Literature DB >> 23257113

High-quality four-dimensional cone-beam CT by deforming prior images.

Jing Wang1, Xuejun Gu.   

Abstract

Due to a limited number of projections at each phase, severe view aliasing artifacts are present in four-dimensional cone beam computed tomography (4D-CBCT) when reconstruction is performed using conventional algorithms. In this work, we aim to obtain high-quality 4D-CBCT of lung cancer patients in radiation therapy by deforming the planning CT. The deformation vector fields (DVF) to deform the planning CT are estimated through matching the forward projection of the deformed prior image and measured on-treatment CBCT projection. The estimation of the DVF is formulated as an unconstrained optimization problem, where the objective function to be minimized is the sum of the squared difference between the forward projection of the deformed planning CT and the measured 4D-CBCT projection. A nonlinear conjugate gradient method is used to solve the DVF. As the number of the variables in the DVF is much greater than the number of measurements, the solution to such a highly ill-posed problem is very sensitive to the initials during the optimization process. To improve the estimation accuracy of DVF, we proposed a new strategy to obtain better initials for the optimization. In this strategy, 4D-CBCT is first reconstructed by total variation minimization. Demons deformable registration is performed to register the planning CT and the 4D-CBCT reconstructed by total variation minimization. The resulted DVF from demons registration is then used as the initial parameters in the optimization process. A 4D nonuniform rotational B-spline-based cardiac-torso (NCAT) phantom and a patient 4D-CBCT are used to evaluate the algorithm. Image quality of 4D-CBCT is substantially improved by using the proposed strategy in both NCAT phantom and patient studies. The proposed method has the potential to improve the temporal resolution of 4D-CBCT. Improved 4D-CBCT can better characterize the motion of lung tumors and will be a valuable tool for image-guided adaptive radiation therapy.

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Year:  2012        PMID: 23257113     DOI: 10.1088/0031-9155/58/2/231

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  23 in total

1.  3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models.

Authors:  S Dhou; M Hurwitz; P Mishra; W Cai; J Rottmann; R Li; C Williams; M Wagar; R Berbeco; D Ionascu; J H Lewis
Journal:  Phys Med Biol       Date:  2015-04-23       Impact factor: 3.609

2.  A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging.

Authors:  Hao Yan; Xin Zhen; Michael Folkerts; Yongbao Li; Tinsu Pan; Laura Cervino; Steve B Jiang; Xun Jia
Journal:  Med Phys       Date:  2014-07       Impact factor: 4.071

3.  A method for volumetric imaging in radiotherapy using single x-ray projection.

Authors:  Yuan Xu; Hao Yan; Luo Ouyang; Jing Wang; Linghong Zhou; Laura Cervino; Steve B Jiang; Xun Jia
Journal:  Med Phys       Date:  2015-05       Impact factor: 4.071

4.  Automatic large quantity landmark pairs detection in 4DCT lung images.

Authors:  Yabo Fu; Xue Wu; Allan M Thomas; Harold H Li; Deshan Yang
Journal:  Med Phys       Date:  2019-08-07       Impact factor: 4.071

5.  Estimating 4D-CBCT from prior information and extremely limited angle projections using structural PCA and weighted free-form deformation for lung radiotherapy.

Authors:  Wendy Harris; You Zhang; Fang-Fang Yin; Lei Ren
Journal:  Med Phys       Date:  2017-03       Impact factor: 4.071

6.  A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction.

Authors:  Xiaokun Huang; You Zhang; Jing Wang
Journal:  Phys Med Biol       Date:  2018-02-08       Impact factor: 3.609

7.  A Biomechanical Modeling Guided CBCT Estimation Technique.

Authors:  You Zhang; Joubin Nasehi Tehrani; Jing Wang
Journal:  IEEE Trans Med Imaging       Date:  2016-11-01       Impact factor: 10.048

8.  dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images.

Authors:  H Dang; A S Wang; Marc S Sussman; J H Siewerdsen; J W Stayman
Journal:  Phys Med Biol       Date:  2014-08-06       Impact factor: 3.609

9.  4D liver tumor localization using cone-beam projections and a biomechanical model.

Authors:  You Zhang; Michael R Folkert; Bin Li; Xiaokun Huang; Jeffrey J Meyer; Tsuicheng Chiu; Pam Lee; Joubin Nasehi Tehrani; Jing Cai; David Parsons; Xun Jia; Jing Wang
Journal:  Radiother Oncol       Date:  2018-11-14       Impact factor: 6.280

10.  PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction.

Authors:  J Webster Stayman; Hao Dang; Yifu Ding; Jeffrey H Siewerdsen
Journal:  Phys Med Biol       Date:  2013-10-10       Impact factor: 3.609

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