3D/2D Registration of Mapping Catheter Images for Arrhythmia Interventional Assistance

Radiofrequency (RF) catheter ablation has transformed treatment for tachyarrhythmias and has become first-line therapy for some tachycardias. The precise localization of the arrhythmogenic site and the positioning of the RF catheter over that site are problematic: they can impair the efficiency of the procedure and are time consuming (several hours). Electroanatomic mapping technologies are available that enable the display of the cardiac chambers and the relative position of ablation lesions. However, these are expensive and use custom-made catheters. The proposed methodology makes use of standard catheters and inexpensive technology in order to create a 3D volume of the heart chamber affected by the arrhythmia. Further, we propose a novel method that uses a priori 3D information of the mapping catheter in order to estimate the 3D locations of multiple electrodes across single view C-arm images. The monoplane algorithm is tested for feasibility on computer simulations and initial canine data.Comment: International Journal of Computer Science Issues, IJCSI, Volume 4, Issue 2, pp10-19, September 200

An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration

We propose numerical algorithms for solving large deformation diffeomorphic image registration problems. We formulate the nonrigid image registration problem as a problem of optimal control. This leads to an infinite-dimensional partial differential equation (PDE) constrained optimization problem. The PDE constraint consists, in its simplest form, of a hyperbolic transport equation for the evolution of the image intensity. The control variable is the velocity field. Tikhonov regularization on the control ensures well-posedness. We consider standard smoothness regularization based on $H^1$- or $H^2$-seminorms. We augment this regularization scheme with a constraint on the divergence of the velocity field rendering the deformation incompressible and thus ensuring that the determinant of the deformation gradient is equal to one, up to the numerical error. We use a Fourier pseudospectral discretization in space and a Chebyshev pseudospectral discretization in time. We use a preconditioned, globalized, matrix-free, inexact Newton-Krylov method for numerical optimization. A parameter continuation is designed to estimate an optimal regularization parameter. Regularity is ensured by controlling the geometric properties of the deformation field. Overall, we arrive at a black-box solver. We study spectral properties of the Hessian, grid convergence, numerical accuracy, computational efficiency, and deformation regularity of our scheme. We compare the designed Newton-Krylov methods with a globalized preconditioned gradient descent. We study the influence of a varying number of unknowns in time. The reported results demonstrate excellent numerical accuracy, guaranteed local deformation regularity, and computational efficiency with an optional control on local mass conservation. The Newton-Krylov methods clearly outperform the Picard method if high accuracy of the inversion is required.Comment: 32 pages; 10 figures; 9 table

Practical application of contrast-enhanced magnetic resonance mammography [CE-MRM] by an algorithm combining morphological and enhancement patterns

The purpose of this article is to report our practical utilization of dynamic contrast-enhanced magnetic resonance mammography [DCE-MRM] in the diagnosis of breast lesions. In many European centers, was preferred a high-temporal acquisition of both breasts simultaneously in a large FOV. We preferred to scan single breasts, with the aim to combine the analysis of the contrast intake and washout with the morphological evaluation of breast lesions. We followed an interpretation model, based upon a diagnostic algorithm, which combined contrast enhancement with morphological evaluation, in order to increase our confidence in diagnosis. DCE-MRM with our diagnostic algorithm has identified 179 malignant and 41 benign lesions; final outcome has identified 178 malignant and 42 benign lesions, 3 false positives and 2 false negatives. Sensitivity of CE-MRM was 98.3%; specificity, 95.1%; positive predictive value 98.9%; negative predictive,. value, 92.8% and accuracy, 97.7%. (C) 2008 Elsevier Ltd. All rights reserve

Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy

The current state-of-the-art assessment of treatment response in breast cancer is based on the response evaluation criteria in solid tumors (RECIST). RECIST reports on changes in gross morphology and divides response into one of four categories. In this paper we highlight how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) may be able to offer earlier, and more precise, information on treatment response in the neoadjuvant setting than RECIST. We then describe how longitudinal registration of breast images and the incorporation of intelligent bioinformatics approaches with imaging data have the potential to increase the sensitivity of assessing treatment response. We conclude with a discussion of the potential benefits of breast MRI at the higher field strength of 3T. For each of these areas, we provide a review, illustrative examples from clinical trials, and offer insights into future research directions

Incorporating Breast Asymmetry Studies into CADx Systems

Breast cancer is one of the global leading causes of death among women, and an early detection is of uttermost importance to reduce mortality rates. Screening mammograms, in which radiologists rely only on their eyesight, are one of the most used early detection methods. However, characteristics, such as the asymmetry between breasts, a feature that could be very difficult to visually quantize, is key to breast cancer detection. Due to the highly heterogeneous and deformable structure of the breast itself, incorporating asymmetry measurements into an automated detection system is still a challenge. In this study, we proposed the use of a bilateral registration algorithm as an effective way to automatically measure mirror asymmetry. Furthermore, this information was fed to a machine learning algorithm to improve the accuracy of the model. In this study, 449 subjects (197 with calcifications, 207 with masses, and 45 healthy subjects) from a public database were used to train and evaluate the proposed methodology. Using this procedure, we were able to independently identify subjects with calcifications (accuracy = 0.825, AUC = 0.882) and masses (accuracy = 0.698, AUC = 0.807) from healthy subjects

Computer vision and optimization methods applied to the measurements of in-plane deformations

fi=vertaisarvioitu|en=peerReviewed