DETECTING CEREBRAL DEGENERATION IN ALS USING A MULTICENTRE APPLICATION OF 3D TEXTURE ANALYSIS

• Author / Creator

  Chunn, Michael

• Amyotrophic lateral sclerosis (ALS) is a highly heterogeneous disease in terms of its clinical presentation, progression, and detected pathology in the body. It is a multi-system degenerative disorder, though a diagnosis is made based on the presence of both upper motor neuron (UMN) and lower motor neuron (LMN) degeneration. However, the disease rests on a spectrum of both motor and cognitive systems degeneration. ALS is fatal with a typical course of 2-5 years. The diagnostic process is long and complicated, often taking about one year from symptom onset. For these reasons, ALS needs a biomarker – an objective measure of disease presence and progression. This would simplify diagnosis and allow treatment and drug trials to be implemented earlier in the disease process. Studies have explored neuroimaging as a source of potential biomarkers. Techniques such as voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) have been previously used to study cerebral degeneration but have yet to be refined as highly sensitive and specific diagnostic tools. A more novel tool called texture analysis (TA) has recently been applied to various diseases as a means to examine pathology in vivo. Furthermore, TA has been applied in two previous studies of ALS, finding high sensitivity and specificity in differentiating patients from controls. TA examines both grey matter and white matter in the brain simultaneously by quantifying the relationships between grey level intensities in neighbouring voxels of a 3D magnetic resonance image. It does so using T1-weighted images – often acquired as part of the diagnostic process. The present study aims to examine cerebral degeneration in ALS patients as detected by TA in a multicentre dataset, and how degeneration correlates with clinical signs of UMN degeneration. Furthermore, this study aims to test the reliability of TA both within and between sites of acquisition to further support the future implementation of TA as a clinical biomarker.

• Subjects / Keywords

  • MRI
  • amyotrophic lateral sclerosis
  • neuroimaging
  • multicentre
  • texture analysis
  • ALS

• Graduation date

  Fall 2018

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3C24R40C

• License

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