General orientation transform for the estimation of fiber orientations in white matter tissues

计算机科学 方向(向量空间) 估计员 纤维束成像 稳健性(进化) 人工智能 磁共振弥散成像 模式识别(心理学) 成像体模 算法 数学 物理 统计 磁共振成像 化学 光学 医学 生物化学 几何学 基因 放射科
作者
Diwei Shi,Sisi Li,Li Chen,Xuesong Li,Hua Guo,Quanshui Zheng
出处
期刊:Magnetic Resonance in Medicine [Wiley]
卷期号:88 (2): 945-961 被引量:3
标识
DOI:10.1002/mrm.29256
摘要

Purpose The orientation distribution function (ODF), which is obtained from the radial integral of the probability density function weighted by ( is the radial length), has been used to estimate fiber orientations of white matter tissues. Currently, there is no general expression of the ODF that is suitable for any n value in the HARDI methods. Theory and methods A novel methodology is proposed to calculate the ODF for any through the Taylor series expansion and a generalized expression for is provided. Then a series of single‐shell HARDI methods, termed the general orientation transform (GOT), is developed based on the obtained expression. By combining complementary GOTs, a composite estimator is obtained and further optimized via constrained optimization to take full advantage of individual merits. The final optimized HARDI method is termed the combined GOT with constrained optimization (coGOT). The proposed method is compared with other commonly used HARDI methods on the simulated data, the physical phantom data, the ISMRM 2015 Tractography challenge data, and in vivo HCP datasets. Results coGOT can resolve crossing fibers with higher resolution, performs better robustness, generates fewer spurious lobes in glyphs, and thus provides distinct improvement in the tractography. The evaluations show coGOT's superior capability in reconstructing the fiber orientations from dMRI signals. Conclusions Generalization of the ODF allows us to obtain a wide range of HARDI estimators to select suitable candidates for composite formulation. The optimized estimator coGOT has great potential for studying neural architecture and serving as fiber tracking tools.

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
CipherSage应助zhuojiu采纳,获得10
刚刚
刚刚
大闲鱼铭一完成签到 ,获得积分10
刚刚
哦哦哦完成签到,获得积分10
1秒前
2秒前
繁荣的从露完成签到,获得积分10
3秒前
4秒前
啊喔完成签到,获得积分20
5秒前
慕青应助jack采纳,获得10
6秒前
7秒前
团子发布了新的文献求助10
8秒前
8秒前
闲之野鹤完成签到,获得积分10
9秒前
健忘向露关注了科研通微信公众号
9秒前
wy.he应助易安采纳,获得10
10秒前
H_完成签到 ,获得积分10
11秒前
Lesley完成签到 ,获得积分10
11秒前
12秒前
12秒前
13秒前
甜甜奇迹发布了新的文献求助10
14秒前
完美世界应助十分喜欢采纳,获得10
14秒前
16秒前
keep完成签到 ,获得积分10
16秒前
科研通AI6应助啊喔采纳,获得10
16秒前
19秒前
21秒前
浮游应助丝竹丛中墨未干采纳,获得10
22秒前
灿灿发布了新的文献求助20
23秒前
Jie完成签到,获得积分10
23秒前
量子星尘发布了新的文献求助10
24秒前
上官若男应助Cyuan采纳,获得10
25秒前
27秒前
27秒前
甜甜奇迹完成签到,获得积分10
28秒前
30秒前
健忘向露发布了新的文献求助10
30秒前
石友瑶发布了新的文献求助10
32秒前
zouni完成签到,获得积分10
32秒前
32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
Encyclopedia of Reproduction Third Edition 3000
Comprehensive Methanol Science Production, Applications, and Emerging Technologies 2000
化妆品原料学 1000
Psychology of Self-Regulation 600
1st Edition Sports Rehabilitation and Training Multidisciplinary Perspectives By Richard Moss, Adam Gledhill 600
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5638086
求助须知:如何正确求助?哪些是违规求助? 4744566
关于积分的说明 15001034
捐赠科研通 4796214
什么是DOI,文献DOI怎么找? 2562406
邀请新用户注册赠送积分活动 1521889
关于科研通互助平台的介绍 1481759