Catálogo de publicaciones - libros

A sampled-data filtering framework is presented for cardiac motion recovery from periodic medical image sequences. Cardiac dynamics is a continuously evolving physiological process, whereas the imaging data can provide only sampled measurements at discrete time instants. Stochastic multi-frame filtering frameworks are constructed to couple the continuous cardiac dynamics with the discrete measurements, and to deal with the parameter uncertainty of the biomechanical constraining model and the noisy nature of the imaging data in a coordinated fashion. The state estimates are predicted according to the continuous-time state equation between observation time points, and then updated with the new measurements obtained at discrete time instants, yielding physically more meaningful and more accurate estimation results. Both continuous-discrete Kalman filter and sampled-data filter are applied, and the scheme can give robust estimation results when the noise statistics is not available a priori. The sampled-data estimation strategies are validated through synthetic data experiments to illustrate their advantages and on canine MR phase contrast images to show their clinical relevance.

Laparoscopic surgery poses many different constraints to the operating surgeon, this has resulted in a slow uptake of advanced laparoscopic procedures. Traditional approaches to the assessment of surgical performance rely on prior classification of a cohort of surgeons’ technical skills for validation, which may introduce subjective bias to the outcome. In this study, Hidden Markov Models (HMMs) are used to learn surgical maneuvers from 11 subjects with mixed abilities. By using the leave-one-out method, the HMMs are trained without prior clustering subjects into different skills levels, and the output likelihood indicates the similarity of a particular subject’s motion trajectories to the group. The experimental results demonstrate the strength of the method in ranking the quality of trajectories of the subjects, highlighting its value in minimizing the subjective bias in skills assessment for minimally invasive surgery.

We have developed a novel multiscale localized image texture analysis technique, based upon the polar Stockwell Transform (PST). In this paper we characterized image texture in vivo using the PST in histologically verified lesion areas in T2-weighted MRI of an animal model of multiple sclerosis. Both high and low frequency signals, representing inflammation and demyelination, were significantly increased in pathological regions compared to normal control tissue. This suggests that this new local spatial-frequency measure of image texture may provide a sensitive and precise indication of disease activity.

The subthalamic nucleus (STN) has been adopted as a commonly used surgical target in deep brain stimulation (DBS) procedures for the treatment of Parkinson’s disease. Many techniques have been developed to facilitate STN DBS targeting, and consequently to improve the surgical outcome. In this work, we conducted a retrospective study on 10 patients who were treated with bilateral STN DBS to assess the target localization accuracy and precision of six methods in STN DBS surgery. A visualization and navigation system integrated with normalized functional and anatomical information was employed to perform the targeting procedures. Actual surgical target location determined by an experienced neurosurgeon with pre-operative image-guided surgical target/trajectory planning and intra-operative electrophysiological exploration and confirmation was considered as the “gold standard” in this evaluation and was compared with those localized using each of the six targeting methods. The mean distance between the actual surgical targets and those planned was 3.0 ± 1.3mm, 3.2 ± 1.1mm, 2.9 ± 1.1mm, 2.7 ± 1.2mm, 2.5 ± 1.0mm, and 1.7 ± 0.8mm for targeting approaches based on T-weighted magnetic resonance image (MRI), brain atlas, T and T maps, electrophysiological database, collection of final surgical targets of previous patients, and the combination of these functional and anatomical data respectively. The results demonstrated that the use of functional data along with anatomical data provides reliable and accurate target position for STN DBS.

A new method is proposed to unambiguously define a geometric partitioning of 3D models of female thorax. A breast partitioning scheme is derived from simple geometric primitives and well-defined anatomical points. Relevant measurements can be extrapolated from breast partition. Our method has been tested on a number of breast 3D models acquired by means of a commercial scanner on real clinical cases.

Ground Glass Opacity (GGO) is defined as hazy increased attenuation within a lung that is not associated with obscured underlying vessels. Since pure (nonsolid) or mixed (partially solid) GGO at the thin-section CT are more likely to be malignant than those with solid opacity, early detection and treatment of GGO can improve a prognosis of lung cancer. However, due to indistinct boundaries and inter- or intra-observer variation, consistent manual detection and segmentation of GGO have proved to be problematic. In this paper, we propose a novel method for automatic detection and segmentation of GGO from chest CT images. For GGO detection, we develop a classifier by boosting -NN, whose distance measure is the Euclidean distance between the nonparametric density estimates of two examples. The detected GGO region is then automatically segmented by analyzing the texture likelihood map of the region. We applied our method to clinical chest CT volumes containing 10 GGO nodules. The proposed method detected all of the 10 nodules with only one false positive nodule. We also present the statistical validation of the proposed classifier for GGO detection as well as very promising results for automatic GGO segmentation. The proposed method provides a new powerful tool for automatic detection as well as accurate and reproducible segmentation of GGO.

We present a model-based framework for imaging 3D cardiac transmembrane potential (TMP) distributions from body surface potential (BSP) measurements. Based on physiologically motivated modeling of the spatiotemporal evolution of TMPs and their projection to body surface, the cardiac electrophysiology is modeled as a stochastic system with TMPs as the latent dynamics and BSPs as external measurements. Given the patient-specific data from BSP measurements and tomographic medical images, the inverse problem of electrocardiography (IECG) is solved via state estimation of the underlying system, using the unscented Kalman filtering (UKF) for data assimilation. By incorporating comprehensive physiological information, the framework enables direct recovery of intracardiac electrophysiological events free from commonly used physical equivalent cardiac sources, and delivers accurate, robust, and fast converging results under different noise levels and types. Experiments concerning individual variances and pathologies are also conducted to verify its feasibility in patient-specific applications.

We have developed a method for the automated segmentation of the internal limiting membrane and the pigment epithelium in 3-D OCT retinal images. Each surface was found as a minimum cut from a geometric graph constructed from edge/regional information and -determined surface constraints. Our approach was tested on 18 3-D data sets (9 from patients with normal optic discs and 9 from patients with papilledema) obtained using a Stratus OCT-3 scanner. Qualitative analysis of surface detection correctness indicates that our method consistently found the correct surfaces and outperformed the proprietary algorithm used in the Stratus OCT-3 scanner. For example, for the internal limiting membrane, 4% of the 2-D scans had minor failures with no major failures using our approach, but 19% of the 2-D scans using the Stratus OCT-3 scanner had minor or complete failures.

Spinal cord analysis is an important problem relating to the study of various neurological diseases. We present a novel approach to spinal cord segmentation in magnetic resonance images. Our method uses 3D “deformable organisms” (DefOrg) an artificial life framework for medical image analysis that complements classical deformable models (snakes and deformable meshes) with high-level, anatomically-driven control mechanisms. The DefOrg framework allows us to model the organism’s body as a growing generalized tubular spring-mass system with an adaptive and predominantly elliptical cross section, and to equip them with spinal cord specific sensory modules, behavioral routines and decision making strategies. The result is a new breed of robust DefOrgs, “spinal crawlers”, that crawl along spinal cords in 3D images, accurately segmenting boundaries, and providing sophisticated, clinically-relevant structural analysis. We validate our method through the segmentation of spinal cords in clinical data and provide comparisons to other segmentation techniques.

Accurate patient registration and referencing is a key element in navigated surgery. Unfortunately all existing methods are either invasive or very time consuming. We propose a fully non-invasive optical approach using a tracked monocular endoscope to reconstruct the surgical scene in 3D using photogrammetric methods. The 3D reconstruction can then be used for matching the pre-operative data to the intra-operative scene. In order to cope with the near real-time requirements for referencing, we use a novel, efficient 3D point management method during 3D model reconstruction.

The presented prototype system provides a reconstruction accuracy of 0.1 mm and a tracking accuracy of 0.5 mm on phantom data. The ability to cope with real data is demonstrated by cadaver experiments.