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Abstract #0235

FieldMapNet MRI: Learning-based mapping from single echo time BOLD fMRI data to fieldmaps with model-based image reconstruction

Melissa W. Haskell1, Anish Lahiri1, Jon-Fredrik Nielsen2, Jeffrey A. Fessler1, and Douglas C. Noll2
1Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States, 2Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States


Artifacts due to B0 field off-resonance result in image distortions and blurring in non-Cartesian acquisitions. FieldMapNet is a learning-based method to map from each image in a spiral-in BOLD fMRI acquisition to a corresponding B0 fieldmap at that timepoint. We train FieldMapNet in a supervised fashion using custom data acquired at three echo times to generate ground truth dynamic fieldmaps. We then perform a tailored B0 correction at each fMRI timepoint using a model-based image reconstruction (MBIR). We show improved image space RMSE using FieldMapNet vs. a separately acquired GRE fieldmap, and a reduction in distortion artifacts and blurring.

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