Skip to main content


We're creating a new version of this page. See preview

  • Meeting abstract
  • Open Access

PET motion correction using MR-derived motion parameters

  • 1,
  • 2, 3,
  • 2, 3 and
  • 1
EJNMMI Physics20141 (Suppl 1) :A53

  • Published:


  • Motion Parameter
  • Motion Correction
  • Siemens Biograph
  • Angle Radial
  • Coarse Sampling

With the improving resolution of modern PET scanners, any slight motion during the scan can cause significant blurring and loss of resolution. MRI scanners have the capacity to perform quick successive scans and thus provide a means to track motion during a scan. Hence, with the advent of simultaneous PET-MR scanners, it has become possible to use the MR scanner to track the motion and thereby provide the necessary motion parameters to correct the PET data. Using a suitable segmentation approach a separate MR scan can provide the attenuation map to produce quantitative PET images.

An FDG brain scan was acquired on a Siemens Biograph mMR PET-MR scanner. The MR scan was acquired using the Golden Angle Radial Sparse Parallel (GRASP) sequence [1], simultaneously with a 5 minute PET scan, while the patient was asked to move his head repetitively from side to side for proof-of-principle purposes. A separate static scan was also acquired prior to the motion scan, to be used as a control. The MR data were divided into a series of 268 images with a frequency of approximately 1 Hz. The motion parameters were derived by performing a rigid (6 degrees-of-freedom) registration of the masked MR images to a chosen reference image. The PET list-mode data were corrected on an event-by-event basis [2, 3]. List-mode maximum likelihood expectation-maximisation (accelerated with ordered subsets [4]) was used for the reconstruction, incorporating the attenuation correction (as a pre-correction to the data) as well as weighted-average sensitivity [2] to achieve a quantitative reconstruction.

Motion correction successfully removed almost all motion artefacts, recovered the resolution and allowed for quantitative images to be produced. Techniques to improve upon the coarse sampling of the MR images, such as interpolating between motion data points, will be investigated.

Authors’ Affiliations

Department of Nuclear Medicine, KU Leuven, Medical Imaging Research Center, Belgium
Center for Advanced Imaging Innovation and Research, New York University, USA
Bernard & Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA


  1. Feng L, Grimm R, Tobias Block K, Chandarana H, Kim S, Xu J, Axel L, Sodickson DK, Otazo R: Golden-angle radial sparse parallel MRI: Combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn. Reson. Med. 2013., 00: 10.1002/mrm.24980Google Scholar
  2. Rahmim A, Bloomfield P, Houle S, Lenox M, Michel C, Buckley KR, Ruth TJ, Sossi V: Motion Compensation in Histogram-Mode and List-Mode EM Reconstructions: Beyond the Event-Driven Approach. IEEE Trans. Nucl. Sci. 2004,51(5):2588–2596. 10.1109/TNS.2004.835763View ArticleGoogle Scholar
  3. Kyme AZ, Zhou VW, Meikle SR, Baldock C, Fulton RR: Optimised motion tracking for positron emission tomography studies of brain function in awake rats. PLoS One 2011,6(7):e21727. 10.1371/journal.pone.0021727PubMed CentralPubMedView ArticleGoogle Scholar
  4. Hudson HM, Larkin RS: Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans. Med. Imaging 1994, 13: 601–609. 10.1109/42.363108PubMedView ArticleGoogle Scholar


© Bickell et al; licensee Springer 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.