Spine Computed Tomography to Magnetic Resonance Image Synthesis Using Generative Adversarial Networks : A Preliminary Study.

图像(数学) 计算机科学 计算机视觉 放射科 生成对抗网络 脊柱(分子生物学) 深度学习
作者
Jung Hwan Lee,In Ho Han,Dong Hwan Kim,Seung Han Yu,In Sook Lee,You Seon Song,Seongsu Joo,Cheng-Bin Jin,Hakil Kim
出处
期刊:Journal of Korean Neurosurgical Society [Korean Neurosurgical Society]
卷期号:63 (3): 386-396 被引量:15
标识
DOI:10.3340/jkns.2019.0084
摘要

Objective To generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. Methods GANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results The mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). Conclusion This was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
江中猴叔发布了新的文献求助10
刚刚
刚刚
cst发布了新的文献求助10
刚刚
动听雨梅完成签到,获得积分10
刚刚
AlissaIodice发布了新的文献求助10
1秒前
hduGL发布了新的文献求助10
1秒前
潇洒的惋清应助zjz采纳,获得10
1秒前
小希发布了新的文献求助10
2秒前
白黑茶叶发布了新的文献求助10
3秒前
空白完成签到,获得积分10
3秒前
Lee应助我滴个采纳,获得20
3秒前
3秒前
shirelylee发布了新的文献求助30
3秒前
4秒前
nico完成签到,获得积分10
4秒前
等等发布了新的文献求助30
4秒前
4秒前
ayw发布了新的文献求助10
4秒前
fengdang完成签到,获得积分10
4秒前
奔跑的考拉完成签到,获得积分10
4秒前
4秒前
潘名超发布了新的文献求助10
5秒前
大模型应助凤凰山采纳,获得30
5秒前
小希发布了新的文献求助10
6秒前
6秒前
CipherSage应助王建采纳,获得10
6秒前
6秒前
7秒前
xuan完成签到,获得积分10
7秒前
Avalonx给化学天空的求助进行了留言
8秒前
xdr完成签到,获得积分10
8秒前
小西发布了新的文献求助10
8秒前
8秒前
coco完成签到,获得积分10
8秒前
nico发布了新的文献求助10
8秒前
8秒前
8秒前
9秒前
Rae应助qihangyang采纳,获得10
9秒前
潇洒的惋清应助qihangyang采纳,获得10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Developing Genetic Editing Tools for Lysobacter 2000
卤化钙钛矿人工突触的研究 2000
Моделирование процессов самоорганизации в кристаллообразующих системах 1000
History of U.S. Space Surveillance and Satellite Cataloging 1000
Adhesion Science: Principles & Practice 800
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6521006
求助须知:如何正确求助?哪些是违规求助? 8314078
关于积分的说明 17784237
捐赠科研通 5623133
什么是DOI,文献DOI怎么找? 2927524
邀请新用户注册赠送积分活动 1904249
关于科研通互助平台的介绍 1764486