Biomedical subjects

Isabelle E Magnin

Publications and source records attributed to Isabelle E Magnin.

9 recordsLinked to original sources

PubMed2006

MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information.

A novel approach for correcting intensity nonuniformity in magnetic resonance imaging (MRI) is presented. This approach is based on the simultaneous use of spatial and gray-level histogram information. Spatial information about intensity nonuniformity is obtained using cubic B-spline smoothing. Gray-level histogram information of the image corrupted by intensity nonuniformity is exploited from a frequential point of view. The proposed correction method is illustrated using both physical phantom and human brain images. The results are consistent with theoretical prediction, and demonstrate a new way of dealing with intensity nonuniformity problems. They are all the more significant as the ground truth on intensity nonuniformity is unknown in clinical images.

Algorithms↗

PubMed2005

A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography.

We describe a level set formulation using both shape and motion prior, for both segmentation and region tracking in high frame rate echocardiographic image sequences. The proposed approach uses the following steps: registration of the prior shape, level set segmentation constrained through the registered shape and region tracking. Registration of the prior shape is expressed as a rigid or an affine transform problem, where the transform minimizing a global region-based criterion is sought. This criterion is based on image statistics and on the available estimated axial motion data. The segmentation step is then formulated through front propagation, constrained with the registered shape prior. The same region-based criterion is used both for the registration and the segmentation step. Region tracking is based on the motion field estimated from the interframe level set evolution. The proposed approach is applied to high frame rate echocardiographic sequences acquired in vivo. In this particular application, the prior shape is provided by a medical expert and the rigid transform is used for registration. It is shown that this approach provides consistent results in terms of segmentation and stability through the cardiac cycle. In particular, a comparison indicates that the results provided by our approach are very close to the results obtained with manual tracking performed by an expert cardiologist on a Doppler Tissue Imaging (DTI) study. These preliminary results show the ability of the method to perform region tracking and its potential for dynamic parametric imaging of the heart.

Algorithms↗

PubMed2004

Quantification of multicontrast vascular MR images with NLSnake, an active contour model: in vitro validation and in vivo evaluation.

Vessel-wall measurements from multicontrast MRI provide information on plaque structure and evolution. This requires the extraction of numerous contours. In this work a contour-extraction method is proposed that uses an active contour model (NLSnake) adapted for a wide range of MR vascular images. This new method employs length normalization for the purpose of deformation computation and offers the advantages of simplified parameter tuning, fast convergence, and minimal user interaction. The model can be initialized far from the boundaries of the region to be segmented, even by only one pixel. The accuracy and reproducibility of NLSnake endoluminal contours were assessed on vascular phantom MR angiography (MRA) and high-resolution in vitro MR images of rabbit aorta. An in vivo evaluation was performed on rabbit and clinical data for both internal and external vessel-wall contours. In phantoms with 95% stenoses, NLSnake measured 94.3% +/- 3.8%, and the accuracy was even better for milder stenoses. In the images of rabbit aorta, variability between NLSnake and experts was less than interobserver variability, while the maximum intravariability of NLSnake was equal to 1.25%. In conclusion, the NLSnake technique successfully quantified the vessel lumen in multicontrast MR images using constant parameters.

Animals↗

PubMed2003

Towards ultrasound cardiac image segmentation based on the radiofrequency signal.

In echocardiography, the radio-frequency (RF) image is a rich source of information about the investigated tissues. Nevertheless, very few works are dedicated to boundary detection based on the RF image, as opposed to envelope image. In this paper, we investigate the feasibility and limitations of boundary detection in echocardiographic images based on the RF signal. We introduce two types of RF-derived parameters: spectral autoregressive parameters and velocity-based parameters, and we propose a discontinuity adaptive framework to perform the detection task. In classical echographic cardiac acquisitions, we show that it is possible to use the spectral contents for boundary detection, and that improvement can be expected with respect to traditional methods. Using the system approach, we study on simulations how the spectral contents can be used for boundary detection. We subsequently perform boundary detection in high frame rate simulated and in vivo cardiac sequences using the variance of velocity, obtaining very promising results. Our work opens the perspective of a RF-based framework for ultrasound cardiac image segmentation and tracking.

Algorithms↗

PubMed2003

A 3-D model-based registration approach for the PET, MR and MCG cardiac data fusion.

In this paper, a new approach is presented for the assessment of a 3-D anatomical and functional model of the heart including structural information from magnetic resonance imaging (MRI) and functional information from positron emission tomography (PET) and magnetocardiography (MCG). The method uses model-based co-registration of MR and PET images and marker-based registration for MRI and MCG. Model-based segmentation of MR anatomical images results in an individualized 3-D biventricular model of the heart including functional parameters from PET and MCG in an easily interpretable 3-D form.

Aged↗

PubMed2002

Exploratory analysis of the spatio-temporal deformation of the myocardium during systole from tagged MRI.

Myocardial contractile function is, with perfusion, one of the main affected factors in ischemic heart diseases. In this paper, we propose an original framework based on functional data analysis for the quantitative study of spatio-temporal parameters related to the myocardial contraction mechanics. The mechanical strains in the left-ventricular (LV) myocardium are computed from tagged magnetic resonance imaging cardiac sequences. A statistical functional model of the normal contractile function of the LV is build from the study of eight examinations on healthy subjects. We show that it is possible to detect abnormal strain patterns comparatively to this model, by generating distance maps at rest and under pharmacological stress. We demonstrate the consistency of the results for the circumferential deformation parameter on healthy and pathological data sets.

Algorithms↗

PubMed2002

A review of cardiac image registration methods.

In this paper, the current status of cardiac image registration methods is reviewed. The combination of information from multiple cardiac image modalities, such as magnetic resonance imaging, computed tomography, positron emission tomography, single-photon emission computed tomography, and ultrasound, is of increasing interest in the medical community for physiologic understanding and diagnostic purposes. Registration of cardiac images is a more complex problem than brain image registration because the heart is a nonrigid moving organ inside a moving body. Moreover, as compared to the registration of brain images, the heart exhibits much fewer accurate anatomical landmarks. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time.

Diagnostic Imaging↗

PubMed2002

Processing radio frequency ultrasound images: a robust method for local spectral features estimation by a spatially constrained parametric approach.

Spectral estimation is a major component in studies aiming at characterizing biological tissues through the analysis of backscattered radio frequency (RF) ultrasonic signals and images. However, conventional spectral estimation techniques yield a well-known trade-off between spatial resolution and variance. The backscattered signals are stochastic by nature, so short-term local analysis results in a high variance of the estimates, which cannot efficiently be reduced through conventional spatial averaging. We address this issue by describing a spectral estimation technique that reduces the variance of the estimates (by smoothing the local estimates in spectrally homogeneous regions) while preserving spectral discontinuities (i.e., the smoothing is not performed across regions with different spectral contents). The proposed approach is set in a Bayesian framework and is based on local autoregressive (AR) estimation, constrained by smoothness priors. These smoothness priors are introduced through a Markov random field in which the associated potential functions are nonquadratic, allowing thereby to preserve discontinuity. The method is validated on simulated RF images and tested on echocardiographic images acquired in vivo. The results are compared to the estimates provided by the conventional Burg technique. These results clearly demonstrate the ability of the proposed approach to improve spectral estimation in terms of variance reduction and discontinuity detection.

Algorithms↗

PubMedDate not supplied

Computer-assisted analysis of three-dimensional MR angiograms.

The software tools required for postprocessing of magnetic resonance (MR) angiograms include the following functions: data handling, image visualization, and vascular analysis. A custom postprocessing software called Magnetic Resonance Angiography Computer Assisted Analysis (MARACAS) has been developed. This software combines the most commonly used three-dimensional visualization techniques with image processing methods for analysis of vascular morphology on MR angiograms. The main contributions of MARACAS are (a) implementation of a fast method for stenosis quantification on three-dimensional MR angiograms, which is clinically applicable in a personal computer-based system; and (b) portability to the most widespread platforms. The quantification is performed in three steps: extraction of the vessel centerline, detection of vessel boundaries in planes locally orthogonal to the centerline, and calculation of stenosis parameters on the basis of the resulting contours. Qualitative results from application of the method to data from patients showed that the vessel centerline correctly tracked the vessel path and that contours were correctly estimated. Quantitative results obtained from images of phantoms showed that the computation of stenosis severity was accurate.

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