Mr. Jian Xu Profile

Jian Xu

at United Imaging Healthcare Co Ltd

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 1 April 2024 Poster + Paper

MBURegNet: an unsupervised model-based deep unrolling registration network for deformable image registration in dual-energy CT

Rui Liao, Peng Wang, Wenjing Cao, Yuan Bao, Jian Xu, Guotao Quan, Wenying Wang

Proceedings Volume 12925, 129254O (2024) https://doi.org/10.1117/12.3006887

KEYWORDS: Model based design, Image registration, Deformation, Computed tomography, Imaging systems, Visualization, Motion models, Mathematical optimization

Read Abstract +

Dual-energy CT (DECT) provides additional material-based contrast using spectral information. The realization of DECT using a rotation-to-rotation kVp switching may suffer from structure misalignment due to patient’s motion and requires deformable image registration (DIR) between the two kVp images. Recent studies in DIR has highlighted deep-learning-based methods which can achieve superior registration accuracy with reasonable computational time. However, current deep-learning-based DIR methods may eliminate important anatomical features or hallucinate faked structures. The lack of interpretability complicates the robustness verification. Alternatively, recent studies have introduced the algorithm unrolling method that provides a concrete and systematic connection between model-based iterative methods and data-driven methods. In this work, we present an unsupervised Model-Based deep Unrolling Registration Network (MBURegNet) for DIR in DECT. MBURegNet comprises a sequence of stacked update blocks to unroll the Large Deformation Diffeomorphic Metric Mapping method, where each block samples the velocity field that follows the diffeomorphism physics. Preliminary studies using clinical data has shown that the proposed network can achieve superior performance improvement compared to the baseline deep-learning-based method, as evidenced by both qualitative and quantitative analyses. Additionally, the network can generate a sequence of intermediate images connecting the initial and final motion states, effectively illustrating the continuous flow of diffeomorphisms.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years