Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique

You Zhang; Liyuan Chen; Bin Li; Michael Folkert; Xun Jia; Xuejun Gu; Jing Wang

doi:10.1117/12.2512649

1 March 2019 Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique

You Zhang, Liyuan Chen, Bin Li, Michael Folkert, Xun Jia, Xuejun Gu, Jing Wang

Author Affiliations +

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109482I (2019) https://doi.org/10.1117/12.2512649
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

You Zhang, Liyuan Chen, Bin Li, Michael Folkert, Xun Jia, Xuejun Gu, and Jing Wang "Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109482I (1 March 2019); https://doi.org/10.1117/12.2512649

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available