Catálogo de publicaciones - libros

Human brain atlases are indispensable tools in model-based segmentation and quantitative analysis of brain structures. However, adult brain atlases do not adequately represent the normal maturational patterns of the pediatric brain, and the use of an adult model in pediatric studies may introduce substantial bias. Therefore, we proposed to develop a digital atlas of the pediatric human brain in this study. The atlas was constructed from T1-weighted MR data set of a 9-year old, right-handed girl. Furthermore, we extracted and simplified boundary surfaces of 25 manually defined brain structures (cortical and subcortical) based on surface curvature. We constructed a 3D triangular mesh model for each structure by triangulation of the structure’s reference points. Kappa statistics (cortical, 0.97; subcortical, 0.91) indicated substantial similarities between the mesh-defined and the original volumes. Our brain atlas and structural mesh models (www.stjude.org/brainatlas) can be used to plan treatment, to conduct knowledge and model-driven segmentation, and to analyze the shapes of brain structures in pediatric patients.

In this work, we proposed a discriminative model of Alzheimer’s disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.

In order to achieve diagnostically useful CT (computed tomography) images of the moving heart, the standard image reconstruction has to be modified to a phase-correlated reconstruction, which considers the motion phase of the heart and generates a quasi-static image in one defined motion phase. For that purpose a synchronization signal is needed, typically a concurrent ECG recording. Commonly, the reconstruction phase is adapted by the user to the patient-specific heart motion to improve the image quality and thus the diagnostic value. The purpose of our work is to automatically identify the optimal reconstruction phase for cardiac CT imaging with respect to motion artifacts. We provide a solution for a patient- and heart rate-independent detection of the optimal phase in the cardiac cycle which shows a minimum of cardiac movement. We validated our method by the correlation with the reconstruction phase selected visually on the basis of ECG-triggering and used for the medical diagnosis. The mean difference between both reconstruction phases was 12.5 % with respect to a whole cardiac motion cycle indicating a high correlation. Additionally, reconstructed cardiac images are shown which confirm the results expressed by the correlation measurement and in some cases even indicating an improvement using the proposed method.

Freehand 3D ultrasound can be acquired without a position sensor by finding the separations of pairs of frames using information in the images themselves. Previous work has not considered how to reconstruct entirely freehand data, which can exhibit irregularly spaced frames, non-monotonic out-of-plane probe motion and significant in-plane motion. This paper presents reconstruction methods that overcome these limitations and are able to robustly reconstruct freehand data. The methods are assessed on freehand data sets and compared to reconstructions obtained using a position sensor.

MR imaging of the heart valve leaflets is a challenging problem due to their large movements throughout the cardiac cycle. This paper presents a motion-compensated imaging approach with COMB tagging for valve imaging. It involves an automatic method for tracking the full 3D motion of the valve plane so as to provide a motion-tracked acquisition scheme. Super-resolution enhancement is then applied to the slice-select direction so that the partial volume effect is minimised. results have shown that in terms of slice positioning, the method has equivalent accuracy to that of a manual approach whilst being quicker and more consistent. The use of multiple parallel COMB tags will permit adaptive imaging that follows tissue motion. This will have significant implications for quantification of myocardial perfusion and tracking anatomy, functions that are traditionally difficult in MRI.

This paper describes a rapid method for intraoperative recovery of liver motion and deformation due to respiration by using a laparoscopic freehand 3D ultrasound (US) system. Using the proposed method, 3D US images of the liver can be extended to 4D US images by acquiring additional several sequences of 2D US images during a couple of respiration cycles. Time-varying 2D US images are acquired on several sagittal image planes and their 3D positions and orientations are measured using a laparoscopic ultrasound probe to which a miniature magnetic 3D position sensor is attached. During the acquisition, the probe is assumed to move together with the liver surface. In-plane 2D deformation fields and respiratory phase are estimated from the time-varying 2D US images, and then the time-varying 3D deformation fields on the sagittal image planes are obtained by combining 3D positions and orientations of the image planes. The time-varying 3D deformation field of the volume is obtained by interpolating the 3D deformation fields estimated on several planes. The proposed method was evaluated by experiments using a pig liver.

We present a model for the numerical simulation of radio frequency (RF) ablation of tumors with mono- or bipolar probes. This model includes the electrostatic equation and a variant of the well-known bio-heat transfer equation for the distribution of the electric potential and the induced heat. The equations are nonlinearly coupled by material parameters that change with temperature, dehydration and damage of the tissue. A fixed point iteration scheme for the nonlinear model and the spatial discretization with finite elements are presented. Moreover, we incorporate the effect of evaporation of water from the cells at high temperatures using a predictor-corrector like approach. The comparison of the approach to a real ablation concludes the paper.

Digital brain atlases can be used in conjuction with magnetic resonance imaging (MRI) and computed tomography (CT) for planning and guidance during neurosurgery. Digital atlases are advantageous since they can be warped nonlinearly to fit each patient’s unique anatomy. Functional neurosurgery with implantation of deep brain stimulating (DBS) electrodes requires accurate targeting, and has become a popular surgical technique in Parkinsonian patients. In this paper, we present a method for integrating postoperative data from subthalamic (STN) DBS implantation into an antomical atlas of the basal ganglia and thalamus. The method estimates electrode position from post-operative magnetic resonance imaging (MRI) data. These electrodes are then warped back into the atlas space and are modelled in three dimensions. The average of these models is then taken to show the region where the majority of STN DBS electrodes were implanted. The group with more favorable post-operative results was separated from the group which responded to the STN DBS implantation procedure less favourably to create a probablisitic distribution of DBS in the STN electrodes.

We present a novel method to visualize registration uncertainty and a simple study to motivate the use of uncertainty visualization in computer–assisted surgery. Our visualization method resulted in a statistically significant reduction in the number of attempts required to localize a target, and a statistically significant reduction in the number of targets that our subjects failed to localize. Most notably, our work addresses the existence of uncertainty in guidance and offers a first step towards helping surgeons make informed decisions in the presence of imperfect data.

A rapid approach to monitor ablative therapy through optimizing shape and elasticity parameters is introduced. Our motivating clinical application is targeting and intraoperative monitoring of hepatic tumor thermal ablation, but the method translates to the generic problem of encapsulated stiff masses (solid organs, tumors, ablated lesions, etc.) in ultrasound imaging. The approach involves the integration of the following components: a biomechanical computational model of the tissue, a correlation approach to estimate/track tissue deformation, and an optimization method to solve the inverse problem and recover the shape parameters in the volume of interest. Successful conver-gence and reliability studies were conducted on simulated data. Then ex-vivo studies were performed on 18 ex-vivo bovine liver samples previously ablated under ultrasound monitoring in controlled laboratory environment. While B-mode ultrasound does not clearly identify the development of necrotic lesions, the proposed technique can potentially segment the ablation zone. The same framework can also yield both partial and full elasticity reconstruction.