Phase Recovery And Holographic Image Reconstruction Using Neural Networks

Fast computing timeSmall imaging system form factorLow costWidely applicableRequires less imaging stepsEasy to operate

Any phase recovery problem Electron holographyX-ray imagingDiffraction TomographyQuantitative phase microscopyAll forms of medical imaging

A novel microscopy method was developed to extract quantitative phase images and intensity images from a single hologram. The approach combines a lens-free microscope with a CMOS sensor and a deep neural network training algorithm. Many hologram images, intensity images as well as reconstructed phase images of pathology slides were fed into the deep neural training algorithm to compute the fixed deep neural network of convolutional layers, unsampling blocks and nonlinear activation functions. The fixed deep neural training algorithm outputs intensity and phase images of the pathology slides with extremely fast computing time and incredible accuracy.

Background

Quantitative phase imaging is widely applicable in medical imaging. Conventional microscopy methods have bulky optic setups and require a lot of resources and time to produce images. Newer lens-free microscopy methods cut down the form factor of imaging systems and speed up the imaging process for obtaining regular intensity microscopy images. However, these newer methods involve using sensors such as charge coupled devices (CCDs), or complementary metal-oxide-semiconductor (CMOS). These sensors, though work well for obtaining intensity images, cannot directly record quantitative phase information or light diffraction. Previous works have applied digital holography to resolve these issues, but the approaches require reconstruction of multiple holograms and long computing time.

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Tech ID/UC Case

29015/2017-782-0

Related Cases

2017-782-0

USA