respiratory motion detection and correction for mr using the...

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Respiratory Motion Detection and Correction for MR Usingthe Pilot Tone

Applications for MR and Simultaneous PET/MR Examinations

Thomas Vahle, PhD,*Mario Bacher, MSc,*†David Rigie, PhD,‡Matthias Fenchel, PhD,* Peter Speier, PhD,*Jan Bollenbeck,* Klaus P. Schäfers, PhD,§ Berthold Kiefer, PhD,* and Fernando E. Boada, PhD||

Objectives: The aim of this study was to develop a method for tracking respira-tory motion throughout full MR or PET/MR studies that requires only minimaladditional hardware and no modifications to the sequences.Materials and Methods: Patient motion that is caused by respiration affects thequality of the signal of the individual radiofrequency receive coil elements. Thiseffect can be detected as a modulation of a monofrequent signal that is emitted bya small portable transmitter placed inside the bore (Pilot Tone). The frequency isselected such that it is located outside of the frequency band of the actual MRreadout experiment but well within the bandwidth of the radiofrequency receiver,that is, the oversampling area. Temporal variations of the detected signal indicatemotion. After extraction of the signal from the raw data, principal componentanalysis was used to identify respiratory motion. The approach and potential ap-plications during MR and PET/MR examinations that rely on a continuous respira-tory signalwere validatedwith an anthropomorphic, PET/MR-compatible motionphantom as well as in a volunteer study.Results:Respiratorymotion detection and correction were presented for MR andPET data in phantom and volunteer studies. The Pilot Tone successfully recov-ered the ground-truth respiratory signal provided by the phantom.Conclusions: The presented method provides reliable respiratory motion track-ing during arbitrary imaging sequences throughout a full PET/MR study. All re-sults can directly be transferred to MR-only applications as well.

Key Words: motion detection, motion correction, free-breathing examinations,PET/MR, MR

(Invest Radiol 2020;55: 153–159)

T he introduction of integrated PET/MR scanners1 provides the op-portunity to correct PET data degraded by respiratory motion using

high spatial and temporal resolution MR images. Various approacheshave been presented by different groups to create a motion-correctedPET image using MR information.2–6 Basically, the presented approachesrely on amotionmodel, that is, a combination of (1) a respiratory signal forMR and PET data sorting into different motion states and (2) motionvectors describing the deformation between these motion states. If PETdata outside of the acquisition of the actual motion modeling sequenceshould be motion corrected, a reliable respiratory motion surrogate sig-nal throughout the whole PET/MR study is required that can be used to

extrapolate information about the motion state beyond the acquisitionof the actual motion modeling sequence.

Furthermore, free-breathing MR abdominal imaging becomesmore important due to higher patient comfort and the potential to re-duce the need for rescans. High image quality requires approaches tocorrect for respiratory motion. Again, a reliable respiratory motion sur-rogate is required to sort the MR data into different motion states.

Several methods have been presented to detect and monitor respira-tory motion. Respiratory motion can be detected based on the PET listmodedata.7 However, these approaches are dependent on the tracer used in thePET/MR study and obviously will not work in case of MR-only studies.

External hardware like respiratory bellows or cameras has beensuccessfully used for this purpose. However, the bellows lead to a pro-longed patient preparation time, because it must be carefully placed towork accurately. Due to the body array coils and sometimes blankets placedon top of the patient for body imaging examinations in clinical settings, thevalue of cameras for respiratory motion detection can be limited.

As another option, the sequence itself can act as a source for themotion surrogate, for example, pencil beam navigators. It was shownthat these types of navigators can be used for tracking diaphragm mo-tion8 at the cost of an additional radiofrequency (RF) pulse and readout;however, this additionally required time can have an impact on scan ef-ficiency and image contrast. In addition, the sampling rate of this tech-nique is limited to the TR of the underlying basic sequence. Othertrajectory types that repeatedly sample the center of k-space, such as ra-dial, allow a “self-navigation.”3,9 In this case, no additional RF pulse isrequired because the actual data samples provide the motion informa-tion. There are also concepts to add the self-navigation feature to arbi-trary sequences10,11 as required for monitoring all sequences duringthe whole MR-only or PET/MR study. The required additional readoutdoes not necessarily change image contrast or scan efficiency but stillrelies on a dedicated modification of every single sequence. Next tothat, further sequence modifications might be required, for example,in case of 2-dimensional pulse sequences where the excited volume isnot constant throughout the acquisition.11

In this work, we discuss a flexible approach to detect and monitorrespiratorymotion throughout a full PET/MR examination that does not re-quire modifications of the sequence. A reference RF signal called PilotTone12–15 is generated by a small transmitter and tracked through thecourse of the whole examination. This additional surrogate signal is de-tected by the RF receive chain of theMR system during every readout. Ide-ally, the frequency of the signal is moved outside of the frequency band ofthe actual imaging object into the oversampling area. Patient motion causesa modulation of the amplitude and the phase of the signal and hence allowsextracting a respiratory surrogate. There is no additional time required forpatient setup besides placing the transmitter on the body array matrix coil.To obtain the respiratory signal from measured raw data, only a simple fil-tering step followed by a principal component analysis (PCA) is required.

First, we validate the Pilot Tone–based respiratory signals for dif-ferent free-breathing sequences using an anthropomorphic, PET/MR-compatible phantom16 and corresponding reference motion signals. Af-terwards, we transfer the protocol to a volunteer study to demonstratethat the approach can also be used in a more realistic and clinical setup.

Received for publication June 24, 2019; and accepted for publication, after revision,August 20, 2019.

From the *Siemens Healthcare GmbH, Erlangen, Germany; †Department of Radiology,University Hospital and University of Lausanne, Lausanne, Switzerland; ‡RefleXionMedical, Hayward, CA; §European Institute for Molecular Imaging, University ofMünster,Münster, Germany; and ||NYU LangoneMedical Center, New York, NY.

Conflicts of interest and sources of funding: This research was supported by the Centerfor Advanced Imaging Innovation and Research, a National Institute for Biomed-ical Imaging and Bioengineering Biomedical Technology Resource Center (NIHP41 EB017183). The authors report no conflicts of interest.

Correspondence to:ThomasVahle,PhD,SiemensHealthcareGmbH,AlleeamRoethelheimpark2, 91052 Erlangen, Germany. E-mail: [email protected].

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.ISSN: 0020-9996/20/5503–0153DOI: 10.1097/RLI.0000000000000619

ORIGINAL ARTICLE

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Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

mailto:[email protected]

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We also present examples for 2 applications that rely on a continuous re-spiratorymonitoring throughout different sequences of the study, namely,PET motion correction2 and 3-dimensional DWI motion correction.17

All experiments shown in this work were performed on a PET/MR scanner but can directly be transferred to an MR-only scanner aswell, besides the PET motion correction.

MATERIALS AND METHODSAll scans were performed using a 3T Biograph mMR (Siemens

Healthcare, Erlangen, Germany).18 Because this work focuses on respira-tory motion detection (and correction), the study protocol was designedto contain only sequences with free-breathing protocols. All protocolsare free-breathing variants of standard MR liver imaging protocols (leav-ing out additional breath-hold protocols). The Pilot Tone was active dur-ing the whole study to monitor the motion.

At first, a prototype radial VIBE sequence was acquired to pro-vide the vector fields for the motion correction approaches for PETandMR presented later in this section. It was followed by prototypesequences for free-breathing EPI-based diffusion-weighted imaging(DWI), radial turbo spin echo T2 (mapping), and dynamic contrast en-hancement (DCE) imaging.

All reconstructions of the sequences include a filtering step,which is applied before the actual reconstruction to remove the PilotTone from the raw data. The sorting of the MR and PET raw data into5 motion states was performed based on the extracted and processedPilot Tone signal. All sequences were scanned during a phantom studyand volunteer measurements.

Pilot Tone Generation and DetectionThe Pilot Tone was generated by a standalone battery-powered

prototype device (shown in Fig. 1) operating at a fixed frequency inthe examination volume. The frequency was selected such that it wasoutside the MR signal bandwidth but within the receiver bandwidthin order not to interfere with the actual imaging signal which is typicallyoversampled by a factor of 2. Hence, the Pilot Tone was received by allRF channels and could easily be separated from the MRI signal. Phys-iological movement changes the distribution of tissues in the examina-tion volume and thus the electromagnetic environment (conductivitiesof tissue). This change in the local electromagnetic environment causesmodulation of the generated Pilot Tone signal, which can be detected bythe MR system's receive coils. The Pilot Tone amplitude was adjustedto avoid saturation of the MR receiver.

The algorithm to automatically detect the Pilot Tone in the MRdata works as follows:

1. The Fourier transform in readout direction gives the projection of theobject and shows the Pilot Tone signal. See Figure 1 for an example.

2. Taking the derivatives and searching for the highest absolute value inthe oversampling bandwidth provides the location of the Pilot Tone.This can be done because the Pilot Tone signal is strong compared

with theMR signal, and the physiologic modulation of the Pilot Toneis slow compared with the readout.

3. The detected location of the Pilot Tone signal is used to generate areference signal in the time domain.

4. As described in Speier et al,12 this reference signal is scaled to matchthe data (by multiplication of the complex conjugate) and finallysubtracted from the raw data to remove the Pilot Tone signal. Thecorresponding complex valued scaling factor is the actual Pilot Tone.

The scaled reference signal that is available for each channel cannow be used to monitor the actual patient's motion. The subtraction fromthemeasured data also guarantees that the Pilot Tone data does not have aharmful effect on the remaining pipeline.

Detection of Respiratory and Cardiac MotionThe Pilot Tone extraction described in the previous section provides

one signal per channel with a length equal to the number of acquired sam-ples. Each receive channel now carries a coarsely localized but unknownlinear mixture of modulations due to physiological motions, for example,respiratory, cardiac, or bulk motion. Therefore, source separation is re-quired to obtain the targeted respiratory surrogate signal. For the initialexperiments presented in this work, a simple PCAwas performed. Afterthe PCA, the most suitable signal with a frequency between 0.1 to0.6 Hz was selected manually. No further signal processing steps wererequired besides a drift correction for the signals extracted during DWI.

In the single example of cardiac motion, a simple PCAwas notsufficient (as shown before in Bacher et al19) to detect the cardiac sig-nal. Hence, for this special application, the fast independent componentanalysis (FICA)20 was used and the most suitable signal was selected.

Principal component analysis generates unmixing vectors suchthat these vectors are orthogonal to each other and point in the directionof maximum variance. Therefore, PCA theoretically only works onmixtures of gaussian variables. Though not gaussian, the respiratorycomponent is by far the strongest signal in the mixture and accountsfor most of the variance. Hence, a PCA is sufficient to identify the respi-ratory component. FICA aims to find an unmixing by solving an opti-mization problem based on statistical independence of sources and cangenerate an unmixing of sources of arbitrary distribution.

We refer to Rigie et al11 for a detailed discussion of the applica-tion of blind deconvolution to disturbed cardiac signals.

Phantom StudyA PET/MR-compatible, anthropomorphic motion phantom was

used to evaluate the Pilot Tone signals.16 The life-sized human torsophantom consists of a plastic thorax and includes inflatable silicone lungs,a deformable left ventricle model, and a liver compartment. A smalltube can be attached to the moving diaphragm to emulate a lesion.

The phantom can simulate respiratory as well as cardiac motion.Respiratory motion is modeled by moving the diaphragm with a pneu-matic piston, which leads to a lesion displacement of 2 cm. Cardiac motionis simulated by inflating the inner cavity of the left ventricle model with

FIGURE 1. Left, Prototype Pilot Tone transmitter used in the experiments. Right, The Pilot Tone signal can easily be identified in the raw data after takingthe Fourier transform in readout dimension. The signal can be detected and removed by subtracting a derived scaled reference signal. Amplitudevariations of the modulated Pilot Tone signal are in the range of a few percent. Hence the Pilot Tone signal nearly looks constant in this figure.

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water. Both position of the diaphragm and cardiac triggering are recordedand serve as a ground-truth for the Pilot Tone signal to compare with.

A dose of 350 MBq 18FDG was injected into the phantom, andthe data acquisition was performed approximately 2 hours after injec-tion due to the required preparation time for the phantom.

Two different experiments were run as presented in Figure 2. Forthe first experiment (setup A), only aperiodic respiratory motion (nocardiac motion) was enabled. MR measurements included the DixonVIBE for MR-based PET attenuation correction (MRAC), DWI, T2mapping, DCE, and of course the motion modeling. PET data wereacquired simultaneously. The second experiment (setup B) includedcardiac motion of the phantom as well to verify whether the PilotTone is still able to identify respiratory motion in more realistic sit-uations when other motion components are present, too. Eleven mi-nutes of PET data were acquired next to the simultaneous acquisitionof the MRAC for attenuation correction and the prototype sequencefor the motion modeling.

The gating signals were extracted from all MR sequences in bothsetups and compared with the ground-truth data provided by the phan-tom. For the first experiment, motion-corrected PET reconstructionswere performed using only data from the motion modeling sequence.Additional motion correction resultswere created by using the PET dataacquired during the other MR sequences as well. These motion correc-tion results were compared with a non–motion-corrected PET recon-struction as well as with an image reconstructed from data acquiredonly during the reference state (end-expiration).

For PETattenuation correction, an additional CT scan (140 kVp)was performed right after the PET/MR scans using the Biograph mCT(Siemens Healthcare, Forchheim, Germany). The Hounsfield units ofthe CT image were converted to PET μ-values as described in Carneyet al.21 A manual alignment of the converted CT image with the PET im-age was followed by a resampling to PET resolution. This process is ofcourse only required for the phantom study where the standard clinicalMR-based attenuation correction cannot provide suitable results.

Human StudiesThe Pilot Tonewas also tested on humanvolunteers to verify that

respiratorymotion can be detected in a more realistic setting aswell. Allvolunteers provided written informed consent before the measurements.Cardiac motion was not investigated and only treated as a disturbance ofthe targeted respiratory signal. Identical protocols were used as in thephantom study (setup A) besides small changes to the sequence band-widths to ensure that the Pilot Tone frequency fits right in the oversamplingbandwidth for all sequences. These modifications were required be-cause the volunteer MR measurements were performed using anotherBiograph mMR located at a different site and the center frequenciesof the systems differ slightly, whereas the frequency of the prototypeportable transmitter is fixed and cannot be changed.

Because no ground-truth motion was available for the humansubjects as in case of the phantom study, a vendor-provided cushion wasused to acquire an additional respiratory signal for comparison. Thecushion was placed on the abdomen and a belt was wrapped aroundthe volunteer to hold the cushion in place. The respiration of the volun-teer causes a compression/decompression of the cushion and hence pro-duces a respiratory signal. For all sequences, both signals (Pilot Toneand cushion) were presented and compared. Examples for gated recon-structions using the Pilot Tone signals were performed to verify if respi-ratory motion was accurately detected.

MR Sequences/Protocols

Motion ModelA prototype T1-weighted, golden angle radial stack-of-stars VIBE

sequencewas used for themotionmodel (TR/TE = 2.97/1.32milliseconds,BW = 810 Hz/pixel, 88 � 4.5 mm slices, 1.56 mm2 in plane resolution,sagittal orientation). The corresponding Pilot Tone signal was extracted,and the raw data were sorted into 5 motion states accordingly. Based onthe reconstructed images for these 5 datasets, motion estimates were cal-culated per voxel with end-expiration used as a reference motion state.22

Diffusion-Weighted ImagingDiffusion-weighted imaging data were acquired in axial orienta-

tion using a prototype diffusion-weighted single-shot echo-planar imag-ing sequencewith Cartesian k-space sampling. Three b-values (50, 400,800 s/mm2) were acquired in 3D diagonal mode with 6, 9, and 15 aver-ages (TR/TE = 6100/57 milliseconds, BW = 1954 Hz/pixel, 35 � 5mm slices, 1.48 mm2 in plane resolution, axial orientation).

Radial T2 MappingFor T2 mapping, a prototype radial 2-dimensional TSE-sequence23

was used (TR/TE = 4000/8 milliseconds, BW = 611 Hz/pixel,28 � 6.0 mm slices, 1.0 mm2 in plane resolution, axial orientation).

Dynamic Contrast EnhancementThe RAVE sequence24,25 was used as an example for DCE although

no contrast agent was used in this proof-of-concept. Like the sequence usedfor the motion modeling, it is based on a golden angle radial stack-of-starsVIBE sequence (TR/TE = 3.87/1.52 milliseconds, BW = 810 Hz/pixel,160 � 2.0 mm slices, 1.4 mm2 in plane resolution, axial orientation).

FIGURE 2. Overview of the 2 PET/MR experiments. During setup A, 4free-breathing sequences are acquired after the required MRAC scan,while setup B only includes MRAC and the motion model. For bothphantom experiments, aperiodic respiratory motion was enabled. Forsetup B, cardiac motion was enabled as well.

FIGURE 3. Comparison between the aperiodic respiratory ground truthsignal (blue) of the phantom and the corresponding extracted PilotTone signal (red) shown for the different sequences. The Pilot Tone nicelyrecovers the respiratory signal in all cases. The dip in the middle of thesignal for the T2mapping results from the fact that the protocol includes ashort pause before the start of the second repetition. During this time,no Pilot Tone data are acquired.

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The Pilot Tone signalwas filtered from the raw data of all sequencesbefore the reconstruction as described in the previous sections, and arespiratory gating signal was created. The time required for the dataprocessing depends on the file size and took—running a prototypeimplementation in MATLAB 2016a (The MathWorks, Inc, Natick, MA)as used for most of the presented results—some minutes, for exam-ple, approximately 2 minutes for the DWI data. An optimized C++implementation was used for the processing of the motion modeldata and reduced the required time to seconds.

PET Motion CorrectionThe listmode PET data were acquired simultaneously with the MR

data. Pilot Tone–based gating signals for respiratory motion extractedfrom the MR raw data were transferred to the PET listmode for thehistogramming of 5 sinograms, one for each respective motion state.The MR-based motion vector fields of the motion modeling sequencewere resampled to the resolution, orientation, and location of the PETimages. Both gated sinogram data and motion vector fields were used

as input for the motion-corrected PET reconstruction as described inFürst et al.2 The motion-corrected PET images were compared withthe gated and uncorrected PET images. All reconstructions were per-formed with an image matrix size of 172 � 172 and pixel size of4.17 � 4.17 mm. In total, 127 slices with a slice thickness of 2.03 mmwere reconstructed. Due to the high amount of 18FDG for the phantom,only the first 40 seconds of the PET data acquired during each sequencewas used leading to 160 seconds of PET data for the non–motion-corrected image. For the reference reconstruction at end-expiration,only 160 seconds of data were used as well.

3D Motion Correction for DWIAccording to the method presented in David et al,17 all averages

were reconstructed separately, and the slices for the different b-valueswere sorted into 5 respiratory motion states resulting in one sparse4-dimensional (4D) volume per b-value. In case more than one averagewas acquired for a b-value and motion state, the corresponding slice ofthe volumewas averaged according to the number of available averages.

The previously introduced motion vector fields were againresampled to the resolution, orientation, and location of the DWI images.Afterwards, the 4D volumes for each b-value were reduced to 3D volumesby warping all motion states to the state representing end-expiration.

For this proof-of-concept, only a visual comparison between theb-values without and with motion correction (toward end-expiration) wasperformed. The motion states for end-expiration and end-inspirationresulting from the data sorting explained above are shown as well forcomparison. Motion correction results are only presented for the humanstudies since there is no (reasonable) diffusion in the phantom.

RESULTS

Phantom StudyFigure 3 shows the recovered respiratory signals of the Pilot

Tone extracted from the measurement data of setup A. For each of the4 sequences in the study (plotted in red), the corresponding referencesignal provided by the phantom is plotted for comparison (in blue). Inall cases, respiratory signals extracted from the Pilot Tone and referencesignal match well. The displacement error between the reference signal

FIGURE 4. Top, Comparison between the aperiodic respiratory groundtruth signal (blue) of the phantom and the corresponding extractedPilot Tone signal (red). Bottom, The cardiac trigger of the phantom (blue)provided as ground truth is comparedwith the detected and recoveredcardiac signal (red) of the Pilot Tone. For a better comparison between thecardiac signals, a discrete version (sorted into 5 motion states) of thePilot Tone signal (green) is plotted as well. For both types of motion(respiratory and cardiac), the Pilot Tone can recover a signal similar tothe ground truth.

FIGURE 5. Coronal views of the different PET reconstructions. Panel A shows a static reconstruction of data from the end-expiration state only as areference. The impact of motion can clearly be seen in panel B, where a static reconstruction of the data acquired during the 4 free-breathing sequencesis shown. The lesion at the liver dome (and a hot spot inside the liver) vanished due tomotion. Panel C shows amotion-corrected reconstruction using thedata from B. Both lesion and hot spot are again clearly visible. The remaining 3 panels (D–F) show reconstructions of data acquired during 3, 2, and 1sequence only. As fewer data are used, the noise level increases and both the lesion and the hot spot inside the liver are again (partly) lost.





and the Pilot Tone signals was on average less than 1.37 mm for 50% ofthe sampled points, less than 2.28mm for 75% of the sampled points, and95% of the sampled points had on average an error less than 3.81 mm.

Figure 4 shows the comparison between the respiratory referencesignal of setup B and the extracted and processed respiratory signalfrom the Pilot Tone. The reference signal matches again well with therecovered Pilot Tone signal. In addition to that, the detected cardiac sig-nal is shown in comparison to the reference cardiac trigger signal of thephantom. For a better comparison of the piecewise constant trigger sig-nal with the continuous Pilot Tone signal, the latter one was also sortedinto 5 discrete motion states. The result of the Pilot Tone signal proc-essed with FICA matches up well with provided reference signal.

The first 2 panels in Figure 5 show the reference images. Panel Ais the image reconstructed from data of the end-expiration motion stateonly. Panel B is the image reconstructed without motion correctionfrom data acquired during the 4 MR sequences using the moving phan-tom. Motion blurring of the liver and the heart is clearly visible, and thesmall lesion at the diaphragm is completely gone. The different PET re-constructions usingmotion correction (panels C–G) show a clear reductionin motion blur while having different levels of noise. Panel G showing animage reconstructed from data acquired during only one of the 4 sequences

in the study is very noisy. Panel C reconstructed from identical data asin B shows a similar result as the reference image in panel A.

Human StudiesFigure 6 shows the Pilot Tone–based respiratory surrogates for

the differentMR sequences acquiredwith 2 volunteers. For all sequences,the surrogates show a reasonable respiratory pattern. The results are againindependent of the sequence used or the orientation of the acquired data.Figures 7 and 8 presenting the reconstructions of the data for the motionmodel and for DWI confirm that the detected signals from Figure 6A arevalid respiratory signals. The comparison of the Pilot Tone signals andthe respiratory belt shown in Figure 6B indicates that both approachesprovide similar results. Only small differences in the amplitude canbe observed for the various sequences. Toward the end of the DWImea-surement, the volunteer started to breath unregularly, which is picked upby both methods.

The gated DWI images in Figure 8A and 8B clearly show respi-ratory motion. Missing slices in the gated images are a result of the factthat averages were not necessarily acquired for each motion state and nointerpolation was applied. Ignoring the motion as in the non–motion-corrected image of Figure 8C and simply averaging data of the different

FIGURE 6. Respiratory signals for different volunteers extracted from Pilot Tone data acquired using setup A. The volunteer in A started to breathunregularly toward the end of the RAVE (DCE) acquisition. For the second volunteer presented in B, the signal acquired with the respiratory belt isshown for comparison as well. In all cases, the Pilot Tone signals look comparable to the signals provided by the belt.

FIGURE 7. Reconstructions of the motion model data for 5 different respiratory states determined by the Pilot Tone signal. Coronal slices are shown forbetter visualization of the respiratory motion. The different motion states from end-expiration (left) to end-inspiration (right) can clearly be identified.





motion states fills the missing slices but automatically leads to motion blurand in this example to a loss of signal in the liver dome. Themotion correc-tion can recover the missing slices by warping the information from theother motion states. Figure 8D not only consists of all acquired data,but due to the motion correction, the missing signal is recovered aswell.

DISCUSSIONWe have presented a method to monitor respiratory motion

throughout a full MR or PET/MR examination. In this prototype im-plementation, the new method requires only minor modifications tothe MR image reconstruction (adding a simple filter step before theactual reconstruction) as well as only very limited additional hardware(the Pilot Tone prototype generator).

The stability of the Pilot Tone signal for respiratory monitoringwas tested with an MR-compatible phantom as well as with volunteerscans. Provided ground truth signals for the phantom were reliablyrecovered. For volunteer scans, the Pilot Tone–based respiratory sig-nals were successfully comparable to signals acquired with a respira-tory belt. Gated 4D reconstructions for both phantom and volunteer scanswere used as visual confirmation that the detected motion is respiratorymotion. Next to the monitoring of the respiratory signal and performinggated reconstructions, potential applications for motion correction inPET and MR were discussed that require a stable respiratory signal.Both PETandMRmotion correction applications indicate why it is im-portant to have a reliable respiratory motion surrogate over the fullexamination.

The PET motion correction results clearly demonstrate (as hasbeen shown in other publications before) that it should be the aim touse as much PET data for the motion correction as possible. Even if cur-rent PET/MR protocols used in clinical routine probably provide enoughPET counts for reasonable motion correction results of shorter timeframes at the moment, it is obviously desired to reduce the injected doseas far as possible. These developments will then require techniques likethe Pilot Tone to use PET data acquired simultaneously next to arbitraryMR sequences.

Three-dimensional DWI motion correction using motion vectorsprovided by an additional sequence is a new method to obtain a motionfree image in combination with free-breathing acquisitions. This ap-proach is particularly interesting for PET/MR applications since the re-quired additional sequence for the motion model is already availabledue to the PETmotion correction. As for the application of PETmotioncorrection, a consistent surrogate of the respiratory motion is required.

The Pilot Tone was tested with several different sequence typesand protocols. The presented approach can be easily adapted to arbi-trary other sequences, which leverages huge additional potential forpractical applicability.

For the experiments presented in this work, a simple PCA wasenough to detect respiratory motion. More sophisticated algorithms like

FICA20 or SOBI26 can help to get even better results, for example, incase of cardiac motion. Those algorithms are probably also requiredto automate the detection process as presented in Rigie et al.11 It wasshown for one example that it is possible to detect cardiac motion aswell when using FICA.

In its current version, due to the minor but still required modifi-cations to the sequences, the approach will not work out of the box withstandard product protocols. It must be assured that the Pilot Tone fre-quency falls into the oversampling bandwidth for all protocols. Thismight require small protocol modifications or adjusting the Pilot Tonefrequency for each single sequence. These modifications are, however,minor compared with other state-of-the-art approaches like embeddednavigators. Also, in this prototype implementation, a Pilot Tone–basedrespiratory surrogate is only available during the time that a sequence isrunning, specifically while it is acquiring MR data.

The initial results presented in this work indicate that the PilotTone is a promising tool for free-breathing examinations in MR andPET/MR.More experiments, especially in a clinical setting, and a betterintegration of software and hardware into the scanner are required tofurther investigate the potential of the Pilot Tone.

ACKNOWLEDGMENTSThe authors would like to thank Björn Czekalla and Lynn

Frohwein from the European Institute for Molecular Imaging (EIMI)in Münster for supporting the acquisition of the phantom data.

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FIGURE 8. Subfigures (A) and (B) showend-expiration and end-inspiration, respectively, resulting from the sorting of all acquired averages into 5 differentmotion states. Coronal slices are again shown for better visualization of the respiratory motion. Missing slices indicate that, for the given motion state,no average was acquired. The displacement of the liver dome due to respiratory motion is clearly visible. Subfigure C shows the non–motion-correctedimage resulting from averaging all acquired averages and ignoring motion. The drop in the signal in the liver dome (indicated by the arrow) is a resultof the averaging of different motion states. Applying the motion vector fields provided by the motion model to the gated results leads to themotion-corrected result (end-expiration) in subfigure D. Gaps resulting from missing slices are filled by the motion correction resulting in a completeimage. Themissing signal described in subfigure Cwas recovered by themotion correction. In this example, data are shown for a b-value of 800 acquiredwith 15 averages.





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https://doi.org/10.1007/s10334-015-0487-2

http://indexsmart.mirasmart.com/ISMRM2016/PDFfiles/0410.html



https://diglib.tugraz.at/cardiac-triggering-based-on-locally-generated-pilot-tones-in-a-commercial-mri-scanner-a-feasibility-study-2017




respiratory motion detection and correction for mr using the...

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