Module susep_net 

SUSEP-Net deep-learning χ-separation (onnx feature).

SUSEP-Net (Li/Gao/Sun 2025) is a dual-branch 3D U-Net that maps three guidance maps — QSM (χ_total, ppm), R2′ (Hz), local field (ppm) — to paramagnetic (χ+) and diamagnetic (χ−) source magnitudes. Clean NCDHW ONNX export (three inputs qsm,r2prime,lfs; two outputs chi_pos,chi_neg).

Recipe (mirrors the authors’ recon.py): z-score each input by the training stats, zero outside the mask, post-pad each dim to a multiple of 8, run, de-normalize the outputs, crop, and mask. The network’s ReLU makes both outputs non-negative magnitudes; 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 susep-net.onnx bytes (see crate::models).

Structs

SusepNetNorm

Training z-score constants for SUSEP-Net (all_mean_std.mat): each field is (mean, std). Inputs are normalized (x-mean)/std; outputs de-normalized y*std + mean.

Functions

susep_net

Run SUSEP-Net χ-separation.