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Proceedings Paper

Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique
Author(s): You Zhang; Liyuan Chen; Bin Li; Michael Folkert; Xun Jia; Xuejun Gu; Jing Wang
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Paper Abstract

Deformation-driven CBCT reconstruction techniques can generate accurate and high-quality CBCTs from deforming prior CTs using sparse-view cone-beam projections. The solved deformation-vector-fields (DVFs) also propagate tumor contours from prior CTs, which allows automatic localization of low-contrast liver tumors on CBCTs. To solve the DVFs, the deformation-driven techniques generate digitally-reconstructed-radiographs (DRRs) from the deformed image to compare with acquired cone-beam projections, and use their intensity mismatch as a metric to evaluate and optimize the DVFs. To boost the deformation accuracy at low-contrast liver tumor regions where limited intensity information exists, we incorporated biomechanical modeling into the deformation-driven CBCT reconstruction process. Biomechanical modeling solves the deformation on the basis of material geometric and elastic properties, enabling accurate deformation in a low-contrast context. Moreover, real clinical cone-beam projections contain amplified scatter and noise than DRRs. These degrading signals are complex, non-linear in nature and can reduce the accuracy of deformation-driven CBCT reconstruction. Conventional correction methods towards these signals like linear fitting lead to over-simplification and sub-optimal results. To address this issue, this study applied deep learning to derive an intensity mapping scheme between cone-beam projections and DRRs for cone-beam projection intensity correction prior to CBCT reconstructions. Evaluated by 10 liver imaging sets, the proposed technique achieved accurate liver CBCT reconstruction and localized the tumors to an accuracy of ~1 mm, with average DICE coefficient over 0.8. Incorporating biomechanical modeling and deep learning, the deformation-driven technique allows accurate liver CBCT reconstruction from sparse-view projections, and accurate deformation of low-contrast areas for automatic tumor localization.

Paper Details

Date Published: 1 March 2019
PDF: 10 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109482I (1 March 2019); doi: 10.1117/12.2512649
Show Author Affiliations
You Zhang, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Liyuan Chen, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Bin Li, Sun Yat-sen Univ. Cancer Ctr. (China)
Michael Folkert, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Xun Jia, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Xuejun Gu, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Jing Wang, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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