Research Terms
Researchers at the University of Central Florida have developed a lensless fiber-optic imaging system that overcomes the expense and complications associated with existing multicore and multimode fiber-based imaging systems (MCFs, MMFs). More robust, compact and durable, the UCF technology can produce artifact-free images and reach an imaging depth of up to several millimeters without any distal optics. The new system also tolerates bending and temperature changes better than MCFs and MMFs.
The innovation could help to reduce the size of image-transmitting endoscopes down to the diameter of the fiber itself and minimize penetration damage without degrading image quality. As a result, the fiber could collect artifact-free images of organs without touching them directly; thus, enabling a minimally invasive, high-performance imaging system.
Technical Details
The UCF fiber optical imaging system comprises two main parts: a disordered glass-air Anderson localized optical fiber (GALOF) that provides low-loss image transmission and a trained deep convolutional neural network (DCNN) of algorithms that enable the accurate reconstruction of raw images. The unique DCNN-GALOF combination offers advantages in resolution, depth perception, and environmental stability over conventional fiber-optic imaging methods. It also allows for physical movement of the specialty fiber without interruption of the real-time imaging process. Experimental results indicate that image quality and system performance are unaffected by a bending angle of approximately 3 degrees or by heating of up to 50 degrees Celsius.
In one example application of the system, the imaging fiber is a meter-long GALOF made of air and silica glass (to create the random refractive index structure--though other oxide glass mixtures are usable). It uses no distal optics. The system employs incoherent light (a low-cost LED) to illuminate various structures of human red blood cells. Within the system, a DCNN model is tailored (trained) to reconstruct and classify images. Instead of relying on known models and priors, the DCNN undergoes a training process using a large dataset collected from samples of the cell structures. In this way, it directly learns the underlying physics of the imaging transmission system without any advanced knowledge. In effect, it optimizes the network and enables it to reconstruct and classify the input images even if a particular type of image is not in the training data set. The trained DCNN is a precise approximation of the mapping function between the measured imaging data and the input imaging data and therefore enables a prediction process that takes less than one second.
Partnering Opportunity
The research team is looking for partners to develop the technology further for commercialization.
Stage of Development
Demonstration system available.
The University of Central Florida invention demonstrates pulse compression and supercontinuum generation in optical fibers with transverse random and longitudinal uniform refractive index distributions.