Development of a mobile and automated platform for multiplexed multi-modality imaging

开发用于多重多模态成像的移动自动化平台

• 批准号: 9347144
• 负责人: Ryan Gessner
• 金额: $ 108.32万
• 依托单位: SONOVOL, INC.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-15 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9347144
• 关键词: Acoustics; Address; Algorithmic Analysis; Algorithmic Software; Algorithms; Anatomy; Angiography; Animals; Antineoplastic Agents; Architecture; Assessment tool; Bioluminescence; Blood Vessels; Breast Cancer Model; Clinical; Computer Assisted; Contrast Media; Data; Data Set; Development; Devices; Evaluation; Human; Hybrids; Image; Image Analysis; Imaging Device; Imaging technology; Ionizing radiation; Legal patent; Link; Longitudinal Studies; Malignant Neoplasms; Maps; Methods; Modality; Modeling; Molecular Target; Morphology; Multimodal Imaging; Mus; Optics; Organ; Performance; Pharmaceutical Preparations; Phase; Protocols documentation; Research Personnel; Resolution; Robotics; Rodent; Scanning; Signal Transduction; Sledding; Structure; System; Three-dimensional analysis; Time; Tissues; Translational Research; Treatment outcome; Tumor Oxygenation; Tumor Tissue; Tumor Volume; Ultrasonography; Work; anatomic imaging; anticancer research; base; bioluminescence imaging; blood vessel visualization; cancer imaging; cancer therapy; clinically actionable; clinically relevant; clinically translatable; cost; cost effective; data integrity; design; imaging approach; imaging modality; imaging system; improved; in vivo; in vivo imaging; innovation; novel; optical imaging; photoacoustic imaging; portability; pre-clinical; pre-clinical research; preclinical trial; research and development; response; soft tissue; success; targeted agent; targeted imaging; tissue oxygenation; tool; translational cancer research; treatment response; tumor; tumor progression; tumor vascular supply; usability; validation studies; vascular sonography

Abstract Bioluminescence imaging (BLI) systems are installed in thousands of facilities and labs, and with their straightforward and low cost workflow for longitudinal studies, are the most commonly used preclinical modality for assessing tumor models in rodents. There currently is no high throughput and low cost system enabling BLI images to be combined with anatomical images of soft tissue to confirm tumor volume, context, or vascularity. In our Phase I work, we demonstrated the feasibility of mapping data from our whole body ultrasound (US) system to 2D BLI images. The additional US data dramatically reduced inter-user quantification variability of the BLI signal (>80%). Furthermore, we showed that our clinically translatable microvessel imaging technology, acoustic angiography, can be mapped to the BLI data. Those tumor microvessels were analyzed using patented vessel analysis algorithms, yielding quantifiable vascular morphology metrics, previously shown to be reliable predictors of tumor malignancy and response to therapy in humans. Thus our commercialized hybrid modality US+BLI device, the Alpheidae Platform, will allow angiogenic tumors and anti-angiogenic therapies to be studied in ways current in vivo imaging tools do not allow. In Phase II, we propose to bring the Alpheidae Platform to market, leveraging whole body tissue and vascular US imaging to improve cancer research with BLI. The team includes experts in optical imaging system design, photoacoustic system design, and US imaging system design. Specifically, in Phase II, we will address the following aims: (Aim 1) Hardware R&D for multi-animal tri-modality imaging. Six animals will be scanned sequentially by our robotic system in each of the three modes. Throughput for six animals will be <15 min. (Aim 2) Software and algorithm R&D to enable automated targeted US and PA acquisitions based on BLI images, and leveraging algorithms for improved spatial resolution in both US and PA datasets. (Aim 3) Validation studies within in vivo tumor drug response study. Device performance will be assessed by comparison to standard BLI in a murine breast cancer model. Its capacity to reliably predict eventual drug response within one week of starting therapy will be the criteria for success. Once Phase II is completed we will have created a novel high throughput and portable tool enabling tumor tissue and microvessel images to be mapped to BLI data. Importantly from a commercial perspective, the Alpheidae Platform can be sold for a fraction of what competitive systems cost. From a translational research perspective, the device includes a clinically translatable US microvessel imaging approach for tumor assessment, and thus forms a direct link between preclinical findings in mice and actionable clinical cancer assessment protocols.

摘要 生物发光成像（BLI）系统安装在数千个设施和实验室中， 纵向研究的简单和低成本工作流程是最常用的临床前模式 用于评估啮齿动物的肿瘤模型。目前还没有高吞吐量和低成本的系统使BLI 图像与软组织的解剖图像相结合，以确认肿瘤体积、背景或血管分布。 在我们的第一阶段工作中，我们证明了从我们的全身超声（US）映射数据的可行性 系统到2D BLI图像。额外的美国数据大大减少了用户间的量化 BLI信号的可变性（>80%）。此外，我们发现我们的临床可转移微血管 成像技术，声学血管造影，可以映射到BLI数据。这些肿瘤微血管 使用获得专利的血管分析算法进行分析，产生可量化的血管形态指标， 先前显示是人类肿瘤恶性程度和对治疗反应的可靠预测因子。因此我们的 商业化的混合模式US+BLI设备，Alpheidae平台，将允许血管生成肿瘤， 抗血管生成疗法将以当前体内成像工具不允许的方式进行研究。 在第二阶段，我们建议将Alpheidae平台推向市场，利用全身组织和血管 美国成像，以改善癌症研究与BLI。该团队包括光学成像系统设计专家， 光声系统设计和US成像系统设计。具体而言，在第二阶段，我们将处理 （目的1）多动物三模态成像系统的硬件研发。将扫描6只动物 我们的机器人系统在这三种模式中依次进行。6只动物的放置时间<15 min。 (Aim 2）软件和算法研发，以实现基于BLI的自动化目标US和PA收购 图像，并利用算法提高US和PA数据集的空间分辨率。(Aim第三章 体内肿瘤药物反应研究中的验证研究。器械性能将通过以下方式进行评估： 在鼠乳腺癌模型中与标准BLI进行比较。其可靠预测最终药物的能力 开始治疗后一周内的反应将是成功的标准。 一旦第二阶段完成，我们将创造一种新的高通量和便携式工具，使肿瘤 组织和微血管图像映射到BLI数据。从商业角度来看， Alpheidae平台的售价仅为竞争对手系统成本的一小部分。从一个转化研究 从另一个角度来看，该设备包括一种临床上可转移的肿瘤微血管成像方法， 评估，从而形成小鼠临床前发现和可操作的临床癌症之间的直接联系 评估协议。