Volume 1 Supplement 1

Proceedings of the 3rd PSMR Conference on PET/MR and SPECT/MR

Open Access

Fast 2D MRI acquisitions for motion correction in PET-MRI

  • Michael Fieseler1, 2,
  • Christopher Glielmi4,
  • Thomas Kösters3,
  • Lynn Frohwein1,
  • Fernado Boada3,
  • Xiaoyi Jiang1, 2 and
  • Klaus P Schäfers1
EJNMMI Physics20141(Suppl 1):A58

DOI: 10.1186/2197-7364-1-S1-A58

Published: 29 July 2014

We performed continuous, fast acquisitions of 2D MR slices covering the thorax under free breathing.

In present work, acquired 2D stacks are re-stored using a respiration signal. The usage of 2D slices is similar to the method described in [1]. The proposed method, however, does not include a navigator and acquisition times are shorter.

Dara were acquired from two patients on a Siemens Biograph mMR scanner (Siemens Healthcare, Erlangen, Germany) using a FLASH sequence, TE 1.38ms, TR 26ms, flip angle 12o, a 32-channel body-coil (acceleration factor of 8).

20 coronal slices were acquired with 3.9 x 6.25mm2 in-plane resolution (HF, LR), 128 x128 pixel, slice thickness 9mm, slice spacing 9mm, 26ms per 2D slice (total acquisition time 160s).

A respiratory signal was estimated from affine registrations of an area showing respiratory motion and subsequently used to sort the acquired 2D stacks into 8 respiratory phases.

Figure 1 shows input data and re-gated data. Noise is reduced by averaging of several 2D stacks. Additionally, cardiac motion is eliminated to a large extent, thus the generated dataset can be used for respiratory motion correction. Average correlation of 2D stacks assigned to a phase is 0.958 for dataset 1 (randomly selected gates: 0.933, SD 0.03). For dataset 2 the average correlation of 2D stacks assigned to a phase is 0.94 (randomly selected gates: 0.9, SD 0.036).
Figure 1

Datasets a) & c) show 2D slice of two acquired datasets. b) & d) show a slice from the re-gated, averaged datasets. Note the reduction of noise in the regated dataset.

The results indicate that the proposed method is suitable for use in respiratory motion correction of PET data. In future work we will evaluate our approach on more datasets. Additionally, we will use motion estimates for each acquired 2D image stack to correct motion frame-wise.

Authors’ Affiliations

European Institute for Molecular Imaging, University of Münster
Department of Computer Science, University of Münster
Center for Advanced Imaging Innovation and Research, NYU Langone Medical Center
Siemens Medical Solutions USA


© Fieseler 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 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.