Q-space trajectory imaging with positivity constraints: a machine learning approach

Q-space trajectory imaging (QTI) is a diffusion MRI framework which access features of the microstructure through the statistical moments of the diffusion tensor distribution. To overcome unreliable estimates obtained with standard fitting methods, a constrained estimation framework named QTI+ was recently proposed. Constrained optimization however typically requires sophisticated fitting routines which introduce a heavy computational burden. In this work we thus explore the possibility of speeding up the QTI parameter estimation, while retaining strict positivity constraints, using artificial intelligence. Results are shown on synthetic datasets as well as for healthy subjects and data from brain tumor patients.