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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

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.

Original language	English (US)
Title of host publication	Medical Imaging 2019
Subtitle of host publication	Physics of Medical Imaging
Editors	Taly Gilat Schmidt, Guang-Hong Chen, Hilde Bosmans
Publisher	SPIE
ISBN (Electronic)	9781510625433
DOIs	https://doi.org/10.1117/12.2512649
State	Published - 2019
Event	Medical Imaging 2019: Physics of Medical Imaging - San Diego, United States Duration: Feb 17 2019 → Feb 20 2019

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	10948
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2019: Physics of Medical Imaging
Country/Territory	United States
City	San Diego
Period	2/17/19 → 2/20/19

Keywords

Biomechanical modeling
Cone-beam computed tomography
Contour propagation
DICE coefficient
Deep learning
Deformation vector field
Liver

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging
Biomaterials

Access to Document

10.1117/12.2512649

Cite this

Zhang, Y., Chen, L., Li, B., Folkert, M., Jia, X., Gu, X., & Wang, J. (2019). Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique. In T. G. Schmidt, G.-H. Chen, & H. Bosmans (Eds.), Medical Imaging 2019: Physics of Medical Imaging Article 109482I (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10948). SPIE. https://doi.org/10.1117/12.2512649

Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique. / Zhang, You ; Chen, Liyuan; Li, Bin et al.
Medical Imaging 2019: Physics of Medical Imaging. ed. / Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans. SPIE, 2019. 109482I (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10948).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, Y , Chen, L, Li, B, Folkert, M, Jia, X, Gu, X & Wang, J 2019, Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique. in TG Schmidt, G-H Chen & H Bosmans (eds), Medical Imaging 2019: Physics of Medical Imaging., 109482I, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10948, SPIE, Medical Imaging 2019: Physics of Medical Imaging, San Diego, United States, 2/17/19. https://doi.org/10.1117/12.2512649

Zhang Y , Chen L, Li B, Folkert M, Jia X, Gu X et al. Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique. In Schmidt TG, Chen GH, Bosmans H, editors, Medical Imaging 2019: Physics of Medical Imaging. SPIE. 2019. 109482I. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). doi: 10.1117/12.2512649

@inproceedings{dea3fdaf9849416fa9e3051ce626e05d,

title = "Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique",

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.",

keywords = "Biomechanical modeling, Cone-beam computed tomography, Contour propagation, DICE coefficient, Deep learning, Deformation vector field, Liver",

author = "You Zhang and Liyuan Chen and Bin Li and Michael Folkert and Xun Jia and Xuejun Gu and Jing Wang",

note = "Publisher Copyright: {\textcopyright} SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2019: Physics of Medical Imaging ; Conference date: 17-02-2019 Through 20-02-2019",

year = "2019",

doi = "10.1117/12.2512649",

language = "English (US)",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Schmidt, {Taly Gilat} and Guang-Hong Chen and Hilde Bosmans",

booktitle = "Medical Imaging 2019",

}

TY - GEN

T1 - Incorporating biomechanical modeling and deep learning into a deformation-driven liver CBCT reconstruction technique

AU - Zhang, You

AU - Chen, Liyuan

AU - Li, Bin

AU - Folkert, Michael

AU - Jia, Xun

AU - Gu, Xuejun

AU - Wang, Jing

N1 - Publisher Copyright: © SPIE. Downloading of the abstract is permitted for personal use only.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Biomechanical modeling

KW - Cone-beam computed tomography

KW - Contour propagation

KW - DICE coefficient

KW - Deep learning

KW - Deformation vector field

KW - Liver

UR - http://www.scopus.com/inward/record.url?scp=85068354172&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068354172&partnerID=8YFLogxK

U2 - 10.1117/12.2512649

DO - 10.1117/12.2512649

M3 - Conference contribution

AN - SCOPUS:85068354172

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2019

A2 - Schmidt, Taly Gilat

A2 - Chen, Guang-Hong

A2 - Bosmans, Hilde

PB - SPIE

T2 - Medical Imaging 2019: Physics of Medical Imaging

Y2 - 17 February 2019 through 20 February 2019

ER -

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this