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.

Practical quantification of image registration accuracy following the AAPM TG‐132 report framework

“…A survey of the literature on validation of commercial DIR algorithms reveals a number of conceptual approaches to the problem. In decreasing order of generality, they are: (a) comparison of the deformation vector field (DVF) with the ground truth one; (b) examining propagation of the large number of anatomical landmarks (points) to determine TRE; (c) investigating the overlap of the deformably propagated contours with the known segmentation results; and, finally, (d) physical phantom evaluations . It appears that comparison of the deformation vector fields should be the most comprehensive method of validating DIR.…”

Quantitative analysis of respiration-induced motion of each liver segment with helical computed tomography and 4-dimensional computed tomography

“…Deformable image registration (DIR) is the key component of the image-guided adaptive strategy in radiotherapy [ 12 ]. Nowadays, DIR is widely used not only for contouring but also for re-planning, dose mapping, and dose evaluation in many kinds of cancers throughout the body [ 13 , 14 ]. Several studies have focused on measuring respiration-induced organ motion using DIR [ 15 – 17 ].…”

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

“…With the aim of improving the DIR accuracy, some commercial software packages now provide the ability to drive the DVF using contours [127,139] or corresponding points [140]. For this purpose, specific tools, developed in academic institutions, are imported to commercial RT TPS [12,139,141]. Therefore, DIR algorithms require further development to better meet clinical needs, such as accounting for the different imaging modalities between planning and treatment delivery [133,142], near-real-time algorithms with graphic processing unit-based frameworks [139,[143][144][145], and anatomical properties simulated by finite element models [146][147][148][149][150].…”