Time-frequency analysis of optical and electrical cardiac signals with applications in ultra-high-field MRI

Electrocardiography (ECG) is the standard method for assessing the state of the cardiovascular system non-invasively. In the context of magnetic resonance imaging (MRI) the ECG signal is used for cardiac monitoring and triggering, i.e., the acquisition of images synchronized to the cardiac cycle. However, ECG acquisition is impeded by the static and dynamic magnetic fields which alter the measured voltages and may reduce signal-to-noise ratio (SNR), leading to false alarms during cardiac monitoring or to image artifacts during cardiac triggering. A major source of noise is the magnetohydrodynamic (MHD) effect as it is proportional to field strength and represents a key challenge in application of ultra-high-field (UHF) MRI >=7 T.

In this work, two approaches for overcoming these limitations are proposed: i) Development of a hardware and software system based on the principal of photoplethysmography imaging (PPGi) as an optical method for acquiring a cardiac signal, and ii) development of algorithms for detecting fiducial points in ECG signals despite the low SNR.

Due to the non-stationary dynamics of the cardiac activity, extraction of information from both types of signals is realized by time-frequency analysis. The feasibility of the PPGi system for heart rate measurement is demonstrated in an UHF MRI study where PPGi signals acquired from the forehead outperformed ECG in terms of accuracy and reliability. Application of the system at the sole of the foot for triggering allowed producing UHF MR angiography images with a quality similar to pulse oximetry triggering in a healthy volunteer.

During the work on ECG algorithms, a general framework for multiscale parameter estimation (msPE) is developed. First, it is customized for delineation of (non-MR related) ECG signals from the reference QT database. Second, it is used for QRS detection in ECG signals acquired within UHF MRI. Processing the QT database shows that msPE is well-suited for processing ECG waves and outperforms state-of-the-art algorithms w.r.t. sensitivity in 4/9 fiducial points. QRS detection in ECG signals acquired within 7 T is shown to be robust with a sensitivity >=95% and an accuracy degraded by 1 ms compared to ECG signals without MHD noise.

In summary, the proposed methods may provide useful steps towards unlocking the full potential of cardiac assessment in UHF MRI.


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