ELECTRONIC POSTER - ismrm

15:00 4894. Faster Acquisition of MR Images with Double Quantum Filtering by Regularization
Genevieve Guillot 1 , Yongchao Xu 1 , Slawomir Kusmia 1 , Hadia Hanachi 1 , Jean-François
Giovannelli 2 , Alain Herment 3
1 U2R2M UMR8081 CNRS, Orsay, France, France; 2 LAPS / IMS UMR5218, Bordeaux, France, France; 3 3-
LIF U678 INSERM / UMR-S UPMC, Paris, France, France
MRI with Double Quantum Filter (DQF) gives a direct access to water linked to macromolecules, but requires 16 up to 64 repetitions
of the acquisition scheme with different phases of the RF pulses in the DQ filter to select the DQ signal. We reduced the number of
phase encoding lines kept in the data for each DQF step, employing a regularization method to compute each image. The acquisition
time could be reduced by 2/3 without any significant loss of contrast and minor loss of contrast on contours. Even faster acquisition
should be possible with radial or spiral k-space trajectories.
Topics in Parallel Imaging
Hall B Monday 14:00-16:00 Computer 113
14:00 4895. Noise-Facilitated GRAPPA Reconstruction for FMRI
Hu Cheng 1 , Wei Lin 2 , Feng Huang 2
1 Indiana University, Bloomington, IN, United States; 2 Invivo Diagnostic Imaging, Gainesville, FL, United
States
In fMRI, temporal SNR is the main concern in the optimization of parallel imaging algorithms such as GRAPPA. It is shown in this
work that adding noise to the auto-calibration signal (ACS) region of GRAPPA data can increase the temporal SNR of fMRI series,
with a minimal impact on image quality. Simulation on the EPI images of a phantom and human subject demonstrated that image
quality can be improved by adding a certain amount of noise to the raw data of reference scans, while the temporal SNR can be further
improved with a higher level of additive ACS noise.
14:30 4896. Undersampled Multi Coil Image Reconstruction for Fast FMRI Using Adaptive
Linear Neurons
Thimo Grotz 1 , Benjamin Zahneisen 1 , Marco Reisert 1 , Maxim Zaitsev 1 , Jürgen Hennig 1
1 Dept. of Diagnostic Radiology, Medical Physics, University Hospital Freiburg, Freiburg, Germany
Standard fMRI experiments have a rather limited temporal resolution of 1-3s. The temporal resolution of fMRI experiments can be
increased by an order of magnitude by acquiring less k-space and using a high number of receive channels. Image reconstruction is
thus an ill-posed inverse problem. Here we would like to introduce a new approach, based on neural networks, to reconstruct the
undersampled fMRI data that offers a significantly improved point spread function with reduced spatial spread and hence improved
spatial localization of activation.
15:00 4897. Time Dependent Regularization for Functional Magnetic Resonance Inverse
Imaging
Aapo Nummenmaa 1,2 , Matti S. Hamalainen 1 , Fa-Hsuan Lin 1,3
1 MGH-MIT-HMS Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States;
2 Department of Biomedical Engineering and Computational Science, Helsinki University of Technology,
Espoo, Finland; 3 Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
We propose a novel method for time dependent regularization of functional magnetic resonance Inverse Imaging (InI). A Variational
Bayesian approximation with a dynamic model for the regularization is constructed to obtain an automatic, temporally adaptive
estimation algorithm. The proposed method is compared with the standard Minimum-Norm Estimate (MNE) by using simulated InI
data. The dynamic dMNE shows significant improvements in determining the activation onset from the baseline period.
15:30 4898. Magnetic Resonance Multi-View Inverse Imaging (MV InI) for Human Brain
Kevin Wen-Kai Tsai 1 , Thomas Witzel 2 , Fa-Hsuan Lin 1,3
1 Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan; 2 A. A. Martinos Center; 3 A.
A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
To solve the anisotropic spatial resolution of MR inverse imaging (InI) reconstruction method, we propose the multi-view InI (MV
InI) to using a few projections and a highly parallel detection to achieve high spatiotemporal MR dynamic imaging. Specifically, we
used three orthogonal projections and a 32-channel head coil array to achieve the effective TR of 300 ms and 4 mm3 isotropic spatial
resolution. We demonstrated the acquisitions and reconstruction of MV InI using in vivo data. This method achieved a 8 times faster
temporal resolution than conventional multi-slice EPI acquisitions.
Tuesday 13:30-15:30 Computer 113
13:30 4899. Homotopic l 0 Minimization Technique Applied to Dynamic Cardiac MR Imaging
Muhammad Usman 1 , Philip G. Batchelor 1
1 King's College London, London, United Kingdom
The L1 minimization technique has been empirically demonstrated to exactly recover an S-sparse signal with about 3S-5S
measurements. In order to get exact reconstruction with smaller number of measurements, recently, for static images, Trzasko has