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Tomographic reconstruction based on deep learning

  • US 10,475,214 B2
  • Filed: 04/05/2017
  • Issued: 11/12/2019
  • Est. Priority Date: 04/05/2017
  • Status: Active Grant
First Claim
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1. A method, comprising:

  • obtaining measured data from a tomography scanner;

    calculating one or more tomographic transforms of the measured data, wherein the one or more tomographic transforms comprise at least one of a backprojection, a weighted backprojection, a reprojection, a plurality of diagonal elements of a Fisher information matrix, a variance image, a noise correlation image, or a polynomial of the Fisher information matrix;

    providing inputs to a trained neural network, wherein the inputs comprise the one or more tomographic transforms and an input reconstructed image, and the input reconstructed image comprises a filtered backprojection image with a special filter kernel, wherein the filter coefficients are selected that preserve the original sinogram after a reprojection; and

    obtaining one or more outputs from the trained neural network based on the inputs.

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