Ge Precision Healthcare LLC

General Electric Company

Cytiva Sweden Ab

Rajesh Langoju

Jiang Hsieh

Bipul Das

Utkarsh Agrawal

Sandip Maity

Yasuhiro Imai

Sameer Dinkar Vartak

Masako Yamada

Abhijit Vishwas Patil

Bhushan Dayaram Patil

Risa Shigemasa

Anusha Rammohan

Umakant Rapol

Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

Method and systems for aliasing artifact reduction in computed tomography imaging

A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.

System and method for utilizing a deep learning network to correct for a bad pixel in a computed tomography detector

A system for detecting an array of samples having detectable samples and at least one reference sample is provided. The system comprises an electromagnetic radiation source, a sensing surface comprising a plurality of sample fields, wherein the plurality of sample fields comprise at least one reference field, a phase difference generator configured to introduce differences in pathlengths of one or more samples in the array of samples, and an imaging spectrometer configured to image one or more samples in the array of samples.

Systems and methods for self-referenced detection and imaging of sample arrays

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Bengaluru, India

Rajesh Langoju