Nuclear Instruments and Methods in Physics Research A 702 (2013) 59–63

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Nuclear Instruments and Methods inPhysics Research A

0168-90

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journal homepage: www.elsevier.com/locate/nima

A dynamic thorax phantom for the assessment of cardiac and respiratorymotion correction in PET/MRI: A preliminary evaluation

Michael Fieseler a,b,n, Harald Kugel d, Fabian Gigengack a,b, Thomas Kosters a, Florian Buther a,Harald H. Quick c, Cornelius Faber d, Xiaoyi Jiang b, Klaus P. Schafers a

a European Institute for Molecular Imaging, University of Munster, Mendelstrasse 11, 48149 Munster, Germanyb Department of Computer Science, University of Munster, Einsteinstrasse 62, 48149 Munster, Germanyc Institute of Medical Physics, University of Erlangen-Nurnberg, Henkestrasse 91, 91052 Erlangen, Germanyd Department of Clinical Radiology, University Hospital of Munster, Albert-Schweitzer-Campus 1, 48149 Munster, Germany

a r t i c l e i n f o

Available online 26 September 2012

Keywords:

Motion correction

PET/MRI

Phantom study

02/$ - see front matter & 2012 Elsevier B.V. A

x.doi.org/10.1016/j.nima.2012.09.039

esponding author at: European Institute for M

ter, Mendelstrasse 11, 48149 Munster, Germ

ail address: michael.fieseler@uni-muenster.de

a b s t r a c t

Respiratory and cardiac motion are a known source of image degradation and quantification

impairment in positron emission tomography (PET). In this study we use near-realistic PET/MRI data

acquired using a custom-built human torso phantom capable of simulating respiratory and cardiac

motion. We demonstrate that a significant reduction in motion-induced artifacts in PET data is possible

using MR-derived motion estimates.

& 2012 Elsevier B.V. All rights reserved.

1. Introduction

Respiratory and cardiac motion are a known source of imagedegradation and quantification impairment in PET. Extensivework has been done regarding motion correction in the contextof PET/CT. Various approaches have been applied for motioncorrection of gated PET data, including optical flow [4], B-splinebased methods [1], and standard registration methods includingmass-preservation [6]. The use of gated PET data for motionestimation, however, relies on sufficient statistics per gate [13]and works only in regions of sufficent tracer uptake.

Apart from motion estimation based on the gated PET data, theusage of 4D-CT data has been proposed as a source of motioninformation [8]. The advantage of this approach lies in the usage ofanatomical data, which is independent of tracer uptake. The usage of4D-CT data, however, increases the radiation burden for the patient.

With the recent development of whole body PET/MRI scan-ners, the detailed anatomical information of MRI data is apromising source of motion information for correction of PETdata. The benefit of using MRI-derived motion information hasbeen shown for a hardware phantom [12], animals [3], and usingsimulation data [5]. In this work we show the preliminary resultsfor MRI-based motion correction of PET data for data acquired of acustom-built human torso phantom. The focus here is on theseparate correction of cardiac and respiratory motion.

ll rights reserved.

olecular Imaging, University

any. Tel.: þ49 251 834 9309.

(M. Fieseler).

2. Methods

2.1. Phantom

We have developed a life-size human torso phantom capable ofsimulating respiratory and cardiac motion. The phantom consists of aplastic thorax with inflatable lungs made of silicone, a deformable leftventricle model (BSI, Germany), and a liver compartment. An imageand the schematic design of the phantom are given in Fig. 1. Thediaphragm is moved by a pneumatic piston. The maximum motionrange is 3 cm. In this work the motion range was limited to 2 cm toreflect a typical value [10]. Cardiac motion is simulated by inflatingthe inner cavity of the ventricle model with water. Contraction isachieved passively through the elasticity of the ventricle model. Bothcardiac and respiratory motion are controlled using LabVIEW(National Instruments, USA).

The contrast agent Lumirems is used to modulate MRI con-trast of the different phantom compartments (given as percentageof Lumirem in water: myocardium, 18%; myocardial cavity, 9%;liver, 25%). The phantom’s compartments were filled with18F-FDG (background and blood: 10 kBq/ml; liver: 30 kBq/ml;myocardium: 90 kBq/ml for the respiratory data set, 70 kBq/mlfor the cardiac data set). For the respiratory data set, an additionaltube (1.5 ml) was attached to the diaphragm to simulate a smalllesion (300 kBq/ml).

2.2. MRI and PET data acquisition

Two different data sets are used in this study: a respiratory dataset was acquired in two separate scanners (Philips Intera/Achieva

Fig. 1. Photography (top) and schematic design (bottom) of the human torso

phantom.

Fig. 2. Respiratory motion correction: Results for cardiac region, coronal views.

(a) MRI data set used as the reference frame. (b) Ungated reconstruction of

Listmode PET data. (c) Averaged corrected PET gates. (d) Single PET gate corres-

ponding to the reference MRI frame and (e) Static acquisition reconstructed on

scanner. The amount of motion-induced blurring is reduced in the corrected PET

data. The corrected data match the single gate closely, yet noise is reduced. Lines

indicate the position of line profiles shown in Fig. 3.

M. Fieseler et al. / Nuclear Instruments and Methods in Physics Research A 702 (2013) 59–6360

MRI scanner, Siemens Biograph 16 PET scanner), and a cardiacdata set was acquired in a PET/MRI scanner (Siemens BiographmMR).

Respiratory case: For acquisition of respiratory data the phan-tom was set to a breathing frequency of 6 cycles per minute. MRIdata were acquired using a dynamic sequence of fast gradientecho scans (3D Turbo Field Echo, 366�366�55 voxels of1.49�1.49�5.0 ml edge length, TE/TR 0.69/2.95 ms, flip angle 51,parallel imaging (SENSE) factor 8, 16 channel receive coil) on a 3.0 TPhilips Intera/Achieva scanner. A total of 35 volumes was acquired,acquisition of one volume took approximately 0.7 s [11]. An exam-ple MRI data set is shown in Fig. 2. PET data were acquired after theMRI scans using a Siemens Biograph 16 scanner (Listmode, 20 min).PET data were gated retrospectively into 10 gates using a data-driven technique [2]. An additional scan without respiratory motionwas performed (3 min).

To examine how MR-based motion correction performs for alow statistics situation, we performed an additional gating, usingonly the first 5 s of Listmode data which correspond to half abreathing cycle in this setup.

Cardiac case: Cardiac motion data were acquired using aretrospectively gated 2D FLASH sequence (156�192 pixels of1.6�1.6 mm edge length, 3 mm slice spacing, 32 slices, TE/TR2.94/45.44 ms, flip angle 121, 25 frames, Siemens Biograph mMR).The phantom’s trigger output was used as an ECG supplement.PET data were acquired simultaneously as Listmode data (20 min)and gated retrospectively to 25 gates. The phantom’s heart wasset to beat at a rate of 50 bpm.

2.3. Motion detection

For motion detection on MRI data, we use a B-spline basedregistration approach [9]. The advantage of B-spline based regis-tration over other methods lies in the implicit regularization. Thecoarseness of the transformation can be regulated through the

spacing of B-spline nodes. The following functional is to beminimized:

JðwÞ ¼

Z

O

D½Tðyðw,xÞÞ,RðxÞ�þaSðwÞ dx

where R denotes the reference volume, T is the template volumewhich is transformed to match R, yðw,�Þ is a B-spline transformationusing the coefficients w, S is a regularizer of the transformation’scoefficients, a is a scalar value, and O denotes the domain. Asdistance measure D we use sum of squared differences (SSD). Toreflect the more global motion in the respiratory data set, we choosea coarser spacing of B-spline nodes of 3 cm and a spacing of 1 cm forthe cardiac data set.

Respiratory case: We extract a respiration signal from theacquired 35 MRI volumes using an image-based navigator.

Table 1Respiratory motion correction: Correlation values of all PET gates and the PET gate

matching the reference MRI frame. Mean and standard deviation of correlation

values are given for ROIs at different regions.

Region Uncorrected Motion-corrected

Mean Std Mean Std

Global 0.9383 0.0269 0.9841 0.0070

Heart 0.8396 0.0832 0.9664 0.0230

Liver 0.7171 0.1346 0.9402 0.0441

Lesion 0.6270 0.2046 0.9235 0.0718

Table 2Respiratory motion correction: Maximum,

mean and standard deviation of detected

motion for cubic regions of interest.

Region Detected motion (mm)

Max Mean Std

Global 12.0 0.85 1.27

Heart 11.9 3.20 2.14

Liver 12.0 4.13 2.57

Lesion 9.5 3.21 2.09

M. Fieseler et al. / Nuclear Instruments and Methods in Physics Research A 702 (2013) 59–63 61

This respiration signal is used to select six consecutive MRIvolumes covering the range from expiration to inspiration.

Since the gating of PET data (10 gates) does not match therespiratory MRI data sets with regard to its phase, a navigatorsignal was extracted from the PET data as well. The PET signal ismatched with the MRI signal to identify the respiratory phase ofthe PET gates with respect to the MRI frames. Since the PETrespiratory phase does not necessarily coincide with a single MRIrespiratory phase, linear interpolation between the two nearestadjacent motion fields is applied.

Cardiac case: The 25 frames from the cardiac data set cover onefull cardiac contraction cycle, so all frames were used for motiondetection. PET data for this case were gated in 25 gates whichcould be identified directly with the 25 MRI frames.

All MRI data were registered with respect to a selectedreference MRI data set. For the respiratory data set we choseframe 3 of 6, for the cardiac data set frame 11 of 25. The PET gateswere geometrically transformed using the motion estimatesderived from MRI data.

Attenuation correction was performed using the CT data in therespiratory case. For the cardiac case, an MRI-based attenuationmap generated from the standard image reconstruction workflowwas used. The m-maps were geometrically transformed to therespective PET phases using motion estimates from the MRI data.For the low statistics data set, a motion-corrected reconstructionhas been performed [7].

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200

kBq

/ m

l

correcteduncorrected

3. Results

3.1. Respiratory motion

Results for the cardiac region are presented in Fig. 2. Theuncorrected PET data show blurring particularly in the cranio-caudal direction. After motion correction the blurring isreduced and the cardiac wall is resolved more clearly. The lineplots through the cardiac wall (Fig. 3) show a good fit of thecorrected data and the PET gate corresponding to the MRIreference data set. Note that the corrected data contain lessnoise compared to the reference gate. In the corrected data,the peak activity is resolved better. Compared to the uncor-rected data, the cardiac wall appears narrower. In Table 1

−40 −30 −20 −10 0 10 20 30 400

5

10

15

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25

30

35

40

45

50

location (mm)

kBq

/ ml

correcteduncorrectedreference gate

Fig. 3. Respiratory motion correction. Line profile of cardiac wall in corrected,

uncorrected, and reference gate PET data. Line profile location is shown in Fig. 2.

The corrected PET data fits the single gate corresponding to the MRI target phase.

Peak activity is not recovered in uncorrected PET data.

−50 −40 −30 −20 −10 0 10 20 30 40 500

20

40

60

location (mm)

Fig. 4. Respiratory motion correction: Line profile for the lesion region. Without

motion correction the activity is blurred along the direction of motion. After

motion correction the lesion appears as a single, larger peak. The drop in activity

at the diaphragm location is noticeable in the line profile of the corrected images.

Location of line profile is indicated in the images in the top row.

correlation values for uncorrected and corrected PET data areshown. Correlation values have been measured in cubicregions around the heart, liver, and the lesion. A large improve-ment in correlation values for the heart, liver, and lesionregions can be observed. Statistics of the detected motion forthe regions used above are summarized in Table 2. Since thereference MRI data set lies between inspiration and expira-tion the expected maximum amount of motion is on theorder of 1 cm.

Fig. 5. Respiratory motion correction: Low statistics data set using 5 s from Listmode data gated in 10 gates, coronal views. Top: Ungated reconstruction. Middle: Motion-

corrected reconstruction using the motion determined on MRI data. Bottom: Single gate.

Table 3Mean, standard deviation and maximum values for a ROI around the lesion, given for full statistics

data set (top), low statistics data set (mid) and a static acquisition (bottom). The values are given as

kBq/ml.

Mean Std Max

Full statistics

Uncorrected 52.1 27.6 119.7

Motion-corrected 64.1 39.9 187.2

Single gate 67.6 55.7 244.4

Low statistics

Uncorrected 52.4 27.8 123.5

Motion-corrected 53.0 33.2 162.5

Single gate 56.0 54.2 328.7

Static acquisition 69.2 54.1 241.9

Fig. 6. Cardiac motion correction: Overlays of reference MRI and PET gates, coronal view. Top row: Uncorrected PET gates 1, 5, and 15. Bottom row: Corrected PET gates

1, 5, and 15. Without motion correction the PET data do not fully fit the contours in the MRI data. After motion correction the PET data fit the contours in the MRI data

(arrows).

M. Fieseler et al. / Nuclear Instruments and Methods in Physics Research A 702 (2013) 59–6362

M. Fieseler et al. / Nuclear Instruments and Methods in Physics Research A 702 (2013) 59–63 63

Results for the lesion region are shown in Fig. 4. After motioncorrection the lesion appears as a single peak. This can be verifiedin the line profile through the lesion region.

Results for the low statistics data set are given in Fig. 5. The sliceshown from a single gate indicates that motion estimation based onthe PET data alone would prove difficult. The motion-correctedreconstruction of this low statistics data set shows the lesion clearlywithout motion blurring. Statistics for a cylindrical region of interestaround the lesion are summarized in Table 3. For both the fullstatistics and low statistics data sets the peak as well as the meanactivity is clearly underestimated in the uncorrected data.

3.2. Cardiac motion

In Fig. 6 an overlay of the reference MRI data set withuncorrected and corrected PET gates is shown. As can be appre-ciated, after motion correction the PET data fit the contours of thereference MRI data set.

4. Discussion and conclusion

In this preliminary study, the presented MRI based motioncorrection of PET data proved successful in reducing motion-induced blurring artifacts. However, the true activity is not fullyrecovered. In future work, we will examine the quality of themotion detection results from MRI data more closely.

Our custom-built human torso phantom allows for the genera-tion of near-realistic image data from PET/MRI scanners includingcardiac and respiratory motion. Besides software-based simulations,this phantom may serve as an additional stage for the validation ofcorrection algorithms under real-world conditions. Regardingmotion correction, the generation of motion-free ground-truth datais invaluable. Currently, the silicone lungs are not attached to thediaphragm so that a gap of varying width between lungs anddiaphragm exists, which poses an artificial challenge to the motionestimation. A new set of silicone lungs is currently under construc-tion to address this issue.

In the low statistics example we have shown that MRI-basedmotion correction may in particular prove useful when small lesions,respectively lesions with low uptake are under examination. In thisstudy only one lesion with relatively high activity was used. We arecurrently preparing experiments with smaller, low-uptake lesions.

In the presented study we acquired two isolated data sets forcardiac and respiratory motion. The combination of both cardiacand respiratory motion correction poses an additional challengewhich we are going to address in a dual-gating study.

Additionally, we plan to apply our approach to real patientdata.

Acknowledgements

This study was supported by the Deutsche Forschungsge-meinschaft (DFG), Sonderforschungsbereich 656 (projects B2and B3) and a research grant to the European Institute forMolecular Imaging (EIMI) from Siemens Medical Solutions(Erlangen, Germany).

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