Mr. Jiale Min Profile

Jiale Min

at United Imaging Healthcare

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 | 7 April 2023 Poster + Paper

Millimeter wave radar-based motion monitoring method in magnetic resonance imaging system

Jiale Min, Yiran Li, Xinyuan Xia, Han Wang, Peng Liu, Tuoyu Cao, Hao Ren

Proceedings Volume 12463, 124632F (2023) https://doi.org/10.1117/12.2653549

KEYWORDS: Head, Radar, Magnetic resonance imaging, Radar sensor technology, Imaging systems, Motion detection, Radar signal processing, Antennas, Spatial filtering, Image quality

Read Abstract +

Head magnetic resonance imaging (MRI) is susceptible to motion artifacts. Images with gross motion artifacts cannot be easily corrected, which makes the monitoring of patient movement is essential to improve the acquired image quality. Several methods have been proposed to detect patient movement during MRI scanning, such as navigator, optical tracking system and the image quality assessment. However, they have some disadvantages such as insufficient real-time performance and requiring additional MR data acquisition. Herein, we propose an MR-compatible millimeter wave (mmWave) radar system to monitor some typical types of movements during head MRI scanning. The radar sensor is installed in a 3T MR system. When MRI system scanning, the radar installed will vibrate. A novel algorithm is proposed to generate the spectrogram without vibration interference. Due to limited movement, we explore how the range information rather than micro-Doppler information can assist the activity classification. After data processing, convolutional neural network (CNN) is applied to extract the features in multiple range bins. The extracted features will be inputted to a shallow neural network for recognizing the moving parts. The result shows four different activities are classified with an overall accuracy of (96.81±0.69)%. Finally, experimental results with acquired head MR images prove our system’s sufficient capability of recognizing nodding head yet inadequate capability of detecting head shaking movement.

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