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Entropy-based analysis for diffusion anisotropy mapping of healthy and myelopathic spinal cord

By Y Hu, TH Li, KDK Luk, JL Cui and CY Wen

Abstract

The present study utilized diffusion MR imaging and fractional anisotropy (FA) mapping to delineate the microstructure of spinal cord. The concept of Shannon entropy was introduced to analyze the complex microstructure of healthy and injured spinal cords based on FA map. A total of 30 volunteers were recruited in this study with informed consent, including 13 healthy adult subjects (group A, 25. ±. 3. years), 12 healthy elderly subjects (group B, 53. ±. 7. years) and 5 cervical spondylotic myelopathy (CSM) patients (group C, 53. ±. 15. years). Diffusion MRI images of cervical spinal cord were taken using pulsed gradient spin-echo-echo-planar imaging (SE-EPI) sequence with a 3. T MR system. The region of interest was defined to cover the spinal cord in FA maps. The Shannon entropy of FA values of voxels in the cord was calculated as well as the average FA values. The significant differences were determined among three groups using one-way ANOVA and post-hoc test. As compared with adult and elderly healthy subjects, the entropy of whole spinal cord was significantly lower in CSM patients (group A: 6.07. ±. 0.18; B: 6.01. ±. 0.23; C: 5.32. ±. 0.44; p<. 0.05). Whereas there were no significant difference in FA values among groups (group A: 0.62. ±. 0.08; B: 0.64. ±. 0.09; C: 0.64. ±. 0.12). In CSM patients, there was a loss of architectural structural complexity in the cervical spinal cord tissue as noted by the lower Shannon entropy value. It indicated the potential application of entropy-based analysis for the diagnosis of the severity of chronic compressive spinal cord injuries, i.e. CSM. © 2010 Elsevier Inc.link_to_subscribed_fulltex

Topics: Diffusion tensor imaging (DTI), Fractional anisotropy (FA), Microstructure, Shannon entropy, Spinal cord, Anisotropy, Image Processing, Computer-Assisted - methods, Spinal Cord - pathology, Spinal Cord Diseases - pathology, Diffusion Tensor Imaging - methods
Publisher: 'Elsevier BV'
Year: 2011
DOI identifier: 10.1016/j.neuroimage.2010.10.018
OAI identifier: oai:hub.hku.hk:10722/135300
Provided by: HKU Scholars Hub
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