TRADITIONAL POSTER - ismrm

Poster Sessions
registered by elastic registration to this virtual template. The algorithm successfully reduces motion artifacts. Comparisons between pre and post registration
underlines the importance of image registration in DCE-MRI examinations.
3055. Flow Compensation in Frequency-Encode Direction for the Fast Spin Echo Triple-Echo Dixon (FTED)
Sequence
Kaining Shi 1,2 , Russell Low 3 , Shanglian Bao 1 , Jingfei Ma 2
1 Beijing City Key Lab of Medical Physics and Engineering, Beijing University, Beijing, China; 2 Imaging Physics, University of Texas
MD Anderson Cancer Center, Houston, TX, United States; 3 Sharp and Children's MRI Center, San Diego, CA, United States
The triple echo readout in the FTED sequence presents a challenge to achieve flow-compensation along the frequency-encode direction. In this work, two
flow-compensation methods were proposed. In the first method, gradient moments are nulled at every RF locations so that the CPMG condition is always
maintained. In the second method, the spin echo component of the signal is nulled at the 1st and 3rd echo locations and the stimulated component is
minimized at different echo locations. The effectiveness of both methods in reducing the flow-induced artifacts was examined with a numerical calculation
and demonstrated in a phantom testing.
3056. On the Optimization of Parallel Imaging for Ghost Reduction: A Blood Flow Example
Feng Huang 1 , Wei Lin 1 , Yu Li 1 , Arne Reykowski 1
1 Invivo Corporation, Gainesville, FL, United States
A parallel imaging based technique, COnvolution and Combination OperAtion (COCOA), has been proposed recently to efficiently remove ghost artifacts
due to non-rigid motion. COCOA has two steps: a convolution step for a synthetic k-space with redistributed error, and a combination step for the final
reconstructed k-space with reduced error. In this work, by using blood flow artifact as an example, the optimization schemes for these two steps are
introduced to improve the ability for ghost suppression.
3057. Undersampled Reconstruction of Multiple 3D High-Resolution Respiratory Phases Using Non-Rigid
Registration
Christian Buerger 1 , Andrew Peter King 1 , Tobias Schaeffter 1 , Claudia Prieto 1
1 Division of Imaging Sciences, King's College London, London, United Kingdom
A method for reconstructing multiple high-resolution respiratory phases from free-breathing 3D-MRI is presented. The proposed method combines an
undersampled self-gating acquisition with a non-rigid image registration scheme. This approach uses all the acquired data to reconstruct a single high spatial
resolution (HSR) phase at the most visited respiratory position and multiple respiratory resolved (RR) images at the remaining phases followed by an
improving of image quality for all RR images (suffering from remained aliasing artifacts) using a registration procedure. This aligns the features of HSR
with the remaining RR phases, leading to a sequence of time-resolved high resolution respiratory phases.
3058. MOtion Correction Using Coil Arrays (MOCCA)
Peng Hu 1 , Mehdi H. Moghari 1 , Beth Goddu 1 , Lois A. Goepfert 1 , Thomas H. Hauser 1 , Warren J. Manning 1 ,
Reza Nezafat 1
1 Beth Israel Deaconess Medical Center, Boston, MA, United States
We present a novel motion correction method using coil arrays (MOCCA). In MOCCA, the elements of a coil array are used as individual motion “sensors”
which detect the motion-induced signal variations that are modulated by coil sensitivity maps. The inclusion of multiple coils by stacking multi-coil data into
a column vector increased the accuracy of motion detection compared to existing methods based on projections. We evaluate the accuracy of MOCCA in a
phantom and demonstrated the application of MOCCA on healthy volunteers for bulk motion correction in brain imaging and for respiratory and cardiac
self-gating in cardiac cine imaging.
3059. Handling Motion in Sparse Reconstruction with Whiskers
Jason K. Mendes 1 , Dennis L. Parker 1
1 UCAIR, University of Utah, Salt Lake CIty, UT, United States
In general, the minimum number of K-Space samples required to produce good results in sparse reconstruction is approximately four times the number of
sparse coefficients. Patient motion that is neither periodic nor smooth will reduce sparsity in the temporal direction and degrade the success of the sparse
reconstruction. It is therefore beneficial to detect and correct as much patient motion as possible to maximize temporal sparsity and thus reduce the total
number of K-Space samples required. This is accomplished using a hybrid Radial-Cartesian sampling technique called. This sequence has an inherent
ability to correct bulk patient motion and is well suited to non-linear sparse reconstruction.
3060. Towards Lissajous Navigator-Based Motion Correction for MR-PET
Marcus G. Ullisch 1,2 , Tony Stöcker 1 , Kaveh Vahedipour 1 , Eberhard D. Pracht 1 , Lutz Tellmann 1 , Hans
Herzog 1 , Nadim Jon Shah 1,3
1 Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich GmbH, Jülich, Germany; 2 Department for Psychiatry and
Psychotherapy, RWTH Aachen University, Aachen, Germany; 3 Faculty of Medicine, Department of Neurology, RWTH Aachen
University, Aachen, Germany
With the combination of Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) into a single combined system, a novel imaging
modality has become available. Previous approaches to patient motion tracking for PET data correction are difficult to use in the combined MR-PET
environment. Thus, alternative methods for motion tracking have to be developed. Here, a novel approach for MR-PET motion correction utilising the
Lissajous navigator is presented.