Purpose: Magnetic resonance imaging (MRI) exams include multiple series with varying contrast and redundant information. For instance, T2-FLAIR contrast is based upon tissue T2 decay and the presence of water, also present in T2- and diffusion-weighted contrasts. T2-FLAIR contrast can be hypothetically modeled through deep learning models trained with diffusion- and T2-weighted acquisitions. Methods: Diffusion-, T2-, T2-FLAIR-, and T1-weighted brain images were acquired in 15 individuals. A convolutional neural network was developed to generate a T2-FLAIR image from other contrasts. Two datasets were withheld from training for validation. Results: Inputs with physical relationships to T2-FLAIR contrast most significantly impacted performance. The best model yielded results similar to acquired T2-FLAIR images, with a structural similarity index of 0.909, and reproduced pathology excluded from training. Synthetic images qualitatively exhibited lower noise and increased smoothness compared to acquired images. Conclusion: This suggests that with optimal inputs, deep learning based contrast generation performs well with creating synthetic T2-FLAIR images. Feature engineering on neural network inputs, based upon the physical basis of contrast, impacts the generation of synthetic contrast images. A larger, prospective clinical study is needed.
@article{arxiv.1901.04871,
title = {Build-A-FLAIR: Synthetic T2-FLAIR Contrast Generation through Physics Informed Deep Learning},
author = {Andrew S. Nencka and Andrew Klein and Kevin M. Koch and Sean D. McGarry and Peter S. LaViolette and Eric S. Paulson and Nikolai J. Mickevicius and L. Tugan Muftuler and Brad Swearingen and Michael A. McCrea},
journal= {arXiv preprint arXiv:1901.04871},
year = {2019}
}
Comments
Submitted to Magnetic Resonance in Medicine for peer review on 15 January 2019