension). This study aims to present high resolution (isotropic ~0.5 mm), black blood MRI for the visualization and characterization of LSAs at both 3T and 7T.Materials and MethodsT1-weighted 3D turbo spin-echo with variable flip angles (T1w TSE-VFA) sequences were optimized for the visualization of LSAs by performing extended phase graph (EPG) simulations. Twenty healthy volunteers (15 under 35 years old, 5 over 60 years old) were imaged with the T1w TSE-VFA sequences at both 3T and 7T. Contrast-to-noise ratio (CNR) was quantified, and LSAs were manually segmented using ITK-SNAP. Automated Reeb graph shape analysis was performed to extract features including vessel length and tortuosity. All quantitative metrics were compared between the two field strengths and two age groups using ANOVA.ResultsLSAs can be clearly delineated using optimized 3D T1w TSE-VFA at 3T and 7T, and a greater number of LSA branches can be detected compared to those by time-of-flight MR angiography (TOF MRA) at 7T. The CNR of LSAs was comparable between 7T and 3T. T1w TSE-VFA showed significantly higher CNR than TOF MRA at the stem portion of the LSAs branching off the medial middle cerebral artery. The mean vessel length and tortuosity were greater on TOF MRA compared to TSE-VFA. The number of detected LSAs by both TSE-VFA and TOF MRA was significantly reduced in aged subjects, while the mean vessel length measured on 7T TSE-VFA showed significant difference between the two age groups.ConclusionThe high-resolution black-blood 3D T1w TSE-VFA sequence offers a new method for the visualization and quantification of LSAs at both 3T and 7T, which may be applied for a number of pathological conditions related to the damage of LSAs.P41 EB015922/EB/NIBIB NIH HHS/United StatesUH2 NS100614/NS/NINDS NIH HHS/United StatesS10 OD025312/OD/NIH HHS/United StatesS10 OD025312/CD/ODCDC CDC HHS/United StatesK25 AG056594/AG/NIA NIH HHS/United StatesUH3 NS100614/NS/NINDS NIH HHS/United StatesP01 AG052350/AG/NIA NIH HHS/United States2020-10-01T00:00:00Z31158475PMC66889588401vault:3371

VALIDATION OF COMPUTATIONAL FLUID DYNAMIC SIMULATIONS OF MEMBRANE ARTIFICIAL LUNGS WITH X-RAY IMAGING

The functional performance of membrane oxygenators is directly related to the perfusion dynamics of blood flow through the fiber bundle. Non-uniform flow and design characteristics can limit gas exchange efficiency and influence susceptibility of thrombus development in the fiber membrane. Computational fluid dynamics (CFD) is a powerful tool for predicting properties of the flow field based on prescribed geometrical domains and boundary conditions. Validation of numerical results in membrane oxygenators has been predominantly based on experimental pressure measurements with little emphasis placed on confirmation of the velocity fields due to opacity of the fiber membrane and limitations of optical velocimetric methods. A novel approach was developed using biplane X-ray digital subtraction angiography to visualize flow through a commercial membrane artificial lung at 1–4.5 L/min. Permeability based on the coefficients of the Ergun equation, α and β, were experimentally determined to be 180 and 2.4, respectively, and the equivalent spherical diameter was shown to be approximately equal to the outer fiber diameter. For all flow rates tested, biplane image projections revealed non-uniform radial perfusion through the annular fiber bundle, yet without flow bias due to the axisymmetric position of the outlet. At 1 L/min, approximately 78.2% of the outward velocity component was in the radial (horizontal) plane verses 92.0% at 4.5 L/min. The CFD studies were unable to predict the non-radial component of the outward perfusion. Two-dimensional velocity fields were generated from the radiographs using a cross-correlation tracking algorithm and compared with analogous image planes from the CFD simulations. Velocities in the non-porous regions differed by an average of 11% versus the experimental values, but simulated velocities in the fiber bundle were on average 44% lower than experimental. A corrective factor reduced the average error differences in the porous medium to 6%. Finally, biplane image pairs were reconstructed to show 3-D transient perfusion through the device. The methods developed from this research provide tools for more accurate assessments of fluid flow through membrane oxygenators. By identifying non-invasive techniques to allow direct analysis of numerical and experimental velocity fields, researchers can better evaluate device performance of new prototype designs

Improved modelling of the human cerebral vasculature

Ph.DDOCTOR OF PHILOSOPH

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography