title: A novel digital tomosynthesis (DTS) reconstruction method using a deformation field map.
abstract: We developed a novel digital tomosynthesis (DTS) reconstruction method using a deformation field map to optimally estimate volumetric information in DTS images. The deformation field map is solved by using prior information, a deformation model, and new projection data. Patients' previous cone-beam CT (CBCT) or planning CT data are used as the prior information, and the new patient volume to be reconstructed is considered as a deformation of the prior patient volume. The deformation field is solved by minimizing bending energy and maintaining new projection data fidelity using a nonlinear conjugate gradient method. The new patient DTS volume is then obtained by deforming the prior patient CBCT or CT volume according to the solution to the deformation field. This method is novel because it is the first method to combine deformable registration with limited angle image reconstruction. The method was tested in 2D cases using simulated projections of a Shepp-Logan phantom, liver, and head-and-neck patient data. The accuracy of the reconstruction was evaluated by comparing both organ volume and pixel value differences between DTS and CBCT images. In the Shepp-Logan phantom study, the reconstructed pixel signal-to-noise ratio (PSNR) for the 60 degrees DTS image reached 34.3 dB. In the liver patient study, the relative error of the liver volume reconstructed using 60 degrees projections was 3.4%. The reconstructed PSNR for the 60 degrees DTS image reached 23.5 dB. In the head-and-neck patient study, the new method using 60 degrees projections was able to reconstruct the 8.1 degrees rotation of the bony structure with 0.0 degrees error. The reconstructed PSNR for the 60 degrees DTS image reached 24.2 dB. In summary, the new reconstruction method can optimally estimate the volumetric information in DTS images using 60 degrees projections. Preliminary validation of the algorithm showed that it is both technically and clinically feasible for image guidance in radiation therapy.
authors.affiliation: lr23@duke.edu
authors.forename: Lei
authors.initials: L
authors.lastname: Ren
authors.forename: Junan
authors.initials: J
authors.lastname: Zhang
authors.forename: Danthai
authors.initials: D
authors.lastname: Thongphiew
authors.forename: Devon J
authors.initials: DJ
authors.lastname: Godfrey
authors.forename: Q Jackie
authors.initials: QJ
authors.lastname: Wu
authors.forename: Su-Min
authors.initials: SM
authors.lastname: Zhou
authors.forename: Fang-Fang
authors.initials: FF
authors.lastname: Yin
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D003201:Computers; D054893:Cone-Beam Computed Tomography / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009369:Neoplasms / Q000175:diagnosis / Q000473:pathology; D019047:Phantoms, Imaging; D011878:Radiotherapy / Q000379:methods; D011880:Radiotherapy Planning, Computer-Assisted; D015203:Reproducibility of Results; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014584:User-Computer Interface