Feasibility and quantitative analysis of a biomechanical model-guided lung elastography for radiotherapy. (21st February 2017)

• Record Type: Journal Article
• Authors: Hasse, Katelyn; Neylon, John; Santhanam, Anand P
• Is Part Of: Biomedical physics & engineering express. Volume 3:Number 2(2017)
• Publication Date: 2017-02-21
• Subjects: lung elastography -- inverse problem -- physics-based biomechanical model -- radiotherapy; Medical physics -- Periodicals; Biophysics -- Periodicals; Biomedical engineering -- Periodicals; Medical sciences -- Periodicals; 610.153
• DOI: 10.1088/2057-1976/aa5d1c
• Languages: English
• ISSNs: 2057-1976

Abstract

Abstract: We present a novel methodology for performing lung elastography within the radiotherapy context. Our approach employed a physics-based model and a novel convergence magnification approach to estimate the lung elasticity distribution. For a systematic analysis, we employed a physics-based virtual lung phantom with CT source geometry and a heterogeneous voxel-to-voxel elasticity distribution. A set of 18 synthetic CT image datasets with known ground-truth elasticity representing normal, emphysematous, and tumor tissue within the lungs was generated and used as input for the lung elastography. During the lung elastography, we start by re-estimating the lung deformations using a deformable image registration procedure. For known phantom geometry, boundary conditions and lung deformation, we solve for the elasticity distribution by iteratively optimizing the tissue elasticity and deforming the lung model for given boundary constraints. To improve the estimation accuracy, a convergence magnification approach was formulated using a physics-based process that amplified the geometrical differences between the ground-truth and the deformed geometry. Our results showed that the model-guided approach estimated the elasticity with 91.94 ± 5.20% of voxels within 0.5 mm of ground-truth displacement and 1 kPa of the ground-truth elasticity. The novel convergence technique presented in this paper improved the accuracy of the estimated elasticity from 77.05% to 91.94%. In the systematic analysis, variations in forward model, ground-truth elasticity distribution, boundary deformation, and geometry were investigated with respect to their effect on the accuracy of the elasticity estimation technique.