Aldo R. Mejia-Rodriguez

Elastic registration based on particle filter in radiotherapy images with brain deformations

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 patients follow up treatment.

Non-rigid Multimodal Image Registration Based on the Expectation-Maximization Algorithm

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.

Beat-to-beat response patterns of spectral sympathetic estimators to the cold face test and their comparison to those of the active orthostatic test

We assessed the effects of cold face test (CFT) 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 (LFRR, 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 CFT 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 CFT 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 CFT is smaller.

Rigid Multimodal/Multispectral Image Registration Based on the Expectation-Maximization Algorithm

In this paper, we present a novel methodology for multimodal/multispectral rigid image registration. The proposed approach is formulated by using the Expectation-Maximization (EM) technique in order to estimate a geometric transformation that aligns the multiparametric images to register. In this approach, the images alignment relies on hidden stochastic random variables which allow to compare the multiparametric intensity values between images of different modalities. The methodology is basically composed by an EM-technique approach, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the rigid transformation are computed. The proposed algorithm was tested with different kinds of medical images; our results show that the proposed methodology is a good alternative for rigid multimodal/multispectral registration.

A new Probabilistic Active Contour region-based method for multiclass medical image segmentation

AbstractIn medical imaging, the availability of robust and accurate automatic segmentation methods is very important for a user-independent and time-saving delineation of regions of interest. In this work, we present a new variational formulation for multiclass image segmentation based on active contours and probability density functions demonstrating that the method is fast, accurate, and effective for MRI brain image segmentation. We define an energy function assuming that the regions to segment are independent. The first term of this function measures how much the pixels belong to each class and forces the regions to be disjoint. In order for this term to be outlier-resistant, probability density functions were used allowing to define the structures to be segmented. The second one is the classical regularization term which constrains the border length of each region removing inhomogeneities and noise. Experiments with synthetic and real images showed that this approach is robust to noise and presents an accuracy comparable to other classical segmentation approaches (in average DICE coefficient over 90% and ASD below one pixel), with further advantages related to segmentation speed. Graphical Abstract

Multimodal image registration based on the expectation–maximisation methodology

In this study, a new framework for multimodal image registration is proposed based on the expectation–maximisation (EM) methodology. This framework allows to address simultaneously parametric and elastic registrations independently on the modality of the target and source images without making any assumptions about their intensity relationship. The EM formulation for the image registration problem leads to a regularised quadratic optimisation scheme to compute the displacement vector field (DVF) that aligns the images and depends on their joint intensity distribution. At the first stage, a parametric transformation is assumed for the DVF, where the resulting quadratic optimisation is computed recursively to calculate its optimal parameters. Next, a general unknown deformation models the elastic part of the DVF, which is represented by an additive structure. The resulting optimisation process by the EM formulation results in a cost function that involves data and regularisation terms, which is also solved recursively. A comprehensive evaluation of the parametric and elastic proposals is carried out by comparing to state-of-the-art algorithms and images from different application fields, where an advantage is visualised by the authors’ proposal in terms of a compromise between accuracy and robustness.

An innovative multimodal/multispectral image registration method for medical images based on the Expectation-Maximization algorithm.

In this paper, we present a methodology for multimodal/ multispectral image registration of medical images. This approach is formulated by using the Expectation-Maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

Multimodal Non-Rigid Registration Methods Based on Demons Models and Local Uncertainty Quantification Used in 3D Brain Images

In this work, we propose a novel fully automated method to solve the 3D multimodal non-rigid image registration problem. The proposed strategy overcomes the monomodal intensity restriction of fluid-like registration (FLR) models, such as Demons-based registration algorithms, by applying a mapping that relies on an intensity uncertainty quantification in a local neighbourhood, bringing the target and source images into a common domain where they are comparable, no matter their image modalities or mismatched intensities between them. The proposed methodology was tested with T1, T2 and PD weighted brain magnetic resonance (MR) images with synthetic deformations, and CT-MR brain images from a radiotherapy clinical case. The performance of the proposed approach was evaluated quantitatively by standard indices that assess the correct alignment of anatomical structures of interest. The results obtained in this work show that the addition of the local uncertainty mapping properly resolve the monomodal restriction of FLR algorithms when same anatomic counterparts exists in the images to register, and suggest that the proposed strategy can be an option to achieve multimodal 3D registrations.

Non-rigid registration based on local uncertainty quantification and fluid models for multiparametric MR images

In this work, we present a novel fully automated multimodal elastic registration method for medical images. The new methodology combines a novel mapping based on the quantification of the intensity uncertainty of the neighborhood pixels, with a monomodal fluid like registration technique; thus the methodology can be summarized as a two-step technique. First, a mapping over both multimodal images is applied. This mapping provides information about the intensity uncertainty of the neighborhood pixels in both images, and it is based on the entropy computed over a local region. Second, a monomodal non-rigid registration is achieved between the transformed images. For this step, it is proposed to use a registration based on fluid-models: demons, diffeomorphic-demons, and a variation of the classical optical-flow. To evaluate the algorithm, a set composed by 12 magnetic resonance images of different modalities (T1, T2 and proton density) were taken from a brain model, and these images were modified by a set of controlled elastic deformations (using splines), in order to generate ground-truths to be registered with the proposed technique. The obtained results in this work showed an average error of less than 1.3 mm by combining the local uncertainty mapping with the diffeomorphic-demons technique, suggesting that the proposed methodology could be considered as a new alternative for fully automated multimodal non-rigid registrations on medical applications, which also ensures to obtain only possible physically deformations.

Mesh-based approach for the 3D analysis of anatomical structures of interest in Radiotherapy

In this paper a method based on mesh surfaces approximations for the 3D analysis of anatomical structures in Radiotherapy (RT) is presented. Parotid glands meshes constructed from Megavoltage CT (MVCT) images were studied in terms of volume, distance between center of mass (distCOM) of the right and left parotids, dice similarity coefficient (DICE), maximum distance between meshes (DMax) and the average symmetric distance (ASD). A comparison with the standard binary images approach was performed. While absence of significant differences in terms of volume, DistCOM and DICE indices suggests that both approaches are comparable, the fact that the ASD showed significant difference (p=0.002) and the DMax was almost significant (p=0.053) suggests that the mesh approach should be adopted to provide accurate comparison between 3D anatomical structures of interest in RT.