Emiliano D'Agostino

A viscous fluid model for multimodal non-rigid image registration using mutual information.

We propose a multimodal free-form registration algorithm based on maximization of mutual information. The warped image is modeled as a viscous fluid that deforms under the influence of forces derived from the gradient of the mutual information registration criterion. Parzen windowing is used to estimate the joint intensity probability of the images to be matched. The method is evaluated for non-rigid inter-subject registration of MR brain images. The accuracy of the method is verified using simulated multi-modal MR images with known ground truth deformation. The results show that the root mean square difference between the recovered and the ground truth deformation is smaller than 1 voxel. We illustrate the application of the method for atlas-based brain tissue segmentation in MR images in case of gross morphological differences between atlas and patient images.

Nonrigid Coregistration of Diffusion Tensor Images Using a Viscous Fluid Model and Mutual Information

In this paper, a nonrigid coregistration algorithm based on a viscous fluid model is proposed that has been optimized for diffusion tensor images (DTI), in which image correspondence is measured by the mutual information criterion. Several coregistration strategies are introduced and evaluated both on simulated data and on brain intersubject DTI data. Two tensor reorientation methods have been incorporated and quantitatively evaluated. Simulation as well as experimental results show that the proposed viscous fluid model can provide a high coregistration accuracy, although the tensor reorientation was observed to be highly sensitive to the local deformation field. Nevertheless, this coregistration method has demonstrated to significantly improve spatial alignment compared to affine image matching.

On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain

Voxel based morphometry (VBM) has been increasingly applied to detect diffusion tensor (DT) image abnormalities in patients for different pathologies. An important requisite for a robust VBM analysis is the availability of a high-dimensional non-rigid coregistration technique that is able to align both the spatial and the orientational DT information. Consequently, there is a need for an inter-subject DTI atlas as a group specific reference frame that also contains this orientational DT information. In this work, a population based DTI atlas has been developed that incorporates such orientational DT information with high accuracy and precision. The proposed methodology for constructing such an atlas is compared with a subject based DTI atlas, in which a single subject is selected as the reference image. Our results demonstrate that the population based atlas framework is more accurate with respect to the underlying diffusion information.

A Viscous Fluid Model for Multimodal Non-rigid Image Registration Using Mutual Information

We propose a multimodal free-form registration algorithm based on maximization of mutual information. The warped image is modeled as a viscous fluid that deforms under the influence of forces derived from the gradient of the mutual information registration criterion. Parzen windowing is used to estimate the joint intensity probability of the images to be matched. The method is evaluated for non-rigid inter-subject registration of MR brain images. The accuracy of the method is verified using simulated multi-modal MR images with known ground truth deformation. The results show that the root mean square difference between the recovered and the ground truth deformation is smaller than 1 voxel. We illustrate the application of the method for atlas-based brain tissue segmentation in MR images in case of gross morphological differences between atlas and patient images.

Non-rigid atlas-to-image registration by minimization of class-conditional image entropy

We propose a new similarity measure for atlas-to-image matching in the context of atlas-driven intensity-based tissue classification of MR brain images. The new measure directly matches probabilistic tissue class labels to study image intensities, without need for an atlas MR template. Non-rigid warping of the atlas to the study image is achieved by free-form deformation using a viscous fluid regularizer such that mutual information (MI) between atlas class labels and study image intensities is maximized. The new registration measure is compared with the standard approach of maximization of MI between atlas and study images intensities. Our results show that the proposed registration scheme indeed improves segmentation quality, in the sense that the segmentations obtained using the atlas warped with the proposed non-rigid registration scheme better explain the study image data than the segmentations obtained with the atlas warped using standard intensity-based MI.

Non-rigid brain image registration using a statistical deformation model

In this article, we propose a new registration method, based on a statistical analysis of deformation fields. At first, a set of MRI brain images was registered using a viscous fluid algorithm. The obtained deformation fields are then used to calculate a Principal Component Analysis (PCA) based decomposition. Since PCA models the deformations as a linear combination of statistically uncorrelated principal components, new deformations can be created by changing the coefficients in the linear combination. We then use the PCA representation of the deformation fields to non-rigidly align new sets of images. We use a gradient descent method to adjust the coefficients of the principal components, such that the resulting deformation maximizes the mutual information between the deformed image and an atlas image. The results of our method are promising. Viscous fluid registrations of new images can be recovered with an accuracy of about half a voxel. Better results can be obtained by using a more extensive database of learning images (we only used 84). Also, the optimization method used here can be improved, especially to shorten computation time.

Hippocampal malrotation in pediatric patients with epilepsy associated with complex prefrontal dysfunction

Purpose:  The cognitive consequences of hippocampal malrotation (HIMAL) were investigated in a matched control study of children with epilepsy.

Linear normalization of MR brain images in pediatric patients with periventricular leukomalacia

The feasibility of linear normalization of child brain images with structural abnormalities due to periventricular leukomalacia (PVL) was assessed in terms of success rate and accuracy of the normalization algorithm. Ten T1-weighted brain images from healthy adult subject and 51 from children (4-11 years of age) were linearly transformed to achieve spatial registration with the standard MNI brain template. Twelve of the child brain images were radiologically normal, 22 showed PVL and 17 showed PVL with additional enlargement of the lateral ventricles. The effects of simple modifications to the normalization process were evaluated: changing the initial orientation and zoom parameters, masking non-brain areas, smoothing the images and using a pediatric template instead of the MNI template. Normalization failure was reduced by changing the initial zoom parameters and by removing background noise. The overall performance of the normalization algorithm was only improved when background noise was removed from the images. The results show that linear normalization of PVL affected brain images is feasible.

Measuring material/tissue permittivity by UWB Time-domain Reflectometry techniques

A model for simulating reflections of ultrawideband (UWB) pulses in multilayered tissue structures, has been implemented and verified using commercially available software. The permittivity of the different layers was altered sequentially and the corresponding reflections have been analyzed. Building on these experiments, the use of pulsed UWB is envisaged for the in-vivo measurement of complex tissue permittivity, in a non invasive way. Eventually, we will apply this technique to radiotherapy, trying to correlate the changes in permittivity with the absorbed radiation dose. A measurement setup using Time-Domain Reflectometry (TDR) is proposed.

Geometric Regularization for 2-D Myocardial Strain Quantification in Mice: An In-Silico Study

Myocardial strain quantification in the mouse based on 2-D speckle tracking using real-time ultrasound datasets is feasible but remains challenging. The major difficulty lies in the fact that the frame rate-to-heart rate ratio is relatively low, causing significant decorrelation between subsequent frames. In this setting, regularization is therefore particularly important to discard motion estimates that are improbable. Different regularization methods have been proposed, among which is a class of regularizers based on enforcing preset geometrical characteristics of the motion field. To date, these regularization methods have not been contrasted. The aim of this study was thus to compare the performance of different geometric regularizers in the setting of myocardial motion and strain estimation in murine echocardiography using simulated datasets. In normal models, restricting the spatial curvature of the motion fields resulted in worse radial strain estimates (mean root-mean-square [RMS] error increased from 0.06 to 0.09; p < 0.05), but better circumferential strain estimates (mean RMS error decreased from 0.035 to 0.01; p < 0.05). More accurate circumferential strain estimates were also obtained by convolving a Gaussian function with the lateral motion components (mean RMS error decreased to 0.015; p < 0.05). In infarcted models, no significant differences were found between regularized and nonregularized radial strains. However, for circumferential strain, the curvature method yielded better strain estimates in all regions (mean RMS error decreased from 0.043 to 0.015; p < 0.05), whereas the Gaussian method only improved strain assessment in the remote myocardium (mean RMS error decreased to 0.021; p < 0.05).