title: Using patient-specific phantoms to evaluate deformable image registration algorithms for adaptive radiation therapy.
abstract: The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B-spline-based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast-Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM-DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0 ~ 3.1 mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0-1.9 mm in the prostate, 1.9-2.4mm in the rectum, and 1.8-2.1 mm over the entire patient body. Sinusoidal errors induced by B-spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient-specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that DIR algorithms need to be verified for each registration instance when implementing adaptive radiation therapy.
authors.affiliation: Henry Ford Health System. Nick.Stanley@mclaren.org.
authors.forename: Nick
authors.initials: N
authors.lastname: Stanley
authors.forename: Carri
authors.initials: C
authors.lastname: Glide-Hurst
authors.forename: Jinkoo
authors.initials: J
authors.lastname: Kim
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Adams
authors.forename: Shunshan
authors.initials: S
authors.lastname: Li
authors.forename: Ning
authors.initials: N
authors.lastname: Wen
authors.forename: Indrin J
authors.initials: IJ
authors.lastname: Chetty
authors.forename: Hualiang
authors.initials: H
authors.lastname: Zhong
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D054893:Cone-Beam Computed Tomography; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D008297:Male; D011467:Prostate / Q000000981:diagnostic imaging* / Q000528:radiation effects*; D011857:Radiographic Image Interpretation, Computer-Assisted; D011879:Radiotherapy Dosage; D012189:Retrospective Studies