ELECTRONIC POSTER - ismrm

Wednesday 13:30-15:30 Computer 123
13:30 5063. Jacobian Weighting of Distortion Corrected EPI Data
Stefan Skare 1,2 , Roland Bammer 1
1 Radiology, Stanford University, Stanford, CA, United States; 2 MR Center, Clinical Neuroscience, Karolinska
Institute, Stockholm, Sweden
By acquiring EPI data both with positive and negative phase encoding blips one obtains two oppositely distorted images. The
reversed gradient polarity (RGPM) method can be used to correct these images by searching for a displacement field that explains
their difference. However, even if the estimated displacement field is adequate, the two corrected EPI images have a very low
resolution in anatomical regions that have been too compressed. In this work, we use a Jacobian weighting scheme to make an
informative choice about the combination of the two images that avoids the inclusion of signals from very compressed regions.
14:00 5064. Using PLACE for EPI Distortion Correction of Diffusion Weighted Images (DWIs)
Sofia Chavez 1 , Elizabeth Ramsay 1 , Donald Plewes 1,2 , Greg Stanisz 1,2 , Q-San Xiang 3
1 Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; 2 Medical Biophysics,
University of Toronto, Toronto, Ontario, Canada; 3 Department of Radiology, University of British Columbia,
Vancouver, B.C., Canada
A feasibility study for the application of PLACE, an EPI distortion correction, to diffusion weighted images (DWIs) is presented.
PLACE requires a minimum of two input images which differ by an extra “blip” along the phase encode (PE) direction. The phase
relation between the images encodes the PE coordinate, allowing for correction of the EPI-based distortion along the PE direction.
Results show successful distortion correction for DWIs of a phantom despite the lower SNR, partial k-space and ramp sampling
typical of standard DWI sequences. In vivo application of PLACE to DWI is currently under investigation.
14:30 5065. Accelerating Phase Modulation for Correcting EPI Geometry Distortion by Modern
GPGPU Parallel Computation.
Yao-Hao Yang 1 , Teng-Yi Huang, Fu-Nien Wang 2 , Nan-Kuei Chen 3
1 National Taiwan University of Science and Technology, Taipei, Taiwan; 2 Department of Biomedical
Engineering and Environmental Sciences, National Tsing Hua university; 3 Brain Imaging and Analysis Center,
Duke University Medical Center
Phase modulation combined with field mapping can correct the EPI geometry distortion but it is a time-consuming algorithm. We
proposed to incorporate the GPGPU technique into phase-modulation calculation to reduce the whole computation time. Applying on
the PROPELLER EPI data set, the parallel algorithm reduced the computation time from ~1750 seconds to ~100 seconds. We
conclude that the GPU computing is a promising method to accelerate EPI geometry correction.
15:00 5066. Probabilistic Reconstruction of Undistorted EPI Images Using a Rician Noise Model
Jesper Leif Roger Andersson 1 , Mark Jenkinson 1
1 fMRIB, Oxford University, Oxford, Oxfordshire, United Kingdom
We have developed a method for estimating and correcting distortions from reverse-blip data with poor SNR. It is based on a forward
model that allows us to make predictions about the images and a Rician noise model that enables us to calculate the probability of
observed images. Bayesian inversion is used to find the most probable distortion-free image and field. It performs well even on data
with very poor SNR.
Thursday 13:30-15:30 Computer 123
13:30 5067. New Calculation Method of Pixel Shift Map on PSF Mapping Technique: A Study
on 7T MRI
Se-Hong Oh 1 , Jun-Young Chung 1 , Myung-Ho In 2 , Maxim Zaitsev 3 , Oliver Speck 2 , Young-
Bo Kim 1 , Zang-Hee Cho 1
1 Neuroscience Research Institute, Gachon University of Medicine and Science, Incheon, Korea, Republic of;
2 Department of Biomedical Magnetic Resonance, Institute for Experimental Physics, Otto-von-Guericke
University Magdeburg, Magdeburg, Germany; 3 Department of Radiologic Research, Medical Physics,
University Hospital of Freiburg, Freiburg, Germany
Echo-planar imaging (EPI) is one of the fastest and most widely used MRI pulse sequences in the field of MRI. Compared to
conventional imaging sequence, EPI is more prone to a variety of artifacts. A prominent EPI artifact is geometric distortion due to
strong magnetic field inhomogeneity and susceptibility. Previous PSF mapping method, which was implemented by Zaitsev et al. used
GE (Gradient Echo) StdDev (standard deviation) image as a base and produced a ¡°mask¡± to extrapolate pixel shift map. Flow
artifact as well as setting of the parameters (i.e. threshold value) can affect the result of mask. And the extrapolated shift map which
resulting shift maps with extrapolation eventually have error. Consequently corrected images will also have errors induced by mask
errors and flow artifacts. So we propose new mask calculation method based on using a 2D PSF data based, not based on the GE
StdDev image as previously used. This method is capable of making automatic mask calculation procedure, along with the advantage
of eliminating flow induced ghost artifact all together.