Monitoring and synchronization of cardiac and respiratory traces in magnetic resonance imaging: A review

200 IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 15, 2022 Monitoring and Synchronization of Cardiac and Respiratory Traces in Magnetic Resonance Imaging: A Review Martina Ladrova , Radek Martinek , Jan Nedoma , Pavla Hanzlikova , Michael Douglas Nelson , Radana Kahankova , Jindrich Brablik , and Jakub Kolarik (Methodological Review) Abstract—Synchronization of human vital signs, namely the cardiac cycle and respiratory excursions, is necessary during magnetic resonance imaging of the cardiovascular system and the abdominal cavity to achieve optimal image quality with minimized artifacts. This review summarizes techniques currently available in clinical practice, as well as methods under development, outlines the benefits and disadvantages of each approach, and offers some unique solutions for consideration. Index Terms—Magnetic resonance imaging (MRI), cardiac magnetic resonance imaging (CMRI), MRI triggering, cardiac triggering, respiratory triggering. I. INTRODUCTION MAGNETIC Resonance Imaging (MRI) is a powerful non-invasive tool for imaging human body structure and function. The main advantages of MRI include the absence of ionizing radiation, high contrast between different types of soft tissues, and its ability to image in arbitrary spatial orientations. In addition to providing high-resolution images of the body structure, MRI also provides novel pathophysiologic insight into basic bodily functions (e.g., molecular water diffusion, Manuscript received August 28, 2020; revised December 22, 2020; accepted January 25, 2021. Date of publication January 29, 2021; date of current version January 24, 2022. This work was supported in part by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project, under Project CZ.02.1.01/0.0/0.0/16 019/0000867 within the Operational Programme Research, Development and Education, and in part by the Ministry of Education of the Czech Republic under Projects SP2020/156 and SP2021/32. (Corresponding author: Radana Kahankova.) Martina Ladrova, Radek Martinek, Radana Kahankova, Jindrich Brablik, and Jakub Kolarik are with the Department of Cybernetics and Biomedical Engineering, Technical University of Ostrava, 70800 Ostrava, Czechia (e-mail: [email protected]; [email protected]; radana.kahankov[email protected]; jindrich.brablik@ vsb.cz; [email protected]). Jan Nedoma is with the Department of Telecommunications, Technical University of Ostrava, 70800 Ostrava, Czechia (e-mail: [email protected]). Pavla Hanzlikova is with the Ostravska Univerzita, 70103 Ostrava, Czechia (e-mail: pavla.hanzlikov[email protected]). Michael Douglas Nelson is with The University of Texas, Arlington, TX 76019 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/RBME.2021.3055550 Fig. 1. Examples of MR motion artifacts: (a) blurring due to more random respiratory motion and (b) ghosting artifacts caused by periodic breathing [3]. tissueperfusion,orMRspectroscopy).Together,theinformation provided can help make more accurate diagnoses, and improves our ability to monitor treatment outcomes [1]. This review focuses on MRI applications that require vital sign synchronization, namely with the cardiac cycle and/or respiratoryexcursions.Indeed,triggering andgatingdevicesand algorithms are necessary to ensure high image quality of regions within the chest and abdomen (e.g. heart, liver, pancreas). The task is to perform imaging during minimal movement of the heart and thorax, which causes motion artifacts, appearing as shadows or blurred contours on the image (referred to as “ghosting artifacts” see Figure 1). Synchronization serves to suppress these artifacts but is not without several major challenges. In particular, synchronization of biological signals is limited by inherent high-frequency disturbances and extreme magnetic induction, which interferes with the measurement of vital signs, and ultimately compromises MRI sequence synchronization [2], [3]. A. Abdominal MRI Abdominal MRI is widely used due to its ability to extract information at the level of tissue composition and to assess functional status, including the metabolic structure of the tissue. It allows clinicians to respond to tissue damage or dysfunction and to adapt the therapy or disease prevention strategy accordingly. Given the versatility of MRI, it can detect a plethora of abdominal disorders, such as steatosis, fibrosis, inflammation, This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ LADROVA et al.: MONITORING AND SYNCHRONIZATION OF CARDIAC AND RESPIRATORY TRACES 201 and tumors (malignant and benign). For example, when examining the liver, bile ducts, and pancreas, MR cholangiopancreatography begins to replace the invasive endoscopic method in diagnostic indications. Also, MRI can be used to identify inflammatory or neoplastic diseases with bowel wall abnormalities to classify some types of Crohn’s disease by accurate detection of individual lesions and evaluation of disease activity. Moreover, MRI is also an excellent tool to differentiate the benign nature of asymptomatic adrenal lesions, the probability of which increases with higher age and is becoming a common problem. Clinical evaluation with MRI also plays a crucial role in the diagnosis, planning, and assessment of treatment of both benign and malignant gynecological conditions, and has even proved to be superior to computer tomography in the diagnosis of uterine and cervical cancer [1], [4]. B. Cardiovascular MRI Cardiovascular magnetic resonance imaging (CMRI) is increasingly used to examine heart structure and function. Its non-invasive nature, together with its avoidance of ionizing radiation, allow for repeatable, low-risk characterization of the myocardium and its associated components, throughout disease progression/regression. One of the most frequent applications of CMRI in clinical practice is the assessment of atrial and ventricular morphology. Due to CMRI’s high spatial resolution, three-dimensional coverage, and excellent high contrastto-noise ratio (CNR), evaluation of chamber dimension, size and shape is highly robust and reproducible [5]–[7]. As such, CMRI is recognized for its accurate characterization and detection of cardiomyopathies (hypertrophic or restrictive cardiomyopathy, etc.), paracardiac masses (both malignant and benign cardiac tumorsincluding secondary tumors, pseudotumors andintracavitary thrombi), congenital heart diseases, and conditions of the pericardium, septal defects, obstructive lesions, postoperative states and more. Cardiovascular MRI also includes structures beyond the heart, including the aorta and other major blood vessels. For example, MRI can be used to detect aortic aneurysm and dissection, as well as atherosclerotic plaque [5], [6], [9]. Synchronizing CMRI with the cardiac cycle provides additional insight into cardiac function and myocardial viability, allowing examination of myocardial ischemia and/or acute/chronic myocardial infarction. Integration of CMRI with velocity/flow measurements provides insight into cardiac hemodynamics and/or valvular function, such as mitral, tricuspid and pulmonary inflow versus regurgitation, valvular stenosis, pulmonary hypertension, or prosthetic valve function [5], [8], [9].Furthermore,asCMRIhasevolved,particularlyoverthepast 10-15 years, more novel applications have emerged, including MR angiography, myocardial tissue characterization, and myocardial perfusion imaging [10]–[12]. C. High-Field MRI There is a general trend in the field of MRI to increase magnetic field strength. In just the past 10 years, we have witnessed an extensive expansion of clinical scanners from 1.5 T to 3 T, with two of the major vendors now offering FDA-approved Fig. 2. Comparison of the quality of cerebral angiography images at (a) 1.5 T, (b) 3 T, and (c) 7 T magnetic field strengths [8]. 7 T clinical scanners. In principle, increasing the magnetic field strength is directly associated with increasing signal intensity; however, it brings other sources of error. With the higher magnetic field strength, the inhomogeneity both of magnetic and radiofrequency field contribute to the degradation of image quality, which has to be reclaimed by different optimization techniques [13]–[16]. Thus, before the increased signal intensity andisotropicresolutioncanberealized,sophisticatedalgorithms must first be developed and integrated. Another limitation of imaging in high magnetic fields are the magnetic effects on the electrical signals used for monitoring vital signs of the patient; challenging synchronization (see more in Section II-A). Together, these inherent limitations often lead to prolonged scan times, and in some cases, impracticable long breath-hold times [17], [18]. Nevertheless, imaging at higher field strengths holds great promise. The contrast between blood and tissue is much more resolved, and there is great potential for accelerated imaging and reducing imaging artifacts; major advantages when imaging small, fast-moving structures [8], [19]. For example, the increased signal energy has a great advantage in MR angiography, where tiny vessels, such as cerebral arteries, are more visible at 7 T than at 1.5 T (see Figure 2). In this context, high field strength MRI promises to provide new insight into previously unresolved anatomical structure and physiological function in vivo [8], [18]. D. MRI Synchronization Synchronization of MRI (or gating/triggering) is dependent upon real-time acquisition of cardiorespiratory function. As such, MRI gating can be divided into two main categories: rrespiratory, which allows reducing motion artifacts in the image resulting from the chest movement during a patient’s free breathing (when scanning of an abdominal and thoracic area), and rcardiac, the goal of which is to eliminate the effect of myocardial movement and capture a heart scan in the specific phase of its cycle (when scanning of a thoracic area), usually monitored by electrocardiography (ECG) signal. 202 IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 15, 2022 Fig. 3. The basic principle of the prospective triggering for (a) morphological imaging, (b) cine imaging. Synchronization of MRI can be divided into two basic principles ”prospective and retrospective. In the case of prospective triggering, the MRI acquisition is triggered by the desired physiologic event (e.g. R wave of ECG signal) with a specific delay or by reaching the specified level of inspiration. Thus, the acquisition usually coincides with the most mechanically quiet phase of the cardiac/respiratory cycle (see Figure 3) [20]. Prospective triggering in cine imaging allows data acquisition covering most of the cardiac cycle, determined by the number of cardiac phases or segments within an R-R interval. The acquired dataare sorted intok-spaces(representing the individualframes) according to the cardiac phase, and each k-space is filled over several heart beats. This functional set starts with the R wave. Since the duration of the R-R interval varies slightly from the heartbeat to the end, the last 10% of the R-R interval is usually not sampled (see Figure 3(b)). Prospective triggering has advantages in noise filtration. The interval of acquisition is often set such that the detector intentionally ignores any high amplitudes other than R waves, preventing false triggering. This ability, however, is problematic when monitoring patients suffering from rapid or irregular activity ”arrhythmias. In patients with premature heartbeats, the earlier R wave is passed because the gradient pulses triggered by the previous R wave are still running, which results in extended acquisition time. This heart rate variability must be considered when planning the acquisition window positioning for a prospectively triggered examination. For the detection of the next R wave, the acquisition window position should not be too close to the next R wave. An acquisition window placed too close may interfere with the next R wave and shorten the corresponding R-R interval. During the scan, the examiner must Fig. 4. The basic principle of the retrospective gating. Fig. 5. The principle of cardiac triggering. monitor changes in the heart cycle and appropriately adapt the triggering parameters [21], [22]. Retrospective gating allows continuous acquisition of MR data (see Figure 4). Both the image and the heart/respiratory signal are captured over several cycles and used to reconstruct the image by reordering, grouping, or correlating with phase of the cardiorespiratory cycle. This procedure requires unconventional “real-time” pulse sequence (e.g., for functional imaging) and acquisition software decisions, because the method of data acquisition changes as it runs. In this way, retrospective gating uses complex methods for interference filtering [20], [21]. The main advantage of retrospective acquisition is the ability to collect data from all heart phases. Like prospective triggering, retrospective methods may have problems with arrhythmias and low R-wave amplitudes. The systolic and diastolic periods are unevenly altered in case of heart rate variability or arrhythmia events, so retrospective software is unable to compensate by appropriate data segmentation, resulting in error [21], [22]. The following chapters outline common problems related to cardiac cycle and respiratory synchronization, summarize available synchronization techniques commonly used in clinical practice, as well as introduce several novel synchronization approaches currently under development. We discuss the advantages and disadvantages of each approach, especially in the case of the increasingly popular high field strength MRI. II. CARDIAC GATING METHODS Cardiac gating is critically important for CMRI. In current practice, cardiac activity is most commonly monitored by ECG signal. The principle of cardiac triggering (see Figure 5)is based on the detection of a change in signal of the heart (e.g., the R wave of ECG signal, ventricular depolarization). Given the prominence of the R wave, and its routine use in cardiac LADROVA et al.: MONITORING AND SYNCHRONIZATION OF CARDIAC AND RESPIRATORY TRACES 203 Fig. 6. Comparison of ECG-triggered and non-triggered images using (a) T2-Weighted Fat-Suppressing Sequence, and (b) T1-Weighted Myocardial-Suppressing Sequence. physiology to denote the beginning of the cardiac cycle, cardiac imaging is often triggered by the R wave [23]. Depending on the imaging being performed, detection of the R wave may initiate a series of evenly timed acquisitions across the R-to-R interval in order to reconstruct a cinematic image of cardiac systole and diastole.Alternatively,theR-wavemaybeusedtodefinethetime period of a single acquisition (e.g. end-systole or mid-diastole). The most commonly used heart scan sequences are cine T1/T2-weighted sequences, fat-suppressing T2-weighted sequences, and myocardial-suppressing T1-weighted sequences. These examinations are usually performed under respiratory quiescence, with the subjects holding their breath for the entire measurement period “from 6 to 25 seconds ”to avoid respiratory movement artifact. Figure 6 shows examples of scans triggered by combination of ECG signal and a breath-holding/freebreathing. As can be appreciated, combining ECG triggering with respiratory quiescence improves overall image sharpness and eliminates motion artifacts. A. Cardiac Gating Challenges Artifacts during MRI may originate from both the patient and the measurement system. Firstly, the electrodes and associated connectorsandcablesshouldbe made of non-ferromagnetic material to reduce magnetic field distortion, whereas interference mayfalselytriggerthe scanner andcause loss of synchronization with the heart [21], [24]. Blood flow in the heart can induce voltage and generate hydrodynamic artifacts. Since blood is an electrically conductive liquid whose movement produces an electrical current added to the heart signal, a magnetohydrodynamic effect occurs. The electrical signal generated in this way usually affects the T wave of ECG signal (see Figure 7). If the Fig. 7. Demonstration of magnetohydrodynamic artefact affecting ECG waveform measured: (a) outside the influence of MR magnetic field, (b) in the 1.5 T magnetic field, where both the R wave and the T wave acquire similar amplitudes. T wave occurs at a time of rapid movement of blood from the heart, the T wave is distorted [25]. In addition, surface receiver coils are commonly used in CMRI examinations to amplify and improvetheacquired signal, causingthis interferencetoincrease significantly. Other problems, such as disorders of cardiac conduction system or unrelated physiological processes (e.g., muscle tremor), are additive to the interference mentioned above. The optimal image quality requires appropriate electrode placement that maximizes the amplitude of the R-wave while minimizing these fundamental artifacts. Extreme changes, such as the noise of a high-amplitude gradient system or the small electrical currents caused by a change of magnetic field gradient, are very disruptive. However, due to the large frequency difference between 204 IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 15, 2022 Fig. 8. ECG waveform with its triggering signal. the noise and ECG, high frequencies can often be suppressed by filtering [21], [26]. In many applications, both prospective and retrospective rejection of arrhythmia has been integrated into acquisition and reconstruction software. In this case, an acceptance window that allows R-R variability must be specified. This step usually increases the acquisition time, and the examiner has to decide whether to reduce the scan time by increasing the number of segments at the expense of image quality. However, the use of prospective gating instead of retrospective gating can overcome extreme deviations of the R-R interval. Typically, the shorter R-R interval should be used to schedule the acquisition window, whichmustbe slightlyshorterthantheshortest R-Rinterval.The resulting image will represent an incomplete heart cycle, but a calculationof ejectionfraction (amongother outcomemeasures) is still possible [21], [24]. The artifacts related to magnetic field are particularly pronouncedduringmeasuring athighmagneticfieldstrength. When using field strengths higher than 3 T, the influence of the magnetohydrodynamic effect on the signal increases, and obtaining a clear signal of sufficient quality for faultless triggering becomes more difficult [27], [28]. As ultra-strong MRI fields become more widespread, solutions to this sensitivity issue are increasingly needed. Thus, new methods of triggering CMRI, discussed in more detail below, have been developed and are currently being studied. B. Electrocardiography Sensing of the ECG signal (see Figure 8) is one of the best known and most commonly used methods of monitoring cardiac activity. The ECG measurements consist of placing the electrodes on the patient’s chest at the specific locations. It is measured in current leads, usually in 12 leads for standard nonMR diagnostic measurements with 3 limb electrodes included. When triggering CMRI, fewer leads are sufficient, because the signal does not serve as a tool for a diagnose of the heart function disorders but only of the QRS complexes detection. Thus, the number of electrodes is commonly reduced to three or four (see Figure 9) [24]. Although ECG is a means of quick cardiac function recording from which each phase of the cardiac cycle can be determined for the purpose of MRI triggering, many problems with the MRI device itself and its magnetic field, such as artifacts or complications when measuring with certain materials, arise. For the best possible prevention of artifacts, it is necessary to Fig. 9. Typical distribution of ECG electrodes in MR environments: (a) – parallel distribution advantageous for high magnetic field strengths, (b) – electrode distribution normally used at 1.5 T field strengths. Fig. 10. Example of available ECG-based sensors: (a) Wireless ECG unit for sensing in MR environment [32]. (b) Quadtrode ECG electrode for the MR gating [33]. prepare the skin well at the locations of ECG electrodes (most often floating electrodes) placement, since accurate detection of low potentials requires minimal skin impedance and optimal electrodecontact with the skin. Theadded noise that results from poor electrode contact with the skin then leads to erroneous triggering during the CMRI examination. Immediately before the examination, the hair must be shaved, and the skin surface scrubbed off with a mild abrasive soap or gel before electrodes are applied; complicating the exam and adding discomfort to the patient [20], [21]. Furthermore, ECG measurements require some safety precautions due to the interaction of the ECG system with RF and gradient systems, since ECG, as a measurement with electrically active components, brings the risk of superficial heating of the patient’sskinorevenburnsresultingfromhighvoltageinduction in ECG hardware [29], [30]. To prevent currents occurring in the ECG leads due to the rapid switching of gradient fields, the location of the leads has to comprise no loops, and the wires should be as short as possible. The possibility of burns due to interaction with the RF field should be reduced by location of the wires outside the resonators, and the battery-powered ECG measuring unit (see Figure 10(a)) should be used to ensure galvanic separation of the MR system and the patient [31]. All of the major MRI vendors offer ECG gating products, and several commercially available products are also available through third party vendors. Modern products include MR-compatible 3-lead ECG triggering device using a secure fixed electrode layout (see Figure 10(b)), which restricts cable length by dictating a tight electrode placement pattern and thus, LADROVA et al.: MONITORING AND SYNCHRONIZATION OF CARDIAC AND RESPIRATORY TRACES 205 Fig. 11. The example of the ECG waveform for cardiac triggering: (a) before (I) and after (II) sequence start, (b) and (c) during the acquisition with failed triggering, (d) during the acquisition with successful triggering, (e) with improperly positioned ECG electrodes, (f) with motion artifacts. provides optimal signal performance and safety due to large electrode contact area. This new design significantly lowers resistance using the unique gel, and reduces at least partly patient preparation time and discomfort thanks to its form of disposable patch [33]. Several commercially available systems allow measurements of more vital signs together, such as ECG, respiratory function and blood pressure, so that provide a detailed information about patient’s condition during examination [34]. As described above, it is also important to consider the influence of magnetohydrodynamic effects during ECG synchronization. This effect causes ECG signal distortion and prevents a proper detection of R wave and subsequent synchronization. For example, Krug et al. [35] and Snyder et al. [36] have shown that ECG triggering is not appropriate for high fields. At field strengths beyond 3 T, the ECG recording is both spatially and temporally distorted, to the point that the R wave may no longer be clearly identifiable or overshadowed by the T wave dominance, exceeding the R wave by 20% of its amplitude. Indeed, the amplitude of the T wave can even be augmented at field strengths as low as 1.5T [37], challenging translation of ECG monitoring/synchronization at higher field strengths. While the prevalence of this problem remains incompletely understood, some reports estimate the problem to exist in as many as 30% of cases [27], [38]. This is particularly troubling, given the importance of accurate R wave registration for image acquisition [27], [28], [39]. Figure 11 shows examples of the ECG signal within examination with various deformations that prevent proper synchronization. To clearly demonstrate the effects of the MR environment on the ECG signal, Figure 11(a) shows the ECG signal prior to initiating a scan sequence (I), and during acquisition (II). Note how the ECG signal is distorted by the RF pulse during acquisition, preventing R wave delineation, similarly to other cases in Figure 11(b) and Figure 11(c), when only several R waves were detected. The correct trigger signal obtained from the distorted ECG during acquisition is only achieved in Figure 11(d).Figure 11(e) shows an example of low amplitude R-waves lost in the baseline noise and thus, their failed delineation. It is caused by poor electrodes cleaning and sticking, insufficient shaving, and improper positioning of electrodes. Motion artifacts are denoted in Figure 11(f), when the signal is completely distorted, and R waves are not detectable. For all these reasons, more advanced methods of R wave detection and magnetohydrodynamic artifact suppression have been developed and used, such as noise cancellation [40], independent component analysis [41], [42], nonlinear Bayesian filtering [42], wavelet transform [44] or their combinations. For example, Abi-Abdallah et al. [45] proposed signal decomposition using wavelet transformation and subsequent adaptive filtering. For this approach, an off-line wavelet transformation process is made to eliminate the delay in signal filtering as much as possible, generating a reference signal that can be used with an adaptive filter during the real-time calculation phase. The algorithm also calculates the respiratory synchronization signal by extracting a breath waveform that modulates the ECG signal. Then, a combination of both heart and respiratory triggering signals performs MR synchronization. Moremodernapproachincludesareal-timehigher-orderQRS detector based on adaptive thresholding [46]. The threshold uses a fourth central moment calculation that represents a significant signal change within the QRS complex compared to other components, such as the T or P wave. Stäb et al. [47] observed only rare occasions of false negative, false positive or misplaced triggereventsofECGtriggeringatfieldstrength7Tbyincluding a learning phase for the R-wave detection. The proposed trigger algorithm learns the shape of the rising edge of the R-wave while the subject is lying outside the magnet bore, where the magnetohydrodynamic effect is negligible. After learning phase is completed, the algorithm compares different derived entities of the incoming ECG signal with the corresponding entities of the learned shape in real time. 206 IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 15, 2022 Fig. 12. Typical placement of VCG electrodes in MR environment. Although the discussed methods, improving the R wave delineation, reached very high accuracy in cardiac triggering, the processing algorithms have unduly high computational requirements and have not been yet suitable for introduction into the medical practice. The insufficient computing performance in the past was the main limiting factor for using these complex methods, which remained to be used only in the research area. However, the increasing performance of microprocessor technology (multicore processors, programmable gate arrays, etc.) brings new trends and possibilities in available computer equipment, signal processing and analysis over the past few years. On the other hand, although the software improvement of ECG synchronization does not demand the additional hardware, the research of other sensing methods (working on another physical principle) could increase the accuracy of cardiac synchronization right in the lowest sensory layer. Thus, together with advanced processing algorithms, they could provide higher image quality for diagnostics. C. Vectorcardiography Vectorcardiography (VCG) describes electrical activity of the heart by three independent loops, each representing individual phases of the cardiac cycle (P wave, QRS complex, and T wave). The loops can be displayed in a one-dimensional image, similar to the conventional ECG, as well as twoand three dimensional loop reconstructions, represented by the amplitude (mV) of the electrical signal between leads (i.e. X, Y lead; X, Z lead; Y, Z lead; or across each of the X, Y, and Z leads). In terms of diagnostic information, VCG is more sensitive for the detection of hypertrophyand ischemicheartdisease [48]–[51]. In the MR environment, VCG is more resistant to the magnetohydrodynamic effect, since the heart’s electrical axis is oriented in the opposite direction of blood flow through the heart. For this reason, some studies have begun to investigate CMRI synchronization using the VCG instead of the ECG signal, where the vector model is used to approximate the ECG signal from any lead. From an application perspective, the main difference between ECG and VCG resides in the placement of the electrodes attached to the patient’s chest (see Figure 12). As mentioned, VCG has gained in popularity within the MR community, given its resistance to the magnetohydrodynamic Fig. 13. (a) Typical VCG waveform and (b) manifestation of magnetohydrodynamic effect. Fig. 14. Illustration PPG based system principles and deployment: (a) Example of finger PPG unit for measurement in MR environment [32], (b) Principle of PPG signal sensing from finger, (c) PPG waveform with its trigger signal. effect (see Figure 13). Indeed, its utility for cardiac synchronization has been recognized for more than two decades, with a degree of success [24], [26], [52]. Like ECG monitoring, however, the method lags in cases of severe arrhythmia, when the triggering system is unable to differentiate the QRS loop from the loop generated by the ectopic beat. Likewise, VCG is similarly affected by high strength and complexity of the magnetohydrodynamic signal in 7 T field strength [35]. D. Pulse Wave Cardiac synchronization using a peripheral pulse measured by photoplethysmography (PPG) is not as widespread as a standard ECG signal measurement but is a suitable alternative, especially given its resistance to MR artifacts. The PPG signal (see Figure 14(c)) is most often measured by a light sensor (see Figure 14(a)), which works by assessing the blood absorption of light corresponding to changes in blood volume (see Figure 14(b)). The rise and fall of this measured wave reflect the systole and diastole process, making it a suitable tool to trigger CMRI [53]. While pulse wave detection is technically simpler, involving placement of a device on an easily accessible finger (as oppose to electrodes placed on the chest), several major disadvantages have prevented its widespread adoption for routine MR synchronization. First, the light sensor is susceptible to movement, especially movement of the finger/hand on which LADROVA et al.: MONITORING AND SYNCHRONIZATION OF CARDIAC AND RESPIRATORY TRACES 207 Fig. 15. Pulse waves with triggering marks (a) signal without artifacts, (b) signal with artifacts caused by the moderate movement of the finger, (c) signal distorted by strong and sharp movement. Fig. 16. Self-Gating waveform with its trigger signal. the sensor is located. Figure 15 illustrates typical movement artifact, and the associated triggering error that accompanies such movement. Spicher et al. [53], [54] have attempted to overcome this limitation by introducing a contactless videobased PPG system, but have not yet succeeded in widespread clinical integration of this approach. Second, the delay between cardiac activity (i.e. mechanical ejection of blood) and detection of that activity in the periphery creates a sizable time delay (i.e. several hundred millisecond delay needed for pulse to travel to the periphery), the extent to which cannot be accurately, nor reproducibly corrected for [27]. Therefore, in most cases, this triggering method is only used when other triggering methods fail.Themostnotableexceptionisflowimaging ofcerebrospinal fluid through cerebral ventricles and openings, for which PPG triggering is superior to ECG gating [55]. E. Self-Gating Methods Self-gating (S-G) techniques, see [97]–[100], eliminate the need for extra hardware for CMRI synchronization by receiving thetriggeringinformationdirectlyfromMRsignals.Themethod relieseither onacquiring radial(orspiral) k-spacedata thatcover the k-space center in each readout or on acquiring additional (non-phase-encoded) navigator readouts through the k-space center. This major advantage is not without its own drawbacks however,assuchanapproachsignificantlyextendsthescantime. The principle of S-G CMRI is based on changes or movement of volume in the image. That means a series of consecutive echoes demonstrates peak alterations corresponding to proportional changes in total transverse magnetization due to organ movement and changes in blood volume. This data is then processed and segmented to create an ECG compensating signal (see Figure 16), according to which the image is subsequently reconstructed according to the associated cardiac cycle, represented by the amplitude and phase of the echo [97]. Due to its nature, this signal is resistant to artifacts that may affect triggering signals of other methods (ECG, VCG, etc.). The S-G method is based on a first-difference detection algorithm; however, many studies deal with more complex types to increase the accuracy of cardiac synchronization [99]. Nijm et al. [99] compared the classical method, based on the first difference calculation (including the signal filtering by its derivative), with more complex methods, such as the median template matching technique (with calculation of the correlation between the original signal and the template, determined by half the RR median calculated by the first difference method) and polynomial matching technique (a polynomial with length of an RR interval median is used as a template here). The cubic polynomial method achieved the lowest error and the highest SNR and was deemed “comparable” to ECG synchronization. It is worth noting however, that none of the S-G methods outperformed the ECG measurement technique or the original first difference method. In order to eliminate efficiency deficits of the previously introduced S-G methods, the others include using modulation of MR echo that occurs through: (1) tissue movement itself (echo-peak method), (2) kymogram (1D gating signal from the temporally evolving 2D center of mass), and (3) 2D lowresolution image correlation [101]–[105]. In each case, the set of views at each cardiac phase was derived using time-stamp informationandcomplexlinearinterpolationalgorithm.A linear regressionalgorithmwasused tocomputesignalcomponentsfor individual sampled k-space positions (i.e., spatial-frequency domains) across cardiac phases. Then, the convolutional algorithm for image reconstruction created an image of each cardiac cycle. The delay of all obtained S-G signals is 2.5 ms. The echo-peak methodseemstobethe mostpracticalofthethreemethodstested considering the image quality, values of the time variability of the triggering signal, and low computational cost. Moreover, the correlation technique requires an operator’s interaction. All three methods achieved very similar results of image quality compared to ECG synchronization [97]. Most central line S-G methods results in a doubling of the acquisition time, while radial streak artifacts are encountered with the projection reconstruction method and can interfere with image interpretation. To overcome these limitations, Crowe et al. [100] utilized S-G triggering on a double-echo sequence 208 IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 15, 2022 where the second gradient echo without phase coding was used to generate a trigger signal. A sampling of the second echo provided subjective image quality score between good and excellent, which means a satisfactory resolution and contrast for image interpretation and definition of fine anatomic structures. Also, no statistically significant differences were obtainedbetweenS-Gand ECG-gatedimagesandmass parameters measurements in all volunteers at clinically practical acquisition times, extended negligibly. This technique is not susceptible to theradialstreakartifacts,buttherewasnostatisticallysignificant difference in image quality between S-G and ECG synchronization. Most S-G methods have been successfully used for retrospective gating of CMRI, which is not as sensitive to changes in heart rate (as in the case of prospective triggering) and provides an imagethroughouttheheartcycle,whichisparticularlyimportant for determining compulsive systolic or diastolic processes [99]. The S-G technique is unsuitable for prospective triggering, which requires accurate determination of trigger delay in order to place an acquisition window in the target phase of the cardiac cycle [28], [97], [99]. Furthermore, these methods are lagging in very high heart rate and some heart diseases expressing themselves by very mild myocardial contraction and relaxation throughout the heart cycle [100]. Hiba et al. [98] overcame this challenge of scanning at a very fast heart rate (up to 600 bpm) when testing the S-G echo-peak method during imaging a rat’s heart. The S-G triggering provided better imaging of papillary muscles than ECG gating and achieved higher SNR values. F. Optical Methods The use of fiber optic sensors brings an innovative approach to biological signals measurement and is increasingly being handled by current research. Optical sensors enable sensing a wide range of signals, such as sound manifestations, pressure changes, or temperature variations. All these signals are measured non-invasively simply by attaching the sensor to the sensinglocation,e.g.,apatient’schestorback.Theopticalmeasuring of vital signs in the MRI environment has the basic advantages of its harmlessness since the sensor is made only of optical fiber material and protective elements (cases). Moreover, the sensors are highly immune to the artifacts arising from magnetic and high-frequency electromagnetic fields, so no artifacts occur in the image. Other benefits include the very small dimensions of the sensors, their low weight, and the minimization of wires. However, the significant disadvantage of optical methods is the high cost and size of the interrogation and measuring unit, which allows the conversion of signals into digital format. On the other hand, this unit can be used to evaluate the results of multiple sensors. Thus, the price of the system can be decreased when comprised to the department with multiple MR scanners, which is usual in the clinical practice. Today, the most commonly used types of optical sensors include sensors based on light interference, so-called interferometric sensors, and sensors with Fiber Bragg Grating (FBG), whichevaluatechangesoflightreflectedonthegratingstructure. The minor but currently evolving part consists of microand Fig. 17. Principle of micro-bending optical sensor. The device detects changes in the input light due to the mechanical action (force) on the optical fiber. Fig. 18. Illustration of PCG based system principles and deployment: Typical interferometric sensor placement for the cardiac activity measurement in position: (a) “ standing, (b) ” supine; (c) Example of an Interferometric sensor (d). PCG waveform with its trigger signal. macro-bending fiber optic sensors, which work on the principle of evaluating changes in light output caused by optical fiber bends created through a “sandwich” micro-bender structure (see Figure 17) [56]–[58]. The principle of measuring heart rate using interferometric sensors (see Figure 18(c)) is generally based on phonocardiography (PCG) signal (see Figure 18(d)), generally consisting of several heart sounds (S1 ”S4). Still, in most cases, only the first two sounds are evident, of which the first (S1) reflects the ventricularsystole. The mechanical-acousticactivityofthe heart and the mechanical activity of the lungs cause changes in the refractiveindexofthecoreandinthelengthofthemeasuringarm ofthe sensorplaced onthebody (seeFigure 18(a),(b)).The interferometric measuring system then evaluates these changes. This method of cardiac activity monitoring has indeed been described previously [59]–[67]. The principle of heart rate measurement using an FBG sensor is based on ballistocardiography (BCG, see Figure 19(d)) and has been described previously [69]–[76]. Cardiac activity is manifested physiologically in the chest area by a slight pressure action occurring due to the mechanical LADROVA et al.: MONITORING AND SYNCHRONIZATION OF CARDIAC AND RESPIRATORY TRACES 215 Fig. 29. The principle of navigator triggering in CMRI. image data. However, data needs to be acquired throughout the heartcyclein CMRI, sotriggeringby one navigationecho within one cycle is insufficient for this purpose [141]. Peters et al. [139] used two navigation echoes within one heart cycle for CMRI respiratory triggering ” just before the QRS complex occurred and 500 ms after that. Data is received when both navigator positions fall into the acceptance window. There was no significant difference between the image quality or the values of the observed parameters (left ventricular function and volume) by triggering and breath-holding, but the CNR of these images showed differences on behalf of the breath-hold method (47 ±14 vs. 21 ±10). However, the navigation echoes provided sufficient image quality for diagnosis and increased the efficiency and speed of the examination (total time for breath-holding was 10 minutes, including pauses, for triggering 3.7 minutes), which brings benefits to patients under stress or completely incapable of cooperation in breathing. D. Self-Gating Methods Like cardiac synchronization, S-G techniques (see Section II-E) also deal with respiratory triggering, because the afore-mentioned methods of monitoring respiratory activity may lag if the position of the chest wall changes with respect to the heart’s position during several respiratory cycles. The S-G technique often allows triggering by both vital functions in CMRI examinations, where obtaining the individual signals by proper filtration (each signal has a different frequency response), see [98], [141]–[143]. The method is based on calculating the correlation between the trigger image and the target image obtained in the same heart phase and the desired position of the breath cycle. Larson et al. [141] filter the breath curve obtained by S-G signals and correlation using low-pass FIR filter and specify a respiratory threshold (see Figure 30), which defines the data further used to reconstruct the image at each phase of the heart cycle. This procedure increased the sharpness and contrast of the image compared to the free-breathing image without triggering, but the difference in image quality compared to the breath-hold Fig. 30. Respiratory Self-Gating waveform with illustration of gating principle. Fig. 31. Example of the micro-bend sensor implementation into mat [162]. technique was no significant, similarly to [142], [143]. Nevertheless, cardiac and respiratory S-G signals correlate strongly with externally measured signals using ECG (R=1.00 ±0) and respiratory belts (R=0.82 ±0.1) [143]. Other successful applications of the retrospective respiratory S-G methods were carried out in lung UTE (ultra-short echo time) imaging in [165]–[168]]. The lung investigation is particularly challenging not only because of respiratory and cardiac motion limiting the image quality, but also intrinsically low MR signal caused by the low water concentration, multiple air”tissue interfaces, or low proton density of the lung parenchyma. Since navigator sequence causes a concomitant saturation of lung parts, S-G method represents less complicated way of freebreathing lung imaging [166], [169] Real-time S-G triggering has been demonstrated in [144], [145]. This approach promises to significantly reduce acquisition time while maintaining image quality similar to the retrospective method but requires more sophisticated software for signal detection and processing. E. Optical Methods The field of optical sensors and fibers also investigates the respiratory synchronization of MR, similar to the cardiac (see Section II-F), when both vital signs are often combined for the measurement of the patient’s status during MRI. For measurement of breathing activity by optical sensors, researchers use interferometric sensors [61]–[66], [146]–[149], FBG sensors placed under the patient’s back [70]–[73], [75], as well as micro/macro-bending fibers [90], [93], [150]–[154], which are often part of so-called smart textiles [155]–[161]. Example of the micro-bend sensor implementation into mat is shown in Figure 31. The difference in measurement of respiration compared to cardiac activity is the signal filtering in a different frequency 216 IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 15, 2022 band to maintain only the desired breath curve, i.e., within the range of 0.05–2 Hz. Yoo et al. [163] presented an option of measuring only respiratory activity, which responds to changes in air temperature during inhalation and exhalation, acquired by a special sensor. The authors designed two different types of optical sensors for monitoring the respiratory rate ” nasal and abdominal. A sensor attached to the nasal cavity measures the airflow using a thermochromic pigment that changes color depending on the temperature variations within the breath cycle (inhalation/exhalation). The second type of sensor is located on the patient’s abdomen and measures breathing activity as the circumference variance. The authors verified the use of the proposed sensors without deteriorating the MR image, which promises a suitability of the fiber-optic respiration sensors for respiratory monitoring during surgical procedures performed inside an MRI system. Measurement of respiratory rate by FBG sensors in MR environmentwasperformedin[68],[74],[77],[78],[80],[81],[164]. All these studies reached the Bland-Altman derived accuracy of the respiratory rate greater than 95% related to conventional respiratory sensors, so the optical method appears to be a good alternative to MRI respiratory triggering. In connection with heart rate monitoring, this technique becomes very interesting forMRI examinationssince onesensorwouldbeabletomeasure the overall vital signs of the patient, moreover continuously, as described in [83]. Fajkus et al. [132] tested respiratory triggering at a 3 T field strength using an FBG sensor applied to the nasal oxygen capsule. They filtered the obtained signal in the band of 0.1 ” 0.5 Hz and compared the calculated respiratory activity with the conventional measurement by respiratory belts, where the measurement by optical sensor reached an accuracy based on the Bland-Altman analysis of over 95%. According to experts, the images from both triggering methods were diagnostically beneficial, but higher contrast and sharpness of the contours were achieved when using the optical sensor. Also, according to quantitative image analysis, images triggered by FBG achieved better results than respiratory reference. Respiratory triggering using a micro-bend sensor was tested at a field strength of 1.5 T in [162]. The optical signal reached a very high correlation R =0.971 with measurements by standard respiratory sensors. No movement artifacts or blurry parts occurred in the image. The difference between the optical and navigator methods in assessing the SNR of the kidney and the CNR kidney-spleen was not significant, but the navigator method gained a significantly better diagnostic quality of the images. IV. DISCUSSION The review of CMRI synchronization methods shows that today’s research focuses more on the novel ones than improving the techniques most widely used in clinical practice. It is due to relatively complicated patient preparation in case of ECG/VCG and especially due to the increasing complications of high field strength examination. Also, relatively patient-friendly PPG is not suitable because of motion artifact susceptibility and long physiological delay. The contemporary research focuses on two major milestones: (1) advancement of software that can achieve adequate accuracy in the detection of synchronization points in the signal or suppress magnetic field artifacts, and (2) development of new methods of measuring vital signs that could replace standard ECG measurements in future. Thanks to the extreme increase in computing performance in the recent years, it is possible to significantly improve the signal/image quality by using advanced processing techniques. However, relying only on software algorithms can result in a number of computer errors, which can lead to the distortion of some image segments and thus, suppress important diagnostic information. Therefore, it is undoubtedly important to pay attention to the basic entity of the entire measuring chain (sensors), which could provide a desirable accuracy already on the lowest layer and thus, together with using software algorithms, even better overall results. Currently, acoustic and accelerometric triggering methods are most suitable to introduce into practice, providing safety and comfort for the patients and high resistance to artifacts caused by magnetic fields; efficiency of acoustic sensors has already been successfully tested at 7 T. The only drawback is susceptibility of the PCG and SCG to interference caused by gradient coil switching, and accordingly, the delay in signal transmission to the MR unit due to filtration. Other investigations, which are still in their infancy, such as optical or DUS measurements, show high-quality heart rate calculations, but some limitations have not yet proven their usability in medical practice: the optical method requires a very expensive evaluation unit, which could become more accessible over time, and inaccuracy in DUS measurements of patients with cardiac disorders means a major complication in cardiac examinations. The research methods have several common disadvantages: rNon-standardized sensor placement in contrast with ECG or VCG. The sensor location highly depends on the specific patient and his body structure; some signals show very different waveforms when placed at diverse points of the chest (e.g. SCG). Therefore, research should handle the possibilities of measurement without the complicated attachment of the sensor to the patient’s body, such as sensors implemented in textiles or mats. rSignal delay compared to ECG signal which is given by several attributes: (1) a physiological delay between the R wave and the alternative trigger point (first heart sound, J-wave etc., see Figure 32), and (2) other delays superimposed caused by the transmission medium, electronics ensuring signal state and conversion to a digital signal, or filtration used to remove undesirable components. Such a delayed trigger signal may lag behind the ECG trigger for hundreds of milliseconds, collecting data from the wrong phase of the cardiac cycle. Therefore, the image may lose its diagnostic value, and the acquisition needs to be repeated, extending the examination time. Thus, suppression of the signal delay in some way, such as software customization of triggering, should be used. LADROVA et al.: MONITORING AND SYNCHRONIZATION OF CARDIAC AND RESPIRATORY TRACES 217 TABLE I THE CLASSIFICATION OF THE PRESENTED SYNCHRONIZATION METHODS Fig. 32. Physiological delay of the individual heart signals compared to the ECG with marked peaks (trigger). The presented S-G methods could solve the problems of sensor placement and signal delay versus ECG. Another advantage of S-G methods is the possibility of respiratory and cardiac gating altogether. Although this double triggering prolongs the required scan time significantly, the total examination time is shortened in cooperating patients. In case of patients unable to assist in breathing, respiratory synchronization is a necessity for a successful examination. Because respiratory compensation using navigation echoes lags due to the very small acquisition window triggered by only one navigation echo per heart cycle, a single breath-hold examination still prevails, which can cause problems for many patients. However, shortcomings of S-G techniques lie in the realization of synchronization itself. In general, these methods have low hardware requirements at the expense of the complexity of software, which processes MR data to generate a trigger signal. Therefore, methods of measuring vital functions, which enable to distinguish a respiratory function from the heart signal by appropriate filtration (BCG, SCG), become preferred. Considering the unavailability of optical methods, acoustic and SCG methodsare currentlyconsidered asmost promisingmethodsfor combining cardiac and respiratory synchronization and should remain to be the subject of further research in this area. Table I subjectively summarizes methods for cardiac and respiratory MRI synchronization with the classification of their accuracy and artifact resistance: rAccuracy refers to the success of correct detection of cardiac function with sequence initiation in the desired phaseoftheheart/respiratorycycle,whichenhancesimage quality. It is evaluated with respect to cases of some abnormalities (e.g., disease, arrhythmia) or technical limitations, not to the origin of MR artifacts. Accuracy is classified as follows: rHigh ”methodprovidesveryaccurateheart/respiratory phasedetectionandisminimallyaffectedbyabnormalities, patient’s physiology or movements. rMedium ” method provides accuracy suitable for MR gating but is limited by some of the above-mentioned problems. rLow ” method is not recommended for MR gating due to large range of limitations. Resistance to MR artifacts indicates the resistance to effects of the MR environment on a given signal (influence of electromagnetic field, magnetohydrodynamic effect or acoustic disturbance) and degree of independence from magnetic field strength, classified as: rHigh ” method is completely resistant to MR artifacts and independent from magnetic field strength. rMedium ” method is affected only by one of the abovementioned MR influences. rLow ” method is susceptible to all effects of the MR environment. Complexity includes computational requirements, availability, and the price of sensors or measuring unit. Computational cost is a crucial factor in implementation of the measuring system since it determines a function of the system in real-time. Price is also a key point of method 218 IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 15, 2022 feasibility for clinical practice. A method eligible for introducingintopracticeshould accomplishacompromise between its complexity and performance/accuracy. Specific limitations closely relate to the previous parametersbutincludedetailedknowledgeofthedisadvantagesof each method, which must be eliminated or reduced as best as possible in order to introduce the method into practice successfully. V. CONCLUSION Synchronization of the cardiac cycle and respiratory excursions is necessary for cardiovascular and abdominal MRI for optimalrepresentationand imagequality.Thepapersummarizes techniquescurrently available and/or underdevelopment thatfocus on increasing problems with measurement in high magnetic fields, nowadays reaching up to 7 T. With an extreme increase in computing performance in recent years, software-based methods, such as S-G and navigators, are the center of attention, and they underwent a huge expansion. However, they still have some disadvantages (computer errors, disturbing steady-state), which could be solved using the hardware-assisted methods based on the non-electrical measurements. From such these techniques, acoustic and accelerometric ones are the most promising not only due to the high accuracy but also thanks to their low price and easy implementation. Moreover, they would provide the possibilityofmonitoring therespiratorysignal togetherwithcardiac function and thus, would enable a “double” MR triggering approach, important in cardiovascular examinations, especially in non-cooperating patients. With this foundation in place, the future of MR synchronization is encouraging; however, more work is needed in order to maximize the technology available and develop new approaches not yet imagined. 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