Contrast neutralization as a strategy to achieve generalizability in MR deep learning applications

Motivation: Provide flexibility to clinicians to fine-tune protocols/contrasts while still leveraging existing Deep-learning (DL) applications trained with limited set of MR contrasts. Goal(s): Develop task-specific contrast neutralization pre-processing step to handle multiple imaging contrasts, that are different from the contrasts in the trainset. Approach: Investigate Simple Contrast Neutralization (SCNe) approach that leverages Fourier domain filtering to neutralize contrast from objects of desired sizes, and demonstrate its impact on generalization of cervical foramina plane determination. Results: Statistically significant improvements in prediction of planes when SCNe is used on new MERGE T2* contrast with DL-model that was trained only with Ax-T2 images. Impact: Use of our Simple Contrast Neutralization (SCNe) approach as pre-processing step was effective in making DL-model trained only with Ax-T2 images robust to unseen new contrast MERGE T2* MR dataset for Spine cervical foramina (CF) plane determination.