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

摘要 生物发光成像(BLI)系统安装在数以千计的设施和实验室中，其 用于纵向研究的简单且低成本的工作流程是最常用的临床前研究方式 用于评估啮齿动物的肿瘤模型。目前还没有支持BLI的高吞吐量和低成本系统 图像与软组织的解剖图像相结合，以确定肿瘤的体积、背景或血管。 在我们的第一阶段工作中，我们演示了绘制来自全身超声(US)的数据的可行性 系统转换为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(&gt；80%)。此外，我们还展示了我们临床上可翻译的微血管 成像技术，声学血管成像，可以映射到BLI数据。这些肿瘤微血管是 使用专利血管分析算法进行分析，产生可量化的血管形态度量， 以前被证明是人类肿瘤恶性程度和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和 抗血管生成治疗需要以目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 美国成像与BLI合作改善癌症研究。该团队包括光学成像系统设计方面的专家， 光声系统设计，以及美国成像系统设计。具体地说，在第二阶段，我们将解决 主要研究目标如下：(1)多动物三通道成像硬件研发。将对六只动物进行扫描 依次由我们的机器人系统在三种模式中的每一种模式下进行。六只动物的吞吐量为15分钟。 (目标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，并利用算法来提高US和PA数据集中的空间分辨率。(目标3) 体内肿瘤药物反应研究中的验证研究。将通过以下方式评估设备性能 与标准BLI在小鼠乳腺癌模型中的比较。它能够可靠地预测最终的药物 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，我们将创造出一种新的高通量和便携式肿瘤工具 要映射到BLI数据的组织和微血管图像。重要的是，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。来自一项翻译研究 从角度来看，该设备包括临床上可翻译的美国肿瘤微血管成像方法 评估，并因此在小鼠的临床前发现和可操作的临床癌症之间形成直接联系 评估方案。

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.