This paper presents the evaluation of the accuracy of an elastic registration algorithm, based on the particle filter and an optical flow process. The algorithm is applied in brain CT and MRI simulated image datasets, and MRI images from a real clinical radiotherapy case. To validate registration accuracy, standard indices for registration accuracy assessment were calculated: the dice similarity coefficient (DICE), the average symmetric distance (ASD) and the maximal distance between pixels (Dmax). The results showed that this registration process has good accuracy, both qualitatively and quantitatively, suggesting that this method may be considered as a good new option for radiotherapy applications like patient's follow up treatment.

A method to estimate the pulmonary fibrosis in computed tomography (CT) imaging is presented. A semi-automatic segmentation algorithm based on the Chan-Vese method was used. The proposed method shows a similar fibrosis region with respect to clinical expert. However, the results need to be validated in a bigger data base. The proposed method approximates a fibrosis percentage that allows to achieve this procedure easily in order to support its implementation in the clinical practice minimizing the clinical expert subjectivity and generating a quantitativeestimation of fibrosis region.

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This paper presents a novel non-rigid multimodal registration method that relies on three basic steps: first, an initial approximation of the deformation field is obtained by a parametric registration technique based on particle filtering; second, an intensity mapping based on local variability measures (LVM) is applied over the two images in order to overcome the multimodal restriction between them; and third, an optical flow method is used in an iterative way to find the remaining displacements of the deformation field. Hence the new methodology offers a solution for multimodal NRR by a quadratic optimisation over a convex surface, which allows independent motion of each pixel, in contrast to methods that parameterise the deformation space. To evaluate the proposed method, a set of magnetic resonance/computed tomography clinical studies (pre- and post-radiotherapy treatment) of three patients with cerebral tumour deformations of the brain structures was employed. The resulting registration was evaluated both qualitatively and quantitatively by standard indices of correspondence over anatomical structures of interest in radiotherapy (brain, tumour and cerebral ventricles). These results showed that one of the proposed LVM (entropy) offers a superior performance in estimating the non-rigid deformation field

We assessed the effects of cold face test (eFT) and active orthostatic test (AOT) on the RR intervals (RR), systolic pressure (SP) and maximal amplitude of arterial pressure first derivative (dmAP) time series of 25 healthy volunteers, and the instantaneous dynamics of their low frequency powers (LF RR, LFsp and LFdmAP), to characterize their time course, and compare their performance as sympathetic markers as well as the magnitude of the sympathetic response evoked by each maneuver. All the variables studied displayed distinct instantaneous response patterns to each maneuver: while in eFT they increased to a plateau, in AOT they presented overshoots at the beginning and end of the test.In both tests, LFdmAP and LFsp dynamics were similar and strongly correlated, and presented a weak correlation with LFRR. Means of LFdmAP and LFsp in eFT were 7 times smaller than in AOT. Our findings support that LFsp and LFdmAP powers exhibit similar performance as noninvasive sympathetic markers and that all variables studied show distinctive beat-to-beat response patterns to each maneuver. Using the sympathetic response produced by AOT as reference, the one evoked by eFT is smaller.

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Abstract. In this paper, we present a novel methodology for multimodal non-rigid image registration. The proposed approach is formulated by using the Expectation-Maximization (EM) technique in order to estimate a displacement vector field that aligns the images to register. In this approach, the image alignment relies on hidden stochastic random variables which allow to compare the intensity values between images of different modality. The methodology is basically composed of two steps: first, we provide an initial estimation of the the global deformation vector field by using a rigid registration technique based on particle filtering, obtaining, at the same time, an initial estimation of the joint conditional intensity distribution of the registered images; second, we approximate the remaining deformations by applying an iterative EM-technique approach, where at each step, a new estimation of the joint conditional intensity distribution and the displacement vector field are computed. The proposed algorithm was tested with different kinds of medical images; preliminary results show that the methodology is a good alternative for non-rigid multimodal registration.

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