Fingerprinting-Based Neuronal Fiber Identification in Brain Surgery

脑外科手术中基于指纹识别的神经元纤维识别

基本信息

批准号：
10533354
负责人：
Steven H. Baete
金额：
$ 53.01万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10533354
关键词：
Adherence Affect Algorithms Animal Model Anterior Architecture Atlases Biological Biomimetics Brain Brain Neoplasms Brain imaging Clinical Complex Computer Simulation Corticospinal Tracts Data Data Set Dedications Detection Development Dictionary Diffusion Diffusion Magnetic Resonance Imaging Electric Stimulation Excision Fiber Fingerprint Functional Magnetic Resonance Imaging Generations Goals Healthcare Human Image Individual Language Lateral Libraries Location Magnetic Resonance Imaging Manganese Maps Measurement Measures Methods Modeling Motor Mus Nerve Neurons Neurosciences Research Neurosurgeon Noise Operative Surgical Procedures Optics Pathway interactions Patients Performance Play Postoperative Period Probability Process Protocols documentation Radiation Research Resected Resolution Risk Role Shapes Signal Transduction Statistical Methods Statistical Models Structure Surface Surgeon Time Tissues Validation Vision Visual Visualization Width Work brain surgery clinical application connectome diffusion weighted experimental study fundamental research improved in vivo mouse model neglect neurosurgery non-invasive imaging novel preservation resilience response simulation tool tractography tumor white matter

Adherence Affect Algorithms Animal Model Anterior Architecture Atlases Biological Biomimetics Brain Brain Neoplasms Brain imaging Clinical Complex Computer Simulation Corticospinal Tracts Data Data Set Dedications Detection Development Dictionary Diffusion Diffusion Magnetic Resonance Imaging Electric Stimulation Excision Fiber Fingerprint Functional Magnetic Resonance Imaging Generations Goals Healthcare Human Image Individual Language Lateral Libraries Location Magnetic Resonance Imaging Manganese Maps Measurement Measures Methods Modeling Motor Mus Nerve Neurons Neurosciences Research Neurosurgeon Noise Operative Surgical Procedures Optics Pathway interactions Patients Performance Play Postoperative Period Probability Process Protocols documentation Radiation Research Resected Resolution Risk Role Shapes Signal Transduction Statistical Methods Statistical Models Structure Surface Surgeon Time Tissues Validation Vision Visual Visualization Width Work brain surgery clinical application connectome diffusion weighted experimental study fundamental research improved in vivo mouse model neglect neurosurgery non-invasive imaging novel preservation resilience response simulation tool tractography tumor white matter

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项目摘要

PROJECT ABSTRACT When planning brain tumor surgery, neurosurgeons use MRI images to assess the location of tissues affected by the tumor. A key question in this planning is the location and/or displacement of important neuronal connections. For visualization of neuronal connections surgeons rely on fiber tractography results derived from diffusion MRI acquisitions. Current fiber tractography methods however fail to individually detect fiber bundles crossing at angles less than 40°. This fiber detection limitation hampers the reliability of fiber tractography in neurosurgery and neuroscience research. Indeed, in neurosurgery applications, tractography struggles with absent or limited visualization of the lateral corticospinal tract, temporal projections of the arcuate fasciculus and anterior optic radiations, nerve bundles essential for preservation of respectively motor, language and visual function. This proposal is dedicated to the development of novel fiber identification methods for diffusion MRI inspired by MR Fingerprinting. In fingerprinting approaches diffusion weighted signals are matched to a pre-computed signal library of potential fiber configurations. Our preliminary data show that fingerprinting-based fiber identification makes better use of the information available in the diffusion MRI measurement and hence outperforms current methods. This improved characterization of the diffusion signal will better inform tractography algorithms on the underlying tissue microstructure, increasing adherence of tractography results to the biological truth. The initial goals of the proposal are to establish the fiber identification performance of the proposed method using simulations, Human Connectome Protocol datasets and a biomimetic hollow fiber phantom in order to achieve smaller angular resolution and to validate the improved tractography results in an animal model. The progress made in these initial goals will be employed to retrospectively assess the impact of the proposed Fingerprinting-based fiber identification on fiber tractography in in vivo brain, the final goal of the proposal. Attainment of these goals will significantly further the adherence of tractography to tissue microstructure aiding in the understanding and visualizing of brain structure in both fundamental research and clinical applications.

项目摘要在计划脑肿瘤手术时，神经外科医生使用MRI图像评估受影响的组织的位置由肿瘤。该计划中的一个关键问题是重要神经元的位置和/或位移连接。为了可视化神经元连接，外科医生依赖于纤维拖拉术结果扩散MRI获取。但是，当前的纤维拖拉方法未能单独检测纤维束交叉小于40°。该纤维检测限制阻碍了纤维拖拉机的可靠性神经外科和神经科学研究。确实，在神经外科应用中，拖拉术与缺乏或有限的可视化皮质脊髓道的可视化，弓形筋膜的临时项目和前视觉辐射，神经束对于分别保存运动，语言和视觉功能。该建议致力于开发新型纤维识别方法，用于扩散MRI。指纹先生。在指纹方法中，扩散加权信号与预计潜在光纤配置的信号库。我们的初步数据表明，基于指纹的纤维识别可以更好地利用扩散MRI测量中可用的信息，因此胜过当前方法。扩散信号的这种改进的表征将更好地告知基础组织微观结构上的拖拉术算法，增加了拖拉术结果的依从性对生物学真理。该提案的最初目标是建立拟议方法的纤维识别性能使用模拟，人类连接协议数据集和仿生空心纤维幻影实现较小的角分辨率并验证改进的拖拉术导致动物模型。这这些初始目标中取得的进展将用于回顾性评估拟议的影响基于指纹的纤维识别体内大脑的纤维拖拉术，该提案的最终目标。实现这些目标将显着进一步遵守拖拉机对组织微观结构的依从性在理解和可视化基础研究和临床应用中的大脑结构中。