MR imaging from CT scan data using generative adversarial network

MingJie Liu; Wei Zou; ChangHao Piao

doi:10.1117/12.2636840

29 April 2022 MR imaging from CT scan data using generative adversarial network

MingJie Liu, Wei Zou, ChangHao Piao

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 1224708 (2022) https://doi.org/10.1117/12.2636840
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

Magnetic resonance (MR) imaging is an important computer aided diagnosis techniques with rich pathological information. Due to the factor of physical and physiological constraint, it affects the applicability of that technique seriously. However, computed tomography (CT)-based radiotherapy is more popular on account of its imaging rapidity and environmental simplicity. Therefore, it is of great theoretical and practical significance to design a method that can construct MR image from corresponding CT image. In this paper, we treat MR imaging as a machine vision problem and propose a multiconditional generative adversarial network (GAN) for MR imaging from CT scan data. Considering reversibility of GAN, both generator and reverse generator are designed for MR and CT imaging respectively, which can constrain each other and improve consistency between features of CT and MR images. In addition, we use VGG16 model to extract semantic features, perception error and voxel error fusing with original GAN loss is designed to enhance similarity of MR image structure and detail texture features. The experimental results with challenging public CT-MR imaging dataset show distinct performance improvement over other GANs utilized in medical imaging and demonstrate the effect of our method for medical image modal transformation.

Citation Download Citation

MingJie Liu, Wei Zou, and ChangHao Piao "MR imaging from CT scan data using generative adversarial network", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 1224708 (29 April 2022); https://doi.org/10.1117/12.2636840

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Magnetic resonance imaging

Computed tomography

Image processing

Convolution

Data modeling

Image quality

Tissues

Show All Keywords

Keywords/Phrases

Search In:

Publication Years