TRADITIONAL POSTER - ismrm

Poster Sessions
1161. A Novel Data Processing Method for Olfactory FMRI Examinations
Xiaoyu Sun 1 , Jianli Wang 1 , Christopher W. Weitekamp 1 , Qing X. Yang 1,2
1 Radiology, Penn State University College of Medcine, Hershey, PA, United States; 2 Neurosurgery, Penn State University College of
Medicine, Hershey, PA, United States
Here we present an olfactory fMRI data processing method that can significantly improve the data processing quality when the patients’ respiration pattern is
not controlled and doesn’t synchronize with olfactory stimulation paradigm. As an example of implementation we present an olfactory fMRI examination
while the subject’s respiration pattern is not regular. Our data demonstrates that it is critical to consider the subject’s respiratory patterns’ modulation on the
olfactory stimulation paradigm. The presented olfactory fMRI data processing method can be used for various applications. In addition to the example of real
time respiration data, subjective response data (not provided here) can also be convolved with odor delivery data for more improved fMRI data processing.
This experimental set-up will be useful in the olfactory fMRI study of neuropsychiatric and neurologic patients that are not cooperative or be able to follow
the breathing instructions.
1162. Physiological Noise Extraction in FMRI Data Using Empirical Mode Decomposition
Hsu-Lei Lee 1 , Jürgen Hennig 1
1 Department of Diagnostic Radiology, Medical Physics, University Hospital Freiburg, Freiburg, Germany
Physiological noise caused by ecg- and/or breathing related pulsatility may introduce temporal correlations that are unrelated to neuronal processes in a
resting-state network analysis. As physiological noise is often non-linear and non-stationary, signal extracted by simple filtering will deviate from the actual
noise, and so as global regression methods like RETROICOR. In this study we implemented empirical mode decomposition (EMD) on resting-state fMRI
time-series and extracted cardiac components which has a time-frequency curve that well matches the true heart rate acquired by external ECG during the
scan.
1163. Characterization and Correction of Physiological Instabilities in 3D FMRI
Rob Hendrikus Tijssen 1 , Steve M. Smith 1 , Peter Jezzard 1 , Robert Frost 1 , Mark Jenkinson 1 , Karla Loreen
Miller 1
1 FMRIB Centre, Oxford University, Oxford, Oxon, United Kingdom
3D FMRI acquisitions have the advantage of allowing high resolution, isotropic, imaging. However, 3D acquisitions, such as SSFP and SPGR, show
increased signal instabilities in the inferior regions of the brain. Here, we present a characterization of these temporal instabilities and propose a GRAPPAbased
correction method that allows retrospective gating of 3D FMRI data.
1164. Length-Scale Dependent Effects of Noise Reduction in Phase and Magnitude FMRI Time-Series
Gisela E. Hagberg 1 , Marta Bianciardi 2 , Valentina Brainovich 1 , Antonino Maria Cassara 3,4 , Bruno
Maraviglia 3,4
1 Santa Lucia Scientific Foundation, Rome, Italy; 2 Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda,
MD, United States; 3 Dept. Physics, Sapienza University, Rome, Italy; 4 Centro Studi e Ricerche "Enrico Fermi"
fMRI analyses are primarily based on the magnitude information in gradient-echo echo-planar images (GE-EPI) but a growing number of studies also
included the phase information. An issue relates to physiologic large-scale phase effects that are more prominent in phase than magnitude data. In the present
work we explored the phase stability at different length scales at 3T and found that improvements in temporal stability could be achieved by alternative
noise-reduction methods that take into account the differential origin of noise effects in phase and magnitude data.
1165. Comparison of Feature Selection Methods for Classification of Temporal FMRI Volumes Using SVM
Ayse Ece Ercan 1 , Esin Karahan 2 , Onur Ozyurt 2 , Cengizhan Ozturk 2
1 Biomedical Engineering, TU Delft, Delft, Netherlands; 2 Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
High dimensional feature space of fMRI volumes has been a drawback for classification studies since large feature dimension is known to increase the
classification error and the computation time. In this study, we combined PCA with two anatomical feature selection methods: grey matter (GM) and region
of interest (ROI) masking, and investigated the effects of different feature reduction methods on the classification accuracy of a linear SVM classifier. To
apply PCA after anatomical masking is concluded to be a reliable method for preserving the classification accuracy of the anatomical feature selection
methods and reducing the computation time.
1166. Assessment and Improvement of FMRI Normalization Based on Inversion-Recovery Prepared High-
Resolution EPI
Pooja Gaur 1 , Helen Egger 1 , Nan-kuei Chen 2
1 Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States; 2 Brain Imaging and Analysis
Center, Duke University, Durham, NC, United States
EPI based fMRI has several major limitations: distortion, low spatial-resolution, and low anatomic resolvability. Therefore, it is not easy to register fMRI
data to structural images, and to normalize fMRI data. EPI distortion correction and nonlinear normalization methods have been developed to address these
limitations. However, it is not easy to assess how these methods perform on fMRI data with distortions, limited resolution, and anatomic resolvability. Here
we report an imaging protocol based on high-resolution inversion-recovery prepared segmented EPI (with identical distortion patterns as in single-shot EPI),
enabling accurate assessment of the performance for distortion correction and nonlinear normalization algorithm.