Mitigating transmit-B artifacts by predicting parallel transmission images with deep learning: A feasibility study using high-resolution whole-brain diffusion at 7 Tesla.

PURPOSE

To propose a novel deep learning (DL) approach to transmit-B (B )-artifact mitigation without direct use of parallel transmission (pTx), by predicting pTx images from single-channel transmission (sTx) images.

METHODS

A deep encoder-decoder convolutional neural network was constructed and trained to learn the mapping from sTx to pTx images. The feasibility was demonstrated using 7 T Human-Connectome Project (HCP)-style diffusion MRI. The training dataset comprised images acquired on 5 healthy subjects using commercial Nova RF coils. Relevant hyperparameters were tuned with a nested cross-validation, and the generalization performance evaluated using a regular cross-validation.

RESULTS

Our DL method effectively improved the image quality for sTx images by restoring the signal dropout, with quality measures (including normalized root-mean-square error, peak SNR, and structural similarity index measure) improved in most brain regions. The improved image quality was translated into improved performances for diffusion tensor imaging analysis; our method improved accuracy for fractional anisotropy and mean diffusivity estimations, reduced the angular errors of principal eigenvectors, and improved the fiber orientation delineation relative to sTx images. Moreover, the final DL model trained on data of all 5 subjects was successfully used to predict pTx images for unseen new subjects (randomly selected from the 7 T HCP database), effectively recovering the signal dropout and improving color-coded fractional anisotropy maps with largely reduced noise levels.

CONCLUSION

The proposed DL method has potential to provide images with reduced B1 artifacts in healthy subjects even when pTx resources are inaccessible on the user side.