Unspecified Xijun Wang Profile

Xijun Wang

at Northwestern Univ

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 | 10 December 2021 Paper

Motion artifact reduction in abdominal MRIs using generative adversarial networks with perceptual similarity loss

Yunan Wu, Xijun Wang, Aggelos Katsaggelos

Proceedings Volume 12088, 120880H (2021) https://doi.org/10.1117/12.2605824

KEYWORDS: Motion models, Performance modeling, Magnetic resonance imaging, Data modeling, Image processing, Image quality, Denoising, Visualization, Image restoration

Read Abstract +

The respiratory motion and the resulting artifacts are considered to be a big problem in abdominal Magnetic Resonance Imaging (MRI), often burdening hospitals with unnecessary cost of time and money. Many deep learning techniques have recently been developed for removing these respiratory motion artifacts. However, fine-details, such as the vessels in liver MRIs, are often over-smoothed by many models, while these details are most important for medical diagnosis. Therefore, more realistic-looking reconstructed images are needed. To achieve this goal, in this paper, we design a Generative Adversarial Networks (GAN)-based model, and use the perceptual loss as part of our training loss to further enhance the images’ perceptual quality. To ensure the proper convergence of the GAN model, we add a pre-training step before the adversarial training. In addition, due to lack of image pairs of the clean image and its corresponding motion distorted image, we develop a motion simulation method via the k-space to mimic the motion generated artifacts. Benefiting from all these methods, our proposed model achieves high perceptual quality with an MSE of 0.002, an SSIM of 0.942, a PSNR of 33.894 and a perceptual distance metric of 0.015. Finally, it generates motion artifact reduced images with clearer and better fine-details, thus providing to radiologists more realistic MRI images for improved diagnosis. The code is available at [Code for Motion Reduction].

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