Self-Attention MHDNet: A Novel Deep Learning Model for the Detection of R-Peaks in the Electrocardiogram Signals Corrupted with Magnetohydrodynamic Effect

Magnetic resonance imaging (MRI) is commonly used in medical diagnosis and minimally invasive image-guided operations. During an MRI scan, the patient's electrocardiogram (ECG) may be required for either gating or patient monitoring. However, the challenging environment of an MRI scanner, with its several types of magnetic fields, creates significant distortions of the collected ECG data due to the Magnetohydrodynamic (MHD) effect. These changes can be seen as irregular heartbeats. These distortions and abnormalities hamper the detection of QRS complexes, and a more in-depth diagnosis based on the ECG. This study aims to reliably detect R-peaks in the ECG waveforms in 3 Tesla (T) and 7T magnetic fields. A novel model, Self-Attention MHDNet, is proposed to detect R peaks from the MHD corrupted ECG signal through 1D-segmentation. The proposed model achieves a recall and precision of 99.83% and 99.68%, respectively, for the ECG data acquired in a 3T setting, while 99.87% and 99.78%, respectively, in a 7T setting. This model can thus be used in accurately gating the trigger pulse for the cardiovascular functional MRI.

A hardware and software system for MRI applications requiring external device data

PURPOSE

Numerous MRI applications require data from external devices. Such devices are often independent of the MRI system, so synchronizing these data with the MRI data is often tedious and limited to offline use. In this work, a hardware and software system is proposed for acquiring data from external devices during MR imaging, for use online (in real-time) or offline.

METHODS

The hardware includes a set of external devices - electrocardiography (ECG) devices, respiration sensors, microphone, electronics of the MR system etc. - using various channels for data transmission (analog, digital, optical fibers), all connected to a server through a universal serial bus (USB) hub. The software is based on a flexible client-server architecture, allowing real-time processing pipelines to be configured and executed. Communication protocols and data formats are proposed, in particular for transferring the external device data to an open-source reconstruction software (Gadgetron), for online image reconstruction using external physiological data. The system performance is evaluated in terms of accuracy of the recorded signals and delays involved in the real-time processing tasks. Its flexibility is shown with various applications.

RESULTS

The real-time system had low delays and jitters (on the order of 1 ms). Example MRI applications using external devices included: prospectively gated cardiac cine imaging, multi-modal acquisition of the vocal tract (image, sound, and respiration) and online image reconstruction with nonrigid motion correction.

CONCLUSION

The performance of the system and its versatile architecture make it suitable for a wide range of MRI applications requiring online or offline use of external device data.

Magnetic Resonance Imaging of Contracting Ultrathin Cardiac Tissue

OBJECTIVE

Integrating cardiac-tissue patches into the beating heart and evaluating the long-term effects of such integration on cardiac contractility are two challenges in an emerging field of regenerative medicine. This pilot study presents tools for the imaging of contracting multicellular cardiac tissue constructs (MTCs) in vitro and demonstrates the feasibility of tracking the early development of strand geometry and contractions in ultrathin strands and layers of cardiac tissue using CINE MRI.

APPROACH

Cultured, ultrathin (~50-100-micron) MTCs of rat neonatal cardiomyocytes were plated in rectangular cell chambers (4.5 × 2.0 cm) with and without ultrathin, carbon EP electrodes embedded in the floor of the cell chamber. Two-dimensional, steady-state free precession (SSFP) CINE MRI, cell microscopy, and tissue photography were performed on Days 5-9 of cell development. Potential confounders and MRI artifacts were evaluated using non-contracting cardiac tissues and cell-free chambers filled with the cell-culture medium.

MAIN RESULTS

Synchronized contractions formed by Day 7; individual contracting tissue strands became identifiable by Day 9. The global patterns and details of the strand geometry and movement patterns in the SSFP images were in excellent agreement with microscopic and photographic images. No synchronized movement was identifiable by either microscopy or CINE MRI in the non-contracting MTCs or the cell-free medium. The EP recordings revealed well-defined depolarization and repolarization waveforms; the imaging artifacts generated by the carbon electrodes were small.

SIGNIFICANCE

This pilot study demonstrates the feasibility of imaging cardiac-strand patterns and contractile activity in ultrathin, two-dimensional cardiac tissue in commonly used clinical scanners.

ECG Electrode Placements for Magnetohydrodynamic Voltage Suppression

This study aims to investigate a set of electrocardiogram (ECG) electrode lead locations to improve the quality of four-lead ECG signals acquired during magnetic resonance imaging (MRI). This was achieved by identifying electrode placements that minimized the amount of induced magnetohydrodynamic voltages (VMHD) in the ECG signals. Reducing VMHD can improve the accuracy of QRS complex detection in ECG as well as heartbeat synchronization between MRI and ECG during the acquisition of cardiac cine. A vector model based on thoracic geometry was developed to predict induced VMHD and to optimize four-lead ECG electrode placement for the purposes of improved MRI gating. Four human subjects were recruited for vector model establishment (Group 1), and five human subjects were recruited for validation of VMHD reduction in the proposed four-lead ECG (Group 2). The vector model was established using 12-lead ECG data recorded from Group 1 of four healthy subjects at 3 Tesla, and a gradient descent optimization routine was utilized to predict optimal four-lead ECG placement based on VMHD vector alignment. The optimized four-lead ECG was then validated in Group 2 of five healthy subjects by comparing the standard and proposed lead placements. A 43.41% reduction in VMHD was observed in ECGs using the proposed electrode placement, and the QRS complex was preserved. A VMHD-minimized electrode placement for four-lead ECG gating was presented and shown to reduce induced magnetohydrodynamic (MHD) signals, potentially allowing for improved cardiac MRI physiological monitoring.

Adaptive step size LMS improves ECG detection during MRI at 1.5 and 3 T

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.