Physiological Recording in the MRI Environment (PRiME): MRI-Compatible Hemodynamic Recording System

IEEE J Transl Eng Health Med. 2018 Mar 1;6:4100112. doi: 10.1109/JTEHM.2018.2807813. eCollection 2018.


Hemodynamic recording during interventional cardiovascular procedures is essential for procedural guidance, monitoring patient status, and collection of diagnostic information. Recent advances have made interventions guided by magnetic resonance imaging (MRI) possible and attractive in certain clinical scenarios. However, in the MRI environment, electromagnetic interference (EMI) can cause severe distortions and artifacts in acquired hemodynamic waveforms. The primary aim of this paper was to develop and validate a system to minimize EMI on electrocardiogram (ECG) and invasive blood pressure (IBP) signals. A system was developed which incorporated commercial MRI compatible ECG leads and pressure transducers, custom electronics, user interface, and adaptive signal processing. Measurements were made on pediatric patients (N = 6) during MRI-guided catheterization. Real-time interactive scanning, which is known to produce significant EMI due to fast gradient switching and varying imaging plane orientations, was selected for testing. The effectiveness of the adaptive algorithms was determined by measuring the reduction of noise peaks, amplitude of noise peaks, and false QRS triggers. During real-time gradient-intensive imaging sequences, peak noise amplitude was reduced by 80% and false QRS triggers were reduced to a median of 0. There was no detectable interference on the IBP channels. A hemodynamic recording system front-end was successfully developed and deployed, which enabled high-fidelity recording of ECG and IBP during MRI scanning. The schematics and assembly instructions are publicly available to facilitate implementation at other institutions. Researchers and clinicians are provided a critical tool in investigating and implementing MRI guided interventional cardiovascular procedures.

Keywords: Adaptive signal processing; biomedical electronics; biomedical equipment; biomedical signal processing; cardiology; catheterization; magnetic resonance imaging.

Grant support

This work was supported in part by the Intramural Research Program of the NIH Center for Information Technology and the Division of Intramural Research of the National Heart, Lung, and Blood Institute, NIH under Grant Z01-HL006040.