Literature DB >> 27831866

A Biomechanical Modeling Guided CBCT Estimation Technique.

You Zhang, Joubin Nasehi Tehrani, Jing Wang.   

Abstract

Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures. Specifically, Bio-CBCT-est first extracts the 2D-3D deformation-generated displacement vectors at the high-contrast anatomical structure boundaries. The extracted surface deformation fields are subsequently used as the boundary conditions to drive structure-based FEA to correct and fine-tune the overall deformation fields, especially those at low-contrast regions within the structure. The resulting FEA-corrected deformation fields are then fed back into 2D-3D deformation to form an iterative loop, combining the benefits of intensity-based deformation and biomechanical modeling for CBCT estimation. Using eleven lung cancer patient cases, the accuracy of the Bio-CBCT-est technique has been compared to that of the 2D-3D deformation technique and the traditional CBCT reconstruction techniques. The accuracy was evaluated in the image domain, and also in the DVF domain through clinician-tracked lung landmarks.

Entities:  

Mesh:

Year:  2016        PMID: 27831866      PMCID: PMC5381525          DOI: 10.1109/TMI.2016.2623745

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  46 in total

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Authors:  Steve A Maas; Benjamin J Ellis; Gerard A Ateshian; Jeffrey A Weiss
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3.  Technical note: A novel boundary condition using contact elements for finite element based deformable image registration.

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4.  Estimating 3-D respiratory motion from orbiting views by tomographic image registration.

Authors:  Rongping Zeng; Jeffrey A Fessler; James M Balter
Journal:  IEEE Trans Med Imaging       Date:  2007-02       Impact factor: 10.048

5.  4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling.

Authors:  Zichun Zhong; Xuejun Gu; Weihua Mao; Jing Wang
Journal:  Phys Med Biol       Date:  2016-01-13       Impact factor: 3.609

6.  A fast inverse consistent deformable image registration method based on symmetric optical flow computation.

Authors:  Deshan Yang; Hua Li; Daniel A Low; Joseph O Deasy; Issam El Naqa
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7.  Sliding characteristic and material compressibility of human lung: parametric study and verification.

Authors:  A Al-Mayah; J Moseley; M Velec; K K Brock
Journal:  Med Phys       Date:  2009-10       Impact factor: 4.071

8.  Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy.

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Journal:  Int J Radiat Oncol Biol Phys       Date:  2002-07-15       Impact factor: 7.038

9.  Dosimetric verification of lung cancer treatment using the CBCTs estimated from limited-angle on-board projections.

Authors:  You Zhang; Fang-Fang Yin; Lei Ren
Journal:  Med Phys       Date:  2015-08       Impact factor: 4.071

10.  Patient-specific finite element modeling of respiratory lung motion using 4D CT image data.

Authors:  René Werner; Jan Ehrhardt; Rainer Schmidt; Heinz Handels
Journal:  Med Phys       Date:  2009-05       Impact factor: 4.071

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  13 in total

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Authors:  Wendy Harris; Chunhao Wang; Fang-Fang Yin; Jing Cai; Lei Ren
Journal:  Med Phys       Date:  2018-06-13       Impact factor: 4.071

2.  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

3.  Statistical Iterative CBCT Reconstruction Based on Neural Network.

Authors:  Binbin Chen; Kai Xiang; Zaiwen Gong; Jing Wang; Shan Tan
Journal:  IEEE Trans Med Imaging       Date:  2018-06       Impact factor: 10.048

4.  A new CT reconstruction technique using adaptive deformation recovery and intensity correction (ADRIC).

Authors:  You Zhang; Jianhua Ma; Puneeth Iyengar; Yuncheng Zhong; Jing Wang
Journal:  Med Phys       Date:  2017-05-12       Impact factor: 4.071

5.  Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio).

Authors:  Hua-Chieh Shao; Xiaokun Huang; Michael R Folkert; Jing Wang; You Zhang
Journal:  Med Phys       Date:  2021-11-19       Impact factor: 4.071

6.  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

7.  Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties.

Authors:  Liang Liu; Xinxin Li; Kai Xiang; Jing Wang; Shan Tan
Journal:  IEEE Trans Med Imaging       Date:  2017-12       Impact factor: 10.048

8.  Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation.

Authors:  Shanzhou Niu; You Zhang; Yuncheng Zhong; Guoliang Liu; Shaohui Lu; Xile Zhang; Shengzhou Hu; Tinghua Wang; Gaohang Yu; Jing Wang
Journal:  Comput Biol Med       Date:  2018-10-22       Impact factor: 4.589

9.  Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model.

Authors:  You Zhang; Michael R Folkert; Xiaokun Huang; Lei Ren; Jeffrey Meyer; Joubin Nasehi Tehrani; Robert Reynolds; Jing Wang
Journal:  Quant Imaging Med Surg       Date:  2019-07

10.  An unsupervised 2D-3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation.

Authors:  You Zhang
Journal:  Phys Med Biol       Date:  2021-03-24       Impact factor: 4.174

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