Lawrence Livermore National Security, LLC

University of California

Arizona State University

Kyle Champley

Rushil Anirudh

Hyojin Kim

Kadri Aditya Mohan

Maxim Shusteff

William D Brown

Suren Jayasuriya

Hayden Kingsley Taylor

Jerel A Smith

Jeffrey S Kallman

Stephen G Azevedo

Maurice B Aufderheide

Harry E Martz, Jr

Erika Jo Fong

Hossein Heidari

Isaac Seetho

Albert William Reed

Chi Chung Li

Trevor Rongey, Jr

Daniel Schneberk

Sui Man Luk

Samira Feili

G Patrick Roberson

Joseph Toombs

Heting Fu

A system for determining a light intensity field for use in manufacturing a 3D object from a volume of material. The system receives a 3D specification of a 3D geometry for the 3D object that specifies voxels within the volume that contain material that is to be part of the 3D object. The system employs a cost function for effectiveness of a light intensity field in manufacturing the 3D object. The cost function may be an adjoint of an Attenuated Radon Transform that models an energy dose that each voxel would receive during manufacture of the 3D object using the light intensity field. The system applies an optimization technique that employs the cost function to generate a measure of the effectiveness of possible light intensity fields and outputs an indication of a light intensity field that will be effective in manufacturing the 3D object.

Computed axial lithography optimization system

A system for generating 2D slices of a 3D image of a target volume is provided. The system receives a target sinogram collected during a computed tomography scan of the target volume. The system inputs the target sinogram to a convolutional neural network (CNN) to generate predicted 2D slices of the 3D image. The CNN is trained using training 2D slices of training 3D images. The system initializes 2D slices to the predicted 2D slices. The system reconstructs 2D slices of the 3D image from the target sinogram and the initialized 2D slices.

Few-view computed tomography reconstruction using deep neural network inference

A system for generating a 4D representation of a scene in motion given a sinogram collected from the scene while in motion. The system generates, based on scene parameters, an initial 3D representation of the scene indicating linear attenuation coefficients (LACs) of voxels of the scene. The system generates, based on motion parameters, a 4D motion field indicating motion of the scene. The system generates, based on the initial 3D representation and the 4D motion field, a 4D representation of the scene that is a sequence of 3D representations having LACs. The system generates a synthesized sinogram of the scene from the generated 4D representation. The system adjusts the scene parameters and the motion parameters based on differences between the collected sinogram and the synthesized sinogram. The processing is repeated until the differences satisfy a termination criterion.

Reconstruction of dynamic scenes based on differences between collected view and synthesized view

A system for generating a 3D segmentation of a target volume is provided. The system accesses views of an X-ray scan of a target volume. The system applies a 2D CNN to each view to generate a 2D multi-channel feature vector for each view. The system applies a space carver to generate a 3D channel volume for each channel based on the 2D multi-channel feature vectors. The system then applies a linear combining technique to the 3D channel volumes to generate a 3D multi-label map that represents a 3D segmentation of the target volume.

3D segmentation using space carving and 2D convolutional neural networks

A system for characterizing the material of an object scanned via a dual-energy computed tomography scanner is provided. The system generates photoelectric and Compton sinograms based on a photoelectric-Compton decomposition of low-energy and high-energy sinograms generated from the scan and based on a scanner spectral response model. The system generates a Compton volume with Compton attenuation coefficients from the Compton sinogram and a photoelectric volume with photoelectric attenuation coefficients from the photoelectric sinogram. The system generates an estimated effective atomic number for a voxel and an estimated electron density for the voxel from the Compton attenuation coefficient and photoelectric coefficient for the voxel and scanner-specific parameters. The system then characterizes the material within the voxel based on the estimated effective atomic number and estimated electron density for the voxel. This information can be used to provide a mapping of known effective atomic numbers and known electron densities to known materials.

System-independent characterization of materials using dual-energy computed tomography

A CT image reconstruction system is provided that provides a solution to a few-view problem by generating a 3D image from measurements and a forward projection matrix for a CT scan of an object that tends to minimize streaks and other artifacts. The system initially receives measurements and a forward projection matrix for the CT scan. The system during an initial stage generates an initial 3D image based on various constraints so that (1) the reconstruction of the initial 3D image can be perform quickly and (2) the difference between the measurements and the forward projection of the initial 3D image has significantly lower contrast and high frequency content as compared the measurements themselves. The system then during a final stage applies an iterative algorithm to reconstruct the 3D image with the 3D image initialized to the initial 3D image at the beginning of the iterative algorithm.

Few-view image reconstruction

Growing community of inventors

Walnut Creek, CA, United States of America

Kyle Champley