Relapsing-Remitting Multiple Sclerosis RRMS is an autoimmune disease of the central nervous system. The disease attacks myelin, the protective covering of the nerves, causing inflammation and often damaging the myelin. Currently, the McDonald Criteria is used to diagnose patients based on empirical observations from MRI technology or evoked potential. The ineffectiveness and poor efficiency of the diagnostic method result in a late diagnosis. However, Diffusion Tensor Imaging (DTI) is a new technology that is the key to detecting damage to myelin sheaths in an effective manner. The experiment will compare Diffusion Tensor images of healthy adult humans, and adult humans with relapsing-remitting multiple sclerosis to achieve parameters for early diagnosis.New diagnostic criteria based on Diffusion Tensor Imaging can be created. DTI imaging looks at water diffusion throughout the brain. The diffusion of hydrogen molecules is measured by 3 eigenvectors. These 3 values represent the 3 different directions the hydrogen atoms diffuse inside the brain. The largest value of a voxel is used to determine the direction of water diffusion. If all the values are the same, flow is isotropic, but if one eigenvector is different than the flow is anisotropic. Accessible and accurate diagnosis is the first step in providing treatment to a disease. DTI could be a huge leap forward in being able to detect myelin damage in nerve cells. It can be used as a faster and more effective method in detecting demyelination, and the degree of demyelination can be used to assess the severity of the diagnosis.
What inspired you (or your team)?
In 2015 my Grandfather was diagnosed with Relapsing Remitting Multiple Sclerosis(RRMS). As a child I was able to directly witness the poor detection methods of this disease. Doctors were often confused weather the damage in my Grandfathers CC(Genu) were due to ageing or due to RRMS. My passion in innovation and the health science encouraged me to contact professeur Teklab in UFT and some undergraduate students to gather Diffusion Tensor Images in hopes of creating an early detection method than the current one. After 2 years we were able to see a difference in Fractional Antistropy and Mean Diffusion between a healthy patient, a patient with RRMS and patients with no RRMS that suffered from demylination due to aging. On August of 2019 I was able to create a proram which could calculate these values to find the level of progression of the disease through Diffusion Tensor Scans that professeur Teklab provided me with. I am currently working to make my code more accurate and more effective.