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
不期而遇完成签到 ,获得积分10
刚刚
xing_xing应助科研通管家采纳,获得20
刚刚
漂亮白柏完成签到,获得积分10
刚刚
刚刚
汉堡包应助科研通管家采纳,获得10
刚刚
lili应助科研通管家采纳,获得20
刚刚
乐乐应助科研通管家采纳,获得10
1秒前
Ava应助科研通管家采纳,获得10
1秒前
1秒前
所所应助科研通管家采纳,获得10
1秒前
我是老大应助科研通管家采纳,获得10
1秒前
Lucas应助科研通管家采纳,获得10
2秒前
Lio完成签到,获得积分10
2秒前
上官若男应助科研通管家采纳,获得10
2秒前
liuxianjia完成签到,获得积分10
2秒前
星辰大海应助科研通管家采纳,获得10
2秒前
CipherSage应助科研通管家采纳,获得10
2秒前
FashionBoy应助科研通管家采纳,获得10
2秒前
科目三应助科研通管家采纳,获得10
2秒前
CC应助科研通管家采纳,获得10
3秒前
小蘑菇应助LXAYUI采纳,获得10
3秒前
汉堡包应助爱听歌火龙果采纳,获得10
3秒前
4秒前
5秒前
cdercder应助漂亮白柏采纳,获得10
5秒前
6秒前
尊敬的驳完成签到,获得积分10
6秒前
CipherSage应助小熊采纳,获得10
7秒前
负责的灭男完成签到 ,获得积分10
7秒前
充电宝应助赵赵采纳,获得10
7秒前
7秒前
荔枝完成签到,获得积分10
8秒前
8秒前
9秒前
戈天完成签到,获得积分10
9秒前
彼方250521完成签到,获得积分10
10秒前
漂亮糖豆完成签到,获得积分10
10秒前
吴迪发布了新的文献求助10
11秒前
nwds完成签到,获得积分10
11秒前
dcx完成签到 ,获得积分10
12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
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Navigating Normative Orders. Interdisciplinary Perspectives 800
1 Peter and Christ's Descent to the Dead in Its Early Christian Reception 700
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
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 7750236
求助须知:如何正确求助?哪些是违规求助? 9297885
关于积分的说明 20243085
捐赠科研通 7331999
什么是DOI,文献DOI怎么找? 3309594
关于科研通互助平台的介绍 2461167
邀请新用户注册赠送积分活动 2321977