We did something that no one has ever done before: we took a two-dimensional MRI scan of a multiple sclerosis patient’s brain and looked at it in 3D, unveiling new distinct characteristics of the disease completely hidden by the conventional view.
Multiple sclerosis is the most common cause of neurological disability in young individuals, affecting over 400,000 Americans and about 2.5 million people worldwide. The disease is caused by one’s own immune system attacking neurons in your brain and spinal cord, leaving distinct white spots called lesions seen on MRI.
The catch is, similar white spots or lesions can be seen on MRI as the result of non-specific white matter disease, a process of ambiguous but harmless origin commonly seen in people with chronic migraine or as people advance in age. Though difficult, it is highly important to distinguish the two diseases, as multiple sclerosis requires aggressive treatment while non-specific white matter disease does not.
We set out to solve this problem. We took MRI scans from multiple sclerosis and nonspecific white matter disease patients, isolated over a thousand of those tiny white lesions, and printed the lesions out using a 3D printer. The results were astonishing! Through conventional, 2D MRI imaging, the lesions looked very similar and simple, merely bright white spots contrasted against the dark grey color of normal brain tissue. However, in 3D the lesions came to life, illuminating their different shapes, surface characteristics, and complexities. The data showed lesions isolated from multiple sclerosis patients were more complex with rugged surfaces and more unique, asymmetrical shapes. In contrast, nonspecific white matter lesions were more symmetric, smooth, and rounded.
These 3-dimensional lesion data may provide new biologic insights related to injury and offer another approach for determining the origin of lesion types. In the future, fully automated quantitative analyses using machine learning may also improve the understanding of more detailed configuration and surface features with greater specificity for MS as well as other disease types in hopes of one day leading to a paradigm shift in the way MS is diagnosed and evaluated.