Synthetic digital reconstructed radiographs for MR-only robotic stereotactic radiation therapy: A proof of concept

基准标记 赛博刀 放射外科 计算机科学 人工智能 基本事实 图像配准 核医学 可视化 医学影像学 计算机视觉 医学 放射治疗 放射科 图像(数学)
作者
Gregory Szalkowski,Dong Nie,Tong Zhu,Pew‐Thian Yap,Jun Lian
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:138: 104917-104917 被引量:4
标识
DOI:10.1016/j.compbiomed.2021.104917
摘要

To create synthetic CTs and digital reconstructed radiographs (DRRs) from MR images that allow for fiducial visualization and accurate dose calculation for MR-only radiosurgery.We developed a machine learning model to create synthetic CTs from pelvic MRs for prostate treatments. This model has been previously proven to generate synthetic CTs with accuracy on par or better than alternate methods, such as atlas-based registration. Our dataset consisted of 11 paired CT and conventional MR (T2) images used for previous CyberKnife (Accuray, Inc) radiotherapy treatments. The MR images were pre-processed to mimic the appearance of fiducial-enhancing images. Two models were trained for each parameter case, using a sub-set of the available image pairs, with the remaining images set aside for testing and validation of the model to identify the optimal patch size and number of image pairs used for training. Four models were then trained using the identified parameters and used to generate synthetic CTs, which in turn were used to generate DRRs at angles 45° and 315°, as would be used for a CyberKnife treatment. The synthetic CTs and DRRs were compared visually and using the mean squared error and peak signal-to-noise ratio against the ground-truth images to evaluate their similarity.The synthetic CTs, as well as the DRRs generated from them, gave similar visualization of the fiducial markers in the prostate as the true counterparts. There was no significant difference found for the fiducial localization for the CTs and DRRs. Across the 8 DRRs analyzed, the mean MSE between the normalized true and synthetic DRRs was 0.66 ± 0.42% and the mean PSNR for this region was 22.9 ± 3.7 dB. For the full CTs, the mean MAE was 72.9 ± 88.1 HU and the mean PSNR was 31.2 ± 2.2 dB.Our machine learning-based method provides a proof of concept of a way to generate synthetic CTs and DRRs for accurate dose calculation and fiducial localization for use in radiation treatment of the prostate.

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
刚刚
张先生发布了新的文献求助10
刚刚
民生完成签到,获得积分10
1秒前
Longbin李发布了新的文献求助10
2秒前
深情安青应助傅双庆采纳,获得10
2秒前
2秒前
祖诗云发布了新的文献求助30
3秒前
4秒前
Suttier发布了新的文献求助10
4秒前
6秒前
雨梦迟歌发布了新的文献求助10
7秒前
思源应助SSS采纳,获得10
7秒前
乐乐应助liulei采纳,获得10
7秒前
7秒前
踏实的镜子应助acuter采纳,获得10
8秒前
JamesPei应助可爱的刚采纳,获得10
8秒前
桐桐应助kai采纳,获得10
8秒前
9秒前
9秒前
10秒前
lixiaobai发布了新的文献求助10
11秒前
大个应助言字午采纳,获得10
11秒前
11秒前
liu发布了新的文献求助10
11秒前
annali发布了新的文献求助10
12秒前
小科研发布了新的文献求助10
14秒前
tongtong完成签到,获得积分10
15秒前
天天快乐应助WATQ采纳,获得20
16秒前
cdercder应助cc采纳,获得20
16秒前
CipherSage应助俏皮小土豆采纳,获得10
16秒前
yh发布了新的文献求助30
17秒前
17秒前
马111完成签到 ,获得积分10
17秒前
民生发布了新的文献求助10
17秒前
白火发布了新的文献求助10
18秒前
18秒前
周正杨完成签到,获得积分10
19秒前
Demo发布了新的文献求助10
19秒前
言字午完成签到,获得积分10
20秒前
22秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 2000
Advanced Weaponeering Fourth Edition, Volume 2 1000
Weaponeering: An Introduction Fourth Edition, Volume 1 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7553990
求助须知:如何正确求助?哪些是违规求助? 9136498
关于积分的说明 19527398
捐赠科研通 7145288
什么是DOI,文献DOI怎么找? 3260797
关于科研通互助平台的介绍 2427234
邀请新用户注册赠送积分活动 2249806