--- pretty_name: AngioFlow GHD WSS Graphs tags: - graph - mesh - cfd - wall-shear-stress - aneurysm - medical --- # AngioFlow GHD WSS Graphs Per-case graphs of CFD wall shear stress (WSS) on GHD-parameterized cerebral aneurysm surfaces, for training a graph transformer (`AngioWSSGraphGPS`) to predict WSS time series from geometry alone. One graph per `(source, case, frame)`; node count varies case to case (no shared/fixed topology, no common mesh). This archive (`ghd_graphs.zip`) is a full unzip-in-place copy of the shared local dataset root `angioflow_ghd_graphs/` — unzip it and the three subfolders below land next to each other exactly as shown. ## Contents | subfolder | internal tag | cases | geometry source | CFD source | real vs synthetic | |---|---|---|---|---|---| | `AneuGv1/` | `v1` | 634 | legacy canonical GHD reconstruction, ICP'd onto the CFD wall | AngioFlow v1 transient CFD | real patient | | `Angioflow/` | `v2` | 251 (152 `AneuX` + 99 `ImperialNHS`, see `extraction_manifest.csv`'s `dataset` column) | per-case GHD fit placed into the CFD frame | merged v2 CFD | real patient (both sub-cohorts) | | `AneuGv2/` | `aneugv2` | 141 | AneuG-*generated* shapes (`dome.obj`), their own CFD runs | `whding123/AneuGv2` on the Hub | synthetic | Every case currently has **80 WSS frames** (one full cardiac-cycle time series) and **`aneurysm_type == 0`** only (bifurcated/sidewall type-1 CFD data doesn't exist yet upstream — the extraction scripts skip and delete any type-1 output). Total: 1,026 cases x 80 frames = 82,080 `(source, case, frame)` samples, ~18 GB uncompressed. ## Per-case files ``` /static__.pt # one per case -- geometry + static fields /wss___.pt # one per (case, frame) -- [N,3] WSS target, Pa /extraction_manifest.csv # per-case QC row, one file per subfolder /sanity/*.png # pred/GT render samples (training-time renders, not part of the data itself) ``` `static__.pt` is a dict: | field | shape | notes | |---|---|---| | `pos` | `[N,3]` | GHD-surface vertex coordinates, metres | | `faces` | `[F,3]` | triangle connectivity | | `edge_index` | `[2,E]` | **not guaranteed symmetric as stored** — symmetrize before use (see Loading, below) | | `normals` | `[N,3]` | stored value is a QC artifact only — recompute at load time instead (see Loading) | | `geo_dist` | `[N,3]` | geodesic distance to each of the 3 openings (type 0), normalized to `[0,1]` per column | | `cot_eigval` | `[k]` (k=16) | cotangent-Laplacian eigenvalues, **ascending by value, not frequency** — for a connected mesh the smoothest mode is near the END of this array | | `cot_eigvec` | `[N,k]` | matching eigenvectors (positional encoding) | | `ghd_eigvec` | `[N,n_ghd]` (n_ghd=8) | canonical GHD basis carried onto this mesh, unit-norm per column | | `flow_split` | `[2]` | outlet flow ratios | | `aneurysm_type` | scalar | `0` for every case currently present | | `node_type` | `[N]` | **convention differs by source**: `AneuGv1`/`Angioflow` use `0=wall,1=focused,2=inlet,3=outlet_0,4=outlet_1`; `AneuGv2` uses `0=wall`, then `1..n` by that case's own `wall_zones` order (no "focused" class) | | `knn_dist_mm` | `[N]` | distance (mm) from each node to the nearest native CFD wall node — QC signal for extraction fidelity | | `meta` | dict | the same row as this case's line in `extraction_manifest.csv` | `wss___.pt` is a bare `[N,3]` tensor, Pa, one file per frame `t` (0-79). ## Loading The reference loader is `ood_fix/dataset.py::AngioWSSGraphDataset` in the `AneuG-Net` repo (not included in this archive). Key behavior to replicate if loading independently: ```python from ood_fix.dataset import AngioWSSGraphDataset ds = AngioWSSGraphDataset(root="", versions=None, # None = all three sources frames="all", # or e.g. [0] for frame-0 only aneurysm_types=(0,)) data = ds[0] # torch_geometric.data.Data: pos, normals, geo_dist, cot_eigvec, # cot_eigval, ghd_eigvec, edge_index, y (WSS), t (frame idx), # aneurysm_type, version, case ``` - **Normals are recomputed at load time** (`pytorch3d.structures.Meshes.verts_normals_packed()`), not read from the stored `normals` field — the stored value came from whichever renderer wrote it (pyvista for v1/v2, plain numpy for aneugv2) and is not uniform across sources. - **`edge_index` must be symmetrized** before use (`SymmetrizeEdges` in the reference pipeline) — the raw stored value is directed/one-sided. - Apply any positional/value normalization exactly once — a transform chain applied both inside a custom `Dataset.get()` and again by PyG's own `__getitem__` will double-apply (this bit the reference pipeline once; its `dataset.py` fix is to let `get()` return the raw `Data` and apply transforms only through PyG's wrapper). ## Train/test split (as used by the reference training script, `ood_fix/train_wss.py`) - **Test** = `n_test_aneux` (30 by default) cases drawn from `Angioflow`'s own `dataset == "AneuX"` cases — real-patient shapes, held out so evaluation never leaks onto a shape family the model trained on. - **Train** = everything else: all of `AneuGv1`, all of `AneuGv2` (synthetic, never eligible for test), all `ImperialNHS` cases in `Angioflow`, and whatever `AneuX` cases weren't drawn for test. - The eventual target metric is generalization to the held-out **AneuX** cases specifically (real patients) — `AneuGv1` is the most abundant source but the least representative of that target distribution; `Angioflow`/`AneuGv2` are more directly relevant. A `--balance_domains` flag in the reference trainer equalizes per-epoch sampling across the three source folders above (not just real-vs-synthetic) so `AneuGv1`'s raw volume (634 cases x 80 frames) doesn't dominate gradient updates. ## Known caveats - **Patient independence across sources is unverified.** Splitting by `(source, case)` keeps one case's frames together, but does not establish that different sources/cases are distinct patients — exact-geometry checks won't catch refits, re-cuts, or generated descendants of the same base shape. Worth auditing upstream patient IDs / synthetic-generation provenance before assuming no leakage across `AneuGv1`/`Angioflow`/`AneuGv2`. - **Type-1 (two-opening) aneurysms are not represented** in this archive at all — every case is `aneurysm_type == 0`. A model trained on this data assumes 3 geodesic channels (`geo_dist`'s last dim) and would need a schema change to admit type-1. - A separate **waveform file** (`waveform_yiying.txt`, the inlet flow waveform used to condition the time embedding) is **not included** in this archive — it lives outside this dataset root in the source repo (`angioflowv2_merged/_meta/waveform_yiying.txt`) and is a required input to the reference model's forward pass, not optional. ## Provenance Produced by `ood_fix/cfd_data_factory/extract_wss_dataset.py` (`AneuGv1`, `Angioflow`) and `ood_fix/cfd_data_factory/extract_ghd_graphs_aneugv2.py` (`AneuGv2`) in the `AneuG-Net` repo (private). See that repo's `ood_fix/PIPELINE.md` for the full upstream pipeline (CFD registration, GHD fitting/placement, subdivision) if you have access to it.