Medical physics最新文献

筛选
英文 中文
Technical Note: Simultaneous segmentation and relaxometry for MRI through multitask learning. 技术说明:通过多任务学习实现MRI的同时分割和松弛测量。
Medical physics Pub Date : 2019-10-01 Epub Date: 2019-08-31 DOI: 10.1002/mp.13756
Peng Cao, Jing Liu, Shuyu Tang, Andrew P Leynes, Janine M Lupo, Duan Xu, Peder E Z Larson
{"title":"Technical Note: Simultaneous segmentation and relaxometry for MRI through multitask learning.","authors":"Peng Cao,&nbsp;Jing Liu,&nbsp;Shuyu Tang,&nbsp;Andrew P Leynes,&nbsp;Janine M Lupo,&nbsp;Duan Xu,&nbsp;Peder E Z Larson","doi":"10.1002/mp.13756","DOIUrl":"https://doi.org/10.1002/mp.13756","url":null,"abstract":"<p><strong>Purpose: </strong>This study demonstrated a magnetic resonance (MR) signal multitask learning method for three-dimensional (3D) simultaneous segmentation and relaxometry of human brain tissues.</p><p><strong>Materials and methods: </strong>A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multicontrast brain images. The deep neural network contained three residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online-synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter, and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on three healthy volunteers. The mean and standard deviation for the T1 and T2 values in vivo were reported and compared to literature values. Additional animal (N = 6) and prostate patient (N = 1) experiments were performed to compare the estimated T1 and T2 values with those from gold standard methods and to demonstrate clinical applications of the proposed method.</p><p><strong>Results: </strong>In animal validation experiment, the differences/errors (mean difference ± standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 ± 486 and 154 ± 512 ms for T1, and 5 ± 33 and 7 ± 41 ms for T2, respectively. In healthy volunteer experiments (N = 3), whole brain segmentation and relaxometry were finished within ~ 5 s. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1- and T2-weighted images.</p><p><strong>Conclusion: </strong>The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1- and T2-weighted images, in conjunction with segmentation of gray matter, white matter, and CSF.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"46 10","pages":"4610-4621"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/mp.13756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41224235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信