A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation

A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data acquired during neurosurgery

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

b Laboratorio de Procesado de Imagen,

The Wiener filter is the well-known solution for linear minimum mean square error (LMMSE) signal estimation. This filter assumes the mean to be known and usually constant. On the other hand, the Kriging filter is an incremental theory, developed within the Geostatistical community, with respect to that of Wiener filters. The extension relies on adopting a parametric model for the mean (usually a polynomial). The goal of this paper is twofold. First it is intended as a comprehensive treatment of the Kriging approach from a Signal Processing perspective, with previous uses of Kriging in Signal Processing being extended. Second, we are deriving a general methodology for FIR filter design, including any situation where an optimal FIR estimator from possibly incomplete and/or noisy data is needed. Selected examples illustrate the performance of the method on some common Image Processing problems (interpolation, approximation and filtering)

Detection of point landmarks . . .

This paper describes a unified approach to the detection of point landmarks—whose neighborhoods convey discriminant information—including multidimensional scalar, vector, and higher-order tensor data. The method is based on the interpretation of generalized correlation matrices derived from the gradient of tensor functions, a probabilistic interpretation of point landmarks, and the application of tensor algebra. Results on both synthetic and real tensor dat

An Embedding Framework for Myocardial Velocity Field Mapping with MRI

This paper presents an embedding framework for myocardial velocity processing with MRI based on a hollow, semi-spherical template. The relationship between vector bundle and manifold mapping is analysed and three different mapping methods that include tangent mapping, normal mapping, and normal-tangent mapping are assessed for their practical value of myocardial contractility analysis. The proposed method provides a basis for consistent volume matching and vector correspondence, in addition to the ease of calculating biomechanical indices such as radial, circumferential and longitudinal strain rates without the concern of boundary effects. Detailed analysis results with both synthetic and in vivo MR velocity data sets are provided

Nonrigid registration using regularized matching weighted by local structure

Abstract. We present a novel approach to nonrigid registration of volumetric multimodal medical data. We propose a new regularized template matching scheme, where arbitrary similarity measures can be embedded and the regularization imposes spatial coherence taking into account the quality of the matching according to an estimation of the local structure. We propose to use an efficient variation of weighted least squares termed normalized convolution as a mathematically coherent framework for the whole approach. Results show that our method is fast as accurate.