Non-invasive monitoring of pulmonary artery pressure at the bedside.

Current solutions for the monitoring of pulmonary artery pressure (PAP) in patients suffering from pulmonary hypertension are limited to invasive means. Non-invasive alternatives, such as Doppler echocardiography, are incompatible with continuous monitoring due to their dependency on qualified personnel to perform the measurements. In the present study, a novel non-invasive and unsupervised approach based on the use of electrical impedance tomography (EIT) is presented. The approach was evaluated in three healthy subjects undergoing hypoxia-induced variations in PAP. A timing parameter - physiologically linked to the PAP via the so-called pulse wave velocity principle - was automatically extracted from the EIT data. Reference systolic PAP estimates were obtained by echocardiography. Strong correlation scores (r e [0.844, 0.990]) were found between the EIT-derived parameter and the reference PAP, thereby suggesting the validity of the proposed approach. If confirmed in larger datasets, these findings could open the way for a new branch of fully non-invasive hemodynamic monitors for patients with pulmonary hypertension.

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images.

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.