4,591 research outputs found
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
Contrast-enhanced ultrasound tracking of helical propellers with acoustic phase analysis and comparison with color Doppler
Medical microrobots (MRs) hold the potential to radically transform several interventional procedures. However, to guarantee therapy success when operating in hard-to-reach body districts, a precise and robust imaging strategy is required for monitoring and controlling MRs in real-time. Ultrasound (US) may represent a powerful technology, but MRs' visibility with US needs to be improved, especially when targeting echogenic tissues. In this context, motions of MRs have been exploited to enhance their contrast, e.g., by Doppler imaging. To exploit a more selective contrast-enhancement mechanism, in this study, we analyze in detail the characteristic motions of one of the most widely adopted MR concepts, i.e., the helical propeller, with a particular focus on its interactions with the backscattered US waves. We combine a kinematic analysis of the propeller 3D motion with an US acoustic phase analysis (APA) performed on the raw radio frequency US data in order to improve imaging and tracking in bio-mimicking environments. We validated our US-APA approach in diverse scenarios, aimed at simulating realistic in vivo conditions, and compared the results to those obtained with standard US Doppler. Overall, our technique provided a precise and stable feedback to visualize and track helical propellers in echogenic tissues (chicken breast), tissue-mimicking phantoms with bifurcated lumina, and in the presence of different motion disturbances (e.g., physiological flows and tissue motions), where standard Doppler showed poor performance. Furthermore, the proposed US-APA technique allowed for real-time estimation of MR velocity, where standard Doppler failed
Hemodynamic wall shear stress in models of atherosclerotic plaques using phase contrast magnetic resonance velocimetry and computational fluid dynamics
Thesis made openly available per email from author, 5/4/2018.Ph.D.Don P. Gidden
Semiautomated Skeletonization of the Pulmonary Arterial Tree in Micro-CT Images
We present a simple and robust approach that utilizes planar images at different angular rotations combined with unfiltered back-projection to locate the central axes of the pulmonary arterial tree. Three-dimensional points are selected interactively by the user. The computer calculates a sub- volume unfiltered back-projection orthogonal to the vector connecting the two points and centered on the first point. Because more x-rays are absorbed at the thickest portion of the vessel, in the unfiltered back-projection, the darkest pixel is assumed to be the center of the vessel. The computer replaces this point with the newly computer-calculated point. A second back-projection is calculated around the original point orthogonal to a vector connecting the newly-calculated first point and user-determined second point. The darkest pixel within the reconstruction is determined. The computer then replaces the second point with the XYZ coordinates of the darkest pixel within this second reconstruction. Following a vector based on a moving average of previously determined 3- dimensional points along the vessel\u27s axis, the computer continues this skeletonization process until stopped by the user. The computer estimates the vessel diameter along the set of previously determined points using a method similar to the full width-half max algorithm. On all subsequent vessels, the process works the same way except that at each point, distances between the current point and all previously determined points along different vessels are determined. If the difference is less than the previously estimated diameter, the vessels are assumed to branch. This user/computer interaction continues until the vascular tree has been skeletonized
Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation
Purpose: Automatic methods of analyzing of retinal vascular networks, such as retinal
blood vessel detection, vascular network topology estimation, and arteries / veins classi cation
are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide
spectrum of diseases.
Methods: We propose a new framework for precisely segmenting retinal vasculatures,
constructing retinal vascular network topology, and separating the arteries and veins. A
non-local total variation inspired Retinex model is employed to remove the image intensity
inhomogeneities and relatively poor contrast. For better generalizability and segmentation
performance, a superpixel based line operator is proposed as to distinguish between lines and
the edges, thus allowing more tolerance in the position of the respective contours. The concept
of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel
network into arteries and veins.
Results: The proposed segmentation method yields competitive results on three pub-
lic datasets (STARE, DRIVE, and IOSTAR), and it has superior performance when com-
pared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964,
respectively. The topology estimation approach has been applied to ve public databases
1
(DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830,
0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries / veins classi cation
based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and
VICAVR) are 0.90.9, 0.910, and 0.907, respectively.
Conclusions: The experimental results show that the proposed framework has e ectively
addressed crossover problem, a bottleneck issue in segmentation and vascular topology recon-
struction. The vascular topology information signi cantly improves the accuracy on arteries
/ veins classi cation
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