Liang Gao Technology Portfolio

Squeezed Light Field Microscopy (SLIM) (Case No. 2024-153)

UCLA researchers have developed a novel method titled Squeezed Light Field Microscopy (SLIM) to enable precise 3D imaging in real time in the NIR-II window. SLIM leverages computational imaging to provide high-resolution 3D images using a single, low-resolution camera sensor. This imaging modality is capable of depth retrieval, post-capture refocusing and extended depth of field, all necessary for surgical precision. An added benefit of SLIM beyond imaging capability is the reduced data redundancy of its output, resulting in a smaller dataset and fast readout. Ultimately, this technology represents a step forward in NIR-II imaging, improving intraoperative guidance and precision.

Potential Applications:

• Neuro and cardiovascular procedures
• Tumor visualization
• 3D imaging of complex anatomic structures like brains, bones, and eyes
• Preoperative planning

Advantages:

• High resolution (micron-scale) imaging
• Increased penetration depth – several centimeters into tissue
• Improved signal-to-background ratios
• Real-time 3D visualization
• Reduced cost and data load

Light-Field Tomographic Fluorescence Lifetime Imaging Microscopy (Case No. 2023-130)

UCLA researchers led by Dr. Liang Gao have advanced a computational imaging method, light-field tomographic fluorescence lifetime imaging microscopy, LIFT-FLIM, which enables use of low dimensional detectors for high dimensional imaging. The method transforms volumetric images into lines which can then be recorded by linear SPAD cameras, which are lower cost and more accessible to general research labs. This technique shows unparalleled single-photon sensitivity and post-processing time of less than 0.3 seconds.

Potential Applications:

Basic research
Translational research
Organoid imaging
Biological and clinical samples

Advantages:

3D imaging using low-cost detectors
Fast processing time
High pixel fill factor
Compatible with spectral FLIM

Ultrafast Light Field Tomography (LIFT) (Case No. 2020-801)

Non-line-of sight (NLOS) imaging is an important technique that enables ultrafast (picosecond exposure) cameras to visualize objects that are hidden from direct view. Widespread implementation of NLOS is limited by the requirement of a high-resolution, two-dimensional ultrafast camera that can process a long sequence of time-resolved data. Current NLOS-enabled cameras must perform scanning in spatial and/or temporal dimensions, lengthening the acquisition time to seconds and restricting the imaging to static or slowly-moving objects. Therefore, an ultrafast camera capable of imaging objects that are both rapidly moving and out of the field of focus is needed to realize the full utility of NLOS. UCLA researchers in the Department of Bioengineering have developed a technology that enables video-quality, NLOS imaging by capturing the complete four-dimensional space (x, y, z, and time) in a single snapshot. The method, light field tomography (LIFT), exhibited exceptional resolution even when objects were in rapid motion as demonstrated by 3D imaging of a light pulse in a fiber optic at 0.5 trillion frames per second. Further, LIFT is adaptable through deep learning strategies which can be used to improve image quality and vastly accelerate image formation. Overall, LIFT could unravel new insights in the study of ultrafast phenomena and facilitate the broad adoption of time-resolved imaging across various disciplines.

Patent:

Ultrafast light field tomography

Potential Applications:

Cameras
Photonics
Medical imaging
Autonomous vehicle sensing
Sensors

Advantages:

Video capture of non-line-of-sight objects
4D capture of objects (3D and time)
Ultra-fast frame capture

Label-Free Real-Time Hyperspectral Endoscopy (Case No. 2020-759)

The use of autofluorescence imaging (AFI) and fluorescence imaging (FI) are standard-of-care endoscopic techniques for imaging tumor-specific contrast to help guide surgeons during surgery. However, AFI images suffer from low sensitivity and specificity in assessing tumor margins and while FI significantly improve tumor accuracy, it faces significant regulatory challenges since the number of FDA-approved fluorescent dyes are very limited. Furthermore, the expression of a molecular-specific dye in patients is heterogeneous and the expression can vary overtime. Therefore, there is a need for a new endoscopy method that provides high sensitivity and does not require the use of fluorescent labeling. UCLA researchers developed a label-free, real-time hyperspectral imaging endoscopy method for molecular guided surgery. The method demonstrated high sensitivity and specificity for tumor detection without the use of fluorescent labeling. The method combined snapshot hyperspectral imaging and machine learning to implement a real-time data acquisition. Furthermore, it can be applied to standard clinical practice since it requires minimal modification to the established white-light surgical imaging procedure. The method can extend a surgeon’s vision at both the cellular and tissue level to improve the ability to identify the lesion and its margins.

Potential Applications: