High-definition fiber tracking guidance for intraparenchymal endoscopic port surgery

医学 内囊 扣带回(脑) 神经导航 外科 内窥镜检查 胼胝体 病变 解剖 放射科 磁共振弥散成像 磁共振成像 切除术 白质 部分各向异性
作者
Juan C. Fernandez‐Miranda,Johnathan A. Engh,Sudhir Pathak,Ricky Madhok,Fernando E. Boada,Walter Schneider,Amin Kassam
出处
期刊:Journal of Neurosurgery [American Association of Neurological Surgeons]
卷期号:113 (5): 990-999 被引量:48
标识
DOI:10.3171/2009.10.jns09933
摘要

The authors have applied high-definition fiber tracking (HDFT) to the resection of an intraparenchymal dermoid cyst by using a minimally invasive endoscopic port. The lesion was located within the mesial frontal lobe, septal area, hypothalamus, and suprasellar recess. Using high-dimensional (256 directions) diffusion imaging, more than 250,000 fiber tracts were imaged before and after surgery. Trajectory planning using HDFT in a computer model was used to facilitate cannulation of the cyst with the endoscopic port. Analysis of the proposed initial surgical route was overlaid onto the fiber tracts and was predicted to produce substantial disruption to prefrontal projection fibers (anterior limb of the internal capsule) and the cingulum. Adjustment of the cannulation entry point 1 cm medially was predicted to cross the corpus callosum instead of the anterior limb of the internal capsule or the cingulum. Following cyst resection performed using endoscopic port surgery, postoperative imaging demonstrated accurate cannulation of the lesion, with improved quantitative signal from both the anterior limb of the internal capsule and the cingulum. The observed fiber preservation from the cingulum and the anterior limb of the internal capsule, with minor injury to the corpus callosum, was in close agreement with preoperative trajectory modeling. Comparison of pre- and postoperative HDFT data facilitated quantification of the benefits and costs of the surgical trajectory. Future studies will help to determine whether HDFT combined with endoscopic port surgery facilitates anatomical and functional preservation in such challenging cases.
最长约 10秒,即可获得该文献文件

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
1秒前
1秒前
2秒前
YANG完成签到,获得积分10
2秒前
3秒前
早早发布了新的文献求助10
3秒前
hcyl发布了新的文献求助10
4秒前
YANG发布了新的文献求助10
5秒前
v0id应助锦程采纳,获得10
6秒前
何曼慈应助111采纳,获得10
6秒前
molihuakai应助wittig采纳,获得10
6秒前
cxs发布了新的文献求助10
7秒前
英俊的铭应助阔达的心情采纳,获得10
7秒前
8秒前
研研发布了新的文献求助10
8秒前
liaomr完成签到,获得积分10
8秒前
阿飞发布了新的文献求助10
11秒前
xiiiiiiii完成签到,获得积分10
11秒前
搜集达人应助sunwx采纳,获得10
11秒前
11秒前
大个应助hcyl采纳,获得10
12秒前
cici完成签到,获得积分10
12秒前
ywl完成签到,获得积分20
13秒前
li完成签到,获得积分10
14秒前
14秒前
英俊的铭应助噼里啪啦采纳,获得10
15秒前
研友发布了新的文献求助10
15秒前
云梦泽发布了新的文献求助10
15秒前
汉堡包应助湛湛采纳,获得10
16秒前
17秒前
卡皮巴拉发布了新的文献求助10
18秒前
Emper发布了新的文献求助10
18秒前
狂野的蜡烛完成签到,获得积分10
18秒前
典雅的访风完成签到,获得积分10
18秒前
19秒前
共享精神应助尊敬灵采纳,获得10
20秒前
巴哒发布了新的文献求助10
20秒前
20秒前
木南发布了新的文献求助10
21秒前
21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 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
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
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 7584040
求助须知:如何正确求助?哪些是违规求助? 9162784
关于积分的说明 19608034
捐赠科研通 7165941
什么是DOI,文献DOI怎么找? 3266349
关于科研通互助平台的介绍 2431328
邀请新用户注册赠送积分活动 2257917