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Systems and methods for deep learning-based image reconstruction

机译:基于深度学习的图像重建的系统和方法

摘要

Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.
机译:本文公开了基于深度学习的图像重建的方法,装置和系统。 示例至少一个计算机可读存储介质包括指令,当执行时,至少将一个处理器引起至少一个处理器,通过旋转X射线发射器来获得器官的多个二维(2D)Tomosynthesis投影图像 相对于器官的多个取向,并从发射器发射来自发射器的第一级X射线通电,用于多个2D Tomosynest投影图像的每个投影图像; 从多个2D Tomosynest投影图像重建器官的三维(3D)体积; 获得具有第二级X射线通电的器官的X射线图像; 基于合成2D图像与X射线图像之间的相似度量生成从重构的3D体积产生的合成2D图像生成算法; 并部署实例化合成2D图像生成算法的模型。

著录项

  • 公开/公告号US11227418B2

    专利类型

  • 公开/公告日2022-01-18

    原文格式PDF

  • 申请/专利权人 GENERAL ELECTRIC COMPANY;

    申请/专利号US201816235046

  • 发明设计人 SYLVAIN BERNARD;

    申请日2018-12-28

  • 分类号G06T11;

  • 国家 US

  • 入库时间 2022-08-24 23:23:08

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