fix: replace absolute intra-package imports with relative imports

docs/cli_scripts/fit_statistical_model_to_patient.rst

@@ -16,7 +16,7 @@ The registration pipeline consists of four stages:
 
 1. **ICP Alignment**: Rigid/affine alignment using surface matching
 2. **PCA Registration** (optional): Statistical shape model fitting
-3. **Mask-to-Mask Registration**: Deformable registration using distance maps
+3. **Mask-to-Mask Registration**: Greedy affine + ICON deformable registration using distance maps
 4. **Mask-to-Image Refinement** (optional): Final intensity-based refinement
 
 Installation
@@ -120,7 +120,9 @@ Registration Configuration
    ``--template-labelmap`` and template label IDs. Disabled by default.
 
 ``--use-ICON-refinement``
-   Enable ICON deep learning registration refinement (default: disabled)
+   Enable ICON deep learning refinement in the mask-to-image stage (Stage 4).
+   The mask-to-mask stage always uses Greedy affine + ICON deformable.
+   Default: disabled
 
 Output Options
 --------------

experiments/Heart-Create_Statistical_Model/3-registration_based_correspondence.py

@@ -2,19 +2,19 @@
 # %% [markdown]
 # # Registration-Based Correspondence
 #
-# This notebook aligns ICP-aligned models from step 2 to the average surface using **ANTs SyN (Symmetric Normalization)** deformable registration via mask-based registration.
+# This notebook aligns ICP-aligned models from step 2 to the average surface using **Greedy affine + ICON deformable** registration via mask-based registration.
 #
 # **Workflow:**
 # 1. Load ICP-aligned models from `kcl-heart-model/surfaces_aligned/`
 # 2. Load average surface (`average_surface.vtp`)
-# 3. Use `RegisterModelsDistanceMaps` to perform ANTs SyN deformable registration
+# 3. Use `RegisterModelsDistanceMaps` to perform Greedy affine + ICON deformable registration
 # 4. Save corresponded models to `kcl-heart-model/surfaces_aligned_corresponded/`
 # 5. Visualize before/after comparisons
 # 6. Analyze deformation magnitude and registration statistics
 #
 # **Method:**
-# - **ANTs SyN** provides diffeomorphic (smooth, invertible) deformation fields
-# - Progressive registration stages: rigid → affine → SyN deformable
+# - **Greedy** performs fast CPU-based affine pre-alignment
+# - **ICON** provides deep learning deformable registration on the affine-pre-aligned masks
 # - Mask-based approach focuses registration on the anatomical structures
 
 # %%
@@ -110,11 +110,9 @@
         roi_dilation_mm=20.0,  # Dilation for ROI mask
     )
 
-    # Perform ANTs SyN deformable registration
-    # This performs progressive multi-stage registration: rigid → affine → SyN deformable
+    # Perform Greedy affine + ICON deformable registration
     result = registrar.register(
-        transform_type="Deformable",  # Uses ANTs SyN (Symmetric Normalization)
-        use_ICON=False,  # Set to True for additional ICON deep learning refinement
+        transform_type="Deformable",
     )
 
     forward_transform = result["forward_transform"]
@@ -203,7 +201,7 @@
     plotter.show_axes()
     plotter.camera_position = "iso"
 
-    # Right: After distance map registration (ICP + ANTs SyN)
+    # Right: After distance map registration (Greedy affine + ICON deformable)
     plotter.subplot(0, 1)
     plotter.add_mesh(
         fixed_model, color="lightblue", opacity=1.0, label="Average Surface"
@@ -212,7 +210,7 @@
         after_mesh, color="green", opacity=1.0, label=f"Case {case_id} (Corresponded)"
     )
     plotter.add_text(
-        f"After Distance Map Registration (ANTs SyN)\nCase {case_id}",
+        f"After Distance Map Registration (Greedy + ICON)\nCase {case_id}",
         position="upper_left",
         font_size=10,
     )
@@ -329,18 +327,16 @@
 # %% [markdown]
 # ## Summary
 #
-# This notebook performed mask-based deformable registration using **ANTs SyN (Symmetric Normalization)** to establish correspondence between the ICP-aligned models and the average surface.
+# This notebook performed mask-based deformable registration using **Greedy affine + ICON deformable** to establish correspondence between the ICP-aligned models and the average surface.
 #
 # **Next Steps:**
 # - Proceed to step 4: `4-surfaces_aligned_correspond_to_pca_inputs.ipynb` to prepare data for PCA analysis
 # - The corresponded models in `kcl-heart-model/surfaces_aligned_corresponded/` now have improved point-to-point correspondence
 # - The registration statistics show the deformation applied to each model
 #
 # **Registration Details:**
-# - The `RegisterModelsDistanceMaps` class uses **ANTs SyN** for progressive registration:
-#   1. Rigid alignment
-#   2. Affine transformation
-#   3. SyN deformable registration (diffeomorphic)
-# - Setting `use_ICON=True` in the `register()` call would add ICON deep learning refinement after SyN
+# - The `RegisterModelsDistanceMaps` class uses a two-stage pipeline:
+#   1. **Greedy affine** registration (fast CPU-based alignment)
+#   2. **ICON deformable** registration on the affine-pre-aligned masks (deep learning)
 # - The `roi_dilation_mm` parameter controls the dilation of the ROI mask (default 20mm)
-# - SyN registration provides smooth, invertible deformation fields for anatomical correspondence
+# - Composed Greedy + ICON transforms provide smooth, invertible deformation fields for anatomical correspondence

experiments/Heart-Create_Statistical_Model/README.md

@@ -51,9 +51,9 @@ This experiment follows a fully automated multi-step process. Each step is a
    - Prepares aligned data for correspondence computation
 
 3. **`3-registration_based_correspondence.py`**
-   - Establishes point correspondences across the population using ANTs SyN deformable registration
+   - Establishes point correspondences across the population using Greedy affine + ICON deformable registration
    - Uses mask-based distance map registration via `RegisterModelsDistanceMaps`
-   - Performs diffeomorphic (smooth, invertible) deformation to the average surface
+   - Greedy affine pre-aligns masks; ICON deep learning refines with a deformable field
    - Critical step for meaningful PCA analysis
 
 4. **`4-surfaces_aligned_correspond_to_pca_inputs.py`**
@@ -75,15 +75,15 @@ This experiment uses a fully automated approach combining:
 
 Instead of traditional mesh parameterization methods (e.g., SPHARM-PDM), this pipeline uses **deformable image registration** to establish correspondences:
 
-- **ANTs SyN (Symmetric Normalization)** performs diffeomorphic registration
+- **Greedy affine** (PICSL Greedy) performs fast CPU-based affine pre-alignment
+- **ICON deformable** applies deep learning registration on the affine-pre-aligned masks
 - Distance maps from surface meshes create continuous fields for registration
-- Progressive registration stages: rigid → affine → SyN deformable
 - Mask-based approach focuses registration on anatomical structures
 
 **Advantages:**
 - Fully automated (no manual parameter tuning)
 - Handles complex topologies naturally
-- Diffeomorphic guarantees smooth, invertible deformations
+- Composed Greedy + ICON transforms provide smooth, invertible deformation fields
 - Integrates seamlessly with medical imaging pipelines
 
 ### PCA Computation
@@ -108,12 +108,12 @@ cd experiments/Heart-Create_Statistical_Model/
 # in VS Code or Cursor via the `# %%` cell markers):
 python 1-input_meshes_to_input_surfaces.py     # Extract surfaces from volumetric meshes
 python 2-input_surfaces_to_surfaces_aligned.py # Rigid ICP alignment + compute average
-python 3-registration_based_correspondence.py  # ANTs SyN deformable correspondence
+python 3-registration_based_correspondence.py  # Greedy affine + ICON deformable correspondence
 python 4-surfaces_aligned_correspond_to_pca_inputs.py  # Prepare PCA input matrices
 python 5-compute_pca_model.py                  # Compute PCA and export JSON model
 ```
 
-**Total Runtime:** Approximately 2-4 hours depending on hardware (20 heart meshes, ANTs registration is computationally intensive).
+**Total Runtime:** Approximately 1-3 hours depending on hardware (20 heart meshes; Greedy affine is fast on CPU, ICON requires a GPU for reasonable speed).
 
 ## Outputs
 
@@ -167,7 +167,7 @@ registered_mesh = workflow.run_workflow()
 - VS Code or Cursor with the Python extension for cell-by-cell execution
   (optional; scripts also run end-to-end as plain Python)
 - ITK, VTK, PyVista (included with PhysioMotion4D)
-- ANTs (Advanced Normalization Tools) - installed automatically with PhysioMotion4D
+- picsl-greedy and ICON (included with PhysioMotion4D)
 - scikit-learn for PCA computation
 
 ### Data
@@ -176,19 +176,19 @@ registered_mesh = workflow.run_workflow()
 - ~2GB for final outputs
 
 ### Compute
-- CPU: Multi-core processor (8+ cores recommended for ANTs registration)
+- CPU: Multi-core processor (4+ cores recommended for Greedy affine registration)
 - RAM: 16GB minimum (32GB recommended)
-- GPU: Not required for this experiment
-- Time: ~2-4 hours total (ANTs deformable registration is computationally intensive)
+- GPU: Recommended for ICON deformable registration (CUDA-capable GPU)
+- Time: ~1-3 hours total (Greedy is fast; ICON speed depends on GPU availability)
 
 ## Citation
 
 If you use this experiment or the KCL dataset, please cite:
 
 > Rodero et al. (2021), "Linking statistical shape models and simulated function in the healthy adult human heart". *PLOS Computational Biology*. DOI: [10.1371/journal.pcbi.1008851](https://doi.org/10.1371/journal.pcbi.1008851)
 
-For ANTs registration:
-> Avants BB, et al. (2011). "A reproducible evaluation of ANTs similarity metric performance in brain image registration". *NeuroImage*. DOI: [10.1016/j.neuroimage.2010.09.025](https://doi.org/10.1016/j.neuroimage.2010.09.025)
+For ICON registration:
+> Greer et al. (2021). "ICON: Learning Regular Maps Through Inverse Consistency". *ICCV*. DOI: [10.1109/ICCV48922.2021.00129](https://doi.org/10.1109/ICCV48922.2021.00129)
 
 ## Related Experiments
 
@@ -199,7 +199,7 @@ For ANTs registration:
 ## Support and Resources
 
 - **KCL Dataset**: [https://zenodo.org/records/4590294](https://zenodo.org/records/4590294)
-- **ANTs Documentation**: [https://github.com/ANTsX/ANTs](https://github.com/ANTsX/ANTs)
+- **Greedy Documentation**: [https://greedy.readthedocs.io/](https://greedy.readthedocs.io/)
 - **PhysioMotion4D Documentation**: See main repository README and API documentation
 - **Issues**: Report bugs or request features on the PhysioMotion4D GitHub repository
 
@@ -210,27 +210,25 @@ For ANTs registration:
 - Check `data/KCL-Heart-Model/README.md` for download instructions
 - Verify all 20 heart mesh files (`.vtk` format) are present
 
-### ANTs Registration Taking Too Long
-- ANTs SyN registration is computationally intensive (5-15 minutes per subject)
-- Total time for 20 subjects: 2-4 hours is normal
-- Consider using a machine with more CPU cores
-- Progress is saved incrementally - can resume if interrupted
+### Registration Taking Too Long
+- Greedy affine is fast (< 1 minute per subject on CPU)
+- ICON deformable is GPU-accelerated; without a GPU it falls back to CPU and will be significantly slower
+- Total time for 20 subjects: 1-3 hours depending on GPU availability
 
 ### Memory Issues
 - Close other applications to free RAM
-- ANTs registration can use 4-8GB per process
+- ICON can use 4-8GB GPU VRAM; reduce batch size or iterations if needed
 - Process fewer meshes initially to test pipeline
-- Use a machine with more RAM (32GB+ recommended)
 
 ### Correspondence Quality Issues
 - Check alignment quality from step 2 (ICP should produce good initial alignment)
 - Verify average surface looks reasonable before step 3
-- ANTs parameters are pre-tuned for cardiac anatomy
-- If registration fails, check input mesh quality and topology
+- If Greedy affine fails, check input mesh quality and topology
+- If ICON deformable quality is poor, increase `icon_iterations` in the `register()` call
 
 ### Import Errors
 - Ensure all PhysioMotion4D dependencies are installed
-- Check that ANTs is available: `python -c "import ants; print(ants.__version__)"`
+- Check Greedy is available: `python -c "from picsl_greedy import Greedy3D; print('ok')"`
 - Reinstall environment if needed: `pip install -e .` in repository root
 
 ---

experiments/Heart-Statistical_Model_To_Patient/heart_model_to_patient.py

@@ -190,7 +190,7 @@
     # Perform deformable registration
     print("Starting deformable mask-to-mask registration...")
 
-    m2m_results = registrar.register_mask_to_mask(use_ICON_refinement=False)
+    m2m_results = registrar.register_mask_to_mask()
     m2m_inverse_transform = m2m_results["inverse_transform"]
     m2m_forward_transform = m2m_results["forward_transform"]
     m2m_model_surface = m2m_results["registered_template_model_surface"]

src/physiomotion4d/cli/convert_image_4d_to_3d.py

@@ -70,7 +70,7 @@ def main() -> int:
         print(f"Error: input image not found: {args.input_image}")
         return 1
     try:
-        from physiomotion4d import ConvertImage4DTo3D
+        from .. import ConvertImage4DTo3D
 
         converter = ConvertImage4DTo3D()
         print(f"Loading 4D image: {args.input_image}")

src/physiomotion4d/cli/convert_image_to_usd.py

@@ -110,7 +110,7 @@ def main() -> int:
     # Initialize processor
     print("Initializing Image-to-USD processor...")
     try:
-        from physiomotion4d import WorkflowConvertImageToUSD
+        from .. import WorkflowConvertImageToUSD
 
         processor = WorkflowConvertImageToUSD(
             input_filenames=args.input_files,

src/physiomotion4d/cli/convert_image_to_vtk.py

@@ -160,7 +160,7 @@ def main() -> int:
     print("=" * 70)
 
     try:
-        from physiomotion4d import WorkflowConvertImageToVTK
+        from .. import WorkflowConvertImageToVTK
 
         workflow = WorkflowConvertImageToVTK(
             segmentation_method=args.segmentation_method,

src/physiomotion4d/cli/convert_vtk_to_usd.py

@@ -11,7 +11,7 @@
 import os
 import sys
 
-from physiomotion4d.usd_anatomy_tools import DEFAULT_RENDER_PARAMS
+from ..usd_anatomy_tools import DEFAULT_RENDER_PARAMS
 
 # Anatomy types accepted by --anatomy-type, sourced from the renderer's
 # registered defaults so that new groups/organs registered in
@@ -193,7 +193,7 @@ def main() -> int:
             return 1
 
     try:
-        from physiomotion4d import WorkflowConvertVTKToUSD
+        from .. import WorkflowConvertVTKToUSD
 
         workflow = WorkflowConvertVTKToUSD(
             vtk_files=args.vtk_files,

src/physiomotion4d/cli/create_statistical_model.py

@@ -138,7 +138,7 @@ def main() -> int:
     # Run workflow
     print("\nInitializing create statistical model workflow...")
     try:
-        from physiomotion4d import WorkflowCreateStatisticalModel
+        from .. import WorkflowCreateStatisticalModel
 
         workflow = WorkflowCreateStatisticalModel(
             sample_meshes=sample_meshes,

src/physiomotion4d/cli/download_data.py

@@ -8,7 +8,7 @@
 from pathlib import Path
 from typing import Optional
 
-from physiomotion4d.data_download_tools import DataDownloadTools
+from ..data_download_tools import DataDownloadTools
 
 SLICER_HEART_CT = "Slicer-Heart-CT"
 

src/physiomotion4d/cli/fit_statistical_model_to_patient.py

@@ -223,7 +223,7 @@ def main() -> int:
     # Initialize workflow
     print("\nInitializing heart model to patient registration workflow...")
     try:
-        from physiomotion4d import WorkflowFitStatisticalModelToPatient
+        from .. import WorkflowFitStatisticalModelToPatient
 
         workflow = WorkflowFitStatisticalModelToPatient(
             template_model=template_model,

src/physiomotion4d/cli/reconstruct_highres_4d_ct.py

@@ -269,7 +269,7 @@ def main() -> int:
     # Initialize workflow
     print("\nInitializing high-resolution 4D CT reconstruction workflow...")
     try:
-        from physiomotion4d import WorkflowReconstructHighres4DCT
+        from .. import WorkflowReconstructHighres4DCT
 
         workflow = WorkflowReconstructHighres4DCT(
             time_series_images=time_series_images,

src/physiomotion4d/contour_tools.py

@@ -12,9 +12,9 @@
 import pyvista as pv
 import trimesh
 
-from physiomotion4d.image_tools import ImageTools
-from physiomotion4d.physiomotion4d_base import PhysioMotion4DBase
-from physiomotion4d.transform_tools import TransformTools
+from .image_tools import ImageTools
+from .physiomotion4d_base import PhysioMotion4DBase
+from .transform_tools import TransformTools
 
 
 class ContourTools(PhysioMotion4DBase):

src/physiomotion4d/convert_image_4d_to_3d.py

@@ -28,7 +28,7 @@
 import numpy as np
 import pydicom
 
-from physiomotion4d.physiomotion4d_base import PhysioMotion4DBase
+from .physiomotion4d_base import PhysioMotion4DBase
 
 
 class ConvertImage4DTo3D(PhysioMotion4DBase):

src/physiomotion4d/image_tools.py

@@ -13,7 +13,7 @@
 import SimpleITK as sitk
 from numpy.typing import NDArray
 
-from physiomotion4d.physiomotion4d_base import PhysioMotion4DBase
+from .physiomotion4d_base import PhysioMotion4DBase
 
 
 class ImageTools(PhysioMotion4DBase):

src/physiomotion4d/labelmap_tools.py

@@ -13,7 +13,7 @@
 import itk
 import numpy as np
 
-from physiomotion4d.physiomotion4d_base import PhysioMotion4DBase
+from .physiomotion4d_base import PhysioMotion4DBase
 
 
 class LabelmapTools(PhysioMotion4DBase):

src/physiomotion4d/landmark_tools.py

@@ -14,7 +14,7 @@
 import logging
 from pathlib import Path
 
-from physiomotion4d.physiomotion4d_base import PhysioMotion4DBase
+from .physiomotion4d_base import PhysioMotion4DBase
 
 LandmarkDict = dict[str, tuple[float, float, float]]
 

src/physiomotion4d/register_images_ants.py

@@ -18,8 +18,8 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from physiomotion4d.register_images_base import RegisterImagesBase
-from physiomotion4d.transform_tools import TransformTools
+from .register_images_base import RegisterImagesBase
+from .transform_tools import TransformTools
 
 
 class RegisterImagesANTS(RegisterImagesBase):
@@ -296,7 +296,7 @@ def _antsfile_to_itk_displacement_field_transform(
 
         # Convert to the correct Image[Vector[D, 3], 3] type for DisplacementFieldTransform
         # Use ImageTools helper to convert array to vector image with correct type
-        from physiomotion4d.image_tools import ImageTools
+        from .image_tools import ImageTools
 
         image_tools = ImageTools()
 

src/physiomotion4d/register_images_base.py

@@ -20,9 +20,9 @@
 
 import itk
 
-from physiomotion4d.labelmap_tools import LabelmapTools
-from physiomotion4d.physiomotion4d_base import PhysioMotion4DBase
-from physiomotion4d.transform_tools import TransformTools
+from .labelmap_tools import LabelmapTools
+from .physiomotion4d_base import PhysioMotion4DBase
+from .transform_tools import TransformTools
 
 
 class RegisterImagesBase(PhysioMotion4DBase):