Deep learning based volume-to-slice MRI registration via intentional overfitting

Yabo Fu; Yang Lei; Tonghe Wang; Marian Axente; Justin Roper; Jeffrey D. Bradley; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2611898

4 April 2022 Deep learning based volume-to-slice MRI registration via intentional overfitting

Yabo Fu, Yang Lei, Tonghe Wang, Marian Axente, Justin Roper, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 1203412 (2022) https://doi.org/10.1117/12.2611898
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Volume-to-slice image registration can be used to support image guided surgery, volumetric image reconstruction and image guidance in radiotherapy. However, the volume-to-slice image registration is challenging due to its different dimensionality and lack of slice correspondence. In radiotherapy, it is important to track the tumor motion while delivering the radiation in order to maximize target coverage and spare healthy tissue. 2D MRI real-time imaging has been available for tumor tracking during radiotherapy. To support volumetric tumor tracking, pre-treatment volumetric MRI images can be acquired, annotated and registered to the real-time 2D MRI. In this study, a proof-of-concept study was performed to register 3D MRI to 2D MRI slice that was extracted from the 3D MRI after random rotation and translation. We propose an intentional overfitting deep learning based network (ION) to perform volume-to-slice registration for MRI abdominal images. MRI volumetric datasets from 40 patients were used in this study for the network training and testing. As a proof-of-concept study, we simulated the 2D slices by extracting the middle slice from rigidly transformed 3D MR images. The 2D slice and 3D volume were then input to the network to predict a six degree of freedom (DOF) parameters, including 3 translational and 3 rotational parameters that will align the volume to match the slice. The network was first trained using multiple patients’ datasets and then further optimized by a particular testing patient dataset via intentional overfitting. Tested on 10 datasets, the mean square error (MSE) between the 2D slice and the middle slice of the rigidly transformed MRI volume was reduced significantly from 1924 to 27 after image registration, indicating good registration accuracy.

Conference Presentation

Citation Download Citation

Yabo Fu, Yang Lei, Tonghe Wang, Marian Axente, Justin Roper, Jeffrey D. Bradley, Tian Liu, and Xiaofeng Yang "Deep learning based volume-to-slice MRI registration via intentional overfitting", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 1203412 (4 April 2022); https://doi.org/10.1117/12.2611898

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