Expand description
AutoQSM single-step deep-learning reconstruction (onnx feature).
AutoQSM (Wei 2019) is a patch V-Net that maps a total field (ppm) directly
to susceptibility (ppm) — no brain extraction, no separate background removal.
The network takes a fixed 64³ input patch and returns the central 32³. Whole
volumes are reconstructed by overlap-tiled sliding-window inference: 32³ output
patches at stride 24 (8-voxel overlap), each from a 64³ input patch with a
16-voxel context margin, edge-padded and linearly blended across the overlap.
This mirrors the authors’ util.data_predict / patch_process.
Upstream weights are Keras; QSM.rs ships a clean PyTorch re-export
(scripts/onnx-export/export_autoqsm.py) so tract runs it. The network is
all Conv3D+ReLU with no normalization, so no input scaling is applied.
Weights are not bundled; the caller passes the exported autoqsm.onnx bytes.