Abstract
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.
Benefit
Low costReconstructed images are of high quality and artifact-freeWorks without distal optical elementsTolerates bending and temperature variations far better than other technologiesMarket Application
Endoscopes for humans, engines, nuclear power plantsRemote image capturePublications
- A path to high-quality imaging through disordered optical fibers: Applied Optics, Volume 58, Issue 13, Page D50-D60, Published on April 9, 2019 https://doi.org/10.1364/AO.58.000D50
- Cell Imaging Using Glass-Air Disordered Optical Fiber and Deep Learning Algorithms: Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP), OSA Technical Digest (Optical Society of America, 2019), paper CW1A.2.
https://doi.org/10.1364/COSI.2019.CW1A.2
- Deep Learning Imaging through Fully-Flexible Glass-Air Disordered Fiber: ACS Photonics, 2018 5 (10), 3930-3935, DOI: 10.1021/acsphotonics.8b00832
- Deep Learning Cell Imaging through Anderson Localizing Optical Fibre: arXiv.org, arXiv:1812.00982v2 [physics.optics]
Brochure
