A key challenge in the imaging of spinal cord injury (SCI) patients is the ability to accurately determine structural or functional abnormality as well as level and severity of injury. Over the years a substantial number of studies have addressed this issue, however most of them utilized qualitative analysis of the acquired imaging data. Quantitative analysis of patients with SCI is an important issue in both diagnostic and treatment planning. Hence in this work new multispectral magnetic resonance (MR) image based approaches were developed for high-throughput extraction of quantitative features from pediatric spinal cord MR images and subsequent analysis using decision support algorithms. This may potentially improve diagnostic, prognostic, and predictive accuracy between typically developing (TD) pediatric spinal cord subjects and patients with SCI. The technique extracts information from both axial structural MRI images (such as T2-weighted gradient echo images) and functional MRI images (such as diffusion tensor images). The extracted data contains first order statistics (diffusion tensor tractography and histogram based texture descriptors), second order (co-occurrence indices) and high order (wavelet primitives) statistics. MRI data from total of 43 subjects that includes 23 healthy TD subjects with the age range of 6-16 (11.94±3.26 (mean ±standard deviation)) who had no evidence of SCI or pathology and 20 SCI subjects with the age range of 7-16 (11.28±3.00 (mean ±standard deviation)) were recruited and scanned using 3.0T Siemens Verio MR scanner. Standard 4-channel neck matrix and 8-channel spine array RF coils were used for data collection. After data collection various post processing methods were used to improve the data quality. A novel ghost artifact suppression technique was implemented and tested. Initially, 168 quantitative measures of multi-spectral images (functional and structural) were calculated by using regions of interest (ROIs) manually drawn on the whole cord along the entire spinal cord being anatomically localized by an independent board certified neuroradiologist. These measures were then statistically compared between TD and SCI groups using standard least squared linear regression model based on restricted maximum likelihood (REML) method. Statistically, significant changes have been shown in 44 features: 30 features obtained from functional images and 14 features selected from structural images. Also, it has been shown that the quantitative measures of the spinal cord in DTI and T2W-GRE images above and below injury level were altered significantly. Finally, tractography measures were also obtained on a subset of the patients to demonstrate quantitative analysis of the extracted white matter structures. Overall the results show that the proposed techniques may have potential to be used as surrogate biomarkers for detection of the injured spinal cord. These measures enable us to quantify the functional and structural plasticity in chronic SCI and consequently has the potential to improve our understanding of damage and recovery in diseased states of the spinal cord. / Bioengineering
| Identifer | oai:union.ndltd.org:TEMPLE/oai:scholarshare.temple.edu:20.500.12613/665 |
| Date | January 2017 |
| Creators | Alizadeh, Mahdi |
| Contributors | Pleshko, Nancy, Mohamed, Feroze B., Faro, Scott H., Flanders, Adam E. |
| Publisher | Temple University. Libraries |
| Source Sets | Temple University |
| Language | English |
| Detected Language | English |
| Type | Thesis/Dissertation, Text |
| Format | 85 pages |
| Rights | IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available., http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | http://dx.doi.org/10.34944/dspace/647, Theses and Dissertations |
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