| --- |
| 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 |
| |
| ``` |
| <subfolder>/static_<version>_<case>.pt # one per case -- geometry + static fields |
| <subfolder>/wss_<version>_<case>_<t>.pt # one per (case, frame) -- [N,3] WSS target, Pa |
| <subfolder>/extraction_manifest.csv # per-case QC row, one file per subfolder |
| <subfolder>/sanity/*.png # pred/GT render samples (training-time renders, not part of the data itself) |
| ``` |
| |
| `static_<version>_<case>.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_<version>_<case>_<t>.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="<path to this unzipped folder>", |
| 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. |
|
|
