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

• 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
• 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

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 通过肿瘤。 可视化神经元连接的连接依赖于纤维牵引结果 扩散MRI采集。 在小于40°的角度穿越。 神经外科和神经科学研究。 缺乏或有限的可视化皮质脊髓道的可视化，弧形筋膜的时间投影 和前视觉辐射，神经束对于保存尊重运动，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MRIY的启发 指纹识别的MR。 潜在的光纤配置的信号库。 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。 基础组织微观结构上的Tractographhy算法，提高了经过延伸的依从性。 对生物学真理。 该提案的最初目标是建立支撑方法的纤维性能 使用模拟，人类连接组协议数据集和仿生的空心纤维幻影 实现较小的角度分辨率，并验证动物模型的改进片段 在这些最初的目标中取得的进步意志遗嘱遗嘱以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牵引力，这是该提案的最终目标。 明显进一步实现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。