Carl Zeiss X-Ray Microscopy, Inc.

Lars Omlor

Matthew Andrew

Christoph Hilmar Graf Vom Hagen

Hauyee Chang

Hrishikesh Bale

Andriy Andreyev

Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent data—optionally collected at a higher throughput—to ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

Accessible neural network image processing workflow

A collision avoidance system and method for an x-ray CT microscope processes image data of an object at different angles and generates a model of the object. This model is then used to configure the microscope for operation and possibly avoid collisions between the microscope and the object.

Optical three-dimensional scanning for collision avoidance in microscopy system

An x-ray microscopy method that obtains a classification of different particles by distinguishing between different material phases through a combination of image processing involving morphological edge enhancement and possibly resolved absorption contrast differences between the phases along with optional wavelet filtering.

Growing community of inventors

Dublin, CA, United States of America

Lars Omlor