©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.International audienceCardiac Magnetic Resonance Imaging (MRI) is very challenging due to the perpetual heart movements. This movement is pseudo- periodic and implies several issues for image Acquisition. Building a single image requires several shots, done on a specific timing of the cardiac cycle. Nowadays the heart rate is estimated before imaging and then it is assumed not to evolve during acquisition. Additionaly, in order to remove motion artifacts, the patients are asked to perform breathholds. Unfortunately, while performing a breath-hold, the heart rate is changing in a significant way. To address this problem in the framework of clinical applications, we propose a simple method to predict the out-coming RR interval, in order to compute adapted MR parameters. The RR interval is the time separating two consecutive R waves and corresponds to one cardiac cycle. Due to its simplicity this method is clinically applicable. The prediction is performed by modelling the Heart Rate Variation as a linear combination of different sensors (here, respiratory sensor and amplitude of R wave). We evaluated this method on five healthy subjects in a clinical setup

Bosser, Gilles

Felblinger, Jacques

Oster, Julien

Pietquin, Olivier

Crossref

Adaptive RR prediction for cardiac MRI

HAL-CentraleSupelec

Adaptive RR Prediction for Cardiac MRIJulien Oster, Olivier Pietquin, Gilles Bosser, Jacques FelblingerTo cite this version:Julien Oster, Olivier Pietquin, Gilles Bosser, Jacques Felblinger. Adaptive RR Prediction forCardiac MRI. ICASSP 2008, Apr 2008, Las Vegas, United States. pp.513-516, 2008. <hal-00276137>HAL Id: hal-00276137https://hal-supelec.archives-ouvertes.fr/hal-00276137Submitted on 28 Apr 2008HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestine´e au de´poˆt et a` la diffusion de documentsscientifiques de niveau recherche, publie´s ou non,e´manant des e´tablissements d’enseignement et derecherche franc¸ais ou e´trangers, des laboratoirespublics ou prive´s.ADAPTIVE RR PREDICTION FOR CARDIAC MRIJulien Oster1, Olivier Pietquin1,2, Gilles Bosser3, Jacques Felblinger 11IADI, Nancy University, Inserm ERI 13, CHU Nancy Brabois, France2SUPELEC Metz Campus, IMS Research Group, France3Cardiac Rehabilitation, University Hospital, Nancy, FranceABSTRACTCardiac magnetic resonance imaging (MRI) is very challeng-ing due to the perpetual heart movements. This movementis pseudo-periodic and implies several issues for image acqui-sition. Building a single image requires several shots, doneon a speciﬁc timing of the cardiac cycle. Nowadays the heartrate is estimated before imaging and then it is assumed notto evolve during acquisition. Additionally, in order to removemotion artifacts, the patients are asked to perform breath-holds. Unfortunately, while performing a breath-hold, theheart rate is changing in a signiﬁcant way. To address thisproblem in the framework of clinical applications, we pro-pose a simple method to predict the out-coming RR interval,in order to compute adapted MR parameters. The RR in-terval is the time separating two consecutive R waves andcorresponds to one cardiac cycle. Due to its simplicity thismethod is clinically applicable. The prediction is performedby modeling the heart rate variation as a linear combinationof diﬀerent sensors (here, respiratory sensor and amplitude ofR wave). We evaluated this method on ﬁve healthy subjectsin a clinical setup.Index Terms— Heart rate, MRI, Electrocardiography,prediction method.1. INTRODUCTION1.1. MR Cardiac TimingCardiac MRI sequences are generally synchronized on the Rwave of the Electrocardiogram (ECG) (see ﬁg. 1) and theread out is done during the ventricular diastole. Depend-ing on the heart rate (HR) and so on the RR interval, op-timal trigger delay (TD), acquisition time (AT) and triggerwindow (TW) are deﬁned before scanning. Additionally forblack blood imaging, Inversion recovery (IR) pulses are usu-ally played directly on the R wave to have an inversion time(TI) of the blood on read out [1], [2]. These MR parame-ters are set before the scanning and are computed when thepatient is breathing normally. In order to avoid motion arti-facts, cardiac imaging is done while patients are performinga breath-hold. HR is assumed to be constant during breath-hold. So for black blood imaging, the trigger launch the IRpulse and the image acquisition follows with a delay ﬁxedto acquire during diastole. The aim of this study was ﬁrstto show that this assumption is generally violated and thatthe currently used procedure can reduce image quality. Fur-thermore black blood imaging for patients with a HR > 85beat-per-minute (bpm) is theoretically unfeasible, because TIof blood is then greater than the RR interval. In the sameway, accurate acquisition during systolic phase is not possi-ble, because IR pulses should be played before the R wave.This study will then introduce a new method for computingMR parameters based on the variation of HR [3] and whichwould enable the acquisition of black blood systolic imagesfor every patient. For this aim predicting the HR variationduring apnea is required. We will see that RR predictionis even possible during free breathing and will be useful fordouble gating systems, i.e. gating of the heart activity andrespiratory position. The proposed method will be easily im-plemented in actual MR systems or triggering devices. Theycan be used clinically without further constraints for the pa-tient and the medical staﬀ and without lengthening of theexamination duration.Fig. 1. Schematic of an ECG trace and Acquisition Timing.In this example, the IR pulse is played in the heart cycleprevious to the acquisition.1.2. Preliminary AnalysisThe ﬁrst step of the study was to analyze the HR variation intwo diﬀerent breath-hold modes (inspiration and expiration)in ﬁve subjects. We observed that HR variation during ap-nea is not reproducible between the two modes (see ﬁg. 2).Moreover the reproducibility between diﬀerent subjects wasnot obvious. In opposite it appeared that the behavior of HRfor a same subject, in the same breath-hold mode is quitesimilar.Since several breath-holds are performed by one patientduring an examination, the information of the HR variationduring the ﬁrst breath-hold could be used in order to compute5131-4244-1484-9/08/$25.00 ©2008 IEEE ICASSP 2008(a)(b)Fig. 2. Heart rate variation during breath-old in inspiration2(a) and in expiration 2(b).Each curve represent the HR forone subject. X axis represents the cardiac cycle number andY axis represents the RR interval duration in seconds.the parameters of a prediction model of the RR during theothers. Currently, a patient has to perform several breath-holds during one MR examination that could all be used formodeling. The model for prediction must be quite simpleand not require a long period of training since one breath-hold contain generally less than thirty cardiac cycles. Thismeans that complex models such as neural networks cannotbe used for clinical purposes. Thus we ﬁrst tried a simpleAR model with a low order, so that the training can be doneduring one apnea and prediction can easily be implementedin real-time [4]. We have implemented this method in theSAEC [5] in order to achieve systolic cardiac MRI. We haveused a Fast Spin Echo sequence and the trigger was sent tothe MR system using the scheme presented in ﬁgure 1. Theprediction was computed using only the past RR. The resultsare displayed in ﬁgure 3.In this paper, we present a new model for prediction. To(a)(b)Fig. 3. Systolic Cardiac Images in little axis, using RR pre-diction, two diﬀerent slices 3(a), 3(b). Such images are un-feasible with the clinical applications.estimate the model parameters on-line a Kalman-ﬁlter-basedmethod is used.2. THEORYWe propose to apply a physiological multi input regression(PMIR) using information of multiple physiological sensors.In our case, we used cardiac and respiratory physiologicalchanges. For respiration information we combined two meth-ods, (i) a pneumatic belt on the subject’s abdomen and (ii)the variation of the the R waves magnitude. These newinputs will bring information since it is known that HR iscorrelated with respiration, and called the respiratory sinusarrhythmia (RSA). The HR decreases during inspiration andincreases in the other case. Using this information can im-prove the prediction especially for free breathing sequences.The signal of the belt is non-uniformly sampled, so we usedonly the samples when HR is computed. As described in [6],the magnitude of QRS is correlated with respiration, thisinformation should be very useful during breath-holds, sinceit is always available.The assumption done is that the RR interval is a linear com-bination of the previous RR, the respiration, its derivationand the magnitude of the previous RR.Let Xn be the HR, Rn the signal of the respiratory belt at514QRS time n, R′n the derivation the respiratory belt at QRStime n and An the amplitude of the nthQRS, then the PMIRcan be written as :Xn =p1Xi=1aiXn−i+p2Xi=1biRn−i+p3Xi=1ciR′n−i+p4Xi=1diAn−i+εn.(1)The parameters will be estimated using a Kalman ﬁlter sincethis modeling corresponds to a state vector model.In fact, equation 1 can be seen as the observation equation ofa Kalman ﬁlter system.yn = Hnxn + εn, (2)where yn = Xn is the observation vector,Hn = [X1, · · · , Xp1 , R1, · · ·Rp2 , R′1, · · · , R′p3 , A1, · · · , Ap4 ]is the observation model and the parametersxn = [a1 · · · ap1b1 · · · bp2c1 · · · cp3d1 · · · dp4 ]is supposed to be the state vector.As we have no information about their evolution during theexamination, we suppose that it is almost constant. So thestate equation can be written as :xn+1 = xn + un. (3)We assume the dynamic noise and measurement noise (resp.un and εn) are both Gaussian distributed with zero mean andconstant covariance.So we are in front of a state estimation problem given theobservations up to n (y1..n).The ﬁrst breath-hold is used to initialize the model parame-ters.3. METHODSFive healthy subjects performed two breath-holds in inspi-ration and two others in expiration. The subjects were insupine position inside the MR bore of a 1.5T General ElectricsSIGNA Excite HD MR system (General Electrics, Milwaukee,WI). Each breath-hold lasted at least for 30 seconds. Signalfrom a respiratory belt and ECG were carried by a Maglife(Schiller Me´dical, Wissembourg, France) patient monitoringsystem and recorded along with MRI gradients and acquisi-tion window signal on the Signal Analyzer and Event Con-troller (SAEC) custom computer and electronics [5]. All thedata were collected for oﬄine processing (but were imple-mented in the SAEC so as to achieve real-time imaging). Inorder to evaluate HR variation we used R waves detection al-gorithm used in an industrial monitoring device (Argus PB-1000, Schiller AG, Baar, Switzerland). This detection is quitea diﬃcult task when the ECG is collected in a MR environ-ment. Many works have shown that the artifacts induced byMR gradients can produce false triggering. Some methods forECG denoising and MR gradient artifacts suppression havebeen developed. In order to improve automatic detection ofR waves, we preprocessed the ECG signal by using a singularvalue decomposition based method [5]. Since some R-waveswere still missed, the annotations had been corrected manu-ally.Three kinds of PMIR have been tested, the diﬀerence consistsin the number of used parameters, this is explained in table1. Each diﬀerent PMIR has a number, and the pi parameteris the order of the regression for each signal, as deﬁned inequation (1).The prediction using PMIR with diﬀerent state observationTable 1. Diﬀerence of the three PMIRPMIR 1 PMIR 2 PMIR 3p1 2 2 2p2 2 2 0p3 2 2 0p4 2 0 0was analyzed. The criteria chosen is the MSE which is deﬁnedas in equation 4.MSE = 1/n×nXi=1(xˆi − xi)2. (4)4. RESULTSThis study shows that the assumption that heart rate is con-stant during breath-holding is unwarranted. Computing MRparameters, in order to ﬁt at best the RR interval will thenimprove the quality of MR images, in particular for Blackblood images as discussed in introduction.We can see on ﬁgure 4 that the shape of predicted HR varia-tion with PMIR is globally the same as the real HR variation.In fact, as few samples are available, we are obliged to makethe system very sensitive to changes in order to adapt veryfast.For better comprehension, the MSEs have been normalizedby the MSE obtained with a constant RR.The improvement of the MSE compared to a constant RRestimate is quite evident regarding the results approximatelysix to seven times better (tables 2, 3).Table 2. Normalized MSE results of prediction using PMIRfor HR variation during breath-hold in inspiration positionSubject PMIR PMIR PMIR RRId 1 2 3 =S1 0.11 0.09 0.08 1S2 0.55 0.45 0.45 1S3 0.09 0.08 0.07 1S4 0.06 0.05 0.04 1S5 0.35 0.29 0.25 1Table 3. Normalized MSE results of prediction using a PMIRfor HR variation during breath-hold in expiration positionSubject PMIR PMIR PMIR RRId 1 2 3 =S1 0.2 0.15 0.24 1S2 1.22 0.88 0.91 1S3 0.53 0.44 0.45 1S4 0.39 0.29 0.3 1S5 0.03 0.02 0.02 15. DISCUSSIONOur method is based on physiological signals evolution as in-puts of the models. It is obvious that during a breath-hold515(a)(b)Fig. 4. Results of the method using PMIR 2 during freebreathing 4(a) and large breathing 4(b). ◦ represent thereal HR variation and + represent the prediction values usingPMIR 2.these variations are small and correspond to a low input inthe model. However the breath-hold is never achieved per-fectly due mainly to a drift in the position and this probablyaﬀects heart rate variation. Application in free breathing ordouble gating will beneﬁt more from such a method.Comparing the three PMIR proposed, we observe that usingPMIR 3 gives the best results for breath-holds in inspirationposition. This can be explained by the fact that there isprobably some compensatory abdominal motions to hold thebreath.In the opposite, the PMIR 2 is better for breath-holds in ex-piration position. This can be explained by the fact that theabdomen is much more stable, since it is much more diﬃcultto hold breath in expiration position, the respiratory belt con-tains more useful information.Using the prediction will lead to the elimination of wrongcardiac time acquisitions that produce the ghosting artifacts.Unfortunately this will not remove motion artifacts, due tothe movement of the subject. Thus it is diﬃcult to comparetwo images done on two consecutive breath-holds with eachstrategy, since the quality of the subject’s breath-hold cannotbe controlled. This may have a great impact on image qual-ity, with apparition of motion artifacts if the breath is notperfectly held.Moreover, ﬁnding a relevant criteria for assessing the imagequality in relation to the presence of ghosting artifacts ontothe image is not a trivial task.6. CONCLUSION AND PERSPECTIVESOur method based on very basic assumptions, can be veryeﬃcient. Taking into account the HR variation during theacquisition can remove artifacts due to incorrect acquisitions.Due to its simplicity, this method is clinically applicable onactual MR systems and without lengthening the examinationduration.Complicating the model will certainly increase the quality ofprediction, but so will the amount of data needed and thecomputation time such that the new method would not beclinically applicable anymore. Using more breath-holds in or-der to enhance the parameters estimation may provide betterresults. One of the drawback of this method is that samplingthe respiratory sensor at the R-wave leads to a great lossof information. Using asynchronous methods could certainlyimprove the prediction.7. REFERENCES[1] G. S. Slavin, G. Holmvang, and E. Schmidt, “Systolic-phase Black-blood Imaging for Fatty Tissue Detectionin Arrhythmogenic Right Ventricular Dyspalisa,” inISMRM, 2005, p. 83.[2] S. Sinha and U. Sinha, “Black Blood Dual Phase TurboFLASH MR Imaging of the Heart,” Journal of MagneticResonance Imaging, vol. 6, pp. 484–494, 1996.[3] G. Liu and G. A. Wright, “Adapting Trigger delays toHeart Rate for Coronary MR Angiography,” in ISMRM,2006, p. 2157.[4] J. Oster, F. Odille, G. Bosser, O. Pietquin, C. Pasquier,P. A. Vuissoz, and J. Felblinger, “Adaptive Prediction ofRR interval for online MR parameters changes,” in Jointannual meeting ISMRM-ESMRMB, 2007, p. 504.[5] F. Odille, C. Pasquier, R. Aba¨cherli, P. A. Vuissoz, G. P.Zientara, and J. Felblinger, “Noise Cancellation SignalProcessing Method and Computer System for ImprovedReal-time Electrocardiogram Artifact Correction duringMRI Data Acquisition,” IEEE Transactions on Biomedi-cal Engineering, vol. 54, April 2007.[6] J. Felblinger and C. Boesch, “Amplitude Demodulation ofthe Electrocardiogram signal (ECG) for respiration moni-toring and compensation during MR examinations,”Mag-netic Resonance in Medicine, vol. 38, pp. 129–136, 1997.8. ACKNOWLEDGMENTThe authors thank the Schiller Me´dical (France) companyand Re´gion Lorraine (France) for funding.516

Adaptive RR Prediction for Cardiac MRI

https://hal-supelec.archives-ouvertes.fr/hal-00276137

Adaptive RR Prediction for Cardiac MRI

Abstract

Similar works

Full text

Available Versions

Crossref

HAL-CentraleSupelec