Prospective Self-Gating for Simultaneous Compensationof Cardiac and Respiratory Motion

Martin Buehrer, Jelena Curcic, Peter Boesiger, and Sebastian Kozerke*

Segmented cardiac acquisitions generally require the use of anelectrocardiogram (ECG) in combination with a breathhold or arespiratory navigator placed on the diaphragm. These tech-niques necessitate patient cooperation and increase the com-plexity of cardiac imaging. The ECG signal may be distortedinside the magnet by interferences from radiofrequency andgradient action. Breathhold acquisition limits the total scantime, while navigators on the diaphragm might not fully reflectrespiratory-induced motion of the heart. To overcome some ofthese problems, several self-gating (SG) or “wireless” tech-niques have recently been presented. All of these approaches,however, are based on either cardiac triggering or respiratorygating, or the data are processed retrospectively, reducing theefficiency of data acquisition. In this work a prospective SGapproach for free-breathing imaging is presented that requiresneither ECG gating nor respiratory navigation. The motion dataused for cardiac triggering and respiratory gating are extractedfrom the repeatedly acquired k-space center. Based on com-puter simulations and in vivo data of the heart, it is shown thatcardiac as well as respiratory motion can be accurately ex-tracted in real time. Using the method proposed, the scanefficiency could be significantly increased while preserving im-age quality relative to retrospective SG approaches. MagnReson Med 60:683–690, 2008. © 2008 Wiley-Liss, Inc.

Key words: cardiac imaging; self-gating; prospective motioncompensation; respiratory motion; real-time filtering; cardiacmotion

Motion due to respiration and the beating of the heart isone of the major issues in cardiac data acquisitions. Tocompensate for cardiac motion, the MR acquisition is usu-ally synchronized with the cardiac cycle using an electro-cardiogram (ECG). To control the patient’s respiratory mo-tion during data acquisition, the subject is either asked toperform a breathhold or the position of the diaphragm istracked using a respiratory navigator typically placed onthe diaphragm. In the latter case the data are rejected if theposition of the diaphragm falls outside a predefined gatingwindow (1). However, both techniques for cardiac andrespiratory motion compensation can suffer from inaccu-racies. ECG triggering is susceptible to radiofrequency andgradient interferences, which may lead to trigger detectionproblems (2–5). Most of these problems can be addressedby using a vector ECG (6). Nevertheless, additional setup

time is still needed for placing ECG electrodes and han-dling the connections. In a few cases the voltage inducedin the wiring of the ECG or electrodes has caused consid-erable heating on the patient’s skin (7,8), and has even ledto fire in the magnet bore (9,10).

To compensate for respiratory motion, breathholds arefrequently used. This approach, however, limit the totalscan time in patients to 10–20 s. To relax scan time con-straints, respiratory navigators are incorporated, but thedisplacement data extracted therefrom may not reflect mo-tion of the heart accurately (11). Sophisticated motionmodels have been used (12) and yet a perfect descriptionof cardiac deformation due to respiration remains difficultto achieve. In addition, repeated acquisition of respiratorynavigators disrupts the steady state, compromising steady-state free precession (SSFP) sequences.

To overcome these problems, several self-gating (SG)methods have been developed that extract the motion dataused for triggering or gating directly from the acquired MRsignals (13–18). Most SG methods are based on imageprojections or the integral of the object that corresponds tothe central k-space point (DC). Cardiac-motion relatedvariations in the DC signal predominantly arise fromchanges in the blood volume in the heart during the car-diac cycle. In systole the magnitude of the DC signal isdecreased, while in diastole it is increased. Respiratory-related variations in the DC signal over time are inducedby moving structures, such as the liver or abdomen, thatshift in and out of the sensitive imaging volume during therespiratory cycle. To make use of this motion information,a repeated acquisition of the DC signal at the k-spacecenter is therefore required. This can be achieved by usingeither a radial sampling pattern (15,18) or a modified SSFPsequence that was first proposed in Refs. 13 and 14 andrecently presented in Refs. 16 and 18. Using these se-quences the central k-space is acquired every TR, provid-ing a signal that carries cardiac and respiratory-relatedvariations. In general, cardiac and respiratory cycles oc-cupy separate temporal frequency bands. Accordingly, theDC signal can be filtered and split into cardiac and respi-ratory motion signals for simultaneous cardiac triggeringand respiratory gating. Most software-based filtering ap-proaches, however, are time-consuming and are thereforenot suited for prospective motion detection. Consequently,methods so far have focused on either respiratory gating orcardiac triggering without the need for filtering (15,16), orthe data processing has been performed retrospectively(17,18). In retrospective SG techniques the data are evalu-ated after data acquisition has been completed. To permitappropriate coverage of both the cardiac and respiratorycycles, a sufficient amount of temporal oversampling isnecessary to ensure that every profile is at least acquiredonce in an acceptable motion state (16,19). However, this

Institute for Biomedical Engineering, University of Zurich and Swiss FederalInstitute of Technology, Zurich, Switzerland.Grant sponsor: Swiss Commission for Technology and Innovation; Grantnumber: 8005.3 LSPP-LS; Grant sponsor: Philips Medical Systems.*Correspondence to: Sebastian Kozerke, Ph.D., Institute for Biomedical En-gineering, University and ETH Zurich, Gloriastrasse 35, 8092 Zurich. E-mail:kozerke@biomed.ee.ethz.chReceived 10 September 2007; revised 26 March 2008; accepted 24 April2008.DOI 10.1002/mrm.21697Published online in Wiley InterScience (www.interscience.wiley.com).

Magnetic Resonance in Medicine 60:683–690 (2008)

© 2008 Wiley-Liss, Inc. 683

sampling strategy is not time-efficient as many data pointsare discarded in reconstruction.

In this paper a time-efficient, prospective SG approachfor simultaneous cardiac triggering and respiratory motioncompensation is presented. Fast and robust filters for real-time detection of cardiac and respiratory motion from DCsignals are applied. The results obtained are comparedwith those acquired by the retrospective SG approach de-scribed in Ref. 19. It is demonstrated that the acquisitiontime is significantly decreased when using prospective SGrelative to retrospective methods, while image quality iswell preserved.

MATERIALS AND METHODS

Data Acquisition

To repeatedly measure the k-space center, a modifiedSSFP sequence (13,16) is designed. Immediately after therephasing lobe of the slice-encoding gradient, a free induc-tion decay (FID) is formed that, in general, is spoiled by thesimultaneously applied phase-encoding gradient(s). If,however, the overlap is removed by shifting all subsequentgradients, the FID can be acquired and used to derivemotion-related signals (Fig. 1). In order to obtain sufficientSNR, a number of FID samples are taken. As a consequenceof acquiring multiple samples at the k-space center, the TRof the sequence is increased by �TR, which in turn de-pends on the number of FID samples acquired and thelength of the slice rephasing gradient. In this work 50 FIDsamples are collected, resulting in a �TR of approximately900 �s for a standard cardiac SSFP sequence. In signalprocessing, the 50 FID samples acquired per TR are aver-aged and stored as a single point for every TR.

Retrospective SG

To compensate for both cardiac and respiratory motion, amodified variant of retrospective SG was implemented.Cardiac and respiratory-related signal variations were sep-arated using ideal band-pass filters with passages between0.6–3 Hz and 0.1–0.5 Hz for cardiac and respiratory mo-tion, respectively. It was found that some coil elements,owing to their local sensitivities, were better suited forextracting cardiac-related signal variations, while othersproved to be better at detecting signal variations caused byrespiration. For selecting suitable channels for cardiac trig-gering and respiratory gating, the local maxima of thesignals from every channel were calculated. Afterwardsthe temporal variability between these maxima was deter-mined by calculating the standard deviation (SD) of thetime intervals in-between. The coil with the lowest vari-ability, while assuming the cardiac frequency to be rela-tively constant, was selected and its signal was used toderive the cardiac trigger signal (16).

In order to select suitable coil elements for deriving therespiratory-related motion signal, a separate approach wasimplemented. Assuming that expiration lasts longer thaninspiration, a threshold can be set to identify the coilchannels that best capture signal variations from respira-tion. A threshold of 80% of the maximum signal corre-sponding to expiration was set and the coil channel thatyielded the most frequent occurrence of values above thisthreshold was selected.

Upon selecting suitable channels, cardiac trigger pointswere derived. This was achieved by using a peak-finderalgorithm that detected the maxima in the filtered cardiacsignal corresponding to the time point immediately aftersystolic filling. The peak-finder algorithm was designed forphysiological signals and is described in Ref. 20. Thealgorithm removes small unwanted peaks while still ful-filling real-time requirements. In reconstruction, the datawere then sorted according to their position in the heartand respiratory cycles as detailed in the Reconstructionsection below.

Prospective SG

In prospective SG, the extraction of cardiac and respirato-ry-related signal variations has to be performed underreal-time conditions, which implies certain filter proper-ties. First, the filter has to be fast, which limits the filterorder; second, the filter must be causal; and third, thedelay between the original and the filtered curve should beas small as possible. The latter is especially importantwhen extracting respiratory motion information since adelay between the original and filtered signals can causemisinterpretation of desirable and undesirable respiratorystates. The ideal band-pass filter as used in retrospectiveSG exhibits no delay but is not suitable for real-time ap-plications because it requires a Fourier transformation ofthe motion signal in every TR interval, which is computa-tionally expensive. Therefore an alternative filter was im-plemented. For extracting the cardiac signal a Butterworthfilter is used. The parameters are: filter order � 2, band-width � 0.6–3 Hz, passband ripple �0.5 dB, stop-bandripple �20 dB. The phase delay of such a filter is inversely

FIG. 1. Modified SSFP sequence for SG. To acquire motion data, allgradients after the rephasing slice-encoding gradient are shifted torecord an FID at the k-space center.

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proportional to the frequencies to be filtered. This is ac-ceptable for the cardiac-related signal component, whichis relatively constant over time. When filtering the respi-ratory-related signal component, however, the changes inperiodicity of respiration make it necessary to also includevery low frequencies, leading to large filter delays. Mini-mizing the delay requires higher frequencies to be in-cluded in the passband, which can, however, overlap withthe band capturing cardiac frequencies. Since respiratoryfrequencies can be highly variable, it would be difficult tofind a suitable parameter set for a Butterworth filter. There-fore an alternative processing algorithm was designed. Thepeak-to-peak distance in the cardiac curve representingthe length of one cardiac cycle can be used for averagingthe motion signal. Accordingly, cardiac-related signal vari-ations can be averaged out by summing up the signalsacross a full cardiac cycle. The remaining signal variationswill then reflect respiratory motion. In this way an adap-tive moving average filter can be designed that updates thefilter properties every time a new cardiac trigger signal isderived.

The real-time cardiac and respiratory information cannow be used for prospective cardiac triggering and respi-ratory gating. Given a delay of at least 30 ms in the peak-finder algorithm, the cardiac trigger point is set to 100 msafter the actual signal peak in the cardiac motion signal toensure robust performance. To correct for missed or falsetrigger points, arrhythmia detection is implemented to re-ject cardiac cycles longer or shorter than 30% of a refer-ence RR interval that is calculated in the preparation phaseprior to the scan. As arrhythmia rejection is performedprospectively, the rejected data are immediately reac-quired, ensuring complete k-space datasets in image re-construction. For respiratory gating a threshold is defined,with data above it being accepted and data below it beingrejected. The threshold is adapted every 5 s and is definedas:

Threshold � max�Threshold Interval�

� a � range�Threshold Interval� [1]

where Threshold Interval is defined as the respiration sig-nal over the last 10 s, parameter a is a user-defined factor

ranging from 0 to 1 setting the threshold level, and rangedenotes the difference between the maximum and theminimum value of the signal in the Threshold Interval.

The peak-finder algorithm as well as the initial respira-tion threshold are initialized based on data acquired dur-ing a 10-s preparation phase right before the actual scan. Inaddition, the “best” channels for detecting cardiac andrespiratory-related motion signals are determined fromthese data. An outline of the preparation and acquisitionsteps for prospective cardiac and respiratory SG is givenFig. 2.

Performance-Driven vs. Quality-Driven SG

In prospective SG, phase-encode packages are directlycontrolled by the cardiac trigger events derived, therebyavoiding unnecessary temporal oversampling of the car-diac cycle. For respiratory gating, two different methodsare implemented. In the first method, hereafter denoted“performance-driven,” the whole cardiac cycle is acceptedif the respiratory signal is above the threshold at the triggertime. In the second, “quality-driven” method, the respira-tory signal is required to be above the threshold during afull cardiac cycle, otherwise data from the full cycle arerejected and must be remeasured.

The performance-driven method may be compared witha conventional navigator-gated sequence in which only aleading navigator is used for gating, while the secondmethod corresponds to a sequence with both a leading andan additional trailing navigator and a common gating win-dow.

Image Reconstruction

In order to correct for the latency during real-time filtering,the motion data stored during the scan are refiltered inreconstruction using the methods described for retrospec-tive SG. This allows application of the ideal band-passfilter to eliminate residual ghosting artifacts arising fromfilter delays during acquisition. After the refiltering pro-cess, trigger points and the gating window are recalcu-lated. An average RR interval is determined and data fromintervals longer or shorter than 30% of the average arediscarded. The RR intervals are then normalized and the

FIG. 2. Outline of prospective cardiac and respiratory self-navigation consisting of a preparation and an acquisition phase. In thepreparation phase, the coil channels that best capture cardiac and respiratory-related signal variations are selected and the initial thresholdfor respiratory gating is set. During the acquisition phase, cardiac trigger points and respiratory motion states are determined in real timeto prospectively control the data acquisition.

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relative temporal positions in the cardiac cycle are calcu-lated to assign every profile to a heart phase. Thereafter,the respiratory state of every profile is determined and dataoutside the respiratory gating window are discarded. Pro-files that are missing either because they were not acquiredin a suitable motion state or were discarded during thereconstruction process are filled with data from the “clos-est” motion state, defined as the shortest distance in thecardiac or respiratory cycles.

Computer Simulations

To evaluate the method, a computer model was designedthat consisted of a contracting “heart” on a “liver” trans-lating in the feet–head direction to simulate respiratorymotion (Fig. 3). The changes in blood volume in the heartduring the cardiac cycle as well as the translation werederived based on measured in vivo data. To simulate real-time SG, an image was created every TR based on themotion patterns given as input. With noise added, theimages were Fourier-transformed and the value at the k-space center was extracted to obtain motion-related sig-nals. The motion signals were processed as described pre-viously. In case the data were accepted, one profile wascollected from the k-space of the current image using thesame sampling pattern as used in an actual sequence pro-tocol. This process was repeated until all data were col-lected. In image reconstruction, the processing steps de-scribed earlier were applied to obtain 30 heart phase im-ages. Three different simulations were performed. In thefirst, retrospective SG was simulated such that one k-spacesegment was acquired over approximately one respiratory

cycle (respiratory cycle: �5 s, acquisition: 6 s). In thesecond and third simulations, real-time SG was performedusing either the performance-driven or quality-driven ap-proach. The reconstructed self-gated images were com-pared with a reference image representing a motion-free,noiseless reconstruction of the equivalent time frame, andthe root-mean-square (RMS) error relative to the referencewas calculated.

In Vivo Measurements

Two-dimensional (2D) cine short-axis and four-chamberviews were acquired in six healthy volunteers (four male,two female). The cardiac frequencies of the subjects rangedfrom 50 to 94 beats per minute (mean � 70.4). None of thevolunteers had a known history or signs of cardiac disease.All measurements were performed on a 1.5T PhilipsAchieva System (Philips Medical Systems, Best, The Neth-erlands) using a five-channel cardiac coil array coveringapproximately 30 cm in both feet–head and right–left di-rections. The two circular coils of the array were posi-tioned anteriorly on the thorax and the three rectangularelements were placed on the subject’s back. The modifiedSSFP sequence was employed for all SG experiments. Thefollowing parameters were used to obtain four-chamberviews: TR � 4.5 ms, TE � 2.6 ms, flip angle � 60°, scanmatrix � 192 186, FOV � 320 320 mm2, slice thick-ness � 8 mm, 11 lines/segment, and 30 cardiac phases. Forthe short-axis view the FOV in the phase-encode directionwas reduced to 253 mm, while all other parameters re-mained the same. In one volunteer, three subsequent SGacquisitions were run for both views. In the first measure-ment, retrospective SG was used with one segment beingreacquired over 7 RR intervals. In the second and thirdacquisitions, real-time SG was performed using the quali-ty-driven and performance-driven approaches with the re-spiratory threshold level set to a � 0.4. In all other volun-teers only the prospective, quality-driven approach wasperformed. As reference, an ECG-triggered breathheld scanwas acquired with TR and TE reduced to 3.6 ms and 1.8 msand otherwise identical scan parameters. In order to assessthe accuracy of cardiac trigger and respiratory state detec-tion for prospective SG, the ECG as well as the signal froma respiratory belt were recorded during the experiments.The performance of cardiac trigger point detection in SGwas assessed by calculating the temporal difference rela-tive to the corresponding wired ECG trigger points, and theSD thereof was defined as the error measure.

RESULTS

Computer Simulations

The filtered cardiac and respiratory-related signal tracesfor prospective and retrospective SG are shown in Fig. 4.The trigger points detected with real-time filtering aremarked. The cardiac and respiratory curves used as inputin the simulation are given as reference. Both real-time andretrospective processing yielded motion traces very simi-lar to the reference. The filtered cardiac signals were vir-tually identical for both the retrospective and the real-timealgorithm, and the trigger points were accurately detected.Relative to the onset of heart model contraction, SG car-

FIG. 3. Computer model consisting of a contracting “heart” on a“liver” translating in the y-direction to simulate respiratory and car-diac motion.

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diac triggers were shifted to a time-point at approximately70% of the cardiac cycle as a result of filter delay andlatency during real-time processing. Real-time filtered re-spiratory signals exhibited a delay of approximately600 ms on average relative to the true onsets of simulatedrespiratory cycles. This shift is, however, largely correctedfor by refiltering during image reconstruction.

Still frames from reconstructions of the computer modelas well as the reference image are given in Fig. 5. Recon-

structions from the retrospective as well as the real-timeSG are shown using both the quality-driven and the per-formance-driven approaches. The difference images rela-tive to the reference together with the RMS error are dis-played in the second row. As can be seen, the retrospectivemethod yields the best image quality (RMS � 16.15). TheRMS error of the quality-driven approach was increased by9% but acquisition time was greatly reduced. The perfor-mance-driven approach is fastest but exhibits motion arti-facts that result from accepted data outside the gatingwindow. The RMS error is 12% higher than that of theretrospective approach. The theoretical acquisition timesfor the different methods are as follows: retrospective ap-proach: 160 s; quality-driven approach: 64 s 10 s prep-aration; performance-driven approach: 55 s 10 s prepa-ration.

In Vivo Measurements

Unfiltered signal traces of one volunteer obtained in vivowith the five-channel cardiac array are given in Fig. 6. Theindividual channels capture cardiac and respiratory-re-lated signal variations differently. In the preparationphase, the SG algorithm automatically selected channel1 for cardiac triggering and channel 4 for respiratorygating. The real-time filtered signals of these channelsare compared with the “wired” ECG trigger signal andthe signal from the respiratory belt in Fig. 7. The adap-tive threshold used for respiratory gating is shown as aslash-dotted line.

The same comparison was performed for both the short-axis and the four-chamber view scans in the six volun-teers. On average, the SG trigger was set 630 ms after theECG trigger or at 70% of the cardiac cycle. The SD of

FIG. 4. The motion pattern given as input to the model (gray line)together with the motion curves derived using the SG approaches.The retrospectively filtered signal, shown as the dotted line, is invery good agreement with the reference. The real-time filtered re-spiratory signal exhibits a shift relative to the reference. This shift iscorrected for by the retrospective SG step in image reconstruction.The cardiac signal is determined accurately with trigger points afterthe systolic filling phase (marked with an ).

FIG. 5. Image reconstructions ofthe computer model. The refer-ence time frame along with re-constructed images from simu-lated self-gated acquisitions areshown. In the second row the dif-ference images relative to the ref-erence are shown (scaled five-fold to reveal details). Note thatthe result of the quality-drivengating approach (RMS � 17.6) iscomparable to the retrospectiveapproach (RMS � 16.1) despiteconsiderably reduced scan time.The performance-driven approachis fastest but shows motion arti-facts (arrows) due to the accep-tance of data outside the respira-tory gating window (RMS � 18.1).The main differences can be ob-served at the edges of movingstructures, which are increasinglyblurred in the prospective ap-proach.

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temporal differences between the wired ECG trigger andthe SG cardiac trigger was 19.9 � 8 ms. Prospective ar-rhythmia rejection identified missed or false triggers suchthat every SG trigger could be assigned to a correspondingECG trigger. On average, 5% of the SG triggers and corre-sponding cardiac cycles were rejected due to heart rate vari-ations. The respiratory signal from SG showed an averagetemporal shift of 680 � 350 ms relative to the respiratory beltsignal. However, this temporal delay can be largely correctedby refiltering the motion signals in image reconstruction.

Exemplary in vivo images of 2D cine short-axis acquisi-tions are shown in Fig. 8 with diastolic (top row) andsystolic (bottom row) frames. Results from retrospectiveSG, quality-driven, and performance-driven prospectiveSG are compared with a conventional ECG-triggeredbreathheld acquisition. Images obtained with prospectiveSG were found to be very similar to images from retrospec-tive SG, and compared well with images from the refer-ence ECG-triggered breathheld acquisition.

Reconstructions of a four-chamber view are shown inFig. 9. Subtle motion artifacts are seen with the perfor-mance-driven approach due to the acceptance of unfavor-able respiratory positions.

DISCUSSION

In this work, SG approaches to compensate for both car-diac and respiratory motion have been presented. It hasbeen shown that cardiac trigger and respiratory gatingsignals can be derived from the k-space center in real time.

FIG. 6. Raw motion signals obtained in vivo with a five-channel coilarray. Note that the traces from different channels represent theunderlying motion differently. Cardiac variations are best capturedby channel 1, while channel 4 best reflects respiratory motion.

FIG. 7. Filtered cardiac and respiratory motion signals from the rawmotion signal shown in Fig. 6. The trigger points calculated from theSG signals are marked with . Trigger points from the simulta-neously acquired, wired ECG are shown as dotted vertical lines(upper panel). The respiratory trace obtained in real time is com-pared with the signal from a respiratory belt as reference (lowerpanel). Note that the SG signal exhibits a shift relative to the refer-ence due to the averaging filter. Additionally, the adaptive thresholdused for respiratory gating is shown as a slash-dotted line.

FIG. 8. Time-frames of the cardiac short-axis view (top row: sys-tolic; bottom row: diastolic). Retrospective SG as well as the twodifferent real-time SG methods are compared with a standard ECG-triggered breathhold acquisition. Actual scan times are indicated.

FIG. 9. Images from a cardiac four-chamber view. RetrospectiveSG and the two different real-time SG methods are compared witha standard ECG-triggered breathhold acquisition. While overall im-age quality is comparable, motion artifacts (arrows) become notice-able when the performance-driven approach is used. This is due tothe acceptance of unfavorable respiratory positions.

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To partially correct for filter delays, which are unavoidablein real-time processing, refiltering is used in image recon-struction. This prospective SG approach in combinationwith a retrospective reconstruction scheme yielded imagequality comparable to that of retrospective SG approachesat greatly increased acquisition efficiencies.

Image quality from prospective SG was found to belargely comparable to the reference ECG-triggered breath-held scans. Subtle differences in image sharpness wereseen. This is, however, expected since the image quality ofbreathhold acquisitions is usually better when comparedto respiratory-gated sequences (21,22).

A modified version of a Cartesian SSFP sequence withprolonged TR was used to sample the DC component of theobject repeatedly in this study; however, other k-spacetrajectories are more efficient. Radial or spiral k-spaceschemes inherently sample the DC component every TR. Itwill be the subject of future work to combine radial andspiral imaging with the prospective SG principles presentedhere. A comparison between the different techniques can befound in Ref. 23. However, it has to be stated that the methodpresented here requires the magnetization to be in the steadystate. Therefore, it is not applicable to applications withmagnetization preparation or first-pass angiography.

In the present implementation, if the respiratory signalfalls outside the gating window, the current k-space seg-ment is maintained and reacquired until the respiratorysignal is within the window again. Another possibilitywould be to adapt the k-space sampling dependent on therespiratory motion state. An approach for conventionalnavigator gating that is based on k-space weighting hasalready been evaluated and described in Refs. 24 and 25.With this method the central k-space is acquired when theleast respiratory motion is expected, while the outer k-space is measured in the less-favorable motion states. Inprospective SG, such an approach could be used as well tospeed up the acquisition or to increase the SNR if theunfavorable profiles are used for averaging.

In the present study, single coil elements were selectedto derive cardiac and respiratory signals. In order to im-prove the SNR in the motion traces, several suitable chan-nels should be combined, in particular when using largecoil arrays.

For image reconstruction, some profiles may still bemissing because they were either not acquired in a propermotion state or discarded in the reconstruction process.Currently these “holes” in k-space are filled with profilesfrom the motion state that is closest to the desired one. Anattractive alternative might be to use parallel imaging to fill inthe missing data. Thereby the holes could be repopulateddirectly in the k-space using generalized autocalibrating par-tially parallel acquisitions (GRAPPA) or a generalized sensi-tivity encoding (SENSE) algorithm as described in Ref. 26.

A preferred type of application for the prospective SGapproach presented here would be cine 3D imaging cov-ering the whole heart. For these scans breathheld acquisi-tions are impossible and therefore respiratory gating isrequired. This topic, however, remains to be investigatedin future works since current reconstruction hardwarelacks the memory necessary to process the amount of datafor these types of scan.

CONCLUSIONS

It has been shown that cardiac and respiratory motion canbe accurately detected prospectively based on data ob-tained from the center of k-space. Compared with retro-spective SG, the total scan time of the prospective methodis significantly decreased while image quality is wellmaintained. The use of this technique allows the recon-struction of cardiac images without the need to connect anECG or use respiratory navigation, thereby simplifying theexam.

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