Whole-Heart Cine MRI Using Real-Time RespiratorySelf-Gating

Sergio Uribe,1,2 Vivek Muthurangu,2 Redha Boubertakh,1,2 Tobias Schaeffter,2

Reza Razavi,2 Derek L.G. Hill,1* and Michael S. Hansen1,2

Two-dimensional (2D) breath-hold cine MRI is used to assesscardiac anatomy and function. However, this technique re-quires cooperation from the patient, and in some cases thescan planning is complicated. Isotropic nonangulated three-dimensional (3D) cardiac MR can overcome some of theseproblems because it requires minimal planning and can bereformatted in any plane. However, current methods, eventhose that use undersampling techniques, involve breath-hold-ing for periods that are too long for many patients. Free-breath-ing respiratory gating sequences represent a possible solutionfor realizing 3D cine imaging. A real-time respiratory self-gatingtechnique for whole-heart cine MRI is presented. The techniqueenables assessment of cardiac anatomy and function with min-imum planning or patient cooperation. Nonangulated isotropic3D data were acquired from five healthy volunteers and thenreformatted into 2D clinical views. The respiratory self-gatingtechnique is shown to improve image quality in free-breathingscanning. In addition, ventricular volumetric data obtained us-ing the 3D approach were comparable to those acquired withthe conventional multislice 2D approach. Magn Reson Med57:606–613, 2007. © 2007 Wiley-Liss, Inc.

Key words: whole-heart imaging; respiratory gating; cine MRI;free breathing; self navigation

Two-dimensional (2D) cine imaging has been shown to bean accurate method of assessing cardiac anatomy andfunction (1). Unfortunately, it requires multiple breath-holds and the scan planning requires operator knowledgeof cardiac anatomy. Isotropic nonangulated three dimen-sional (3D) cardiac MR can overcome some of these prob-lems because it requires minimal planning and can bereformatted in any plane (2,3). However, such scans lackthe temporal information needed to assess cardiac func-tion. A more optimal solution would be a time-resolved(cine) 3D technique. A fundamental problem with thisapproach is the length of acquisition and the attendantdifficulties with respiratory compensation. Parallel imag-ing techniques (4,5) and other undersampled techniques(6) have been used to acquire 3D cine data sets in a singlebreath-hold (7–10). However, it is desirable to achievebetter combinations of spatiotemporal resolution for an

accurate functional and anatomical analysis. Respiratorycompensation for static 3D whole-heart imaging can beachieved with the use of navigator beams (11). Unfortu-nately, navigator techniques interrupt the acquisition andthus are difficult to combine with steady-state free preces-sion (SSFP) cine sequences (12). Furthermore, interleavingof navigators in a cine acquisition can limit the temporalresolution.

To address these limitations, it would be useful to de-velop 3D acquisition techniques that incorporate respira-tory self-gating. Such techniques would enable improve-ments in both spatial and temporal resolution. Respiratoryself-navigated techniques have been proposed for 2D ra-dial cine MRI (13), 3D whole-heart coronary MR angiogra-phy (MRA) (14) using radial trajectories, and 2D multislicespiral imaging (15). A recent study performed respiratoryself-navigated coronary MRA (16) using Cartesian trajecto-ries with a projection calculated from a center k-spaceprofile. In such studies motion compensation is performedretrospectively. The main problem with retrospective cor-rection schemes is that it is difficult to ensure that allnecessary data are acquired at the correct respiratory po-sition. It would therefore be preferable to use respiratoryself-navigation in a real-time manner such that data cor-rupted by motion can be reacquired.

In this paper we present a general approach for 3D cinewhole-heart imaging with real-time respiratory self-gating,which we implemented on a clinical MR scanner. Thisnew method derives the breathing motion using a centerk-space profile (17,18) and adjusts the acquisition schemein real time to reacquire motion-corrupted data. The scanis performed in a clinically acceptable time and requiresminimal planning. To demonstrate the applicability of thedeveloped technique, we applied it to five healthy volun-teers to study cardiac anatomy and function. The results ofthis respiratory gating approach are compared with non-gated free-breathing scans of the same resolution. Further-more, a preliminary comparison of volume measurementsfrom the new 3D cine approach and the standard 2D pro-tocol is presented.

MATERIALS AND METHODS

We performed a 3D cine acquisition of the whole heartusing a retrospectively ECG gated, balanced SSFP k-spacesegmented sequence. We used a five-element cardiac coilarray to speed up the acquisition by applying sensitivityencoding (SENSE). The 3D cine acquisition was based ona nonangulated sagittal volume covering the whole heart(see Fig. 1). The readout was placed in the foot–head (FH)direction. and therefore a projection (along x) of the entirevolume could be obtained with a 1D fast Fourier transform

1Center for Medical Image Computing, University College London, London,UK.2Cardiac MR Research Group, Division of Imaging Sciences, King’s CollegeLondon, London, UK.Grant sponsor: Engineering and Physical Sciences Research Council/MedicalResearch Council; Guy’s and St. Thomas’ Charity.*Correspondence to: Professor Derek Hill, Center for Medical Image Com-puting, New Engineering Building, University College London, Malet Place,London WC1E 6BT, UK. E-mail: derek.hill@ucl.ac.ukReceived 14 July 2006; revised 26 October 2006; accepted 7 November2006.DOI 10.1002/mrm.21156Published online in Wiley InterScience (www.interscience.wiley.com).

Magnetic Resonance in Medicine 57:606–613 (2007)

© 2007 Wiley-Liss, Inc. 606

(FFT) of the profile passing through the center of ky-kz

space. The center of k-space was revisited throughout theacquisition to provide continuous monitoring of the respi-ratory position. The acquisition itself was divided into a“learning” stage in which certain parameters about therespiratory pattern were derived, followed by a “gating”stage in which the actual image data were acquired. In thefollowing sections the respiratory motion-detection algo-rithm is explained, and the learning and gating stages aredescribed in detail. The acquisition scheme also allows forreadaptation of the gating parameters in the case of respi-ratory drift. This optional stage of the acquisition is alsodescribed below along with the in vivo experiments.

Acquisition Scheme and Motion Detection

To measure breathing motion, we repeatedly acquired acentral profile in k-space. We modified a segmented SSFPcine sequence by adding a center profile at the beginningof each k-space segment. This allowed us to sample thecenter profile at a rate of (Nl � 1) * TR, where Nl is thenumber of k-space profiles in each segment, and TR is therepetition time. It is important to note that, unlike thenavigator beams, this extra readout does not disturb thesteady state. The center profiles were used only for gatingand were not included in the final reconstruction.

Since a phased-array coil was employed, the informa-tion from the center profile was available in each coil. Weobtained the final center profile (used to derive gatinginformation) by adding the contribution of each coil ink-space. This approach was chosen for this initial studybecause it is fast and thus facilitates real-time processingof the gating information; however, weighted combina-tions of coil lines could also be used.

Immediately after the acquisition of each center profile,the respiratory phase was detected with the use of thealgorithm described in Fig. 2. A 1D FFT of the centerprofile was applied. The resulting 1D image corresponds tothe projection of the entire excited volume onto the FHaxis. We obtained the motion signal by calculating thecorrelation coefficient (CC) (Eq. [1]) between a projectionand a reference projection, which was obtained in a pre-vious learning stage:

Corr. Coeff. �

�i�1

N

�RPi � RPm��Pi � Pm�

���i�1

N

�RPi � RPm�2� � ��i�1

N

�Pi � Pm�2�

, [1]

FIG. 1. a,b: Placement of the imaging volume. Ar-row x indicates readout, y indicates phase encode,and z indicates slice direction. c: Projection of thisvolume over time (t) as it is obtained from thecentral k-space profile.

Whole-Heart Cine MRI 607

where N is the total number of reconstructed pixels in thereadout direction; RPi and Pi are the pixel values of thereference projection and a given projection, respectively;and PRm and Pm are the corresponding mean pixel valuesin the two projections.

Learning Stage

To calculate the reference projection, at the beginning ofthe scan the volunteers were asked to hold their breathingin end-expiration for 4 s. During this stage five consecutiveprojections were averaged to obtain the reference projec-tion, which was then correlated with the subsequent pro-jections.

Since the dynamic range of the CC shows considerableintersubject variability, the breath-hold stage was followedby a 12-s free-breathing stage to determine the range of theCC during the respiratory cycle. The user could then selecta window of acceptance as a percentage of this range in thesubsequent gating stage, as described below.

Throughout the scan, the normalized first moment (i.e.,the center of mass) of each projection was calculated ac-cording to

Center of mass �

�z�1

N

m�z� � z

�z�1

N

m�z�

, [2]

where m(z) is the pixel value at position z along the pro-jection. The purpose of this was to have a reference inwhich the center of mass values was associated with in-spiration and expiration. This was used during the driftcorrection stage (see below). A set of in vivo projectionsare shown in Fig. 3 along with the calculated center ofmass and correlation gating signal.

Gating Stage

The CC signal was used to gate the sequence (i.e., datawere accepted or rejected depending on how similar theacquired projections were to the reference projection). Thedata were accepted if the CC was within a certain windowof acceptance. This window of acceptance was a user-

FIG. 2. Block diagram of the algorithm used togate the sequence. Center profiles acquired in dif-ferent coils are added in k-space, resulting in a finalcenter profile. This is processed by FFT and cor-related with a reference projection. The CC is usedto gate the sequence. If the data are accepted, anew k-space segment is acquired; alternatively, itis reacquired.

FIG. 3. In vivo projections along time. A 4-sbreath-hold at the beginning was used to calculatea reference projection, which was then correlatedwith the subsequent central profiles. The free-breathing part of the learning stage was used toderive respiratory parameters, such as the dy-namic range. The CC captures the breathing mo-tion. Notice that the center of the mass signal isnoisy (both signals have been scaled for bettervisualization).

608 Uribe et al.

selectable parameter, defined as a percentage of the rangeof the CC, which was determined in the learning stage.

Since the same k-space segment was acquired severaltimes over the R-R interval (i.e., for different time points inthe cardiac cycle), all of these k-space segments were ac-cepted if all of the corresponding center projection fellwithin the acceptance window. When a k-space segmentwas rejected, it was reacquired continuously until it fellwithin the acceptance window.

Drift Correction Algorithm

To avoid a long scan due to changes in the breathingpattern, we achieved a drift correction by reinitializing thereference projection. This was done only when the gatingefficiency fell below 25% over a user-definable period oftime (Teff). The reference projection was updated by aver-aging five new center profiles over the last Teff. These werechosen as the profiles that had center-of-mass values clos-est to the original end-expiration position (known from thelearning stage).

Real-Time Implementation

The acquisition and gating strategy was implemented on aPhilips Achieva 1.5T clinical scanner (Philips MedicalSystems, Best, The Netherlands). The modifications werefully integrated with the existing scanner software, andsince the gating was used in real time (forcing the reacqui-sition of rejected data), no specialized offline reconstruc-tion was necessary. Parameters such as the window ofacceptance and Teff were adjustable by the user. Moreover,the projections, CC, and acceptance windows were sent tothe navigator (NAV) display in real time as feedback to thescanner operator. Also, the scan efficiency was sent to thedisplay every Teff seconds. This allowed the scanner op-erator to monitor the breathing pattern throughout theacquisition.

In Vivo Experiments

A study protocol consisting of seven imaging series wasused to scan five healthy volunteers (four men and onewoman, 28 � four years old, 65 � 10 kg, 1.75 � 10 m).Initially a SENSE reference scan was used to obtain coilsensitivity maps from the five coil elements in the phased-array cardiac coil. Subsequently, an interactive real-timescan was performed to determine the geometry of the next

three 2D cine exams: a two-chamber (2-CH) view, a four-chamber (4-CH) view, and a stack of short-axis (SA) viewscovering the ventricles, all acquired during breath-hold-ing. After that, two 3D scans of the whole heart wereperformed using the proposed real-time respiratory gatingapproach. Identical parameters were used in the twoscans, except for the window of acceptance. The first scanwas carried out without respiratory gating (i.e., with awindow of acceptance of 100%) and the second scan witha window of acceptance of 15% of the peak-to-peak valueof the CC (the data were accepted in end-expiration). Forevery exam a Cartesian balanced SSFP (bSSFP) sequencewas combined with SENSE (acceleration factor of 2) andpartial Fourier (partial factor of 5/8) (19) reconstructiontechniques, both applied in the phase encoding direction,i.e. Anterior-Posterior direction. The parameters used ineach scan are summarized in Table 1.

Quantitative Analysis

Ventricular function was quantified on the conventionalSA multislice 2D acquisition and on SA images reformat-ted from the gated 3D volumes, which were obtained withthe exactly same geometries (determined in the interactivescan) and slice thickness as the 2D scans. Left and rightventricular end-diastolic volume (EDV) and end-systolicvolume (ESV) were measured using manual segmentation(Viewforum; Philps Medical System, Best, The Nether-lands). The stroke volume (SV) and ejection fraction (EF)were calculated from the EDV and ESV. For each measure-ment the median and range were determined. This analy-sis was performed to investigate whether the 3D acquisi-tion scheme introduced any systematic bias in the func-tional parameters determined from the examination.

RESULTS

Gating Signal and Scan Efficiency

A respiratory gating signal was obtained in all volunteers.The peak-to-peak range of the CC varied depending on theanatomical characteristic of the subjects. For most sub-jects, this range was between 0.8 and 1, and for others itwas between 0.9 and 1. Figure 4 shows the in vivo projec-tions, the correlation gating signal (scaled), and the win-dow of acceptance for two different volunteers. The figuresreflect what is seen by the scanner operator on the NAVdisplay during the acquisition. Notice the robustness and

Table 1Scan Parameters

Single slice 2-CH view Single slice 4-CH view Multislice SA view 3D cine

Field of view (FOV) (mm) 370 x 370 x 10 370 x 370 x 10 430 x 277 x 100 470 x 280 x 145Matrix 192 x 180 192 x 180 192 x 124 192 x 112 x 58Slices 1 1 10 (slice gap 0) 58Voxel size (mm) 2 x 2 x 10 2 x 2 x 10 2.2 x 2.3 x 10 2.5 x 2.5 x 2.5TR (ms)/Echo time (TE) (ms) 3.3/1.6 3.3/1.6 3.1/1.5 3.3/1.6Flip angle (°) 60 60 60 60Cardiac gating Retrospective Retrospective Retrospective RetrospectiveReconstructed cardiac phases 40 40 30 15Lines per segment (Turbo Field Echo

(TFE) factor)/temporal resolution (ms)6/20 6/20 8/25 16/52.8

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smoothness of the correlation gating signal throughout thescan despite the difference in appearance of the centralprofiles. The projections show a bright signal that corre-sponds to fat localized in the abdominal area, and justabove that the projection of the heart throughout the car-diac and respiratory cycle.

The respiratory-gated 3D scan required scan times of270 � 60 s with a window of acceptance of 15%. Theefficiency of these scans was 48% � 15% compared to anequivalent nongated 3D exam, which required scan timesof 130 � 30 s.

Cine Images

Representative ED and ES 2-CH, 4-CH, and SA reformattedimages from one volunteer are shown in Fig. 5. Note theblurring in the nongated examination, which is reducedusing the proposed gating approach, and the improveddelineation of the blood pool myocardial border in the

gated examinations, particularly of small structures suchas the papillary muscles in the SA view.

Quantitative Analysis

The results of the quantitative analysis are shown in Ta-bles 2 and 3. The median and range values for all of thefunctional parameters are comparable using both breath-hold multislice and 3D reformatted SA data. Figure 6shows a comparison of the measured EDV and ESV valuesin the left and right ventricles across the five volunteersusing both breath-hold multislice and 3D reformatted SAdata.

DISCUSSION

This paper demonstrates the feasibility of real-time respi-ratory self-navigation for whole-heart 3D cine imaging. 3Disotropic cine imaging is an optimal solution for assessing

FIG. 4. Projections, gating signal (in red), and win-dows of acceptance (blue lines) of (a) one male and(b) one female volunteer during the first 50 s of ascan. These pictures were obtained during thescan in the NAV display. Notice that the CC is avery robust respiratory motion signal despite thevery different appearances of the central projec-tions. The CCs in a and b have been scaled forbetter visualization according to their dynamicrange in (a) 0.18 and (b) 0.1.

FIG. 5. Representative ED (a and c) and ES(b and d) frame of 3D reformatted data in i)4-CH, ii) 2-CH, and iii) SA views acquired inone volunteer without (a and b) and with (cand d) respiratory gating using a 15% win-dow of acceptance. Notice the improveddelineation of the papillary muscles in theSA views (arrows).

610 Uribe et al.

cardiac anatomy and function, since any 2D slice can bearbitrarily reformatted. However, the acquisition time forsuch data sets is long and therefore respiratory compensa-tion is problematic. The self-navigation technique de-scribed here addresses this problem by allowing a free-breathing scan to be performed in a reasonable time. Themain advantages of the technique are that it is compatiblewith SSFP cine imaging and it does not limit the temporalresolution. In addition, in this real-time implementationmotion-corrupted data are reacquired rather than cor-rected, and therefore no specialized offline reconstructionis necessary.

A robust respiratory gating signal was obtained in allvolunteers despite the varied appearance of the centralprojections and the different breathing patterns of the vol-unteers. The key to this stability across volunteers was theuse of the CC and the reference projection. Previouslydescribed retrospective approaches gate from the center ofmass (14); however, we found that this results in noiseunless filtering is applied, which is not possible in realtime. Effective filtering would cause a phase-shift of thegating signal, and consequently the center of mass wasused only during the drift correction stage.

In the current implementation, a complex addition ofthe center k-space profiles from different coils is done.This could potentially be associated with some phase can-cellation problems; however, no such problems were ob-served in this study. Since other approaches (e.g., a sum-of-squares coil combination) are more time-consuming, wechose to use the simple sum to facilitate real-time process-ing. This problem will be resolved with faster processingcapabilities. Another potential solution would be tochoose a specific coil for the gating signal, and this will beinvestigated in future work.

The use of a reference projection introduces a 4-s breath-hold at the beginning of the acquisition. The techniquewould be more versatile if this could be eliminated. Infuture work we will aim to derive the necessary gatingparameters from a free-breathing learning stage rather thana breath-hold. In the present implementation we chose touse the average of the last five center projections in thebreath-hold. This approach was adopted to ensure that thesteady state had been reached and that any small individ-ual profile variations were averaged out. The methodworked robustly in the current volunteer studies, but morework has to be done to establish whether this is the opti-mum approach.

A reduction of motion artifacts was observed in all vol-unteers, although the magnitude of the improvement var-

ied. This may have been due to variations in respiratoryamplitude or distribution of intra-abdominal fat; however,the exact cause of the large intersubject variability needs tobe investigated. A particularly obvious improvement wasseen in one volunteer’s images, in which the quality washeavily influenced by motion. In these images it was im-possible to identify the outline of the cardiac muscle in thenongated scan (Fig. 7). However, in the gated acquisition,there was marked improvement, and although some arti-facts still remained we were able to segment the ventricle.We reduced residual artifacts by narrowing the acceptancewindow, albeit at the expense of an increase in total scantime. This trade-off between artifacts and scan time de-pends to a large extent on the breathing pattern of thepatient/volunteer, but we found that a window of accep-tance of 15% was a good compromise in the studied vol-unteers. In future work we will consider whether thelearning phase can be used to automatically select a sub-ject-specific window of acceptance.

The proposed drift correction stage was only used onone occasion in a single volunteer. This occurred duringthe nongated scan, so the impact on image quality in agated acquisition has not yet been assessed.

There are two obstacles that hinder the use of volumecine SSFP sequences in clinical applications. Thefirst—the need for scan times longer than a breath-hold—is addressed by our method. The second problemwith volume SSFP sequences is that they have poorercontrast between blood and myocardium than 2D SSFPsequences. This contrast limitation was recently de-scribed by Nehrke et al. (20). They suggested that in 3DbSSFP sequences, increasing slab thickness and spatialresolution reduces inflow enhancement and increasesintravoxel dephasing, both of which reduce myocardialblood pool contrast. This may explain the poorer con-trast and the remaining artifacts in the reformatted im-ages compared to conventional 2D SSFP images. Futurework must be aimed at further understanding and re-solving these contrast issues.

Since the center of k-space is revisited throughout theacquisition, there could potentially be a problem witheddy-current artifacts. We previously investigated theseartifacts and a potential acquisition strategy for avoidingthem in phantoms (21). However, in this work we couldnot observe any image degradation due to eddy-currentartifacts, and consequently the proposed acquisition strat-egy was not used. In the future we will investigate whetherthe image quality can be improved with alternative k-

Table 3Median Right Ventricular End Diastolic Volume (EDV), EndSystolic Volume (ESV), Stroke Volume (SV), and Ejection Factor(EF) Measured Using Conventionally Acquired Multislice andReformatted Short Axis Data

Cine 2D Cine 3D

Median Range Median Range

EDV (ml) 131.6 114–193.8 133.1 113.3–203.3ESV (ml) 48.4 34.8–72 47.2 30.1–85.9SV (ml) 82 73.9–121.8 86 76.5–117.4EF (%) 64.8 62.3–69.9 63 57.8–74.2

Table 2Median Left Ventricular End Diastolic Volume (EDV), End SystolicVolume (ESV), Stroke Volume (SV), and Ejection Factor (EF)Measured Using Conventionally Acquired Multislice andReformatted Short Axis Data

Cine 2D Cine 3D

Median Range Median Range

EDV (ml) 113.8 94.9–179.9 117.3 96.4–173.6ESV (ml) 31.9 20.5–56.1 32 22.2–58.3SV (ml) 83 74.4–123.8 83.3 74.2–115.3EF (%) 75.1 68.8–78.4 74.2 68.2–77

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space sampling strategies. Furthermore, the present imple-mentation does not include the acquired respiratory gatingprofiles in the final image reconstruction (they are onlyused for gating). This could potentially improve the signal-to-noise ratio (SNR) of the reconstructed images, and fu-ture investigations into how the k-space center should beresampled should take this into consideration.

A quantitative analysis of ventricular function showedgeneral agreement among the volumetric data obtainedusing multislice and 3D reformatted SA data. No system-atic bias in the EDV, ESV, SV, and EF was observed usingthis new methodology. Only five volunteers were scannedin this study, and in future work this technique will beapplied to more volunteers and patients. A larger studywould make it possible to test for statistical differencesbetween the ventricular function parameters derived fromthe 2D cine and 3D self-gated cine methods.

In this paper only images from the end-expiration posi-tion are shown. One could image other respiratory posi-tions by using a reference profile from the desired respira-tory position; however, this would prolong the acquisi-tion, since other respiratory positions are not visited for aslong a time as the end-expiration position. The methodcould also be extended to resolve motion during the wholebreathing cycle, which could be useful for studying some

forms of congenital heart disease (22). Furthermore, sincea center profile is acquired in each k-space segment (ap-proximately one every 60 ms when 15 cardiac phases areacquired at a heart rate of 60 bpm), enough information isprovided to produce a cardiac motion gating signal (23).This would enable the development of a double self-gatedacquisition.

The technique holds considerable promise for scanningpatients who are unable to hold their breath for a longperiod, and enabling acquisitions that are not feasible in abreath-hold (e.g., a 7D flow acquisition of the whole heart)(24).

CONCLUSIONS

We have introduced a new method for volume cineimaging of the heart that uses real-time respiratory self-navigation. This technique removes motion artifactsfrom free-breathing images by forcing the reacquisitionof motion-corrupted data. The feasibility of this ap-proach was demonstrated with in vivo experiments. Theproposed technique makes it easier to perform cardiacMRI, and could enable new functional studies of theheart.

FIG. 6. Comparison of measured EDV (aand c) and ESV (b and d) values for the left(a and b) and right ventricle (c and d) usinga multislice SA view (2D) and the developed(3D) free-breathing technique in five volun-teers.

FIG. 7. Reformatted SA view of one volun-teer. The use of the proposed method (b)allows one to differentiate the differentstructures of the heart, which are not iden-tifiable in the nongated scan (a).

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ACKNOWLEDGMENTS

S. Uribe and M. Hansen received support from the Engi-neering and Physical Sciences Research Council (EPSRC)/Medical Research Council (MRC)-funded Medical Imagesand Signals Interdisciplinary Research Collaboration. V.Muthurangu is funded by the Guy’s and St. Thomas’ Char-ity, and R. Boubertakh is funded by the EPSRC. We alsothank Philips Medical Systems for ongoing support of ourresearch program.

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