GDCNet: Calibrationless geometric distortion correction of echo planar imaging data using deep learning
<|reference_start|>GDCNet: Calibrationless geometric distortion correction of echo planar imaging data using deep learning: Functional magnetic resonance imaging techniques benefit from echo-planar imaging's fast image acquisition but are susceptible to inhomogeneities in the main magnetic field, resulting in geometric distortion and signal loss artifacts in the images. Traditional methods leverage a field map or voxel displacement map for distortion correction. However, voxel displacement map estimation requires additional sequence acquisitions, and the accuracy of the estimation influences correction performance. This work implements a novel approach called GDCNet, which estimates a geometric distortion map by non-linear registration to T1-weighted anatomical images and applies it for distortion correction. GDCNet demonstrated fast distortion correction of functional images in retrospectively and prospectively acquired datasets. Among the compared models, the 2D self-supervised configuration resulted in a statistically significant improvement to normalized mutual information between distortion-corrected functional and T1-weighted images compared to the benchmark methods FUGUE and TOPUP. Furthermore, GDCNet models achieved processing speeds 14 times faster than TOPUP in the prospective dataset.<|reference_end|>
@article{jimeno2024gdcnet:,
    title={GDCNet: Calibrationless geometric distortion correction of echo planar
  imaging data using deep learning},
    author={Marina Manso Jimeno, Keren Bachi, George Gardner, Yasmin L. Hurd, John
  Thomas Vaughan Jr., Sairam Geethanath},
    journal={arXiv preprint arXiv:2402.18777},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18777},
    primaryClass={eess.IV cs.CV}
}