In vivo Macroscopic Fluorescence Lifetime Molecular Optical Imaging

体内宏观荧光寿命分子光学成像 摘要 在体内，特别是在临床之前，仍需要更好地确定新的靶向治疗的特征。 翻译。在这方面，临床前分子成像是靶向药物开发管道中的中心工具。 然而，仍然缺乏能够实现纵向(多时间点)的集成成像平台 对包括分子、代谢和功能在内的复杂指纹进行空间分辨率监测 同一肿瘤/受试者的签名。这一新的集成多路传输成像平台将发挥关键作用 在开发下一代靶向药物和阐明(多重)耐药机制方面。 最近，我们展示了光学成像在量化受体靶标方面的独特能力。 通过利用荧光寿命参与活体受试者。这一突出成就是实现的 由于仪器、算法和生物化学创新的结合，第一次能够， 基于二维或三维Förster共振能量结构光的全身时间分辨光学成像 活体受试者的转移(FRET)成像。在此，我们将进一步冲击光学临床前成像领域和 药物传递评估：1)集成尖端高分辨率、时间分辨率SPAD阵列成像仪 提高了光子收集效率、空间分辨率和便携性；2)我们将利用最新的 深度学习(DL)在超快、定量、但无需拟合/迭代的无逆解算图像方面的发展 以2D和3D形式提供图像形成/处理解决方案，以促进多路传输成像；3) 我们将在我们的成像平台中实施新功能，以支持并行纵向成像 多种临床相关的靶-受体相互作用(例如，她的受体家族成员参与了许多 癌症)以及活的完整动物的整个肿瘤区域的代谢和功能状态。

In vivo Macroscopic Fluorescence Lifetime Molecular Optical Imaging ABSTRACT There is still great need in better characterizing new targeted therapies in vivo, especially prior to clinical translation. In this regard, preclinical molecular imaging is a central tool in the targeted drug development pipeline. However, there is still a lack of integrated imaging platforms that can enable longitudinal (multiple time points) and spatially-resolved monitoring of complex fingerprints including molecular, metabolic and functional signatures in the same tumor/subject. This new integrated multiplexing imaging platform will play a crucial role in the development of the next generation of targeted drugs and elucidating (multi-) drug resistance mechanisms. Recently, we have demonstrated the unique capabilities of optical imaging in quantifying receptor-target engagement in live subjects by leveraging fluorescence lifetime. This outstanding achievement was realized thanks to the combination of instrumental, algorithmic and biochemical innovations to enable, for the first time, whole-body time-resolved optical imaging based on structured light for 2D or 3D Förster resonance energy transfer (FRET) imaging in live subjects. Herein, we will further impact the field of optical preclinical imaging and drug delivery assessment by 1) integrating a cutting-edge high-resolution, time-resolved SPAD array imager for improved photon collection efficiency, spatial resolution, and portability; 2) we will harness the latest developments in Deep Learning (DL) for ultra-fast, quantitative, but fitting/iterative inverse solver-free image formation, in both 2D and 3D providing image formation/processing solutions to facilitate multiplexed imaging; 3) we will implement new functionalities in our imaging platform to enable the concurrent longitudinal imaging of multiple clinically relevant target-receptor interactions (e.g. HER receptor family members involved in many cancers) as well as metabolic and functional status across the whole-tumor region in live intact animals.