André Guillou1,2, Jean-Marc Sellal2,3, Sarah Ménétré1, Grégory Petitmangin1, Jacques Felblinger2,4,5, Laurent Bonnemains6,7,8. 1. Schiller SA, 4 rue Pasteur, 67160, Wissembourg, France. 2. INSERM, U947, rue du Morvan, 54511, Vandoeuvre-les-Nancy, France. 3. Department of Cardiology, CHU Nancy, rue du Morvan, 54511, Vandoeuvre-les-Nancy, France. 4. CHU Nancy, CIC-IT 801, rue du Morvan, 54511, Vandoeuvre-les-Nancy, France. 5. Department of Medical Imaging, CHU Nancy, rue du Morvan, 54511, Vandoeuvre-les-Nancy, France. 6. INSERM, U947, rue du Morvan, 54511, Vandoeuvre-les-Nancy, France. laurent.bonnemains@inserm.fr. 7. Department of Cardiac surgery, CHU Strasbourg, 1 rue de l'hopital, 67000, Strasbourg, France. laurent.bonnemains@inserm.fr. 8. University of Strasbourg, 4 rue Kirshleger, 67000, Strasbourg, France. laurent.bonnemains@inserm.fr.
Abstract
OBJECTIVE: We describe a new real-time filter to reduce artefacts on electrocardiogram (ECG) due to magnetic field gradients during MRI. The proposed filter is a least mean square (LMS) filter able to continuously adapt its step size according to the gradient signal of the ongoing MRI acquisition. MATERIALS AND METHODS: We implemented this filter and compared it, within two databases (at 1.5 and 3 T) with over 6000 QRS complexes, to five real-time filtering strategies (no filter, low pass filter, standard LMS, and two other filters optimized within the databases: optimized LMS, and optimized Kalman filter). RESULTS: The energy of the remaining noise was significantly reduced (26 vs. 68%, p < 0.001) with the new filter vs. standard LMS. The detection error of our ventricular complex (QRS) detector was: 11% with our method vs. 25% with raw ECG, 35% with low pass filter, 17% with standard LMS, 12% with optimized Kalman filter, and 11% with optimized LMS filter. CONCLUSION: The adaptive step size LMS improves ECG denoising during MRI. QRS detection has the same F1 score with this filter than with filters optimized within the database.
OBJECTIVE: We describe a new real-time filter to reduce artefacts on electrocardiogram (ECG) due to magnetic field gradients during MRI. The proposed filter is a least mean square (LMS) filter able to continuously adapt its step size according to the gradient signal of the ongoing MRI acquisition. MATERIALS AND METHODS: We implemented this filter and compared it, within two databases (at 1.5 and 3 T) with over 6000 QRS complexes, to five real-time filtering strategies (no filter, low pass filter, standard LMS, and two other filters optimized within the databases: optimized LMS, and optimized Kalman filter). RESULTS: The energy of the remaining noise was significantly reduced (26 vs. 68%, p < 0.001) with the new filter vs. standard LMS. The detection error of our ventricular complex (QRS) detector was: 11% with our method vs. 25% with raw ECG, 35% with low pass filter, 17% with standard LMS, 12% with optimized Kalman filter, and 11% with optimized LMS filter. CONCLUSION: The adaptive step size LMS improves ECG denoising during MRI. QRS detection has the same F1 score with this filter than with filters optimized within the database.
Keywords:
Electric artefact; Magnetic field gradient; Noise reduction
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