Register now After registration you will be able to apply for this opportunity online.
Contactless Fiber-Optic Photoplethysmography-based Gating for MRI
This project aims to collect diverse forehead PPG datasets using a newly developed device, to evaluate variability across populations and sensor placements and, to explore their impact on signal quality. You will apply classical signal processing and machine learning methods to extract reliable MRI triggers from the PPG signal to statistically quantify the pulse arrival time (PAT) and its variability. If time permits, you may further investigate the extraction of respiratory-modulated components from the PPG waveform.
Keywords: Flow MRI, Cardiac Gating, PPG, Contactless, Fiber Optic, Signal Processing, ML
In clinical routine, 2D and 4D Flow MRI are typically performed with cardiac synchronization [1] using an electrocardiogram (ECG) with patch electrodes [2] to bin the data into cardiac phases. Since the ECG signal is distorted by the magneto-hydrodynamic effect [3] and may be compromised by the switching of the MRI gradient fields [4], robust gating can be challenging. Since the quality also depends on the positioning and skin contact of the patch electrodes, the setup process can be time consuming. To address these issues, alternative techniques have been developed such as self-gating techniques [5], pilot tone navigation [6] and contact finger pulse units [7].
Inspired by approaches using camera-based photoplethysmography (PPG) [8], which can be limited by frame rate and/or PPG signal-to-noise ratio, the use of a contactless fiber-optic PPG device on the forehead in a contactless fashion is proposed.
In clinical routine, 2D and 4D Flow MRI are typically performed with cardiac synchronization [1] using an electrocardiogram (ECG) with patch electrodes [2] to bin the data into cardiac phases. Since the ECG signal is distorted by the magneto-hydrodynamic effect [3] and may be compromised by the switching of the MRI gradient fields [4], robust gating can be challenging. Since the quality also depends on the positioning and skin contact of the patch electrodes, the setup process can be time consuming. To address these issues, alternative techniques have been developed such as self-gating techniques [5], pilot tone navigation [6] and contact finger pulse units [7]. Inspired by approaches using camera-based photoplethysmography (PPG) [8], which can be limited by frame rate and/or PPG signal-to-noise ratio, the use of a contactless fiber-optic PPG device on the forehead in a contactless fashion is proposed.
The project aims at:
• Establishing a data quality baseline using contact-based forehead-PPG measurements.
• Collecting data using a contactless PPG acquisition.
• Collection of various PPG datasets: wavelength (green and red), skin type (Fitzpatrick scale), demographics (age, …).
• Study PPG signal quality and apply classical signal processing and machine learning methods to extract triggers/gating signals from the PPG signal.
The project aims at: • Establishing a data quality baseline using contact-based forehead-PPG measurements. • Collecting data using a contactless PPG acquisition. • Collection of various PPG datasets: wavelength (green and red), skin type (Fitzpatrick scale), demographics (age, …). • Study PPG signal quality and apply classical signal processing and machine learning methods to extract triggers/gating signals from the PPG signal.
Sébastien Emery (emery@biomed.ee.ethz.ch), Marco Giordano (marco.giordano@pbl.ee.ethz.ch) , Dr. Tommaso Polonelli (Tommaso.polonelli@pbl.ee.ethz.ch), Prof. Dr. Sebastian Kozerke (kozerke@biomed.ee.ethz.ch)
Sébastien Emery (emery@biomed.ee.ethz.ch), Marco Giordano (marco.giordano@pbl.ee.ethz.ch) , Dr. Tommaso Polonelli (Tommaso.polonelli@pbl.ee.ethz.ch), Prof. Dr. Sebastian Kozerke (kozerke@biomed.ee.ethz.ch)
ContactlessFiberOpticPPG.pdf
