Ashish Raj1, Kyoko Fujimoto2, Thanh Nguyen, Susan Gauthier2
1Radiology, Weill-Cornell Medical College, New York, NY, United States; 2Neurology, Weill-Cornell Medical College, New York
T2-relaxometry, the numerical separation of differently relaxing tissue components in the brain, is a challenging problem due to ill-posedness, instability and extremely demanding requirement for SNR. The resulting myelin water fraction maps are noise, unstable and frequently diagnostically uninterpretable. Here, we completely side-step the inverse problem and instead separate the differentially relaxing components in the brain by principal components analysis (PCA), which is popular in fMRI analysis but has never before been used in T2 Relaxometry. This approach greatly improves computation speed, noise, spatial variations and definition of white matter in the brain.