Augmented Reality Platform for Deep Brain Stimulation

用于深部脑刺激的增强现实平台

基本信息

  • 批准号:
    10132413
  • 负责人:
  • 金额:
    $ 46.2万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-04-01 至 2022-03-31
  • 项目状态:
    已结题

项目摘要

PROJECT SUMMARY Subthalamic deep brain stimulation (DBS) for the treatment of Parkinson's disease (PD) can be highly effective at improving motor symptoms and enhancing the patient's quality of life. However, the specific details of the anatomical target(s) for therapeutic stimulation remain unresolved. Recent DBS surgical targeting hypotheses have evolved to consider that direct stimulation of specific axonal pathways within the subthalamic region may be linked to the control of specific symptoms. Unfortunately, 3D anatomical characterization of the wide array of different axonal pathways in the human subthalamic region is very limited and techniques to visualize the complex neuroanatomy currently focus on 2D computer screens. These limitations hinder our ability to create accurate models and interpret the effects of DBS. Therefore, we propose that significant need exists for an anatomically driven model of subthalamic axonal pathways that can be interactively visualized with holographic 3D imaging and coupled to patient-specific DBS simulations. The goal of this Bioengineering Research Grant (PAR-16-242) is to create next generation visualization tools and surgical targeting models for clinical DBS. The first step of this study will rely on direct input from a collection of world experts in basal ganglia neuroanatomy to help us build a virtual 3D atlas model of 8 different axonal pathways in the subthalamic region. This development will occur within the HoloLens augmented reality (AR) environment, thereby enabling face-to-face discussion among the anatomy experts while visualizing the model hologram and its interactive adjustment. The second step of this study will evaluate the ability of various tractography algorithms to recreate the pathways described by the anatomy experts. We hypothesize that tractography will fail to accurately capture the anatomical trajectory of most subthalamic axonal pathways without extensive modeling constraints. Results from this analysis will have important implications for the rapid growth of tractography in DBS research, as well as clinical practice. Finally, we will translate our subthalamic axonal pathway model system into an interactive HoloLens AR application that works in concert with patient-specific MRI datasets and DBS pathway-activation modeling. We propose that such a tool will be especially useful for DBS surgical education and research investigation.
项目摘要 丘脑底脑深部电刺激(DBS)治疗帕金森病(PD)可以高度 有效改善运动症状并提高患者的生活质量。不过,具体细节 用于治疗刺激的解剖学目标仍然没有解决。近期DBS手术靶向 假设已经发展到考虑直接刺激底丘脑内特定轴突通路 区域可能与特定症状的控制有关。不幸的是, 在人类底丘脑区域中的各种不同的轴突通路是非常有限的, 可视化复杂的神经解剖目前集中在2D计算机屏幕上。这些限制阻碍了我们 创建准确模型并解释DBS效果的能力。因此,我们建议, 存在一个解剖驱动的模型,丘脑底轴突通路,可以交互式可视化 全息3D成像并结合患者特定的DBS模拟。 这项生物工程研究资助(PAR-16-242)的目标是创建下一代可视化 用于临床DBS的工具和手术靶向模型。这项研究的第一步将依赖于来自一个 收集了世界上基底神经节神经解剖学的专家,帮助我们建立了一个虚拟的3D图谱模型, 丘脑底区的轴突通路。这种发展将发生在HoloLens增强 现实(AR)环境,从而使解剖专家之间能够进行面对面的讨论, 可视化模型全息图及其交互式调整。本研究的第二步将评估 各种纤维束成像算法能够重建解剖专家描述的路径。我们 假设纤维束成像将不能准确地捕获大多数丘脑底核的解剖轨迹, 轴突通路而没有广泛的建模约束。这一分析的结果将具有重要意义。 对DBS研究中纤维束成像的快速发展以及临床实践的影响。最后我们将 将我们的底丘脑轴突通路模型系统转化为交互式HoloLens AR应用程序, 与患者特异性MRI数据集和DBS通路激活建模相一致。我们建议, 工具将是特别有用的DBS手术教育和研究调查。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Mark Griswold其他文献

Mark Griswold的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Mark Griswold', 18)}}的其他基金

Augmented Reality Platform for Deep Brain Stimulation
用于深部脑刺激的增强现实平台
  • 批准号:
    9893938
  • 财政年份:
    2018
  • 资助金额:
    $ 46.2万
  • 项目类别:
Optimization of MR Fingerprinting (MRF) for Quantitative MRI
定量 MRI 的 MR 指纹 (MRF) 优化
  • 批准号:
    8696434
  • 财政年份:
    2014
  • 资助金额:
    $ 46.2万
  • 项目类别:
Optimization of MR Fingerprinting (MRF) for Quantitative MRI
定量 MRI 的 MR 指纹 (MRF) 优化
  • 批准号:
    8820913
  • 财政年份:
    2014
  • 资助金额:
    $ 46.2万
  • 项目类别:
Optimization of MR Fingerprinting (MRF) for Quantitative MRI
定量 MRI 的 MR 指纹 (MRF) 优化
  • 批准号:
    9015440
  • 财政年份:
    2014
  • 资助金额:
    $ 46.2万
  • 项目类别:
Optimization of MR Fingerprinting (MRF) for Quantitative MRI
定量 MRI 的 MR 指纹 (MRF) 优化
  • 批准号:
    9242647
  • 财政年份:
    2014
  • 资助金额:
    $ 46.2万
  • 项目类别:
Magnetic Resonance Fingerprinting (MRF) for Improved High Field MR
用于改进高场 MR 的磁共振指纹识别 (MRF)
  • 批准号:
    9107869
  • 财政年份:
    2013
  • 资助金额:
    $ 46.2万
  • 项目类别:
Magnetic Resonance Fingerprinting (MRF) for Improved High Field MR
用于改进高场 MR 的磁共振指纹识别 (MRF)
  • 批准号:
    8721411
  • 财政年份:
    2013
  • 资助金额:
    $ 46.2万
  • 项目类别:
Magnetic Resonance Fingerprinting (MRF) for Improved High Field MR
用于改进高场 MR 的磁共振指纹识别 (MRF)
  • 批准号:
    8557778
  • 财政年份:
    2013
  • 资助金额:
    $ 46.2万
  • 项目类别:
Improved cardiac and vascular MRI using parallel imaging and compressed sensing
使用并行成像和压缩感知改进心脏和血管 MRI
  • 批准号:
    8586534
  • 财政年份:
    2010
  • 资助金额:
    $ 46.2万
  • 项目类别:
Improved cardiac and vascular MRI using parallel imaging and compressed sensing
使用并行成像和压缩感知改进心脏和血管 MRI
  • 批准号:
    8197605
  • 财政年份:
    2010
  • 资助金额:
    $ 46.2万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 46.2万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了