Photoacoustic Image Guidance of Hysterectomies

子宫切除术的光声图像指导

基本信息

  • 批准号:
    10586827
  • 负责人:
  • 金额:
    $ 35.94万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-08-01 至 2027-05-31
  • 项目状态:
    未结题

项目摘要

Project Summary Ureteral injury represents one of the most serious complications of pelvic surgery, with a majority of these injuries occurring during gynecological procedures. This injury is particularly problematic during hysterectomies because of the proximity between the ureter and nearby blood vessels. One barrier to progress is the absence of clinically available technology to identify relative positions of the ureter, uterine arteries, and tool tips with suf- ficient depth penetration and image contrast. We previously demonstrated that photoacoustic imaging achieves simultaneous detection of critical structures with approximately 25-30 dB contrast at centimeter depths, allowing for complete avoidance of the ureter and better targeting of the uterine arteries. However, to advance this tech- nology into surgical practice, we need to establish the optical, acoustic, and navigation parameters necessary to achieve optimal detection of tool tips, blood vessels, and ureters. Optimizing photoacoustic imaging system designs and providing informative real-time feedback during hysterectomies will enable these surgeries to be performed without the complications that are typically associated with ureteral injuries, including extensive re- peat surgeries, complete kidney failure, sepsis, acute renal insufficiency, and patient death. Our long-term goal is to develop guidance technology to differentiate critical structures in real-time during surgery. The overall objective of this proposal is to establish optimal parameters to advance photoacoustic technol- ogy toward differentiation of ureters, uterine arteries, and tool tips during hysterectomies. Aim 1 of this project will define the light delivery requirements for optimal visibility of laparoscopic surgical tool tips and underlying structures. Aim 2 will integrate and optimize sound reception components and parameters for photoacoustic imaging of the ureter, uterine artery, and tool tips. Aim 3 will pursue in vivo demonstrations of robotic hysterec- tomy navigation with photoacoustic imaging system components. These three aims will be tested independently with a combination of simulation, cadaver, swine, and human patient studies, resulting in multiple possibilities for deploying the proposed technology. Successful completion of the proposed project will establish a series of viable photoacoustic imag- ing system designs to enable ureter avoidance during hysterectomies. This project is innovative because of the novel integration and refinement of photoacoustic approaches and techniques to distinguish the ureter from the uterine artery. The project results are anticipated to have a significant impact on patients undergoing laparoscopic hysterectomies, robotic hysterectomies, and other robotic surgeries (e.g., radical prostatectomies, thoracic surgeries), with possible extensions to additional surgeries wherein critical structures reside in close proximity. The proposed research aligns with NIBIB’s mission to accelerate the application of biomedical tech- nologies by supporting research to advance the development of new tools for visualizing critical structures to target or avoid during minimally invasive surgeries.
项目摘要 输尿管损伤是骨盆手术最严重的并发症之一,其中大部分是 妇科手术中发生的损伤。这种损伤在子宫切除术中尤其严重。 因为输尿管和附近血管之间的距离很近。进步的一个障碍是缺席 临床可用的技术,以确定输尿管、子宫动脉和工具提示的相对位置,通过超声检查 充分的深度渗透和图像对比度。我们以前曾演示过,光声成像可以实现 在厘米深度同时检测对比度约为25-30分贝的关键结构,允许 为了完全避开输尿管,更好地瞄准子宫动脉。然而,为了推进这项技术- 在外科实践中,我们需要建立必要的光学、声学和导航参数 以实现对工具提示、血管和输尿管的最佳检测。优化光声成像系统 在子宫切除术期间设计和提供信息丰富的实时反馈将使这些手术 手术中没有输尿管损伤的并发症,包括广泛的再手术。 泥炭手术、完全性肾功能衰竭、败血症、急性肾功能不全和病人死亡。我们的长期目标 是开发引导技术,以便在手术过程中实时区分关键结构。 这项建议的总体目标是建立最佳参数,以促进光声技术的发展。 子宫切除术中输尿管、子宫动脉和工具提示的区分。本项目的目标1 将定义为最佳可见性的腹腔镜手术工具尖端和基础的光传输要求 结构。AIM 2将集成和优化光声接收组件和参数 输尿管、子宫动脉和工具提示的成像。目标3将进行机器人磁滞的活体演示- 我的导航系统带有光声成像系统组件。这三个目标将独立进行测试 通过模拟、身体、猪和人类患者研究的组合,导致了多种可能性 部署建议的技术。 拟议项目的成功完成将建立一系列可行的光声图像- ING系统设计用于在子宫切除术中避免输尿管。这个项目具有创新性,因为 用于区分输尿管的光声方法和技术的新的集成和改进 来自子宫动脉。该项目的结果预计将对接受治疗的患者产生重大影响 腹腔镜子宫切除术、机器人子宫切除术和其他机器人手术(例如,根治性前列腺切除术, 胸部手术),可能扩展到关键结构位于附近的附加手术 接近。这项拟议的研究与NIBIB加快生物医学技术应用的使命一致。 通过支持研究来推进将关键结构可视化的新工具的开发,以 在微创手术中瞄准或避免。

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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Muyinatu A. Lediju Bell其他文献

Overfit detection method for deep neural networks trained to beamform ultrasound images
用于训练以对超声图像进行波束形成的深度神经网络的过拟合检测方法
  • DOI:
    10.1016/j.ultras.2024.107562
  • 发表时间:
    2025-04-01
  • 期刊:
  • 影响因子:
    4.100
  • 作者:
    Jiaxin Zhang;Muyinatu A. Lediju Bell
  • 通讯作者:
    Muyinatu A. Lediju Bell
Deep Learning-Based Displacement Tracking for Post-Stroke Myofascial Shear Strain Quantification
基于深度学习的位移跟踪,用于中风后肌筋膜剪切应变量化
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Md Ashikuzzaman;Jonny Huang;Steve Bonwit;Azin Etemadimanesh;Preeti Raghavan;Muyinatu A. Lediju Bell
  • 通讯作者:
    Muyinatu A. Lediju Bell
Mitigating skin tone bias in linear array emin vivo/em photoacoustic imaging with short-lag spatial coherence beamforming
利用短滞后空间相干波束形成减轻线性阵列体内/体外光声成像中的肤色偏差
  • DOI:
    10.1016/j.pacs.2023.100555
  • 发表时间:
    2023-10-01
  • 期刊:
  • 影响因子:
    6.800
  • 作者:
    Guilherme S.P. Fernandes;João H. Uliana;Luciano Bachmann;Antonio A.O. Carneiro;Muyinatu A. Lediju Bell;Theo Z. Pavan
  • 通讯作者:
    Theo Z. Pavan

Muyinatu A. Lediju Bell的其他文献

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{{ truncateString('Muyinatu A. Lediju Bell', 18)}}的其他基金

Minimizing Uncertainty in Breast Ultrasound Imaging with Real-Time Coherence-Based Beamforming
通过基于实时相干的波束形成最大限度地减少乳房超声成像的不确定性
  • 批准号:
    10417922
  • 财政年份:
    2022
  • 资助金额:
    $ 35.94万
  • 项目类别:
Minimizing Uncertainty in Breast Ultrasound Imaging with Real-Time Coherence-Based Beamforming
通过基于实时相干的波束形成最大限度地减少乳房超声成像的不确定性
  • 批准号:
    10679017
  • 财政年份:
    2022
  • 资助金额:
    $ 35.94万
  • 项目类别:
A Machine Learning Alternative to Beamforming to Improve Ultrasound Image Quality for Interventional Access to the Kidney
波束成形的机器学习替代方案可提高肾脏介入治疗的超声图像质量
  • 批准号:
    10170765
  • 财政年份:
    2020
  • 资助金额:
    $ 35.94万
  • 项目类别:
A Machine Learning Alternative to Beamforming to Improve Ultrasound Image Quality for Interventional Access to the Kidney
波束成形的机器学习替代方案可提高肾脏介入治疗的超声图像质量
  • 批准号:
    9913520
  • 财政年份:
    2018
  • 资助金额:
    $ 35.94万
  • 项目类别:
Coherence-Based Photoacoustic Image Guidance of Transsphenoidal Surgeries
基于相干性的光声图像引导经蝶手术
  • 批准号:
    8891530
  • 财政年份:
    2015
  • 资助金额:
    $ 35.94万
  • 项目类别:
Coherence-Based Photoacoustic Image Guidance of Transsphenoidal Surgeries
基于相干性的光声图像引导经蝶手术
  • 批准号:
    9043878
  • 财政年份:
    2015
  • 资助金额:
    $ 35.94万
  • 项目类别:

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