Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system

Abstract

Purpose: The accuracy of deformable image registration tools can vary widely between imaging modalities and specific implementations of the same algorithms. A biomechanical model-based algorithm initially developed in-house at an academic institution was translated into a commercial radiotherapy treatment planning system and validated for multiple imaging modalities and anatomic sites.

Methods: Biomechanical deformable registration (Morfeus) is a geometry-driven algorithm based on the finite element method. Boundary conditions are derived from the model-based segmentation of controlling structures in each image which establishes a point-to-point surface correspondence. For each controlling structure, material properties and fixed or sliding interfaces are assigned. The displacements of internal volumes for controlling structures and other structures implicitly deformed are solved with finite element analysis. Registration was performed for 74 patients with images (mean vector resolution) of thoracic and abdominal 4DCT (2.8 mm) and MR (5.3 mm), liver CT-MR (4.5 mm), and prostate MR (2.6 mm). Accuracy was quantified between deformed and actual target images using distance-to-agreement (DTA) for structure surfaces and the target registration error (TRE) for internal point landmarks.

Results: The results of the commercial implementation were as follows. The mean DTA was ≤ 1.0 mm for controlling structures and 1.0-3.5 mm for implicitly deformed structures on average. TRE ranged from 2.0 mm on prostate MR to 5.1 mm on lung MR on average, within 0.1 mm or lower than the image voxel sizes. Accuracy was not overly sensitive to changes in the material properties or variability in structure segmentations, as changing these inputs affected DTA and TRE by ≤ 0.8 mm. Maximum DTA > 5 mm occurred for 88% of the structures evaluated although these were within the inherent segmentation uncertainty for 82% of structures. Differences in accuracy between the commercial and in-house research implementations were ≤ 0.5 mm for mean DTA and ≤ 0.7 mm for mean TRE.

Conclusions: Accuracy of biomechanical deformable registration evaluated on a large cohort of images in the thorax, abdomen and prostate was similar to the image voxel resolution on average across multiple modalities. Validation of this treatment planning system implementation supports biomechanical deformable registration as a versatile clinical tool to enable accurate target delineation at planning and treatment adaptation.

Keywords: biomechanical models; deformable image registration; multimodality imaging.

Figure 1

Reference (blue) and target (orange) images with the corresponding controlling ROIs used as inputs for biomechanical DIR (solid and dashed green lines for each image respectively). [Color figure can be viewed at wileyonlinelibrary.com]

Figure 2

Surface ROI accuracy (†denotes structures used as controlling ROIs). [Color figure can be viewed at wileyonlinelibrary.com]

Figure 3

Impact of three different registrations on rib alignment between inhale (blue image) and exhale (orange image) thoracic 4DCT datasets: biomechanical DIR with either fixed (left) or sliding lung interfaces (middle), or baseline rigid registration (right). [Color figure can be viewed at wileyonlinelibrary.com]

Figure 4

Internal ROI accuracy (all organs were used as controlling ROIs during DIR). [Color figure can be viewed at wileyonlinelibrary.com]

Figure 5

Example of biomechanical DIR improving prostate surface alignment (solid surface = reference, mesh = target) vs. rigid registration. Fiducial alignment is similar for both registrations (solid spheres = reference, dashed spheres = target). [Color figure can be viewed at wileyonlinelibrary.com]

Figure 6

Impact of contouring variability on accuracy metrics: (a). Manual original contours vs. manual repeat contours on inhale 4DCT; (b). Accuracy of rigid registration (manual exhale contours vs. manual inhale contours), and biomechanical DIR (manual exhale contours deformed to inhale vs. actual inhale contours) using two different sets of contours (yellow or green). DIR reduces the mean DTA for the liver and stomach to values similar to the baseline variations, irrespective of contours used for DIR. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 7

Cumulative histogram of the 3D differences in the deformation vector fields between biomechanical DIR and hybrid DIR using ROI‐only information. Representative cases for each dataset are shown. [Color figure can be viewed at wileyonlinelibrary.com]