Applying B-spline Transform to RTDose/RTStructre

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1

Hello everyone,

I am working on registering two CT images of the pelvic region, which often exhibit significant anatomical changes, especially in organs like the bladder.

I have performed a B-spline registration using the B-spline deformable registration module in 3D Slicer and obtained an output B-spline transform. My goal now is to apply this transform to the corresponding RTDOSE and RTSTRUCT files.

I have two questions:

1- How to Apply the B-spline Transform: Can I apply the non-rigid B-spline transform to the RTDose and RTStructure simply by dragging and dropping them under the transform in the Data module’s transform hierarchy?

2- Best Module for Large Deformations: Given the large deformations observed in pelvic organs (e.g., bladder filling/emptying), is the B-spline deformable registration module the most suitable choice for this task? Would you recommend other extensions like SlicerElastix or SlicerANTs for potentially better results in such scenarios?

Can you help me?

Thanks a lot

2

The easiest is to right-click the item in the transform column of the subject hierarchy tree in the Data module and select the transform you want to use.

This page in the documentation is a bit outdated (says double-click instead of right-click, and the screenshots are old), but explains the same thing.

I think it definitely makes sense to try those extensions. As far as my personal experience goes, Elastix is more advanced than the BRAINS registration that is in Slicer core. I haven’t tried ANTs yet.

3

hello cpinter

Following the suggestions in this topic, I utilized the SlicerElastix module for deformable registration.
While I have already evaluated the registration utilizing contour-based metrics (Dice Similarity Coefficient and Hausdorff Distance), a reviewer has specifically asked for the validation of the generated Deformation Vector Fields (DVFs) themselves.
Could you recommend any methods or modules within 3D Slicer to validate the physical plausibility of the DVFs or other voxel-based validation techniques to confirm the accuracy of the results?

Thanks a lot

4

I’ve never evaluated deformation fields, but I’d probably check at least

  1. Smoothness vs abrupt jumps in the vectors
  2. Maximum displacement (whether it’s higher than reasonable)

I also asked a chatbot and it gave me these others:

1. Jacobian Determinant

  • What it measures: Local volume change induced by the deformation.

  • Why it matters:

    • A Jacobian determinant of 1 means no volume change.

    • > 0 ensures the transformation is locally invertible (no folding or tearing).

    • ≤ 0 indicates non-physical behavior (folding or inversion).

  • Typical evaluation:

    • Compute the Jacobian determinant at every voxel.

    • Report statistics (mean, min, max) and visualize regions with negative or extreme values.

2. Smoothness / Regularity

  • What it measures: How continuous and differentiable the DVF is.

  • Why it matters: Abrupt changes in displacement can indicate unrealistic deformation.

  • Typical evaluation:

    • Compute the gradient norm or Laplacian of the DVF.

    • Use bending energy:
      E=∫∥∇2u(x)∥2dxE = \int \|\nabla^2 u(x)\|^2 dxE=∫∥∇2u(x)∥2dx
      where u(x)u(x)u(x) is the displacement field.

3. Inverse Consistency

  • What it measures: Whether the forward and backward transformations are consistent.

  • Why it matters: Physically plausible deformations should be invertible.

  • Typical evaluation:

    • Compute the inverse DVF and check the composition error:
      ∥Tforward(Tinverse(x))−x∥\| T_{forward}(T_{inverse}(x)) - x \|∥Tforward​(Tinverse​(x))−x∥

4. Diffeomorphism Check

  • What it measures: Whether the deformation is a diffeomorphism (smooth, invertible, no folding).

  • Why it matters: Many algorithms aim for diffeomorphic transformations for physical plausibility.

  • Typical evaluation:

    • Ensure Jacobian > 0 everywhere.

    • Check for continuity and differentiability.

5. Physical Constraints

  • What it measures: Compliance with biomechanical or anatomical constraints.

  • Examples:

    • Maximum displacement limits (e.g., bones shouldn’t move unrealistically).

    • Tissue incompressibility (e.g., for certain organs).

6. Energy-Based Metrics

  • What it measures: The cost function used during registration (elasticity, fluid-like models).

  • Why it matters: Lower energy often correlates with more plausible deformation.