Respiratory Resolved Attenuation Correction Maps for Motion Compensated PET-MR using Dixon-GRPE
Christoph Kolbitsch1,2, Radhouene Neji3, Matthias Fenchel4, and Tobias Schaeffter1,2

1Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom, 2Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, 3MR Research Collaborations, Siemens Healthcare, Frimley, United Kingdom, 4MR Oncology Application Development, Siemens Healthcare, Erlangen, Germany


Quantitative PET requires accurate attenuation correction (AC) information. For simultaneous PET-MR acquisitions in the thorax or abdomen these MRAC images are obtained in a single breathhold which can lead to misregistration errors between breathhold MRAC and free-breathing PET data. Here we present a method which obtains accurate AC information (Dice coefficient higher than 0.85) during free-breathing and yields additional respiratory motion fields which can be utilised in motion-compensated MR and PET reconstructions. The proposed Dixon-GRPE method led to improvements of up to 50% in sharpness (FWHM) and a 33% improvement in the quantification of the specific uptake value (SUV).


Simultaneous PET-MR yields highly sensitive PET and versatile MR data in a single examination. In order to obtain quantitative PET information, the varying density of different tissue types has to be taken into consideration using attenuation correction (AC) data. Commonly, this AC map is obtained in a short two-point Dixon MR scan before every PET acquisition during a single breathhold1. However, the following PET scans are obtained during free-breathing which can lead to misregistration errors between breathhold AC map and free-breathing PET data causing artefacts at tissue boundaries (Fig. 1a). Here we present a technique which obtains MR data during free-breathing using a multi-echo acquisition with a 3D Golden radial phase encoding (GRPE) sampling scheme2. This approach yields AC information and respiratory motion information allowing for the transformation of AC information to the required respiratory motion phases. In addition, motion information can be utilised in motion compensated image reconstructions to improve MR and PET image qualities2 (Fig. 1b).


Dixon-GRPE: GRPE was implemented as a prototype sequence for multi-echo acquisitions on a 3T PET-MR scanner (Biograph mMR, Siemens Healthcare, Erlangen, Germany) and three-point Dixon data were obtained in a phantom and four volunteers during free-breathing: T1-weighted GRE (FA 10o, TR/TE 5.94/1.23/2.72/4.21ms FOV: 400mm3, 1.92x3.2x3.2mm3 resolution, total acquisition time 96s). Standard MRAC data were also acquired (FA 10o, TR/TE 3.60/1.23/2.46ms FOV: 500x328x400mm3, 2.6x4.1x3.1mm3 resolution) in a single 19s breathhold.

Respiratory motion estimation: A self-gating signal from 1D foot-head projections is used to bin the MR data into respiratory bins. 3D images defining different respiratory phases are reconstructed with an iterative non-Cartesian SENSE reconstruction3 with a generalised total-variation4 constraint in spatial and temporal direction. Non-rigid image registration was performed to derive motion fields describing the transformation between end-expiration and all other respiratory phases5.

Motion compensated MR image reconstruction: The final 3D high-resolution images are reconstructed using a motion compensated image reconstruction which utilises the non-rigid respiratory motion fields to transform the image data to the same respiratory phase at each iteration6.

Calculation of AC information: The motion-compensated MR images are separated into fat and water components7. The images are then classified into the following tissue types: air outside the body (Aout), air inside the body (Ain), fat (F) and water (W).

Evaluation of AC images: The AC information was validated by calculating the Dice similarity coefficient between Dixon-GRPE and standard MRAC images8. In the phantom images this was carried out for different undersampling factors ranging from 2x oversampled (R=0.5) to 16x undersampled (R=16) in the angular direction.

PET simulations: Respiratory resolved PET simulations were carried out based on the motion resolved GRPE images using STIR9. Realistic emission values were assigned to tissue classes and tumor lesions were added in the liver10. PET images were reconstructed using either the Cartesian MRAC data without motion correction (uncorr) or with the Dixon-GRPE MRAC information and a respiratory motion compensated image reconstruction (MCIR10). In the simulated PET images full-width-at-half-maximum (FWHM) and maximum uptake (SUVmax) were assessed and compared to motion-free reference images.


Dice coefficients calculated from the phantom scans were larger than 0.85 for a wide range of undersampling factors suggesting a robust fat-water separation and AC classification (Fig. 2). Based on these phantom experiments the number of respiratory bins was set to 8, i.e. respiratory resolved images with a maximum undersampling factor of 8. A comparison between fat, water and AC images for volunteers shows also very good agreement (Fig 3). In addition, motion vectors from expiration to inspiration are displayed. The Dice coefficients for Aout, Ain and W were 0.99±0.001, 0.85±0.031 and 0.86±0.017, respectively. The Dice coefficient for fat in healthy volunteers was 0.51±0.115, due to the small size of fat structures in healthy volunteers which led to strong reductions of the Dice coefficient even for small differences in the classification. Respiratory motion and misregistration errors between MRAC and PET data can lead to strong artefacts in the PET images (Fig. 4). The error of FWHM is between 10% and 50% and the error of SUVmax is up to 33%.


We have presented an MR technique which yields accurate 3D AC images and respiratory motion information within one short free-breathing scan. The motion information can be utilised in a motion-compensated PET reconstruction to correct for respiratory motion artefacts and to ensure there are no misregistration errors between AC and PET image data. Evaluation of Dixon-RPE in clinical PET-MR scans will be carried out to validate the PET simulation results.


No acknowledgement found.


1. Martinez-Möller A, Souvatzoglou M, Delso G, et al. Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med. 2009;50(4):520-526.

2. Buerger C, Clough RE, King AP, Schaeffter T, Prieto C. Nonrigid motion modeling of the liver from 3-D undersampled self-gated golden-radial phase encoded MRI. IEEE Trans Med Imaging. 2012;31(3):805-815.

3. Pruessmann KP, Weiger M, Boernert P, Boesiger P. Advances in sensitivity encoding with arbitrary k-space trajectories. Magn Reson Imaging. 2001;46(4):638-651.

4. Knoll F, Bredies K, Pock T, Stollberger R. Second order total generalized variation (TGV) for MRI. Magn Reson Med. 2011;65(2):480-491.

5. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999;18(8):712-721.

6. Batchelor PG, Atkinson D, Irarrazaval P, Hill DLG, Hajnal J, Larkman D. Matrix description of general motion correction applied to multishot images. Magn Reson Med. 2005;54(5):1273-1280.

7. Berglund J, Kullberg J. Three-dimensional water/fat separation and T 2 * estimation based on whole-image optimization-Application in breathhold liver imaging at 1.5 T. Magn Reson Med. 2012;67:1684-1693.

8. Dice LR. Measures of the Amount of Ecologic Association Between Species. Ecology. 1945;26(3):297-302.

9. Thielemans K, Tsoumpas C, Mustafovic S, et al. STIR: software for tomographic image reconstruction release 2. Phys Med Biol. 2012;57(4):867-883.

10. Tsoumpas C, Buerger C, King a P, et al. Fast generation of 4D PET-MR data from real dynamic MR acquisitions. Phys Med Biol. 2011;56(20):6597-6613.


Figure 1: (a) For a standard PET-MR scan misregistration errors can occur between breathhold MRAC and free-breathing PET data. (b) The proposed Dixon-RPE MRAC approach overcomes this problem by acquiring data during free breathing yielding accurate AC and respiratory motion information which can be utilized for PET and MR motion correction.

Figure 2: Phantom scans. Attenuation coefficient (AC) images of a fat-water phantom. The Dice coefficient between standard Cartesian MRAC and Dixon-GRPE MRAC with different undersampling factors ranging from 0.5 (2x oversampled) to 16x undersampled. Accurate tissue classification is achieved even for an undersampling factor of 12.

Figure 3: Volunteer study. Fat, water and attenuation coefficients (AC) images obtained with the standard Cartesian and Dixon-GRPE MRAC approach. Dixon-GRPE also yields non-rigid transformation fields (red arrows) describing respiratory motion. The respiratory motion-compensated image reconstruction used for Dixon-GRPE leads to accurate depiction of small vessels in the liver (blue arrow).

Figure 4: PET simulation. (a) Respiratory motion and misregistration errors between PET and MRAC can lead to artefacts at the dome of the liver (white arrows) of more than 50% (b). This can impair the detection of lesions (c1), cause an overestimation of uptake (c2) or lead to blurring (c3). MFree motion-free reference.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)