Modeling lung deformation: A combined deformable image registration method with spatially varying Young's modulus estimates

Abstract

Purpose:Respiratory motion introduces uncertainties in tumor location and lung deformation, which often results in difficulties calculating dose distributions in thoracic radiation therapy. Deformable image registration (DIR) has ability to describe respiratory-induced lung deformation, with which the radiotherapy techniques can deliver high dose to tumors while reducing radiation in surrounding normal tissue. The authors' goal is to propose a DIR method to overcome two main challenges of the previous biomechanical model for lung deformation, i.e., the requirement of precise boundary conditions and the lack of elasticity distribution. Methods: As opposed to typical methods in biomechanical modeling, the authors' method assumes that lung tissue is inhomogeneous. The authors thus propose a DIR method combining a varying intensity flow (VF) block-matching algorithm with the finite element method (FEM) for lung deformation from end-expiratory phase to end-inspiratory phase. Specifically, the lung deformation is formulated as a stress-strain problem, for which the boundary conditions are obtained from the VF block-matching algorithm and the element specific Young's modulus distribution is estimated by solving an optimization problem with a quasi-Newton method. The authors measure the spatial accuracy of their nonuniform model as well as a standard uniform model by applying both methods to four-dimensional computed tomography images of six patients. The spatial errors produced by the registrations are computed using large numbers (>1000) of expert-determined landmark point pairs. Results: In right-left, anterior-posterior, and superior-inferior directions, the mean errors (standard deviation) produced by the standard uniform FEM model are 1.42(1.42), 1.06(1.05), and 1.98(2.10) mm whereas the authors' proposed nonuniform model reduces these errors to 0.59(0.61), 0.52(0.51), and 0.78(0.89) mm. The overall 3D mean errors are 3.05(2.36) and 1.30(0.97) mm for the uniform and nonuniform models, respectively. Conclusions:The results indicate that the proposed nonuniform model can simulate patient-specific and position-specific lung deformation via spatially varying Young's modulus estimates, which improves registration accuracy compared to the uniform model and is therefore a more suitable description of lung deformation.

The ANACONDA algorithm for deformable image registration in radiotherapy

“…The Demon's algorithm, in which the sum of squared differences is used as image similarity measure, can be combined with information from contoured image sets as described by Gu et al 29 Starting from a finite element model approach for geometric alignment, combinations with image similarity measures internally have been proposed. 28,30 In this work, we present ANACONDA for DIR. It is available in the commercial treatment planning system RayStation (RaySearch Laboratories AB, Stockholm, Sweden).…”

“…Hybrid solutions have been proposed already in 2001 by Christensen et al 26 and the interest in such solutions in the field of radiotherapy has increased in recent years due to limitations of pure image intensity based algorithms. 17,19,[27][28][29][30] Godley et al 27 present an algorithm where masks are created for bladder and rectum and subsequently incorporated in a demon-based algorithm for DIR in the pelvic region. In the work by Kim et al, 19 difficulties in performing CT/CBCT DIR for the pelvic region are pin pointed for a commercially available intensity based system.…”

A Biomechanical Modeling Guided CBCT Estimation Technique

“…Compared with the intensity-based approach, biomechanical modeling incorporates the morphology, the material composition, and the tissue elasticity of anatomical structures into the deformation process. The resulting deformation field is thus more physiologically and physically meaningful [22]. In addition, by using biomechanical modeling the whole deformation field can be computed from the provided boundary conditions, potentially generating a more accurate deformation field in low contrast regions within the anatomical structures [21].…”

A Novel Respiratory Motion Perturbation Model Adaptable to Patient Breathing Irregularities

“…Lung motion prediction, including tumors and OARs, has been studied using more advanced physical approaches, including biomechanical modeling with the finite element method (25, 26), motion vector modeling with deformable image registration (DIR) (27, 28), hybrid modeling with biomechanics and DIR (29, 30), and statistical modeling with respiratory parameters (31-33). Although the prediction accuracy might be clinically acceptable, the major limitations included a lack of real-time performance owing to the complex, iterative computation and lack of adaptations to changes of breathing behaviors or irregularities.…”