Showing papers by "Gemma Piella published in 2014"

Diffusion maps for multimodal registration.

Abstract: Multimodal image registration is a difficult task, due to the significant intensity variations between the images. A common approach is to use sophisticated similarity measures, such as mutual information, that are robust to those intensity variations. However, these similarity measures are computationally expensive and, moreover, often fail to capture the geometry and the associated dynamics linked with the images. Another approach is the transformation of the images into a common space where modalities can be directly compared. Within this approach, we propose to register multimodal images by using diffusion maps to describe the geometric and spectral properties of the data. Through diffusion maps, the multimodal data is transformed into a new set of canonical coordinates that reflect its geometry uniformly across modalities, so that meaningful correspondences can be established between them. Images in this new representation can then be registered using a simple Euclidean distance as a similarity measure. Registration accuracy was evaluated on both real and simulated brain images with known ground-truth for both rigid and non-rigid registration. Results showed that the proposed approach achieved higher accuracy than the conventional approach using mutual information.

Improved myocardial motion estimation combining tissue Doppler and B-mode echocardiographic images.

Abstract: We propose a technique for myocardial motion estimation based on image registration using both B-mode echocardiographic images and tissue Doppler sequences acquired interleaved. The velocity field is modeled continuously using B-splines and the spatiotemporal transform is constrained to be diffeomorphic. Images before scan conversion are used to improve the accuracy of the estimation. The similarity measure includes a model of the speckle pattern distribution of B-mode images. It also penalizes the disagreement between tissue Doppler velocities and the estimated velocity field. Registration accuracy is evaluated and compared to other alternatives using a realistic synthetic dataset, obtaining mean displacement errors of about 1 mm. Finally, the method is demonstrated on data acquired from six volunteers, both at rest and during exercise. Robustness is tested against low image quality and fast heart rates during exercise. Results show that our method provides a robust motion estimate in these situations.

Pre to Intraoperative Data Fusion Framework for Multimodal Characterization of Myocardial Scar Tissue

Abstract: Merging multimodal information about myocardial scar tissue can help electrophysiologists to find the most appropriate target during catheter ablation of ventricular arrhythmias. A framework is presented to analyze and combine information from delayed enhancement magnetic resonance imaging (DE-MRI) and electro-anatomical mapping data. Using this information, electrical, mechanical, and image-based characterization of the myocardium are performed. The presented framework allows the left ventricle to be segmented by DE-MRI and the scar to be characterized prior to the intervention based on image information. It allows the electro-anatomical maps obtained during the intervention from a navigation system to be merged together with the anatomy and scar information extracted from DE-MRI. It also allows for the estimation of endocardial motion and deformation to assess cardiac mechanics. Therefore, electrical, mechanical, and image-based characterization of the myocardium can be performed. The feasibility of this approach was demonstrated on three patients with ventricular tachycardia associated to ischemic cardiomyopathy by integrating images from DE-MRI and electro-anatomical maps data in a common framework for intraoperative myocardial tissue characterization. The proposed framework has the potential to guide and monitor delivery of radio frequency ablation of ventricular tachycardia. It is also helpful for research purposes, facilitating the study of the relationship between electrical and mechanical properties of the tissue, as well as with tissue viability from DE-MRI.

An Adaptive Multiscale Similarity Measure for Non-rigid Registration

Abstract: Popular intensity-based similarity measures such as (normalized) mutual information estimate statistics over the entire image, neglecting spatial relationships and local image properties. In this work, we present an adaptive multiscale image similarity measure for non-rigid registration which combines image statistics at multiple scales for a multiscale representation of regional image similarities. We validated the proposed similarity measure on simulated and clinical MR brain datasets. Results show that our approach achieves higher registration accuracy and robustness than conventional global measures or their local variations at a single scale.