Session Index

"Functional near-infrared imaging has been widely applied to a variety of brain function studies and clinical measurements. Imaging phantoms are used to evaluate and calibrate the performance of medical imaging equipment. The phantoms are quite important for near-infrared imaging of brain function because the brain function images measured by the near-infrared imaging instruments are affected by the scattering of the tissue and depend on the probe arrangement on the head. In this talk, I will present two types of the phantoms for functional near-infrared imaging of brain function. The optical phantoms are composed of solid and liquid materials. The image is obtained from the intensity change caused by the absorber mimicking the brain activation detected by the probe pairs attached to the phantom surface. Numerical phantoms are more flexible than the optical phantom and can realize the complex anatomical structure of the head. Light propagation in the phantom is numerically calculated to obtain the functional image. The numerical phantom is effective to evaluate the influence of the extracerebral tissue structure and probe arrangement of the near-infrared imaging instruments on the brain function image. "

 

"Computational Imaging systems consist of two parts: the physical part where light propagates through free space or optical elements such as lenses, prisms, etc. finally forming a raw intensity image on the digital camera; and the computational part, where algorithms try to restore the image quality or extract other type of information from the image data. A broad spectrum of computational imaging approaches exist: in one extreme, computer vision, the physical part typically comprises standard imaging optics; at the other extreme, in lens-less imaging the burden of forming images or extracting other types of information from the optical field falls entirely on the computation. In this talk I will discuss the emerging trend in computational imaging to train deep neural networks (DNNs) to perform image extraction and restoration tasks. In several imaging experiments carried out by our group, the objects rendered “invisible” due to various adverse conditions such as extreme defocus, scatter, or very low photon counts were “revealed” after processing of the raw images by DNNs. The DNNs were trained from examples consisting of pairs of known objects and their corresponding raw images. The objects were drawn from databases of faces and natural images, with the brightness converted to phase through a liquidcrystal spatial phase modulator. After training, the DNNs were capable of recovering unknown, i.e. hitherto not presented during training, objects from the raw images and recovery was robust to disturbances in the optical system, such as additional defocus or various misalignments. This suggests that DNNs may form robust internal models of the physics of light propagation and detection and generalize priors from the training set."

 

We demonstrate coherent brightfield (COBRI) microscopy with enhanced contrast and show its capability of direct visualization of very small nanoparticles (e.g., 10 nm gold nanoparticle) in scattering at high speed (1000 Hz). A quantitative relationship between the linear scattering cross section of nanoparticle and its COBRI contrast is reported.

 

Migraine is a nerves disease with no objective diagnosis, but the abnormal frontal lobe activation was found on migraine patients in previous research. Thus, we use near-infrared spectroscopy (NIRS) to develop a real-time headgear system to detect this difference from the indices of Hemodynamic change and heart rate variability (HRV).

 

Cornea is one of the collagen-rich connective tissue and plays important role in clear vision. Here, we use Fast Fourier Trans-form second harmonic generation microscopy as a tool to determine the directionality of corneal stroma as a function of depth at different position.