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Odette Fokapu

Publications and source records attributed to Odette Fokapu.

2 recordsLinked to original sources

Real-time gating system for mouse cardiovascular MR imaging.

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), spin-echo (SE), and fast SE (FSE) sequences with minimum induced delay and maximum gating efficiency (99% sensitivity and R-peak detection). This simple digital gating interface will allow various gating strategies to be optimized for cardiovascular MR explorations in mice.

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Reference signal extraction from corrupted ECG using wavelet decomposition for MRI sequence triggering: application to small animals.

BACKGROUND: Present developments in Nuclear Magnetic Resonance (NMR) imaging techniques strive for improved spatial and temporal resolution performances. However, trying to achieve the shortest gradient rising time with high intensity gradients has its drawbacks: It generates high amplitude noises that get superimposed on the simultaneously recorded electrophysiological signals, needed to synchronize moving organ images. Consequently, new strategies have to be developed for processing these collected signals during Magnetic Resonance Imaging (MRI) examinations. The aim of this work is to extract an efficient reference signal, from an electrocardiogram (ECG) that was contaminated by the NMR artefacts. This may be used for image triggering and/or cardiac rhythm monitoring. METHODS: Our method, based on sub-band decomposition using wavelet filters, is tested on various ECG signals recorded during three imaging sequences: Gradient Echo (GE), Fast Spin Echo (FSE) and Inversion Recovery with Spin Echo (IRSE). In order to define the most adapted wavelet functions to use according to the excitation protocols, noise generated by each imaging sequence is recorded and analysed. After exploring noise models along with information found in the literature, a group of 14 wavelets, members of three families (Daubechies, Coiflets, Symlets), is selected for the study. The extraction process is carried out by decomposing the contaminated ECG signals into 8 scales using a given wavelet function, then combining the sub-bands necessary for cardiac synchronization, i.e. those containing the essential part of the QRS energy, to construct a reference signal. RESULTS: The efficiency of the presented method has been tested on a group of quite representative signals containing: highly contaminated (mean SNR<--5 dB) simulated ECGs that replicate normal and pathological human heart beats, as well as some pathological and healthy rodents' actual ECG records. Despite the weak SNR of the contaminated ECG, the performances were quite satisfactory. When comparing the wavelet performances, one may notice that for a given sequence, some wavelets are more efficient for processing than others; for GE, FSE and IRSE sequence, good synchronisation condition is accomplished with coif5, sym8, and sym4 respectively. CONCLUSION: Sub-band decomposition proved to be very suitable for extracting a reference signal from a corrupted ECG for MRI triggering. An appropriate choice of the wavelet function, in accordance with the image sequence type, could considerably improve the quality of the reference signal for better image synchronization.

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