Module chisepnet 

χ-sepnet deep-learning χ-separation (onnx feature).

χ-sepnet (SNU-LIST) is a 3D U-Net that maps a 3-channel patch — [QSM (χ_total, ppm), local field (ppm), R2′/Dr] each z-scored by training statistics — to paramagnetic (χ+) and diamagnetic (χ−) source magnitudes (z-scored). The network is a fixed 192×192×128 patch; we run it as an overlapping sliding window over the whole volume and average the overlaps, then de-normalize.

Recipe (mirrors the SNU-LIST / QSM-CI recon.py): z-score each channel, zero outside the mask, end-pad each dim up to the patch size, tile with a 0.75 stride plus a final flush patch, average overlaps, de-normalize, crop, and mask. Dr (114 Hz/ppm, the network’s COSMOS-referenced relaxivity) scales R2′ into the network’s ppm-equivalent input channel. We return χ− as a signed (≤ 0) value to match the crate’s separation convention (chi_pos ≥ 0, chi_neg ≤ 0, chi_total).

Weights are not bundled; the caller passes the exported chi-sepnet.onnx bytes.

Structs

ChiSepNetNorm

Training z-score constants for χ-sepnet (xsepnet_train_patch_norm_factor…mat): each field is (mean, std). Inputs are normalized (x-mean)/std; outputs de-normalized y*std + mean. dr scales R2′ (Hz) into the ppm-equivalent channel (r2prime/dr - mean)/std.

Constants

PD 🔒

PH 🔒

PW 🔒

Functions

chisepnet

Run χ-sepnet χ-separation.