Jose Tamez-Pena¹, Patricia Gonzalez², Joshua Farber², Edward Schreyer², Saara Totterman², and Victor Trevino^1
1Biomedicine, ITESM, Monterrey, Nuevo Leon, Mexico, ²Qmetrics Technologies, Rochester, NY, United States

This work describes the performance of a fully automated MRI image analysis technique to detect or predict clinical symptoms of knee Osteoarthritis.

Paul A. Yushkevich¹, Hongzhi Wang¹, John Pluta¹, Sandhitsu R Das¹, Brian Avants¹, Michael Weiner², Susanne Mueller², and David Wolk^3
1PICSL, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, ²Department of Radiology, University of California, San Francisco, San Francisco, CA, United States, ³Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States

High-resolution T2-weighted MRI has been shown to be a promising modality for imaging the subfields of the hippocampal formation. However, until now, the analysis of this data required tedious manual segmentation. We present a reliable method for automatic segmentation of hippocampal subfields in T2-weighted MRI and apply it to measure group differences in subfield volume and thickness between amnestic MCI patients and controls.

Shawn Sidharthan¹, Ryan Joseph Hutten¹, Christopher Glielmi², Hongyan Du³, Fiona Malone¹, Ann Ragin^1,4, Robert R Edelman¹, and Ying Wu^1,5
1Radiology, NorthShore University HealthSystem, Evanston, IL, United States, ²MR Research and Development, Siemens Healthcare, Chicago, IL, United States, ³Center for Clinical Research Informatics, NorthShore University HealthSystem, Evanston, IL, United States, ⁴Radiology, Northwestern University, Chicago, IL, United States,⁵Radiology, University of Chicago, Chicago, IL, United States

Magnetization transfer ratio (MTR) may detect subtle microscopic changes in the hippocampus before macroscopic anomalies occur. This modality proves to be a crucial tool when evaluating patients with progressive neurological pathologies such as Alzheimer’s disease. In this study, reliability and reproducibility were analyzed for high-resolution MTR at 3T in different metrics (mean and histogram approach) and compared to the more conventional MR volumetric method in the hippocampus. Mean and histogram MTR approach derived from automated post-processing methods, provided excellent scan-rescan results in comparison to volumetry. The results indicate MTR and volumetric analysis to be a useful tool for future studies.

Muqing Lin¹, Kevin Head², Claudia Buss², Tugan Muftuler¹, Elysia Poggi Davis¹, Curt A Sandman², Orhan Nalcioglu¹, and Min-Ying Lydia Su^1
1Tu & Yuen Center for Functional Onco-Imaging and Department of Radiological Sciences, University of California, Irvine, CA, United States, ²Department of Psychiatry & Human Behavior, University of California, Irvine, CA, United States

The shape analysis of hippocampus was applied to evaluate the changes in developmental brain in 48 children from 6 to 9 years old. Three different methods are used, including the surface-to-centerline distance mapping and non-rigid registration using robust point mapping (RPM) and Demons algorithm. The differences between males and females were also analyzed. Although a significant difference was found in some scattered regions, the averaged difference between the two age or sex groups is very small. The results obtained using the distance mapping and RPM registration methods showed similar patterns. The observed differences are mostly likely coming from individual variations.

Delphine Ribes^1,2, Bénédicte Mortamet¹, Meritxell Cuadra Bach³, Clifford R. Jack⁴, Reto Meuli⁵, Gunnar Krueger¹, and Alexis Roche^1
1Advanced Clinical Imaging Technology, Siemens Medical Solutions-CIBM, Lausanne, Switzerland, ²Radiology, UNIL, Lausanne, Switzerland, ³Signal Processing Laboratory (LTS5), EPFL, Lausanne, Switzerland, ⁴Mayo Clin, Rochester, MN USA, ⁵Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

Normal aging and numerous diseases such as Alzheimer’s disease (AD), vascular dementia (VD) and other neurodegenerative diseases lead to brain tissue changes over time. In the interest of disease classification and diagnosis, it is highly desirable to have reliable and automatic tools to measure brain tissue volumes. In this study, we compare volumetric and GM probabilities differences extracted from standard T1-weighted images using SPM8, VBM8 and an in-house automatic tissue classification algorithm called VEMTC.

J.B.M. Warntjes^1,2, J. West^1,3, O. Dahlqvist-Leinhard^1,3, G. Helms⁴, A.-M. Landtblom⁵, and P. Lundberg^6,7
1Linköping University, Center for Medical Image Science and Visualization, Linköping, Sweden, ²Department of Medicine and Health, Division of Clinical Physiology, Linköping, Sweden, ³Department of Medicine and Health, Division of Radiation Physics, Linköping, Sweden, ⁴University Medical Center, MR-Research in Neurology and Psychiatry, Göttingen, Germany, ⁵Department of Clinical Neuroscience, Linköping, Sweden, ⁶Linköping University, Dept of Radiation Physics and Dept of Radiology, IMH, University of Linkoping, Linköping, Sweden, ⁷University Hospital of Linköping, Dept of Radiation Physics and Dept of Radiology, CKOC, University Hospital of Linkoping, Linköping, Sweden

A model is proposed where myelin partial volume in brain parenchyma is estimated utilizing quantitative Magnetic Resonance Imaging. QMRI aims at the absolute measurement of physical parameters such as the relaxation rates R1 and R2 and proton density PD. Data on 9 brain structures of 30 healthy subjects were used to set the model parameters. The model estimated an average 30.6±1.2% myelin in healthy white matter. Examples are shown for clinical cases were both general and local reductions of myelin can be observed.

Niranchana Manivannan¹, Bradley D. Clymer¹, Anna Bratasz^2,3, and Kimerly A. Powell^2,3
1Department Of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, United States, ²Small Animal Imaging Shared Resource, The Ohio State University, ³Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States

The goal of this research is to apply orthogonal super resolution (SR) reconstruction technique to create isotropic 3D MRI images from 2D multislice stacks of images. The evaluation of this technique was performed in ex-vivo mouse model where the results of the SR reconstruction were quantitatively and qualitatively compared to an isotropically acquired 3D image . The structural detail observed in the through-plane direction of the SR reconstructed images was comparable to that observed in the isotropically acquired 3D scans. For the first time SR algorithm is successfully used to reconstruct in-vivo 3D isotropic volume in Ultra high field MRI.

Costin Tanase¹, Tyler Lesh¹, and Cameron Carter^1
1Psychiatry and Behavioral Sciences, University of California at Davis, Sacramento, CA, United States

VBM studies have suggested the presence of reduced gray matter (GM) in relatively focal lateral and medial prefrontal and temporal cortical regions that are present at the first episode schizophrenia and which appear to become more extensive with illness progression. However there are a number of methodological concerns that impact the interpretation of studies using the VBM approach. In this work we address two of the major sources of variability, such as the accuracy of normalization of GM to a standard template and the positioning in the scanner.

Jan Scholz¹, Miriam Klein², and Heidi Johansen-Berg^1
1University of Oxford, FMRIB Centre, Oxford, United Kingdom, ²University College London, Sobell Department of Motor Neuroscience and Movement Disorders, London

Evidence for training-related gray matter changes have been reported in several studies [1,2,3]. However, changes detected with gray matter VBM can potentially be due to global or local intensity changes, lesions, morphological changes, and/or thickness changes. Here we specifically test for changes in cortical thickness over time taking advantage of the longitudinal processing stream of FreeSurfer.

Hongzhi Wang¹, Sandhitsu R. Das¹, Murat Altinay¹, John Pluta¹, Jung Wook Suh¹, caryne craige¹, Brian Avants¹, and Paul Yushkevich^1
1PICSL, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

It is often a nontrivial task to produce optimal segmentation results using existing segmentation software, especially when the user's data and manual segmentation protocol are different from those used by the software developers. We present an open source wrapper algorithm that can automatically improve segmentation accuracy of any existing segmentation software on the user's data using training data provided by the user.