Abstract. This paper investigates the influence of static magnetic field exposure on blood flow. We mainly focus on steady flows in a rigid vessel and review the existing theoretical solutions, each based on some simplifying hypothesis. The results are developed, examined and compared, showing how the magnetohydrodynamic interactions reduce the flow rate and generate electric voltages across the vessel walls. These effects are found to be moderate for magnetic fields such as those used in magnetic resonance imaging. In

We present in this paper

Blood flow in high static magnetic fields induces elevated voltages that contaminate the ECG signal which is recorded simultaneously during MRI scans for synchronization purposes. This is known as the magnetohydrodynamic (MHD) effect, it increases the amplitude of the T wave, thus hindering correct R peak detection. In this paper, we inspect the MHD induced alterations of human ECG signals recorded in a 1.5 Tesla steady magnetic field and establish a primary characterization of the induced changes using time and frequency domain analysis. We also reexamine our previously developed real time algorithm for MRI cardiac gating and determine that, with a minor modification, this algorithm is capable of achieving perfect detection even in the presence of strong MHD artifacts.

Mouse cardiac MR gating using ECG is affected by the hostile MR environment. It requires appropriate signal processing and correct QRS detection, but gating software methods are currently limited. In this study we sought to demonstrate the feasibility of digital real-time automatically updated gating methods, based on optimizing a signal-processing technique for different mouse strains. High-resolution MR images of mouse hearts and aortic arches were acquired using a chain consisting of ECG signal detection, digital signal processing, and gating signal generation modeled using Simulink (The MathWorks, Inc., Natick, MA, USA). The signal-processing algorithms used were respectively low-pass filtering, nonlinear passband, and wavelet decomposition. Both updated and nonupdated gating signal generation methods were tested. Noise reduction was assessed by comparison of the ECG signal-to-noise ratio (SNR) before and after each processing step. Gating performance was assessed by measuring QRS detection accuracy before and after online trigger-level adjustments. Low-pass filtering with trigger-level adjustment gave the best performance for mouse cardiovascular imaging using gradient-echo (GE), spinecho (SE), and fast SE (FSE) sequences with minimum induced delay and maximum gating efficiency (99% sensitivity and Rpeak detection). This simple digital gating interface will allow various gating strategies to be optimized for cardiovascular MR explorations in mice. Magn Reson Med 57:29 -39, 2007.

This paper addresses a complex multi-physical phenomenon involving cardiac electrophysiology and hemodynamics. The purpose is to model and simulate a phenomenon that has been observed in magnetic resonance imaging machines: in the presence of a strong magnetic field, the T-wave of the electrocardiogram (ECG) gets bigger, which may perturb ECG-gated imaging. This is due to a magnetohydrodynamic (MHD) effect occurring in the aorta. We reproduce this experimental observation through computer simulations on a realistic anatomy, and with a three-compartment model: inductionless MHD equations in the aorta, bi-domain equations in the heart and electrical diffusion in the rest of the body. These compartments are strongly coupled and solved using finite elements. Several benchmark tests are proposed to assess the numerical solutions and the validity of some modeling assumptions. Then, ECGs are simulated for a wide range of magnetic field intensities (from 0 to 20 T).

Atherosclerosis initially develops predominantly at the aortic root and carotid origin, where effective visualization in mice requires efficient cardiac and respiratory gating. The present study sought to first compare the high-resolution MRI gating performance of two digital gating strategies using: 1) separate cardiac and respiratory signals (double-sensor); and 2) a singlesensor cardiorespiratory signal (ECG demodulation), and second, to apply an optimized processing technique to dynamic contrast-enhanced (CE) carotid origin vessel-wall imaging in mice. High-resolution MR mouse heart and aortic arch images were acquired by ECG signal detection, digital signal processing, and gating signal generation modeled using Simulink (MathWorks, USA). Double-sensor gating used a respiratory sensor while single-sensor gating used breathing-modulated ECG to generate a demodulated respiratory signal. Pre-and postcontrast T 1 -weighted images were acquired to evaluate vessel-wall enhancement with a gadolinium blood-pool agent (P792; Guerbet, France) at the carotid origin in vivo in ApoE -/-and C57BL/6 mice, using the optimized cardiorespiratory gating processing technique. Both strategies provided images with improved spatial resolution, less artifacts, and 100% correct transistor-to-transistor logic (TTL) signals. Image quality allowed vessel-wall enhancement measurement in all the ApoE -/-mice, with maximal (32%) enhancement 27 min postinjection. The study demonstrated the efficiency of both cardiorespiratory gating strategies for dynamic contrast-enhanced vesselwall imaging.

Cardiac Magnetic Resonance Imaging (MRI) requires synchronization to overcome motion related artifacts caused by the heart's contractions and the chest wall movements during respiration. Achieving good image quality necessitates combining cardiac and respiratory gating to produce, in real time, a trigger signal that sets off the consecutive image acquisitions. This guarantees that the data collection always starts at the same point of the cardiac cycle during the exhalation phase. In this paper, we present a real time algorithm for extracting a cardiac-respiratory trigger signal using only one, adequately placed, ECG sensor. First, an off-line calculation phase, based on wavelet decomposition, is run to compute an optimal QRS filter. This filter is used, afterwards, to accomplish R peak detection, while a low pass filtering process allows the retrieval of the respiration cycle. The algorithm's synchronization capabilities were assessed during mice cardiac MRI sessions employing three different imaging sequences, and three specific wavelet functions. The prominent image enhancement gave a good proof of correct triggering. QRS detection was almost flawless for all signals. As for the respiration cycle retrieval it was evaluated on contaminated simulated signals, which were artificially modulated to imitate respiration. The results were quite satisfactory.

A technique is proposed that allows automatic decomposition of electromyographic (EMG) signals into their constituent motor unit action potential trains (MUAPTs). A specific iterative algorithm with a classification method using fuzzy-logic techniques was developed. The proposed classification method takes into account imprecise information, such as waveform instability and irregular firing patterns, that is often encountered in EMG signals. Classification features were determined by the combining of time position and waveform information. Statistical analysis of inter-pulse intervals and spike amplitude provided an accurate estimation of features used in the classification step. Algorithm performance was evaluated using simulated EMG signals composed of up to six different discharging motor units corrupted with white noise. The algorithm was then applied to real signals recorded by a high spatial resolution surface EMG device based on a Laplacian spatial filter. On six groups of 20 simulated signals, the decomposition algorithm performed with a maximum and an average mean error rate of 2.13% and 1.37%, respectively. On real surface EMG signals recorded at different force levels (from 10% to 40% of the maximum voluntary contraction), the algorithm correctly identified 21 MUAPTs, compared with the 29 MUAPTs identified by an experienced neurophysiologist. The efficiency of the decomposition on surface EMG signals makes this method very attractive for non-invasive investigation of physiological muscle properties. However, it can also be used to decompose intramuscularly recorded EMG signals.