M. F. Santarelli

Multichannel techniques for motion artifacts removal from electrocardiographic signals.

Electrocardiographic (ECG) signals are affected by several kinds of artifacts, that may hide vital signs of interest. Motion artifacts, due to the motion of the electrodes in relation to patient skin, are particularly frequent in bioelectrical signals acquired by wearable systems. In this paper we propose different approaches in order to get rid of motion confounds. The first approach we follow starts from measuring electrode motion provided by an accelerometer placed on the electrode and use this measurement in an adaptive filtering system to remove the noise present in the ECG. The second approach is based on independent component analysis methods applied to multichannel ECG recordings; we propose to use both instantaneous model and a frequency domain implementation of the convolutive model that accounts for different paths of the source signals to the electrodes

A model of ultrasound backscatter for the assessment of myocardial tissue structure and architecture

A statistical parametric model of returning echoes from myocardium is theorized in order to investigate the relationship between normal myocardium structure and spectral signatures with the use of ultrasonic tissue characterization. It is hypothesized, that in a clinical setting the normal myofiber architecture in the left ventricular wall is structured as a matrix of cylindrical scatterers whose orientation and spatial distribution vary according to two different statistical distribution laws: (1) a Gaussian law to approximate parametric angular myofiber variability at each site within the myocardial wall; (2) a gamma distribution law to describe parametric regularity in scatterer interdistance. In the model, the effect of the angle of insonification with respect to the alignment of myofibers on ultrasound backscatter was considered. The slope of the power spectral density (PSD) evaluated within the echocardiographic transducer bandwidth has been used as a ultrasonic tissue characterization parameter. The model has been tested by computer simulation and in vitro measurements on myocardial pig tissue specimens. The concordance between experimental and simulated results confirms that the model accounts for the process underlying the echo formation from normal myocardium. Moreover, it provides a simple method of simulation which can be easily implemented and used for the assessment of pathologic alterations.

Frequency domain approach to blind source separation in ECG monitoring by wearable system

In this paper we present a method for removing artifacts from biomedical signals acquired by wearable systems, taking advantage of multichannel data acquisition since both artifacts and signals of interest show common features in different channels. In order to take into account the effects of the different paths from the source signals to the sensors, we propose a method based on blind separation of convolutive mixtures: the observed data are seen as linear mixtures of filtered source signals where neither the source signals nor the convolution and mixing processes are known. The only hypothesis we make to recover the original sources is the statistical independence among them. The proposed method was applied on real ECG signals corrupted by motion artifacts with satisfactory results

Quantitative 3D assessment of myocardial viability with MRI delayed contrast enhancement

Myocardial viability is a fundamental question in the clinical and therapeutic decision making process. Contrast-enhanced MRI can distinguish between viable and necrotic myocardium in non-invasive manner and with excellent definition of endocardial and epicardial borders. Aim of this study is to propose a software methodology that allows to assess the global, transmural and intramural extent of myocardial necrosis providing both bull-eyes and 3D representation of contrast delayed enhanced area in MRI cardiac images.

Nonlinear anisotropic filtering as a tool for SNR enhancement in cardiovascular MRI

This article deals with an anisotropic filtering technique for the enhancement of the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) without loss of resolution in Magnetic Resonance Imaging (MRI). This technique overcomes the major drawbacks of conventional filters, namely the blurring of organ boundary and the suppression of fine details in MR images. The authors demonstrated that the application of such a filter in the post-processing phase is equivalent to time domain averaging during MR image acquisition. It preserves acquisition speed and image quality, making this technique particularly adapted to cardiovascular applications. This new technique has been tested on simple test objects and on human images of the heart.

A new algorithm for 3D automatic detection and tracking of cardiac wall motion

We present a new algorithm for surface tracking of volumetric cardiac data with a gradient-vector-flow (GVF)-based deformable parametric model applied to non-linear anisotropic diffusion filtered images. The system allows automated quantitative image analysis of sequences of volumetric medical data in order to track endocardium and epicardium in anatomical studies with magnetic resonance. In order to test the developed algorithm on real data, dynamic sequences of multislice magnetic resonance images have been processed.

Independent component analysis of fMRI data: a model based approach for artifacts separation

Independent component analysis applied to functional magnetic resonance imaging is a promising technique for non invasive study of brain function. We examine the behavior of spatial ICA decomposition applying ICA to simulated data sets. We study the ICA performances in presence of movement correlated and uncorrelated with activation task, also taking into account the presence of rician distributed noise. We show that the presence of image artifacts due to simulated subject movement and MRI noise greatly affects the method ability to reveal the activation, especially in the presence of movement correlated with activation task. Spatial smoothing of data, before ICA, seems to overcome this problem, allowing us to retrieve the original sources also in the presence of both correlated movement and high noise level.

Volume rendering in medicine: the role of image coherence

A study is described dealing with the role of image coherence on medical volume rendering algorithm performances. In particular, a simulator has been implemented in order to assess how algorithm performance can be improved by exploiting the amount of coherence for volumetric images. Moreover, typical cardiac images of different imaging modalities, were analysed in order to derive experimental coherence degree and relevant performance optimization indexes.

Multimodal cardiac image fusion by geometrical features registration and warping

Multimodal image fusion is an important step in multiparametric analysis from different cardiac images recorded over time from different imaging modalities. In fact, low resolution images, such as PET or SPECT can be integrated with high resolution images as MRI or CTI, to show both perfusion and structure information in the same representation. A fundamental step in this integration process is to bring the modalities involved into spatial alignment, a procedure referred as multimodal image fusion. In the present paper we describe a method for multimodal cardiac images fusion, consisting on segmentation, registration and warping phases.