TRADITIONAL POSTER - ismrm

Poster Sessions
3120. Assessing the Accuracy of Detecting Mouse Brain Structure Changes from MRI Using Simulated
Deformations
Matthijs Christiaan van Eede 1 , R Mark Henkelman 1 , Jason P. Lerch 1
1 Mouse Imaging Centre, Toronto, Ontario, Canada
The use of image registration to investigate shape differences in mouse brain MRIs have become a significant area of interest. It is unknown how accurately
structural changes can be detected or whether this sensitivity varies with structure shape. We present a novel method to simulate deformation fields with
known structural tissue change and subsequently attempt to recover the induced changes in 21 structures. We demonstrate that image based registration
algorithms can reliably detect structural shape differences down to 5% in the structures with a lower surface to volume ratio, and reliably down to 10% in all
others.
3121. Rapid Semi-Automatic Segmentation of the Spinal Cord from Magnetic Resonance Images
Mark Andrew Horsfield 1 , Stefania Sala 2 , Mohit Neema 3 , Martin Absinta 2 , Anshika Bakshi 3 , Maria Pia
Sormani 4 , Mara Rocca 2 , Rohit Bakshi 3 , Massimo Filippi 2
1 Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom; 2 Neuroimaging Research Unit, Ospedale San Raffaele,
Milan, Italy; 3 Laboratory for Neuroimaging Research, Harvard Medical School, Boston, MA, United States; 4 Department of Health
Sciences (DISSAL), University of Genoa, Genoa, Italy
A new semi-automatic method for rapid segmentation of the spinal cord from MR images is presented, based on an active surface (AS) model of the cord
surface with intrinsic smoothness constraints. The intra- and inter-observer reproducibilities of cord area measures were evaluated, and compared favorably
with an existing cord segmentation method. Correlations between cord area and clinical disability scores confirmed the relevance of the new method in
measuring cord atrophy. A novel form of cord visualization is shown, in which the straightened cord center-line forms one coordinate axis of a new image,
allowing simple visualization of the cord structure.
3122. Semi-Automated Microbleed Identification on Susceptibility Weighted Images
Samuel Barnes 1,2 , E. Mark Haacke 1
1 Wayne State University, Detroit, MI, United States; 2 Loma Linda University, Loma Linda, CA, United States
A method to detect microbleeds in the brain in a semi-automated fashion is presented. The goal of this technique is to reduce the processing time of
quantifying microbleeds. The semi-automated method compares favorably with manual counting achieving approximately 80% sensitivity and 100%
specificity while reducing processing time to under an hour.
3123. Segmentation and Volume Estimation on a Sub-Voxel Basis Using Quantitative MR: A Validation
Study
Janne West 1,2 , Jan B. Warntjes, 23 , Peter Lundberg 1
1 Department of Medical and Health Sciences, Division of Radiation Physics, Linköping, Östergötland, Sweden; 2 Center for Medical
Imaging Science and Visualization, Linköping, Östergötland, Sweden; 3 Department of Medicine and Health, Division of Clinical
Physiology, Linköping, Östergötland, Sweden
Using an MR quantification sequence; specific brain-tissues typically exhibit a narrow range of R1, R2 and PD values, and thus the tissues in the brain can
be identified as clusters in the three dimensional R1-R2-PD space. In partial volume voxels (voxels containing two or more tissue types) the R1-R2-PD
values are a combination of the values from the contributing tissues. By using a partial volume model a segmentation method to assess fractional brain-tissue
volumes of white matter (WM), grey matter (GM) and CSF for the complete brain on a sub-voxel basis was created and validated on 7 normal subjects.
3124. Brain Extraction Algorithm Using 3D Level Set and Refinement
Jinyoung Hwang 1 , HyunWook Park 1
1 Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of
Skull-stripping methods have been proposed widely, but they usually provide coarse segmentation results. For example, in superior and inferior slices, their
results could serve incorrect result. Thus, we present a brain extraction algorithm using 3D level set and refinement process. First, 3D level set function is
applied to whole brain volume, to find coarse brain region. The refinement process is then applied to the result of 3D level set function, which improves the
accuracy of the final segmentation results. We evaluated the proposed method to normal brain data acquired from BrainWeb, IBSR, 1.5T, and 3T data.
3125. Symmetric and Multi-Scale Features for Automatic Segmentation of Multiple Sclerosis Lesions Using
Pattern Classification
Marco Battaglini 1 , Nicola De Stefano 1 , Mark Jenkinson 2
1 Quantitative Neuroimaging Laboratory, University of Siena, Siena, Italy; 2 Clinical Neurology, FMRIB Centre, University of Oxford,
Oxford, Oxon, United Kingdom
IIn order to develop a fully automated segmentation tool for MS lesions we explore using novel input features with two pattern classification methods
(Neural Networks and Random Forests). Results show a statistically significant improvement in DICE by using the novel multi-scale and symmetry features
with both classifiers. To be useful for clinical trials we use multi-centre real clinical data, segmented by different manual raters, which makes this
challenging. Nonetheless, we still achieve DICE results consistent with state-of-the-art methods, without requiring costly pruning of the Neural Networks,
complicated post-processing, or having to apply any exclusion criteria to the images.