Abstract: A method for quantifying retrograde trans-synaptic degeneration (rTSD) in a patient includes receiving optical coherence tomography (OCT) image data associated with a left retina and a right retina of the patient. The method further includes analyzing the OCT image data to determine a thickness of one or more retinal layers of the left retina and one or more retinal layers of the right retina. The method further includes, based at least in part on the determined thicknesses, determining a value of an rTSD index that is indicative of a level of rTSD in the patient. The rTSD index can be based at least in part on the thicknesses of retinal layers connected to a left optic radiation of the patient versus the thicknesses of retinal layers connected to a right optic radiation of the patient.

Abstract: A method for quantifying retrograde trans-synaptic degeneration (rTSD) in a patient includes receiving optical coherence tomography (OCT) image data associated with a left retina and a right retina of the patient. The method further includes analyzing the OCT image data to determine a thickness of one or more retinal layers of the left retina and one or more retinal layers of the right retina. The method further includes, based at least in part on the determined thicknesses, determining a value of an rTSD index that is indicative of a level of rTSD in the patient. The rTSD index can be based at least in part on the thicknesses of retinal layers connected to a left optic radiation of the patient versus the thicknesses of retinal layers connected to a right optic radiation of the patient.

Abstract: A computer-implemented method for automatically identifying subjects at risk of Multiple Sclerosis (MS) includes acquiring a plurality of images of a subject's brain using a Magnetic Resonance Imaging (MRI) scanner. A contrast enhancement process is applied to each image to generate a plurality of contrast-enhanced images. An automated lesion detection algorithm is applied to detect one or more lesions present in the contrast-enhanced images. An automated central vein detection algorithm is applied to detect one or more central veins present in the contrast-enhanced images. An automated paramagnetic rim detection algorithm is applied to detect one or more paramagnetic rims present in the contrast-enhanced images. The patient's risk for MS may then be determined based on the one or more of the lesions, central veins, and paramagnetic rims present in the contrast-enhanced images.

Abstract: A computer-implemented method for automatically identifying subjects at risk of Multiple Sclerosis (MS) includes acquiring a plurality of images of a subject's brain using a Magnetic Resonance Imaging (MRI) scanner. A contrast enhancement process is applied to each image to generate a plurality of contrast-enhanced images. An automated lesion detection algorithm is applied to detect one or more lesions present in the contrast-enhanced images. An automated central vein detection algorithm is applied to detect one or more central veins present in the contrast-enhanced images. An automated paramagnetic rim detection algorithm is applied to detect one or more paramagnetic rims present in the contrast-enhanced images. The patient's risk for MS may then be determined based on the one or more of the lesions, central veins, and paramagnetic rims present in the contrast-enhanced images.