Abstract

The posterior visual pathway is commonly affected by multiple sclerosis (MS) pathology that results in measurable clinical and electrophysiological impairment. Due to its highly structured retinotopic mapping, the visual pathway represents an ideal substrate for investigating patho-mechanisms in MS. Therefore, a reliable and robust imaging segmentation method for in-vivo delineation of the optic radiations (OR) is needed. However, diffusion-based tractography approaches, which are typically used for OR segmentation are confounded by the presence of focal white matter lesions. Current solutions require complex acquisition paradigms and demand expert image analysis, limiting application in both clinical trials and clinical practice. In the current study, using data acquired in a clinical setting on a 3T scanner, we optimised and compared two approaches for optic radiation (OR) reconstruction: individual probabilistic tractography-based and template-based methods. OR segmentation results were applied to subjects with MS and volumetric and diffusivity parameters were compared between OR segmentation techniques. Despite differences in reconstructed OR volumes, both OR lesion volume and OR diffusivity measurements in MS subjects were highly comparable using optimised probabilistic tractography-based, and template-based, methods. The choice of OR reconstruction technique should be determined primarily by the research question and the nature of the available dataset. Template-based approaches are particularly suited to the semi-automated analysis of large image datasets and have utility even in the absence of dMRI acquisitions. Individual tractography methods, while more complex than template based OR reconstruction, permit measurement of diffusivity changes along fibre bundles that are affected by specific MS lesions or other focal pathologies.

Highlights

  • Precise localisation of focal pathology to function-specific regions within the central nervous system (CNS) is a critical step toward defining biomarkers of MS disease activity and progression [1,2].The visual pathways constitute an ideal model for probing patho-mechanisms of multiple sclerosis (MS) in vivo since structural and functional properties of the visual system, which is frequently affected by MS pathology, can be assessed by a number of objective, quantitative techniques [3,4]

  • The binarised template-optic radiation (OR) was derived from the probability weighted OR template, optimised using a threshold of 0.32, which produced a maximum average Dice Similarity Coefficient (DSC) (0.72) with individual probabilistic tractography (PT)-OR in HCs (S1 File)

  • We investigated the association between OR volume and conventional measures of disease burden in MS, including normalised brain volume (NBV) and total brain lesion volume

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Summary

Introduction

Precise localisation of focal pathology to function-specific regions within the central nervous system (CNS) is a critical step toward defining biomarkers of MS disease activity and progression [1,2].The visual pathways constitute an ideal model for probing patho-mechanisms of multiple sclerosis (MS) in vivo since structural and functional properties of the visual system, which is frequently affected by MS pathology, can be assessed by a number of objective, quantitative techniques [3,4]. The retina, optic nerves, optic chiasm, optic tract, and visual cortex can be relatively differentiated from surrounding structures, segmentation of the lateral geniculate nuclei (LGN) and optic radiation (OR) is challenging. While susceptibility-weighted imaging (SWI) [10] and phase difference enhanced imaging (PADRE) [11] techniques, which exploit subtle magnetic heterogeneity between the OR and its surrounding white matter fibre structures, more reliably delineate the tract, quantitative techniques based on visualisation of tissue contrast using these sequences have not been validated in MS, where both focal (lesional) pathology and reduced fibre density ( in Meyer’s loop and subcortical regions) may directly impact segmentation quality

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