Assessment Of Intra-coronary Stent Location And Extension In Intravascular Ultrasound Sequences.

Purpose An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter-based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment. Methods The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm. Results The technique was extensively validated on a set of 103 IVUS of sequences of in vivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F-measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5 mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F-measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5 mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present. Conclusions Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method

Automatic segmentation of cross-sectional coronary arterial images

We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels

Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives

Combinatorial optimisation for arterial image segmentation.

Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods

Flipping Biological Switches: Solving for Optimal Control: A Dissertation

Switches play an important regulatory role at all levels of biology, from molecular switches triggering signaling cascades to cellular switches regulating cell maturation and apoptosis. Medical therapies are often designed to toggle a system from one state to another, achieving a specified health outcome. For instance, small doses of subpathologic viruses activate the immune system’s production of antibodies. Electrical stimulation revert cardiac arrhythmias back to normal sinus rhythm. In all of these examples, a major challenge is finding the optimal stimulus waveform necessary to cause the switch to flip. This thesis develops, validates, and applies a novel model-independent stochastic algorithm, the Extrema Distortion Algorithm (EDA), towards finding the optimal stimulus. We validate the EDA’s performance for the Hodgkin-Huxley model (an empirically validated ionic model of neuronal excitability), the FitzHugh-Nagumo model (an abstract model applied to a wide range of biological systems that that exhibit an oscillatory state and a quiescent state), and the genetic toggle switch (a model of bistable gene expression). We show that the EDA is able to not only find the optimal solution, but also in some cases excel beyond the traditional analytic approaches. Finally, we have computed novel optimal stimulus waveforms for aborting epileptic seizures using the EDA in cellular and network models of epilepsy. This work represents a first step in developing a new class of adaptive algorithms and devices that flip biological switches, revealing basic mechanistic insights and therapeutic applications for a broad range of disorders