Automated Assessment of Structural Changes & Functional Recovery Post Spinal Inju
Principal Investigator: Baba Vemuri
Affiliation: University of Florida
Abstract: DESCRIPTION (provided by applicant): Establishing structure-function correlations is fundamental to understanding how information is processed in the central nervous system (CNS). Axonal connectivity is a key relationship that facilitates information transmission and reception within the CNS. Recently, diffusion weighted magnetic resonance imaging (DW-MRI) methods have been shown to provide fundamental information required for viewing structural connectivity and have allowed visualization of fiber bundles in the CNS in vivo. In this project, we propose to develop methods for extraction and analysis of these patterns from high angular resolution diffusion weighted images (HARDI) that is known to have better resolving power over diffusion tensor imaging (DTI). To this end, a biologically relevant and clinically important model has been chosen to study changes in the organization of fibers in the intact and injured spinal cord. Our hypothesis is that, changes in geometrical properties of the anatomical substrate, identifying the region of injury and neuroplastic changes in distant spinal segments, correlate with different magnitudes of injury and levels of locomotor recovery following spinal cord injury (SCI). Prior to hypothesis testing, we will denoise the HARDI data and then construct a normal atlas cord. Deformable registration and tensor morphometry between a normal atlas and an injured cord would be performed to provide a distinct signature for each type of behavior recovery associated with the SCI substrate. Validation of the hypothesis will be performed through systematic histological analysis of cord samples following acquisition of the HARDI data. Spinal cords will be cut and stained with fiber and cell stains to verify changes in anatomical organization that result from contusive injury (common in humans as well) to the spinal cord. A comparison between anatomical characteristics obtained from histological versus HARDI analysis will provide validation for the image analysis and the hypothesis. Three severities of spinal cord injuries will be produced (light, mild and moderate contusions) based upon normed injury device parameters. The structural signatures of these labeled data subsets will then be identified. Automatic classification of novel &injured cord HARDI data sets will then be achieved using a large margin classifier. Finally, HARDI data acquired over time will be analyzed in order to learn and predict the level of locomotor recovery by studying the structural changes over time and developing a dynamic model of structural transformations corresponding to each chosen class. We will use an auto-regressive model in the feature space to track and predict structural changes in SCI and correlate it to functional recovery. PUBLIC HEALTH RELEVANCE: This project involves the development of automated methods to extract morphological signatures that characterize changes in spinal cord injury (SCI) substrate estimated from Diffusion MRI scans of rats, and predict the functional recovery by correlating to behavioral studies. Although the various algorithms developed here are for analysis of SCI, they can be used in other applications such as traumatic brain injury, in tracking and predicting developmental changes etc. from diffusion MRI scans.
Funding Period: -------------------- - --------------------
more information: NIH RePORT
- Multiple Atlas construction from a heterogeneous brain MR image collectionYuchen Xie
Department of Computer and Information Science and Engineering CISE, University of Florida, Gainesville, FL 32611, USA
IEEE Trans Med Imaging 32:628-35. 2013....
- A robust variational approach for simultaneous smoothing and estimation of DTIMeizhu Liu
Siemens Corporate Research and Technology, Princeton, NJ, 08540, USA
Neuroimage 67:33-41. 2013..We present experimental results depicting the positive performance of our method in comparison to competing methods on synthetic and real data examples...
- Shape retrieval using hierarchical total Bregman soft clusteringMeizhu Liu
Department of CISE, University of Florida, E324, CSE Building, PO Box 11612, Gainesville, FL 32611, USA
IEEE Trans Pattern Anal Mach Intell 34:2407-19. 2012..We evaluate our method on various public domain 2D and 3D databases, and demonstrate comparable or better results than state-of-the-art retrieval techniques...
- An efficient interlaced multi-shell sampling scheme for reconstruction of diffusion propagatorsWenxing Ye
Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA
IEEE Trans Med Imaging 31:1043-50. 2012..The sampling scheme and the reconstruction algorithms were evaluated on simulated data as well as rat brain data collected on a 600 MHz (14.1T) Bruker imaging spectrometer...
- Nonnegative factorization of diffusion tensor images and its applicationsYuchen Xie
Department of CISE, University of Florida, Gainesville, FL 32611, USA
Inf Process Med Imaging 22:550-61. 2011..The proposed method has been validated using both synthetic and real data, and experimental results have shown that it offers a competitive alternative to current state-of-the-arts in terms of accuracy and efficiency...
- Total Bregman divergence and its applications to DTI analysisBaba C Vemuri
Department of Computer and Information Science and Engineering CISE, The University of Florida, Gainesville, FL 32611, USA
IEEE Trans Med Imaging 30:475-83. 2011..Additionally, we derive the piecewise smooth active contour model for segmentation of DT-MRI using the TBD and present several comparative results on real data...