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Noise Cancellation Signal Processing Method and Computer System for Improved Real-Time Electrocardiogram Artifact Correction During MRI Data Acquisition Freddy Odille*, Cédric Pasquier, Roger Abächerli, Member, IEEE, Pierre-André Vuissoz, Gary P. Zientara, and Jacques Felblinger, Member, IEEE
Abstract—A system was developed for real-time electrocardiogram (ECG) analysis and artifact correction during magnetic resonance (MR) scanning, to improve patient monitoring and triggering of MR data acquisitions. Based on the assumption that artifact production by magnetic field gradient switching represents a linear time invariant process, a noise cancellation (NC) method is applied to ECG artifact linear prediction. This linear prediction is performed using a digital finite impulse response (FIR) matrix, that is computed employing ECG and gradient waveforms recorded during a training scan. The FIR filters are used during further scanning to predict artifacts by convolution of the gradient waveforms. Subtracting the artifacts from the raw ECG signal produces the correction with minimal delay. Validation of the system was performed both off-line, using prerecorded signals, and under actual examination conditions. The method is implemented using a specially designed Signal Analyzer and Event Controller (SAEC) computer and electronics. Real-time operation was demonstrated at 1 kHz with a delay of only 1 ms introduced by the processing. The system opens the possibility of automatic monitoring algorithms for electrophysiological signals in the MR environment.
Index Terms—Artifact reduction, electrocardiography, magnetic resonance, real time systems, triggering.
Manuscript received November 8, 2005; revised September 3, 2006. This work was supported in part by the Ministre de l’Industrie et de la Recherche of France under Grant RNTS 2003, in part by INSERM, and in part by Région Lorraine. The work of G. P. Zientara was supported in part by the National Institutes of Health (NIH) under Grant U41-RR019703 and Grant R01-CA86879. Asterisk indicates corresponding author. *F. Odille is with INSERM, ERI13, F-54000 Nancy, France and also with Nancy University, laboratoire Imagerie Adaptative Diagnostique et Interventionnelle and CHU de Nancy Brabois, F-54511 Vandoeuvre-lès-Nancy, France (e-mail:
[email protected]). C. Pasquier and R. Abächerli are with the Schiller Médical, Wissembourg F-67162, France, Nancy University, Nancy, F-54000 France and also with INSERM, ERI13, F-54000 Nancy, France and with Nancy University, laboratoire Imagerie Adaptative Diagnostique et Interventionnelle and CHU de Nancy Brabois, F-54511 Vandoeuvre-lès-Nancy, France (e-mail:
[email protected];
[email protected]). P.-A. Vuissoz and J. Felblinger are with INSERM, ERI13, F-54000 Nancy, France and also with Nancy University, laboratoire Imagerie Adaptative Diagnostique et Interventionnelle and CHU de Nancy Brabois, F-54511 Vandoeuvrelès-Nancy, France (e-mail:
[email protected]; j.felblinger@chu-nancy. fr). G. P. Zientara is with the Department of Radiology, Brigham and Women’s Hospital, Boston, MA 02115 USA (e-mail:
[email protected]). Digital Object Identifier 10.1109/TBME.2006.889174
I. INTRODUCTION ARDIAC triggering is frequently used to synchronize magnetic resonance imaging (MRI) acquisitions from the heart and abdominal organs (e.g., liver, kidney). The most commonly used techniques that can provide the synchronization signal are electrocardiogram (ECG) analysis [1], [2], peripheral pulse oximetry [3], and self-gated approaches [4], [5]. ECG is the accepted gold standard cardiac sensing method during MRI due to the direct information it provides about the electrical activity of the heart. ECG signals have several advantageous properties: they reveal much information about the heart’s activity; ECG can operate in real-time with only a short delay; ECG has high temporal resolution; and, ECG is available during the entire examination. A major disadvantage is that the ECG signal is typically corrupted by artifact during the MRI acquisition. Peripheral pulse oximetry is an easy-to-use technique, but it only provides indirect information about heart’s activity via blood oxygen saturation. The primary disadvantage of pulse oximetry is that the signal experiences a delay due to the propagation delay of the blood flow, reducing its value for synchronization of the acquisition and cardiac cycle. Self-gated approaches have the advantage of being derived directly from acquired image data, which reduces the patient preparation time. However, the achievable temporal accuracy of self-gated approaches is lower than when using ECG, and these methods require either extra acquisition time, the use of specific pulse sequences (e.g., radial or spiral acquisitions), and/or special MRI hardware (e.g., gradient coils and power supplies capable of higher slew rates). One advantage of the choice of ECG as a sensing mechanism for triggering is that ECG is often required for patient monitoring in many cases, such as during anesthesia, pediatric MRI examinations, cardiac stress examinations, and interventional MRI [6]. Using the ECG to provide a reliable trigger signal for MRI can be a difficult task in practice because of distortions that appear, mainly due to magnetic field gradient switching [7], [8] and the magnetohydrodynamic effect [9]. It has been shown that R-wave detection can be performed in an efficient way, even in the presence of these artifacts, using a pseudovectorcardiogram approach [10]. But that method does not provide a corrected ECG signal, making it inappropriate for patient monitoring. Several methods for correction of gradient artifacts in
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ECG signals have appeared [7], [11], but no real-time implementation has yet been realized. Currently, many patients, such as those with arrhythmias, for example, are excluded from MRI protocols because of the forecast of ECG triggering problems caused by unpredictable ECG signal content. Even in the case when a patient’s ECG signal is considered “regular,” ECG signal-based triggering can be affected by MR scanner gradient switching-induced artifacts. For some sequences, such as diffusion imaging, which requires strong dephasing and rephasing gradients, or black blood techniques, artifacts in the ECG signal often appear similar to the QRS complex in terms of shape and amplitude. This may cause misinterpretation in the form of incorrect triggering, especially in case of an irregular heart beat, unless further analysis is performed on the ECG signal. In this paper, we present a signal processing method and specialized computer and electronics system for the real-time determination of the artifacts in an ECG signal due to MR scanner gradient switching, and the subsequent real-time ECG signal correction. Our purpose is to improve the quality of the displayed ECG signal for patient monitoring, and to provide accurate ECG-based MR pulse sequence triggering. In the first section, we review the theoretical model underlying our correction algorithm usable in real-time. We assume a linear time invariant (LTI) model and the separability of the artifact signal components and the true ECG signal components. A noise cancellation (NC) method is described, based on a finite impulse response (FIR) filter, relating the pulse sequence gradient digital waveforms to the observed artifact-corrupted ECG signal. A method is also described for the inversion of the filtering equation to compute an estimation for the digital FIR filter elements that represent the correction algorithm coefficients. Along with our digital noise cancellation method, we present and test a complete practical system for implementing real-time ECG gradient switching artifact correction, for accurate ECG monitoring within a MR scanner. The system incorporates a signal analyzer and event controller (SAEC) computer and electronics system, capable of high level analysis of physiological signals. Validation of the correction algorithm is presented specific to gradient switching-based ECG artifact. An example of the real-time implementation of the system is shown to demonstrate the efficiency of the signal processing method along with the capabilities of the SAEC hardware.
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cardiac cycle and their amplitude and frequency can resemble the QRS complex. The MHD effect on a 1.5 T MR scanner causes less prominent artifacts, so is less problematic when attempting R-wave-based triggering, compared to gradient switching artifacts. MHD effect artifacts are easily discernable from the QRS complex shape, and their occurrence is correlated with the cardiac cycle. Since the MHD effect increases with , it will be more prominent in MRI at higher fields. For monitoring, the MHD effect remains a problem for diagnosing heart dysfunction based on ST deviation, but monitoring of the cardiac rhythm can be achieved. RF artifacts can be suppressed by filtering, since the frequencies of physiological signals are very low relative to the Larmor frequency. However, RF low frequency components entering the ECG amplifier low-pass filter can sometimes result in artifacts. Therefore, for the correction of ECG signals containing artifacts, we focus on those artifacts caused by magnetic field gradient switching. Consider N number of artifact-corrupted ECG in all experiments described below). Call channels ( the artifact caused by the gradient switching on ECG channel , and , and the gradient waveforms in the three orthogonal directions of space, applied during the MR pulse sequence. It is assumed that the artifacts generated by these gradients on the ECG channels can be determined by a LTI model [7]. This implies that the system is fully determined by its impulse and, hence, that artifacts can be predicted by a response, sum of convolution products (1) . In order to correct the artifacts, where , corresponding to the impulse we only need to know the responses for each gradient direction and for each ECG channel. may be viewed as a FIR filter which can be used to Each predict artifacts. The FIR model has the advantage of simplicity, compared to use of an infinite impulse response (IIR) model, since there is no closed-loop effect between generated artifacts and the driving gradients system. B. Real-Time Correction
II. THEORY A. Modeling the ECG Artifact MR acquisitions of electrophysiological signals suffer from artifacts that have three principal sources. First, changes of magnetic flux occur during the MR sequence, due to the magnetic field gradient switching, inducing an additional voltage to the measured signal and creating an artifact [7]. Second, another voltage can be induced by moving charged particles (e.g., blood flow) known as the magnetohydrodynamic (MHD) effect [9], distorting the ECG signal. The third source of artifacts is radiofrequency (RF) induction resulting from MR scanner emissions at the Larmor frequency. Magnetic field gradient switching artifacts are the most prominent and disturbing source, since they can occur at any instant in the
It is possible to predict artifacts that will be generated by any gradient waveform combination on any ECG channel, assuming that all FIR filter terms [for all in (1)] are known and the are computed from LTI model is applicable. The filter terms training ECG signal data and recorded along with the applied magnetic field gradient during a training MRI scan (described later). An important aspect of our method is that real-time implementation of (1) for artifact correction is performed in the time domain, unlike the modern signal processing approach where convolution products are often realized as products in the frequency domain using the fast Fourier transform (FFT). The reason for this is that the time domain method is a direct point-by-point computation and, hence, introduces a delay of only one sample. The FFT method, however, uses buffering to
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accumulate sufficient data (i.e., data values), introducing an unacceptable time delay for a real-time application such as ECG correction. The predicted artifact, once computed from the real-time gradient waveform, is finally subtracted from the measured ECG signal to provide a corrected ECG signal. C. Estimation of the Impulse Response Matrix An accurate estimation of the impulse response filters in (1) obviously conditions the performance of the correction algorithm that is dependent upon it. To compute an estimation of the impulse response filters, several methods can be used. The first approach possible is to play a custom MR pulse sequence with one gradient pulse in each spatial direction during diastole, by the inverse FFT [7]. A second approach then compute is to acquire ECG data during initial (nontriggered) scanning while using an adaptive filter, then wait for convergence of the , and take the filter coefficients as the values of computed [11]. estimate of Here we propose a new method which is more direct and genare computed erally applicable, in which the estimates of based upon any prerecorded discretely-sampled training ECG signal, using inversion of the system seen in (1). A key assumpcan tion is that, for each recorded ECG channel , artifacts be separated from the true ECG signal. In practice, this assumption proves to be appropriate if the training scan is chosen so the gradient pulses are neither synchronized with, nor coincident with, the QRS signal. Another assumption is that the training ECG signal must be acquired during a MR pulse sequence in which magnetic field gradients are pulsed in all three spatial directions. Consider the acquisition of the training ECG signal during the training MR pulse sequence. Let M be the length of each . From (1) the product of the M-point discretized FIR filter, filter, , and the samples of the last M discrete time points from one gradient waveform yields the prediction for a single time point of an artifact . In all of our experiments cited below, we use a sampling window of 100 ms with a 1-kHz sampling rate, . that is, Generalizing this description to N number of ECG channels, (1) becomes, in matrix form:
(2) Each column in the artifact matrix, , contains N data representing one discrete time point e.g., , of the artifact prediction from each ECG signal channel, e.g., for the first column . The FIR filter matrix, , is of equals constructed with N rows, the M-point FIR filters relating to the
x,y,z gradient spatial directions are concatenated to form each row specific to one ECG channel’s signal [see (3) at the bottom of the page]. The three gradient waveforms (i.e., specific to the three spatial directions (x,y,z)) from the M time points (e.g., where prior to , and is the discrete time increment between signal sample points) form the first column of matrix , . These definitions represent a restatement of (1) in terms of discrete signals. To compute the filter value estimates from the full duration of a number of recorded artifact occurrences, a large number, K, of gradient waveforms and ECG signal samples are stored, representing data similar to that described earlier, but specific to other times . For each new instance (at some time ) of recorded waveforms and signals, a new column of 3M elements is added to the gradient waveform matrix, , and a new column of N elements is added to the artifact matrix, . The total number of columns in the two matrices, and , is then equal to K, the total number of artifact samples selected. in size, the FIR filter matrix, , is Matrix , is , and the gradient waveform sample matrix, , is . K should be chosen so in order to provide an overis determined linear algebraic system, so that inversion of mathematically possible. D. Practical Computation of the Impulse Response Matrix Estimate In order to determine the impulse response matrix, , it is necessary to know the gradient matrix, , and the artifact matrix . Columns of are obtained directly from samples of the gradient waveform selected during periods of gradient switching. requires a stratagem. In practice, the The specification of exact artifacts are not known, only their sum with the recorded training ECG signal. To accommodate this, it is assumed that a good estimate, , of the artifact can be obtained from a sample of the ECG signal during the exact same time period during which gradient switching occurs. Based on the heuristic, we perform a “smart” and . The “smart” selection selection of the data to form involves sequentially filling each column of the artifact and gradient matrices according to an algorithm based on decreasing priority. Highest priority is given to choosing sample time points at the instances closest to the appearance of gradients having the highest energy, since these artifacts are likely to produce the greatest perturbation on the ECG signal. ECG and gradient waveform data are acquired during a training MRI scan until samples (i.e., columns of and ) are recorded.
(3)
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In order to reduce the number of columns needed for a good conditioning of the system, an ECG-gated MR pulse sequence is one suggestion for use as the MR training scan. In this case, artifacts occurring during the diastole will provide values for columns of the artifact matrix estimate, , that are better separated from cardiac signals than those artifacts occurring during a QRS complex. However, correct triggering is not possible if the magnitude of the artifacts is too great, relative to the ECG signal. For this reason we need to properly handle the general case of artifacts occurring at any instant throughout the cardiac cycle. In the general case, the approximation, , of , needs to be taken into account when inverting (2). Indeed, the error inherent introduces an ill-conditioning of the system and results in in noise amplification (the noise here being the residual ECG signal contained in the matrix), especially in the case of artifacts occurring during the QRS complex. To minimize this effect, the Moore–Penrose pseudoinverse of the matrix, (where signifies the transpose of ), is computed using a truncated Singular Value Decomposition (SVD) of [12]. The SVD yields the decomposition , ( , are orthogonal matrices of so singular vectors, and is a diagonal matrix of singular values). The truncated SVD involves reducing the rank of , that is, setting to zero those singular values in that are less than a certain minimum threshold value, giving . It is an assumption, supported by our results, that the singular values (and associated singular vectors) of relatively small magnitude primarily contain information relating to the observed noise. The corresponding inverses of the noise-related singular values are elim, with the modified matrix denoted as . As a inated in result, the effect of noise-related terms in the computation of is advantageously reduced using . This yields the following estimate of the impulse response matrix:
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A digital database of discrete artifact-corrupted ECG signals was created with SAEC recordings (14 healthy adult subjects, 201 pulse sequences tested in total). Acquisition of raw ECG and MR gradient waveform signals were performed both at 10 kHz sampling, without processing for use in algorithm prototyping and off-line simulation of real-time behavior; and, at 1-kHz sampling, with real-time ECG correction and QRS detection, under real conditions. For the real-time case with 1-kHz sampling rate, an anti-aliasing filter was necessary for gradient input signals (first order, 500-Hz cutoff frequency). The QRS detection algorithm used is an industrial monitoring QRS detector (Argus PB-1000, Schiller AG, Baar, Switzerland). One to three ECG leads were used. For the FIR matrix estimation, all signals were downsampled from 10 kHz to the desired analysis sampling rate of 1-kHz (employing a digital low-pass filter at 500-Hz cutoff, then decimation). For each volunteer subject, a test set of different MR pulse sequences was used for data acquisition. This test set included: diffusion-weighted EPI (DW-EPI), standard EPI, RARE (FSE), black-blood (double IR FSE), SSFP (2D-FIESTA), spin echo (SE), and gradient-recalled echo (GRE) sequences. A greater proportion of ECG recordings during DW-EPI were included in our testing (Table I). Our experience has shown that these test cases of DW-EPI lead to the maximum ECG signal corruption and, hence, provide the most stringent test bed for ECG correction, due to the large amount of rapid diffusion gradient switching. MRI acquisition parameter values over wide ranges were tested, specifically variations in: the b-value for DW-EPI ); the slice location (head, heart, liver); (from 100–1000 the plane orientation (axial, sagittal, coronal); and, the extent of the field-of-view (24–60 cm). Custom pulse sequences designed to cause ECG signal artifacts were included in our test set, with these pulse sequences producing isolated trapezoidal gradients with user-defined amplitude (1–30 mT/m), rise-time (300–1000 ), and length (1–20 ms).
(4) ECG signal artifacts caused by gradient switching in later pulse sequences of the MR examination are predicted from (1) using the gradient waveforms observed in real-time, and the dis, comprising matrix that is comcrete M-point FIR filters, puted from (4) using the training MRI scan data. III. MATERIAL AND METHODS A. Experimental Details MR examinations were performed on a 1.5T GE SIGNA Excite HD MR system (General Electric, Milwaukee, WI). The MR scanner gradient system is capable of 33 mT/m with a 270 rise time. ECG signals were acquired using the ECG optical sensor of the Maglife (Schiller Médical, France) patient monitoring system. Two types of sensors were used: the first sensor has a 25-Hz bandwidth (commercially available sensor, forth order analog low-pass filter), and the second sensor has a 60-Hz bandwidth (commercial sensor modified for research purposes). MR gradient signals, representing the currents in the gradient coil system, are available from a built-in tap on a circuit in the MR real-time chassis electronics.
B. Validation of ECG Correction Method Using Prerecorded Data Validation of our FIR filter-based ECG correction method was performed using post-processing simulations using Matlab (MathWorks, Natick, MA) of prerecorded signals from our database of artifact-corrupted ECG signals with the accompanying gradient waveforms. For each case of the data obtained from each subject, two correction methods were tested and compared to the unprocessed signals: first, the proposed NC method, whose coefficients were estimated by the method described in the first section; and, second, for comparison purposes, an adaptive filter (LMS coefficient updates [11]) published previously. Among the different MR pulse sequences applied to a given subject, one pulse sequence was chosen as the training pulse sequence. The training pulse sequence provided data for both the matrix computation of the NC method described in this study, and also the data for the transition regime for the LMS adaptive filter computation. Following the training scan simulation, the correction algorithm was run using all other pulse sequences that
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TABLE I NOISE CANCELLATION ECG CORRECTION RESULTS FOR 201 MR PULSE SEQUENCES
The mean ECG artifact energy per cardiac cycle was estimated: before (Raw); and, after correction by the LMS adaptive filter method published previously (LMS); by the NC correction method presented in this study (NC); and the NC method in an auto-calibration mode (NC auto), i.e., using the acquisition to correct as the training scan. For the NC method, the training scan was acquired during free-breathing. The corrections were tested with two ECG sensors with different bandwidths (25 and 60 Hz). Results are divided into four groups, separated according to the increasing amount of artifact energy observed in the corrupted ECG signals. Group limits are: 25 Hz sensor: Group 1 from 0 to 0.04 10 V s; Group 2 from 0.04 to 0.42 10 V s; Group 3 from 0.42 to 15.18 10 V s; Group 4 over 15.18 10 V s. 60 Hz sensor: Group 1 from 0 to 0.04 10 V s; Group 2 from 0.04 to 3.14 10 V s; Group 3 from 3.14 to 14.25 10 V s; Group 4 over 14.25 10 V s; Group 5 for artifacts saturating the acquisition system. Custom pulse sequences: trapezoidal gradients with user-defined amplitude, rise-time and length.
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had been applied to the subject. Subsequently, to study the influence of the training scan, a third correction method was introduced, using the NC method in an auto-calibration mode. This meant that the sequence to correct was systematically used as the training scan to compute the matrix. All results are shown in Table I. For all computations the duration of the impulse response was set to 100 ms for each gradient direction (i.e., 3 100 samples at 1 kHz). An “over-determination” factor of 100 was chosen to of the gradient matrix fill the matrix of dimension with waveform samples, resulting in a 300 30000 whose pseudoinverse was computed as described above. The cutoff used in the truncated SVD method to eliminate noise-related singular values of small magnitude was set to 3% of the largest singular value. To evaluate the efficiency of the correction, ECG signals were compared on a cycle-by-cycle basis. The separation of ECG cycles was accomplished using the results of the abovementioned QRS detector acting on the corrected signals, accompanied by manual checking and, if needed, correction of the R-wave detection. All ECG cycles were normalized with respect to the QRS complex amplitude. First, a reference ECG cycle was constructed using the data recorded when no gradient switching occurred. If the distribution of these ECG cycles was Gaussian, 99% of these ECG cycles would lie within , which the limits defined by the were chosen as the limits of the reference ECG. Cycles which differed significantly from the reference, as measured from the respective correlation coefficient, were considered as abnormal and eliminated. Cycles which were corrupted by gradient artifacts were then compared to the reference ECG signal from the same subject, before and after correction. The artifacts were es-
timated to be the signal lying out of the reference ECG limits . The efficiency of the correction method was assessed by computing the energy of the artifacts before and after correction. Fig. 1 shows an example of the results of ECG signal correction by the NC method, illustrating an example of the data contributing to the validation process. After correction by the NC or LMS method, the ECG was compared to the reference. The residual artifact energy was, then, able to be estimated, giving a quantitative measure of the artifact reduction. Patient motion (breathing) during scans may be an issue in the performance of our method and its influence warranted investigation. The changing position of the heart and electrodes due to respiration is expected to induce a change in the shape of impulse responses. To assess this effect, we compared the mean artifact energy reduction obtained from our NC method’s application to the ECG signals acquired in four different cases of respiration and breathholding, with data obtained from seven of our healthy adult subjects. For each subject, four training scans were performed with the same MR pulse sequence (DW-EPI): first, during free breathing, then two scans were acquired during breathhold (inspiration and expiration), and a fourth scan was acquired during deep breathing. C. Real-Time Implementation of ECG Correction Method To be routinely applicable for real-time application of ECG correction, the FIR filter signal processing method requires: 1) the use of an additional training MRI pulse sequence, in order to determine the impulse response matrix, ; 2) the specialized SAEC computer and electronics. For better conditioning of and matrices used in the system inversion, an MR the pulse sequence that will cause numerous ECG signal artifacts
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Fig. 1. Example of the ECG signal correction of MR gradient artifacts using the NC correction method. The sequence used here is a DW-EPI cardiac acquisition, three diffusion directions, one slice, TR = 1000 ms, TE = 87:5 ms, FOV = 60 cm, five NEX. Notice the occurrence of an artifact during a cardiac QRS complex, and the presence of an extra systole. (a) ECG signal (before and after correction) from the 25-Hz bandwidth sensor, and gradient waveforms (respectively, X, Y, and Z), with their respective spectrum. The pulse sequence used here is a DW-EPI. (b) Statistics obtained by accumulating ECG cycles from example (a) for the reference ECG (without gradients), the corrupted ECG (with gradient artifacts), and the corrected ECG. The black line represents the mean value, and the light gray area represents the statistical variations defined by the mean 3 standard deviation. In these plots, the amplitude of each QRS has been normalized for better superposition of ECG cycles. (c) ECG signal (before and after correction) from the 60-Hz bandwidth sensor, and gradient waveforms with their respective spectrum. The sequence used here is a custom sequence containing trapezoidal gradients pulsed in X, Y, and Z. (d) Statistics obtained by accumulating ECG cycles in example c.
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is most advantageous, that is, a pulse sequence containing a wide range of pulsed gradients in all three spatial dimensions. Mathematically, the training scan gradients have to be linearly independent, otherwise the G matrix would be rank deficient and, hence, the system would not be invertible. This condition is generally fulfilled in an MRI sequence because the slice-select, phase-encoding and readout gradients are not pulsed simultaneously. In our experience, a good candidate for the training scan is the standard diffusion weighted EPI sequence (DW-EPI), with the diffusion gradients pulsing (successively) in the three spatial dimensions, and with at least three as the number of excitations (NEX). The training scan does not require cardiac
triggering, and requires approximately 20 s at the start of the patient examination. D. SAEC Computer and Electronics Real-time implementation of our FIR filter signal processing method for ECG artifact correction requires specialized computer and electronics hardware for rapid signal analysis and computation. Our system employs a SAEC computer and electronics hardware that is designed to have the capability for integration of all relevant physiological information in the MR acquisition pipeline for the purpose of adapting the sequence parameters and the reconstruction.
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Fig. 2. Example of MR gradient artifact impulse responses (coefficients of the H matrix) computed using the proposed estimation method. The impulse responses are given for X, Y, and Z gradient pulses, for the 25 Hz (left) and the 60 Hz (right) bandwidth ECG sensor. Four impulse responses are plotted for each sensor, corresponding to the estimation obtained by four different training scans (DW-EPI) on the same subject: in free-breathing, breathhold (inspiration and expiration), and deep breathing. Although the overall shape is reproducible, slight differences can be observed, especially in inspiration, due to the variation of position of the heart and electrodes. The effect of such variations on the correction efficiency is assessed in Table II.
Physiological signals, such as the ECG signal used in this study, are collected within the MR scanner bore by various sensors, with local amplification, preprocessing and conversion into an optical signal [1]. The signals are transmitted outside the MR bore through optical fibers and then conditioned and analyzed by the SAEC. The SAEC is composed of two modules giving real-time signal analysis and processing performance capability. The first module is a PXI-8186 Embedded Controller (2.2-GHz Pentium 4, 512-MB RAM, National Instruments) with a real-time operating system (OS) and a data acquisition device (32 analog inputs, 8 digital I/O lines, 12-bit resolution, 1.25-MS/s maximum sampling rate). The PXI controller executes the time-critical tasks (synchronous acquisition and conditioning of physiological and MR signals), and ensures accurate and reliable timing of all events. The PXI communicates with the MR scanner host computer receiving gradient waveforms and pulse sequence parameters, and sending the R-wave trigger signal for ECG and/or respiratory-gated pulse sequences. The second module is a host PC computer (3-GHz Xeon dual processor, 2-GB RAM, Windows OS; 100 Mbit Ethernet link with the PXI) that accomplishes remote application control and signal display.
E. Real-Time Software Implementation The kernel of the real-time application was developed using LabVIEW software (National Instruments, Austin, TX). Signal processing, such as artifact correction and QRS detection, were written in C and integrated into the real-time application. The R-wave trigger signal generation, like the acquisition and processing tasks, is hardware-timed. The user interface on the SAEC host PC provides a display of the acquired and processed signals. Algorithms and parameters can be changed interactively. Real-time simulations were performed by redirecting the data stream to be composed of prerecorded data files, rather than having a data acquisition device as its source. This is a capacity exploited in our off-line experiments. From simulations, the maximum processing frequency acceptable for a given algorithm or a given processing pipeline can be determined. IV. RESULTS A. Validation of the Noise Cancellation Method for ECG Correction The amplitude and energy of artifacts in the ECG signal depend not only on the gradient waveform shape and energy
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TABLE II INFLUENCE OF RESPIRATION ON IMPULSE RESPONSES ESTIMATION
Influence of respiration on the estimation of impulse responses representing gradient switching artifact in ECG signal during MRI is studied by evaluating the mean artifact energy before (Raw) and after correction by the NC method using different configurations for the training scan: free-breathing, breathhold (inspiration and expiration) and deep breathing. Subsequent acquisitions include free-breathing and breathholded scans. Group limits are: 25 Hz sensor: Group 1 from 0 to 0.01 10 V s; Group 2 from 0.01 to 0.12 10 V s; Group 3 from 0.12 to 22.04 10 V s; Group 4 over 22.0410 V s. 60 Hz sensor: Group 1 from 0 to 0.03 10 V s; Group 2 from 0.03 to 3.33 10 V s; Group 3 from 3.33 to 16.46 10 V s; Group 4 over 16.46 10 V s; Group 5 for artifacts saturating the acquisition system.
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(and, hence, on the imaging parameters), but also on the placement of electrodes on the patient. To assess the efficiency of our correction method, our results were put into five groups according to the amount of ECG signal artifacts that were created. The artifact energy was expressed as the mean energy in per cardiac cycle. Results are displayed in Table I. Groups 1 and 2 correspond to artifacts which are of small magnitude, or negligible, compared to the intrinsic ECG signal. In Groups 3 and 4, the artifacts can be of the same order of magnitude as the QRS complex, or of greater magnitude (up to a factor of 7 in our database). A fifth group was added in which the magnitude of the artifacts was so great that they saturated the acquisition system. This result was observed only for cases studied using the 60 Hz sensor. The effect of such ECG signal saturation, representing extreme cases during DW-EPI and EPI scanning, is fatal for all the tested methods (i.e., there is an inability to differentiate ECG signal from artifact in these cases), due to the breakdown of the LTI (a hypothesis of our NC method). To prevent this problem, the dynamic range should be extended, and, also, the sensor cables length should be shortened as much as possible. Also shown in Table I is the composition of each group of ECG artifacts in terms of pulse sequence types used for data acquisition. From Table I one observes that when the most disturbing artifacts occur (Groups 3 and 4), the artifact energy can be reduced by more than 90% on average by the NC correction method presented in this study. These results are equivalent or exceed those obtained using the LMS adaptive filter [11]. Importantly, results obtained using ECG signals from pulse sequences that cause few artifacts (i.e., Group 1) show that the correction algorithm itself does not introduce artifacts. The artifact reduction is less accurate for these sequences because the artifact-to-noise ratio is low and the typically small computational error becomes a significant factor.
Results obtained with the NC method in the auto-calibration mode are the worst when the artifacts have the least energy, and perform better at highest artifact energies. This confirms that the conditioning of the filtering equation being inverted is better when artifacts are significant compared to the ECG signal (in the computation, the ECG signal plays the role of the “noise,” i.e., the source of ill-conditioning). An example of impulse responses computed for the four different types of training scans (free breathing, breathhold in inspiration and expiration, and deep breathing) is presented in Fig. 2. The shape of impulse responses depends strongly on the sensor preprocessing and bandwidth. Good reproducibility of the overall shape can be observed from these plots. However slight amplitude variations are visible, especially in the case of inspiration. The effective influence of these variations on the correction has been assessed using the same artifact energy quantification method as above. Detailed results are shown in Table II. Overall, similar results are obtained for the four types of training scans, but the NC in the inspiration training scan is less efficient. This may be explained from the fact that this is the highest magnitude change of position of both the heart and the electrodes placed on the patient thorax. In comparison, during free or deep breathing, an averaging effect may advantageously occur in the estimation process. B. Real-Time Performance of the ECG Correction Method The SAEC architecture has been designed for real-time processing of physiological data, that is, to maximize the throughput of the time-critical tasks (acquisition, processing, and trigger signal generation). For example, if the frequency of the processing task is set to 1 kHz, the corrected ECG will occur in the next sample because of our use of a convolution approach, a delay of 1 ms after the raw ECG sample. This delay is very short relative to the duration of the adult cardiac cycle, and, more importantly, it is relatively short compared to the
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Fig. 3. Example of the real-time ECG signal correction of MR gradient switching artifacts using the NC correction method. The sequence used here is a DW-EPI cardiac acquisition, three diffusion directions, one slice, TR = 1000 ms, TE = 87:5 ms, FOV = 60 cm, five NEX. Notice the occurrence of an artifact during a cardiac QRS complex, and the presence of an extra systole.
duration of a QRS complex (approximately 80 ms). As a result, the correction method we present here makes possible a greater amount of real-time ECG processing in the MR environment, such as QRS detection and automatic heart beat classification. To demonstrate conclusively that the SAEC performance provides computer processing sufficient for the demands required by our method, real-time simulations were performed using prerecorded signals from our ECG signal database. Simultaneous recording of raw and processed ECG signals was performed to show that the delay introduced by the ECG correction method was less than the time interval between two ECG samples (This is demonstrated by simply displaying the difference between the raw and corrected signals). These tests showed that, in the current version of the SAEC computer and electronics and software, the 3-lead correction method works at a maximum frequency of approximately 5 kHz. This frequency should be adequate to provide an accurate shape of the input gradient waveform. However, testing the complete processing pipeline requires the addition of a QRS detection algorithm, and real-time classification. For reliable use of the entire signal processing system, the loop frequency of both the correction and the QRS detection computations was finally set to 1 kHz. C. Practical Real-Time Application of the ECG Correction Method Fig. 3 displays an example of real-time ECG correction, during monitoring of an adult human subject, using the FIR filter signal processing method and the SAEC computer and electronics. The real-time correction method and QRS detection were executed at 1 kHz, using four healthy adult subjects,
demonstrating near-complete artifact removal. The occurrence of an artifact simultaneous with a cardiac QRS complex is handled well by our method, which corrected the artifact without significantly perturbing the underlying ECG signal, as demonstrated in Fig. 3. Suggested by these post-processing results, the training scan was chosen to be the DW-EPI sequence for our tests on prerecorded data. The training scan provided the data filter estimates for each subject, for the computation of the filters that were later used for artifact prediction in the later pulse sequences of the examination. The one-time computation of the impulse response matrix requires only a few seconds, and is insignificant during a typical scanning protocol. The corrected ECG signal seen in Fig. 3 is both easy for a physician to interpret for monitoring, and also sufficiently accurate for automated analysis and R-wave triggering. V. DISCUSSION AND CONCLUSION A new system, consisting of a noise cancellation signal processing method and SAEC computer and electronics, was described and validated for the real-time correction of magnetic field gradient switching artifacts occurring in ECG signals. The NC correction method relies on the hypothesis that artifacts can be predicted by an LTI model. Our results show conclusively that the model is valid whenever the position of the electrodes on the patient and the position of the patient in the magnet are not changed significantly. Otherwise, the shape of the impulse responses is likely to change, and a new calibration has to be made. In particular, the influence of respiration and breathholding was assessed, showing conclusively that efficient NC corrections using our method can be achieved even with free breathing scans.
ODILLE et al.: NC SIGNAL PROCESSING METHOD AND COMPUTER SYSTEM FOR IMPROVED REAL-TIME ECG ARTIFACT CORRECTION
The system requires a training MRI scan and an accompanying ECG signal acquisition. The data from the training scan is used to compute an estimate of the impulse response matrix. Our results show that a standard diffusion-weighted EPI pulse sequence is suitable for the determination of these impulse responses. However, the training scan, as well as the algorithm parameters, need to be optimized for a particular hardware setup. This is necessary since the shape of artifacts and of impulse responses strongly depends on MR scanner and ECG sensor characteristics, as demonstrated in our studies with the higher bandwidth sensor. The time required for the training MRI scan could be further shortened by using a well-defined sequence of gradients which would optimally condition the system for the computation of . Another strategy possible would involve use of the MRI prescan gradient waveform, and associated ECG signal data, acquired just before the start of each pulse sequence. The efficiency and accuracy of the method was demonstrated by postprocessing a large number of prerecorded ECG signals from a widely different set of MR pulse sequences, and also by real-time tests with adult subjects under standard MRI scanning conditions. The real-time demonstration of the new correction method was reliant upon the new SAEC computer and electronics platform, dedicated to the conditioning and analysis of physiological sensor signals during MRI examination with minimal delay. The NC signal processing method and SAEC computer and electronics system are not restricted, in theory or practice, to application to the ECG artifact correction problem. It was demonstrated that the NC method is applicable for signals with a bandwidth as large as 60 Hz, with the exception of extreme cases in which artifacts saturate the acquisition system, invalidating the LTI hypothesis. Other kinds of physiological signals suffering from MR gradient artifacts, such as those from the electroencephalogram or electromyogram [13]–[15] and electrooculogram EOG, may benefit from our correction method, either for post-processing purpose, or for real-time applications. Applications of the system we described in this study include improvement in the synchronization between the scanner pulse sequence timing and actual patient cardiac cycle for an important number of MR pulse sequences that are used routinely (e.g., diffusion weighted sequences, black blood techniques). Additionally, the real-time ECG correction provides more reliable cardiac monitoring information to the physician, and opens the possibility of more accurate automated computer-assisted monitoring. However, even though gradient switching artifacts can be considerably reduced in the ECG signal by use of the method we presented, the ECG signal still appears different when acquired inside the MR bore compared to external from the MRI scanner because of the MHD effect. Therefore, existing automated computer-assisted monitoring algorithms would require special modification in order to operate within the MR environment. Real-time analysis of ECG signals can also help improve other aspects of MRI data acquisition. Generally, all MR imaging techniques are not possible for patients suffering from
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arrhythmias or cardiac rhythm disorders. A complete analysis of the ECG can help adapt the reconstruction algorithms by identifying and possibly correcting corrupted data as accumulated in k-space. Achieving this analysis in real-time also provides the capability for adaptation of MR pulse sequence parameters on-the-fly during an examination to deal with abnormal or irregular heart beats by, for example, re-acquiring artifact-corrupted data. ACKNOWLEDGMENT The authors would also like to thank Schiller Médical SAS, France, for providing an important part of the required hardware. REFERENCES [1] J. Felblinger, C. Lehmann, and C. Boesch, “Electrocardiogram acquisition during MR examinations for patient monitoring and sequence triggering,” Magn. Reson. Med., vol. 32, pp. 523–529, 1994. [2] R. E. Wendt, III, R. Rokey, G. W. Vick, III, and D. L. Johnston, “Electrocardiographic gating and monitoring in NMR imaging,” Magn. Reson. Imag., vol. 6, pp. 89–95, 1988. [3] S. Denslow and D. S. Buckles, “Pulse oximetry-gated acquisition of cardiac MR images in patients with congenital cardiac abnormalities,” Am. J. Roentgenol., vol. 160, pp. 831–833, 1993. [4] A. C. Larson, R. D. White, G. Laub, E. R. McVeigh, D. Li, and O. P. Simonetti, “Self-gated cardiac cine MRI,” Magn. Reson. Med., vol. 51, pp. 93–102, 2004. [5] M. E. Crowe, A. C. Larson, Q. Zhang, J. Carr, R. D. White, D. Li, and O. P. Simonetti, “Automated rectilinear self-gated cardiac cine imaging,” Magn. Reson. Med., vol. 52, pp. 782–788, 2004. [6] T. Birkholz, M. Schmid, C. Nimsky, J. Schuttler, and B. Schmitz, “ECG artifacts during intraoperative high-field MRI scanning,” J. Neurosurg. Anesthesiol., vol. 16, pp. 271–276, 2004. [7] J. Felblinger, J. Slotboom, R. Kreis, B. Jung, and C. Boesch, “Restoration of electrophysiological signals distorted by inductive effects of magnetic field gradients during MR sequences,” Magn. Reson. Med., vol. 41, pp. 715–721, 1999. [8] M. K. Laudon, J. G. Webster, R. Frayne, and T. M. Grist, “Minimizing interference from magnetic resonance imagers during electrocardiography,” IEEE Trans. Biomed. Eng., vol. 45, no. 2, pp. 160–164, Feb. 1998. [9] J. R. Keltner, M. S. Roos, P. R. Brakeman, and T. F. Budinger, “Magnetohydrodynamics of blood flow,” Magn. Reson. Med., vol. 16, pp. 139–149, 1990. [10] S. E. Fischer, S. A. Wickline, and C. H. Lorenz, “Novel real-time R-wave detection algorithm based on the vectorcardiogram for accurate gated magnetic resonance acquisitions,” Magn. Reson. Med., vol. 42, pp. 361–370, 1999. [11] R. Abaecherli, C. Pasquier, F. Odille, M. Kraemer, J. J. Schmid, and J. Felblinger, “Suppression of MR gradient artefacts on electrophysiological signals based on an adaptive real-time filter with LMS coefficient updates,” Magma, vol. 18, pp. 41–50, 2005. [12] C. Hansen, “The truncated SVD as a method for regularization,” BIT, vol. 27, pp. 534–553, 1987. [13] S. M. Mirsattari, J. R. Ives, F. Bihari, L. S. Leung, R. S. Menon, and R. Bartha, “Real-time display of artifact-free electroencephalography during functional magnetic resonance imaging and magnetic resonance spectroscopy in an animal model of epilepsy,” Magn. Reson. Med., vol. 53, pp. 456–464, 2005. [14] G. Garreffa, M. Carni, G. Gualniera, G. B. Ricci, L. Bozzao, D. De Carli, P. Morasso, P. Pantano, C. Colonnese, V. Roma, and B. Maraviglia, “Real-time MR artifacts filtering during continuous EEG/fMRI acquisition,” Magn. Reson. Imag., vol. 21, pp. 1175–1189, 2003. [15] J. Sijbersa, J. Van Audekerke, M. Verhoye, A. Van der Linden, and D. Van Dyck, “Reduction of ECG and gradient related artifacts in simultaneously recorded human EEG/MRI data,” Magn. Reson. Imag., vol. 18, pp. 881–886, 2000.
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Freddy Odille was born in France in 1980. He received the engineering degree from Ecole Nationale Supérieure d’Electricité et de Mécanique, Nancy, France, in 2003. He is currently working towards the Ph.D. degree at Nancy University, Nancy, France, working in the Adaptive, Diagnostic, and Interventional Imaging laboratory (IADI, INSERM, ERI13), Nancy, France. His research interests include signal processing and mathematics applied to adaptive MRI acquisition and reconstruction methods. Mr. Odille is a member of the International Society for Magnetic Resonance in Medicine.
Cédric Pasquier received the Master degree of Biomedical Engineering from Nancy University, Nancy, France in 2003. He is currently working towards the Ph.D. degree at Nancy University working in the Adaptive, Diagnostic, and Interventional Imaging laboratory (IADI, INSERM, ERI13), Nancy, France. He is currently Research Engineer at Schiller Médical, Wissembourg, France. His research interest is centered on the development of new sensors and new methods applied to respiratory motion correction in MRI. Mr. Pasquier is a member of the International Society for Magnetic Resonance in Medicine.
Roger Abächerli (M’01) was born in Sursee, Switzerland, in 1974. He received the diploma in electrical engineering from the University of Technology Lausanne (EPFL), Lausanne, Switzerland, in 2001 after studies at the Georgia Institute of Technology (GaTech), Atlanta, and the University College of Dublin (UCD), Dublin, Ireland. He received the Ph.D. degree from the National Institute of Technology of Lorraine (INPL), Lorraine, France, in 2005. He is currently Research Engineer at Schiller AG, Baar, Switzerland. He is member of the research group that won the KTI-Medtech-Award for the textile-electrode, and recently received the Jos Willems Award from the International Society for Computerized Electrocardiology.
Pierre-André Vuissoz received the diploma of theoretical physics from Swiss Federal Institute of Technology Zurich (ETHZ), Zurich, Switzerland, in 1993. In 1999, he received the Ph.D. degree from the Experimental Physic Laboratory, the Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland, developing the electrochemical NMR. Until 2003, he worked as expert in fuel cell technology for a consortium of French companies. He is a Teacher in mathematics and physics. In 2004, he joined the Diagnostic and Interventional Adaptive Imaging laboratory (IADI, INSERM, ERI13), Nancy, France. Since then he has been a Research Engineer in the development of adaptive MRI pulse sequences and parallel MRI reconstruction algorithms. Dr. Vuissoz is a member of the International Society for Magnetic Resonance in Medicine and of the Swiss Physical Society.
Gary P. Zientara is an Associate Professor of Radiology at Brigham and Women’s Hospital and Harvard Medical School, Cambridge, MA; Visiting Professor of Medical Imaging, Faculty of Medicine, University of Nancy, Nancy, France; a Research Affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology, Cambridge. He is an Investigator in the Children’s Hospital Boston Developmental Disabilities Research Center, Boston, MA, and member of the Harvard Center for Neurodegeneration and Repair. He is an MR Physicist, Imaging Scientist, and Physical Chemist with experience in NMR and ESR, with primary research emphasis in real-time MRI physics and computing, monitoring of imaged-guided thermal therapies and MRI system software.
Jacques Felblinger (M’91) received the Ph.D. degree in electrical engineering from the Institut National Polytechnique de Lorraine in 1990, Nancy, France. His dissertation concerned the development of automatic ventricular fibrillation detection algorithms. From 1991 to 2001, he was with the University of Berne, Berne, Switzerland, in the MR research group with primary research emphasis in the development of tools for patient monitoring during MR examinations and MR spectroscopy. He has been a Professor of Medical Imaging at Nancy University, France, since 2001. He founded the Diagnostic and Interventional Adaptive Imaging laboratory (IADI, Inserm, ERI13) in 2005. His research is centred on developing new acquisition strategies for magnetic resonance and computed tomography.
