DTI-DeformIt: Generating ground-truth validation data for diffusion tensor image analysis tasks

Abstract

We propose DTI-DeformIt: a framework to generate realistic synthetic datasets from a smaller number of, or even one, annotated image(s). Our approach extends the DeformIt technique of Hamarneh et al. [1] to handle the deformations and noise conditions of diffusion tensor images. An implementation of our proposed framework is also provided as a free download. We further show that DTI-DeformIt generates images that, according to eigenvector distance, are no different from real images than other real images, making them suitable for machine learning and validation.