2 March 2018 Quantitative phase and texture angularity analysis of brain white matter lesions in multiple sclerosis
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Abstract
Structural changes to nerve fiber tracts are extremely common in neurological diseases such as multiple sclerosis (MS). Accurate quantification is vital. However, while nerve fiber damage is often seen as multi-focal lesions in magnetic resonance imaging (MRI), measurement through visual perception is limited. Our goal was to characterize the texture pattern of the lesions in MRI and determine how texture orientation metrics relate to lesion structure using two new methods: phase congruency and multi-resolution spatial-frequency analysis. The former aims to optimize the detection of the ‘edges and corners’ of a structure, and the latter evaluates both the radial and angular distributions of image texture associated with the various forming scales of a structure. The radial texture spectra were previously confirmed to measure the severity of nerve fiber damage, and were thus included for validation. All measures were also done in the control brain white matter for comparison. Using clinical images of MS patients, we found that both phase congruency and weighted mean phase detected invisible lesion patterns and were significantly greater in lesions, suggesting higher structure complexity, than the control tissue. Similarly, multi-angular spatial-frequency analysis detected much higher texture across the whole frequency spectrum in lesions than the control areas. Such angular complexity was consistent with findings from radial texture. Analysis of the phase and texture alignment may prove to be a useful new approach for assessing invisible changes in lesions using clinical MRI and thereby lead to improved management of patients with MS and similar disorders.
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Shalese Baxandall, Shalese Baxandall, Shrushrita Sharma, Shrushrita Sharma, Peng Zhai, Peng Zhai, Glen Pridham, Glen Pridham, Yunyan Zhang, Yunyan Zhang, } "Quantitative phase and texture angularity analysis of brain white matter lesions in multiple sclerosis", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741E (2 March 2018); doi: 10.1117/12.2293595; https://doi.org/10.1117/12.2293595
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