Improvement in imaging technologies, such as those featured in this Center, have the potential for dramatically improving human health. This potential will not be realized, however, unless the imaging can be fully utilized at the time of medical intervention and is widely available.

To this end, NCIBT aims to advance two highly promising optical technologies, interventional fluorescence lifetime imaging (iFLIM in TRD-1) and interferometric Diffuse Optical Spectroscopy (iDOS in TRD-2), and also to develop an artificial intelligence-deep learning (AI-DL) enabled platform capable of real-time guidance of decision making during medical and surgical procedures (in TRD-3). Both technologies and the platform will be widely disseminated, through training and education, to ensure availability.

Our technologies provide unique advantages of imaging content, resolution, scale, and tissue-specific contrast. Each technique brings to its respective field a new level of quantitative accuracy; iDOS provides quantitative assessment of optical properties, while iFLIM provides fluorescence measures that are relatively independent of optical properties. Created at UC Davis and sharing potential for synergistic development and intraprocedural use, they are poised for rapid technological improvement and further clinical translation. The platform will also be able to incorporate input from other optical and non-optical imaging modalities and thus serve as a test bed for the technologies of the TRDs and for other emerging intraprocedural approaches.

Interventional Fluorescence Lifetime Imaging (iFLIM)

Technologically advancing and integrating iFLIM technology in clinical settings for real-time in-situ tissue diagnosis and surgical guidance.

Interferometric Diffuse Optical Spectroscpy (iDOS)

Advancing a novel iDOS technology for continuous and non-invasive blood flow monitoring in critical clinical scenarios.

Intelligent data analytics and systems design: AI-ML-DL-VIS

Developing and validating a set of advanced analytical methods including artificial intelligence (AI), machine learning (ML), and deep leaning (DL) for intelligent instrument design, data/image analysis, visualization (VIS), and clinical decision making.