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Development of an ultrasound-optical hybrid modality preclinical imaging tool

Development of an ultrasound-optical hybrid modality preclinical imaging tool
超声光学混合模态临床前成像工具的开发
批准号:
8832323
负责人:
Ryan Gessner
金额:
$21.75万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-04-15 至 2016-09-30

项目摘要

项目成果

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中文摘要
翻译
描述(由申请人提供):临床前动物模型被广泛用于癌症研究,以评估药物疗效和毒性,并更好地了解疾病的复杂基本潜在过程。体内成像研究使研究人员能够纵向评估肿瘤的存在、功能状态,并对治疗做出反应,而不需要为每个读取点牺牲动物。解剖成像方式(MRI、CT和超声)能够可视化和评估组织结构,这对于定位通过分子成像方式(PET、SPECT和光学)获得的信号是必要的,分子成像方式评估组织的功能状态(肿瘤代谢需求、分子信号表达、药物生物分布等)。超声波是解剖学中最便宜的。 然而,目前市场上还没有用于全身成像的超声产品,因此研究人员通常采用基于MR或CT的解剖成像来进行双通道研究。磁共振和CT成像研究缓慢、昂贵,并降低了研究的吞吐量。我们建议建立一个高通量和低成本的超声-光学混合模式系统,以加快临床前药物研究,同时降低成本。我们的公司SonoVol是北卡罗来纳大学Paul Dayton博士的超声成像实验室和Stephen Aylward博士在Kitware的图像分析实验室几年来紧密合作的学术和行业研究的结果。与MR或CT不同,我们的SonoVol设备是一种廉价的台式成像系统,可以在不到5分钟的时间内捕获全身老鼠图像。我们正在提议建立售后市场所需的硬件和软件组件,以便在现有的商业成像系统之间转移鼠标,从而在全身解剖超声图像和生物发光图像之间创建融合。我们将在受控的体外环境和实施SonoVol专有硬件的试验性小动物研究中测试该系统。这款SonoVol设备允许在动物周围操纵任何超声探头,以建立具有凝聚力的全身3D体积,并利用几种强大的图像处理和分析工具来对齐两种模式,从而实现解剖图像和功能图像之间的一对一映射。此外,还可以在运行中将超声图像定向到鼠标的 体内有较强的生物发光信号表达。该产品商业化的下一阶段将是建立一个专用系统,该系统不需要在系统之间进行动物的物理转移,从而进一步增加产量。
英文摘要
DESCRIPTION (provided by applicant): Preclinical animal models are used extensively in cancer research to evaluate drug efficacy and toxicity and to better understand the disease's complex fundamental underlying processes. In vivo imaging studies enable researchers to longitudinally assess tumor presence, functional status, and response the therapy without the need to sacrifice animals for each read point. Anatomical imaging modalities (MRI, CT, and Ultrasound) enable the visualization and assessment of tissue structures, which are necessary to localize the signals acquired via the molecular imaging modalities (PET, SPECT, and Optical), which assess the functional status of tissues (tumor metabolic demand, molecular signal expression, drug biodistribution, etc.). Ultrasound is the least expensive of the anatomical modalities with the fastest acquisition time, but there is no ultrasound product on the market for whole body imaging, thus researchers often resort to MR or CT based anatomical imaging for their dual modality studies. MR and CT imaging studies are slow, expensive, and reduce study throughput. We propose to build a high throughput and low cost ultrasound-optical hybrid modality system which could speed up preclinical drug research, as well as drive down costs. Our company, SonoVol, is the result of several years of strong collaborative academic-industry research between Dr. Paul Dayton's ultrasound imaging lab at UNC and Dr. Stephen Aylward's image analysis lab at Kitware. Unlike MR or CT, our SonoVol device is an inexpensive and benchtop imaging system which can capture a whole body mouse image in less than 5 minutes. We are proposing to build the after- market hardware and software components necessary to transfer a mouse between existing commercially available imaging systems to create a fusion between a whole-body anatomical ultrasound image, and a bioluminescence image. We will test this system in both a controlled in vitro environment, as well as a pilot small animal study implementing SonoVol's proprietary hardware. This SonoVol device allows any ultrasound probe to be manipulated around an animal to build up a cohesive whole-body 3D volume, as well as leverage several powerful image processing and analysis tools to align the two modalities, allowing one-to-one mapping between the anatomical and functional images. Furthermore, it will be possible to target ultrasound images, on-the-fly, to regions in the mouse's body which have strong bioluminescence signal expression. The next phase of commercialization of this product will be to build a dedicated system which does not require a physical transfer of the animal between systems, thereby further increasing throughput.
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A turnkey research platform to accelerate clinical translation of focused-ultrasound (FUS) oncology therapies
  • 批准号:
    9908739
  • 项目类别:
  • 资助金额:
    $30.0万
  • 财政年份:
    2019
  • 负责人:
    Ryan Gessner
  • 依托单位:
Development of a mobile and automated platform for multiplexed multi-modality imaging
  • 批准号:
    9347144
  • 项目类别:
  • 资助金额:
    $108.32万
  • 财政年份:
    2015
  • 负责人:
    Ryan Gessner
  • 依托单位:
海外基金