Dr. Jiong Wu Profile

Dr. Jiong Wu

at University of Pennsylvania

SPIE Involvement:

Author

Publications (2)

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 | 2 April 2024 Poster + Paper

Diffeomorphic image registration with bijective consistency

Jiong Wu, Hongming Li, Yong Fan

Proceedings Volume 12926, 129262V (2024) https://doi.org/10.1117/12.3006871

KEYWORDS: Image registration, Deformation, Education and training, Machine learning, Frequency modulation, Fermium, Brain, Image resolution, Neuroimaging, Magnetic resonance imaging

Read Abstract +

Recent image registration methods built upon unsupervised learning have achieved promising diffeomorphic image registration performance. However, the bijective consistency of spatial transformations is not sufficiently investigated in existing image registration studies. In this study, we develop a multi-level image registration framework to achieve diffeomorphic image registration in a coarse-to-fine manner. A novel stationary velocity field computation method is proposed to integrate forward and inverse stationary velocity fields so that the image registration result is invariant to the order of input images to be registered. Moreover, a new bijective consistency regularization is adopted to enforce the bijective consistency of forward and inverse transformations at different time points along the stationary velocity integration paths. Validation experiments have been conducted on two T1-weighted magnetic resonance imaging (MRI) brain datasets with manually annotated anatomical structures. Compared with four state-of-the-art representative diffeomorphic registration methods, including two traditional diffeomorphic registration algorithms and two unsupervised learning-based diffeomorphic registration approaches, our method has achieved better image registration accuracy with superior topology preserving performance.

Proceedings Article | 2 March 2018 Paper

Fast diffeomorphic image registration via GPU-based parallel computing: an investigation of the matching cost function

Jiong Wu, Xiaoying Tang

Proceedings Volume 10574, 105742S (2018) https://doi.org/10.1117/12.2293082

KEYWORDS: Image registration, Parallel computing, Neuroimaging, Image segmentation, Brain mapping

Read Abstract +

Large deformation diffeomorphic metric mapping (LDDMM) is one of the state-of-the-art deformable image registration algorithms that has been shown to be of superior performance, especially for brain images. LDDMM was originally proposed for matching intra-modality images, with the Sum of Squared Difference (SSD) being used as the matching cost function. Extension of LDDMM to other types of matching cost functions has been very limited. In this paper, we systematically evaluated three different matching cost functions, the SSD, the Mutual Information (MI), and the Cross Correlation (CC) in the LDDMM-image setting, based on 14 subcortical and ventricular structures in a total of 120 pairs of brain images. In addition, we proposed an efficient implementation for those three LDDMM-image settings via GPU-base parallel computing and quantitatively compared with the standard open source implementation of LDDMM-SSD in terms of both registration accuracy and computational time. The proposed parallelization and optimization approach resulted in an acceleration by 28 times, relative to the standard open source implementation, on a 4-core machine with GTX970 card (29.67 mins versus 828.35 mins on average), without sacrificing the registration accuracy. Comparing the three matching cost functions, we observed that LDDMM-CC worked the best in terms of registration accuracy, obtaining Dice overlaps larger than 0.853 for a majority of structures of interest.

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