Mitigating transmit-B1 artifacts by predicting parallel transmission images with deep learning: A feasibility study using high-resolution whole-brain diffusion at 7 Tesla

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Purpose: To propose a novel deep learning (DL) approach to transmit-B1 (B1+)-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.

Original languageEnglish (US)
Pages (from-to)727-741
Number of pages15
JournalMagnetic resonance in medicine
Issue number2
StatePublished - Aug 2022

Bibliographical note

Funding Information:
We thank John Strupp, Brian Hanna, and Jerahmie Radder for their assistance in setting up computation resources. This work was supported in part by National Institutes of Health (NIH) grants U01 EB025144, P41 EB015894, and P30 NS076408.

Publisher Copyright:
© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.


  • Human Connectome Project
  • deep learning
  • diffusion MRI
  • parallel transmission
  • ultrahigh field MRI
  • Artifacts
  • Brain/diagnostic imaging
  • Humans
  • Image Processing, Computer-Assisted/methods
  • Deep Learning
  • Diffusion Tensor Imaging
  • Feasibility Studies

Center for Magnetic Resonance Research (CMRR) tags

  • BFC
  • IRP
  • P41

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural


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