已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

How Accurate Are the Fusion of Cone-Beam CT and 3-D Stereophotographic Images?

锥束ct 叠加 人工智能 均方误差 核医学 颅面 数学 计算机科学 医学 计算机视觉 计算机断层摄影术 放射科 统计 精神科
作者
Yasas S. N. Jayaratne,Colman McGrath,Roger A. Zwahlen
出处
期刊:PLOS ONE [Public Library of Science]
卷期号:7 (11): e49585-e49585 被引量:54
标识
DOI:10.1371/journal.pone.0049585
摘要

Cone-beam Computed Tomography (CBCT) and stereophotography are two of the latest imaging modalities available for three-dimensional (3-D) visualization of craniofacial structures. However, CBCT provides only limited information on surface texture. This can be overcome by combining the bone images derived from CBCT with 3-D photographs. The objectives of this study were 1) to evaluate the feasibility of integrating 3-D Photos and CBCT images 2) to assess degree of error that may occur during the above processes and 3) to identify facial regions that would be most appropriate for 3-D image registration.CBCT scans and stereophotographic images from 29 patients were used for this study. Two 3-D images corresponding to the skin and bone were extracted from the CBCT data. The 3-D photo was superimposed on the CBCT skin image using relatively immobile areas of the face as a reference. 3-D colour maps were used to assess the accuracy of superimposition were distance differences between the CBCT and 3-D photo were recorded as the signed average and the Root Mean Square (RMS) error.The signed average and RMS of the distance differences between the registered surfaces were -0.018 (±0.129) mm and 0.739 (±0.239) mm respectively. The most errors were found in areas surrounding the lips and the eyes, while minimal errors were noted in the forehead, root of the nose and zygoma.CBCT and 3-D photographic data can be successfully fused with minimal errors. When compared to RMS, the signed average was found to under-represent the registration error. The virtual 3-D composite craniofacial models permit concurrent assessment of bone and soft tissues during diagnosis and treatment planning.

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
淡定傲儿发布了新的文献求助10
刚刚
lwl驳回了Ava应助
刚刚
大个应助GSR采纳,获得10
刚刚
yizzy完成签到,获得积分20
1秒前
ricer完成签到 ,获得积分10
1秒前
有病早治完成签到 ,获得积分10
2秒前
茯苓完成签到,获得积分10
2秒前
ricer关注了科研通微信公众号
4秒前
5秒前
6秒前
张欢馨应助非而者厚采纳,获得10
6秒前
白石人家发布了新的文献求助10
9秒前
英姑应助欢喜的凡蕾采纳,获得10
10秒前
11秒前
在水一方应助小季丶二五采纳,获得10
12秒前
13秒前
昵猜发布了新的文献求助10
13秒前
Hello应助FC.M采纳,获得10
13秒前
14秒前
LiSiyi完成签到 ,获得积分10
14秒前
14秒前
mojiali完成签到 ,获得积分10
14秒前
淡定傲儿完成签到,获得积分20
16秒前
crazydick发布了新的文献求助10
18秒前
19秒前
昵猜完成签到,获得积分20
20秒前
20秒前
棋士应助相龙采纳,获得10
20秒前
20秒前
明理的亦寒完成签到 ,获得积分10
22秒前
姜糊完成签到 ,获得积分10
23秒前
23秒前
24秒前
科研通AI6.4应助典雅听枫采纳,获得10
25秒前
夜月残阳发布了新的文献求助10
26秒前
甜甜的问丝完成签到,获得积分10
32秒前
34秒前
烟花应助开放世界采纳,获得10
35秒前
35秒前
互助完成签到,获得积分0
37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
Health Psychology 600
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
Additive Manufacturing Design and Applications (ASM Handbook, Volume 24A) 500
Variations: A More Diverse Picture of Contemporary Art 400
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 7591077
求助须知:如何正确求助?哪些是违规求助? 9168476
关于积分的说明 19624617
捐赠科研通 7169854
什么是DOI,文献DOI怎么找? 3267424
关于科研通互助平台的介绍 2432229
邀请新用户注册赠送积分活动 2259715