ENH: FireANTs registrar, tuned heart/lung presets, Fireants-vs-Greedy

.github/scripts/build_dashboard.py

@@ -72,7 +72,7 @@ def parse_junit(xml_path: Path) -> dict:
             "skipped": skipped,
             "available": True,
         }
-    except Exception as exc:
+    except (ET.ParseError, OSError, ValueError) as exc:
         return {
             "tests": 0,
             "passed": 0,
@@ -118,7 +118,7 @@ def parse_coverage(json_path: Path) -> dict:
         if pct is not None:
             return {"percent": float(pct), "available": True}
         return {"percent": None, "available": False}
-    except Exception as exc:
+    except (OSError, ValueError, TypeError) as exc:
         return {"percent": None, "available": False, "parse_error": str(exc)}
 
 
@@ -168,9 +168,9 @@ def build_html(data: dict) -> str:
     ts = data["timestamp"]
 
     try:
-        dt = datetime.fromisoformat(ts.replace("Z", "+00:00"))
+        dt = datetime.fromisoformat(ts)
         ts_display = dt.strftime("%Y-%m-%d %H:%M UTC")
-    except Exception:
+    except ValueError:
         ts_display = ts
 
     # Determine overall badge
@@ -313,9 +313,9 @@ def build_summary_md(data: dict) -> str:
     ts = data["timestamp"]
 
     try:
-        dt = datetime.fromisoformat(ts.replace("Z", "+00:00"))
+        dt = datetime.fromisoformat(ts)
         ts_display = dt.strftime("%Y-%m-%d %H:%M UTC")
-    except Exception:
+    except ValueError:
         ts_display = ts
 
     if not junit.get("available"):

.github/workflows/ci.yml

@@ -369,6 +369,11 @@ jobs:
         $env:CL = "/std:c++20"
         python -m uv pip install -e ".[dev_cuda13]" --no-build-isolation-package torch-scatter
 
+    - name: Install FireANTs
+      # --no-deps: FireANTs pins would otherwise override the CUDA torch stack.
+      run: |
+        python -m uv pip install fireants --no-deps
+
     - name: Assert CUDA is accessible
       run: |
         python -c "

.github/workflows/nightly-health.yml

@@ -138,6 +138,8 @@ jobs:
           if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE }
           python -m uv pip install -e ".[dev_cuda13]" --no-build-isolation-package torch-scatter
           if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE }
+          python -m uv pip install fireants --no-deps
+          if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE }
 
       - name: Assert CUDA is accessible
         run: |

.github/workflows/test-slow.yml

@@ -60,6 +60,8 @@ jobs:
       # Invoke via python -m uv so uv targets the active venv interpreter.
       run: |
         python -m uv pip install -e ".[dev_cuda13]" --no-build-isolation-package torch-scatter
+        if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE }
+        python -m uv pip install fireants --no-deps
 
     - name: Assert CUDA is accessible
       run: |

.gitignore

@@ -13,6 +13,7 @@ build
 __pycache__
 _version.py
 htmlcov
+.vscode
 .vscode/*
 !.vscode/settings.json
 

.pre-commit-config.yaml

@@ -4,7 +4,7 @@
 repos:
   # Ruff - fast linter and formatter (replaces black, isort, flake8)
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.15.20
+    rev: v0.16.6
     hooks:
       # Run the linter with auto-fixes
       - id: ruff-check

.ruff.toml

@@ -4,3 +4,7 @@
 [lint]
 # Ignore UP007/UP045: Use Optional[X] instead of X | None for itk.Image compatibility
 ignore = ["UP007", "UP045"]
+
+[lint.per-file-ignores]
+# Simpleware loads this script by file name, so the module name is fixed.
+"src/monai_physio/simpleware_medical/SimplewareScript_heart_segmentation.py" = ["N999"]

data/TCIA-4DLung/convert.py

@@ -48,6 +48,7 @@ def convert_series(series_dir, root, patient_name, pct_counts):
             [DCM2NIIX, "-f", "%d", "-b", "n", "-z", "y", "-o", tmp_dir, series_dir],
             capture_output=True,
             text=True,
+            check=False,
         )
         if result.returncode != 0:
             print(f"  FAILED: {series_dir}\n{result.stderr}")

docs/conf.py

@@ -6,7 +6,7 @@
 import sys
 import tomllib
 import warnings
-from datetime import datetime
+from datetime import UTC, datetime
 from pathlib import Path
 from unittest.mock import MagicMock
 
@@ -30,7 +30,7 @@ def __getattr__(cls, name):
 
 # -- Project information -----------------------------------------------------
 project = "MONAI Physio"
-copyright = f"{datetime.now().year}, Stephen R. Aylward, NVIDIA Corporation"
+copyright = f"{datetime.now(tz=UTC).year}, Stephen R. Aylward, NVIDIA Corporation"
 author = "Stephen R. Aylward"
 _repo_root = Path(__file__).resolve().parents[1]
 _pyproject = tomllib.loads((_repo_root / "pyproject.toml").read_text(encoding="utf-8"))

docs/developer/migration_next.md

@@ -37,3 +37,35 @@ new_call(argument, required_option="value")
 
 **Automated conversion:** `<path to script or CLI>`, or `None needed`.
 ````
+
+## InferPhysicsNeMoBase, InferPhysicsNeMoMGN, InferPhysicsNeMoMLP - `model_tag` is now a constructor argument
+
+**Change:** `model_tag` moved from a class attribute to the first `__init__`
+argument of `InferPhysicsNeMoBase` and its MGN and MLP subclasses
+(defaults `"base"`, `"mgn"`, `"mlp"`). `log_level` is now the second
+positional argument. A subclass that only sets a class-level `model_tag`
+now gets the constructor default instead, because `__init__` assigns the
+instance attribute.
+
+**Why:** the checkpoint tag must match the training method that wrote the
+weights (for example `"physics_informed_motion"`). It can now be chosen per
+instance without defining a subclass.
+
+**Before**
+
+```python
+class InferPhysicsInformed(InferPhysicsNeMoMGN):
+    model_tag = "physics_informed_motion"
+
+
+method = InferPhysicsInformed("DEBUG")
+```
+
+**After**
+
+```python
+method = InferPhysicsNeMoMGN(model_tag="physics_informed_motion", log_level="DEBUG")
+```
+
+**Automated conversion:** `None needed`. Pass `log_level` by keyword and
+replace class-level `model_tag` overrides with the constructor argument.

experiments/Heart-Create_Statistical_Model/1-input_meshes_to_input_surfaces.py

@@ -46,7 +46,7 @@
         print(f"    Original mesh: {mesh.n_cells} cells, {mesh.n_points} points")
         print(f"    Surface mesh: {surface.n_cells} cells, {surface.n_points} points")
 
-    except Exception as e:
+    except Exception as e:  # noqa: BLE001 - report and go on to the next mesh
         print(f"  Error processing {vtk_file}: {e!s}")
 
 print("\n" + "=" * 50)

experiments/Heart-GatedCT-OptimizedLongitudinalRegistration/1-initial_registration.py

@@ -432,9 +432,13 @@ def crop_image_to_mask(
             output_dir = _HERE / f"results{cond}"
             detail_landmarks_file = output_dir / "registration_landmarks_init.csv"
             detail_dice_file = output_dir / "registration_dice_init.csv"
-            if subject_index == 0 and image_index == 0 and cond_index == 0:
-                if output_dir.exists():
-                    shutil.rmtree(output_dir)
+            if (
+                subject_index == 0
+                and image_index == 0
+                and cond_index == 0
+                and output_dir.exists()
+            ):
+                shutil.rmtree(output_dir)
             output_dir.mkdir(parents=True, exist_ok=True)
 
             for method_index, method_name in enumerate(methods):

experiments/Heart-GatedCT_To_USD/2-generate_segmentation.py

@@ -1,5 +1,6 @@
 # %%
 import os
+import sys
 
 import itk
 import numpy as np
@@ -36,7 +37,7 @@
             )
         except (FileNotFoundError, OSError):
             print("No image found. Aborting. Please run 1-generate_images.ipynb first.")
-            exit(1)
+            sys.exit(1)
         arr = itk.array_from_image(image)
         print(arr.shape)
         arr = np.where(arr == 0, -1000, arr)

experiments/Heart-Simpleware_Segmentation/simpleware_heart_segmentation.py

@@ -225,7 +225,7 @@
         print("Please check the error messages above for details.")
         result = None
 
-    except Exception as e:
+    except Exception as e:  # noqa: BLE001 - last-resort handler for the script
         print(f"\nUnexpected error: {e}")
         result = None
 

experiments/Heart_and_Lungs_Motion/0-heart_and_lungs_beating_heart.py

@@ -174,7 +174,7 @@ def _ensure_mgn_inference_assets(
             reference_image=reference_image,
             fitted_reference_mesh=registered_mesh_file,
         )
-        pct = int(round(stage * 100))
+        pct = round(stage * 100)
         field_path = output_dir / f"deformation_field_s{pct:03d}.mha"
         normal_path = output_dir / f"surface_normal_field_s{pct:03d}.mha"
         surface_path = output_dir / f"deformed_heart_surface_s{pct:03d}.vtp"
@@ -185,7 +185,7 @@ def _ensure_mgn_inference_assets(
         normal_files.append(normal_path)
         surface_files.append(surface_path)
         deformed_surfaces.append(result["deformed_surface"])
-        logging.info("stage %.2f -> %s", stage, field_path.name)
+        logging.getLogger(__name__).info("stage %.2f -> %s", stage, field_path.name)
 
     # Assemble the ordered per-stage surfaces into one animated 4D USD. The
     # stages loop over one RR interval, so play them back at one beat per second.

experiments/Lung-VesselsAirways/0-GenData.py

@@ -1,5 +1,5 @@
 # %%
-from datetime import datetime
+from datetime import UTC, datetime
 
 import itk
 import matplotlib.pyplot as plt
@@ -296,7 +296,7 @@ def __getitem__(self, _):
             optimizer.step()
             running_loss += loss.item()
         print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {running_loss / len(dataloader)}")
-    date = datetime.now().strftime("%Y.%m.%d")
+    date = datetime.now(tz=UTC).strftime("%Y.%m.%d")
     model_path = f"vessel_seg_resnet18_{date}.pth"
     torch.save(model, model_path)
 

experiments/Registration-Fireants_vs_Greedy/affine_diagnostic.py

@@ -0,0 +1,96 @@
+"""Affine-only diagnostic: FireANTs vs Greedy on selected heart phases.
+
+Registers each listed phase to the heart reference frame with an Affine-only
+registrar (FireANTs class defaults vs the tutorial Greedy registrar) and
+records NCC/MSE of the warped image plus the affine matrix summary.
+
+Usage:
+    py affine_diagnostic.py --out experiments/Registration-Fireants_vs_Greedy/results/affine_diagnostic.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+import itk
+import numpy as np
+import torch
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+import benchmark
+
+
+def affine_summary(transform: itk.Transform) -> dict[str, Any]:
+    """Matrix determinant, singular values and translation of an affine."""
+    matrix = itk.array_from_matrix(transform.GetMatrix())
+    singular = np.linalg.svd(matrix, compute_uv=False)
+    return {
+        "determinant": float(np.linalg.det(matrix)),
+        "singular_values": [float(v) for v in singular],
+        "translation_mm": [float(v) for v in transform.GetTranslation()],
+    }
+
+
+def main() -> None:
+    """Run the diagnostic and write the JSON."""
+    parser = argparse.ArgumentParser(description=__doc__.split("\n")[0])
+    parser.add_argument("--phases", default="3,13,15")
+    parser.add_argument("--out", type=Path, required=True)
+    args = parser.parse_args()
+    logging.basicConfig(level=logging.WARNING)
+
+    files, reference_index = benchmark.load_dataset("heart", False)
+    images = [itk.imread(str(path)) for path in files]
+    reference = images[reference_index]
+    fixed_array = itk.array_from_image(reference)
+    identity = itk.IdentityTransform[itk.D, 3].New()
+
+    result: dict[str, Any] = {"dataset": "heart", "reference_frame": reference_index}
+    rows: dict[str, list[dict[str, Any]]] = {"greedy": [], "fireants": []}
+    for method in ("greedy", "fireants"):
+        registrar = benchmark.build_registrar("heart", method, False, {})
+        registrar.set_transform_type("Affine")
+        registrar.set_fixed_image(reference)
+        registrar.register(images[0])
+        for phase in (int(p) for p in args.phases.split(",")):
+            moving = images[phase]
+            if method == "fireants":
+                torch.cuda.synchronize()
+            start = time.perf_counter()
+            out = registrar.register(moving)
+            if method == "fireants":
+                torch.cuda.synchronize()
+            seconds = time.perf_counter() - start
+            forward = out["fixed_to_moving_transform"]
+            warped = itk.array_from_image(
+                benchmark.resample(moving, forward, reference)
+            )
+            unregistered = itk.array_from_image(
+                benchmark.resample(moving, identity, reference)
+            )
+            rows[method].append(
+                {
+                    "phase": phase,
+                    "seconds": seconds,
+                    "ncc": benchmark.ncc(warped, fixed_array),
+                    "mse": benchmark.mse(warped, fixed_array),
+                    "ncc_unregistered": benchmark.ncc(unregistered, fixed_array),
+                    "mse_unregistered": benchmark.mse(unregistered, fixed_array),
+                    "affine": affine_summary(forward),
+                }
+            )
+    result["greedy"] = rows["greedy"]
+    result["fireants"] = rows["fireants"]
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.out.write_text(json.dumps(result, indent=2), encoding="utf-8")
+
+
+if __name__ == "__main__":
+    main()