GDCNet -- calibrationless geometric distortion correction of EPI data -- GDCNet 3D supervised (unet arch)
Description
GDCNet (Jimeno et al., 2024; arXiv:2402.18777) is a deep-learning
model that predicts a 1-channel voxel displacement map (VDM)
along the phase-encoding (PE) direction of an EPI volume from a
(source, target) pair. The VDM drives a downstream geometric
distortion correction without requiring a separate fieldmap or
reverse-PE calibration acquisition.
Architecture: a vxm-family 3D U-Net (encoder filters
16/32/32/32, decoder filters 32/32/32/32, tail convs
32/16/16; InstanceNorm-free; LeakyReLU(0.2) intra-block)
consuming a 2-channel (source, target) input at the published
64x64x32 grid. The U-Net body produces a 16-channel feature
map; a final Conv3d with kernel (1, 1, 3) collapses the
16-channel features into a 1-channel VDM along the PE
(last spatial) axis.
v0 ships three 3D variants (supervised + semi-supervised +
self-supervised). The architecture is parameter-identical
across variants (~326k trainable scalars); only the optional
spatial-transform step and the training corpus differ.
Intended use
Supervised arch -- forward returns the predicted VDM only. Use when downstream pipelines accept a raw displacement field as input to TopUp / applywarp. Input grid: (1, 64, 64, 32). Output: (1, 64, 64, 32) VDM along the channel-0 (PE) direction of a (vdm, 0, 0) flow.
Usage
1from ilex.models.gdc_net import GDCNet
2model = GDCNet.from_pretrained('ilex-hub/gdc_net.3d-supervised.1')
Authors
Manso Jimeno M., Vaughan J. T., Geethanath S. (Columbia University, Department of Biomedical Engineering)
Citation
Manso Jimeno M., Vaughan J. T., Geethanath S. (2024). GDCNet -- Calibrationless geometric distortion correction of echo planar imaging data using deep learning. arXiv:2402.18777. Zenodo deposit: doi:10.5281/zenodo.10257231.
References
- Manso Jimeno M., Vaughan J. T., Geethanath S. (2024). GDCNet -- Calibrationless geometric distortion correction of echo planar imaging data using deep learning. arXiv:2402.18777.
- Manso Jimeno M. (2023). GDCNet -- Calibrationless geometric distortion correction of echo planar imaging data using deep learning. Zenodo deposit. doi 10.5281/zenodo.10257231.
- Upstream code (no LICENSE file in repo) -- github.com/imr-framework/gdcnet.
License
HF Hub license tag: cc-by-4.0
Effective terms: CC-BY-4.0 (Creative Commons Attribution 4.0). The trained .h5 weights are deposited on Zenodo (zenodo.org/records/10257231) under CC-BY-4.0. The upstream code (github.com/imr-framework/gdcnet) has no LICENSE file; the ilex JAX / Equinox port code is separately licensed under Apache-2.0 / GPL-3.0. Redistribution requires attribution to Manso Jimeno, Vaughan, and Geethanath (2024).
Copyright
Network architecture (github.com/imr-framework/gdcnet) is
unlicensed in the upstream repository -- the trained .h5 weights
deposited on Zenodo (zenodo.org/records/10257231, DOI
10.5281/zenodo.10257231) are CC-BY-4.0 (Creative Commons
Attribution 4.0). The ilex JAX / Equinox port code is separately
licensed under Apache-2.0 / GPL-3.0; the CC-BY terms on the
upstream weights are preserved through the canonical bundle's
_ilex.origin = 'tf' provenance.
Upstream source
Original weights / reference implementation:
https://github.com/imr-framework/gdcnetProvenance
This artefact was produced by
ilex's
save/load pipeline. The architecture is implemented in
ilex.models.gdc_net.GDCNet and the weights have been converted
from their upstream format. See the upstream source above
for the canonical reference.
