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Purpose: Magnetic resonance imaging (MRI) exams include multiple series withvarying contrast and redundant information. For instance, T2-FLAIR contrast isbased upon tissue T2 decay and the presence of water, also present in T2- anddiffusion-weighted contrasts. T2-FLAIR contrast can be hypothetically modeledthrough deep learning models trained with diffusion- and T2-weightedacquisitions.  Methods: Diffusion-, T2-, T2-FLAIR-, and T1-weighted brain images wereacquired in 15 individuals. A convolutional neural network was developed togenerate a T2-FLAIR image from other contrasts. Two datasets were withheld fromtraining for validation.  Results: Inputs with physical relationships to T2-FLAIR contrast mostsignificantly impacted performance. The best model yielded results similar toacquired T2-FLAIR images, with a structural similarity index of 0.909, andreproduced pathology excluded from training. Synthetic images qualitativelyexhibited lower noise and increased smoothness compared to acquired images.  Conclusion: This suggests that with optimal inputs, deep learning basedcontrast generation performs well with creating synthetic T2-FLAIR images.Feature engineering on neural network inputs, based upon the physical basis ofcontrast, impacts the generation of synthetic contrast images. A larger,prospective clinical study is needed.

Build-A-FLAIR: Synthetic T2-FLAIR Contrast Generation through Physics Informed Deep Learning