Systems and methods for deep learning-based image reconstruction

Abstract

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.

What is claimed is: 
     
         1 . At least one computer-readable storage medium including 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 at least one of the plurality of two-dimensional (2D) tomosynthesis projection images or 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.   
     
     
         2 . The at least one computer-readable storage medium of  claim 1 , wherein the x-ray image is to be registered to fit in a geometry of the plurality of 2D tomosynthesis projection images. 
     
     
         3 . The at least one computer-readable storage medium of  claim 1 , wherein the plurality of 2D tomosynthesis projection images of the organ and the x-ray image of the organ are to be obtained during a same compression of the organ. 
     
     
         4 . The at least one computer-readable storage medium of  claim 1 , wherein the plurality of 2D tomosynthesis projection images and the x-ray image are further to be obtained with a detector that receives x-rays emitted from the x-ray emitter, the instructions, when executed, to further cause the at least one processor to:
 applying a dynamic range correction factor to at least one of the plurality of 2D projection images or the x-ray image.   
     
     
         5 . The at least one computer-readable storage medium of  claim 1 , wherein each plane of the reconstructed 3D volume is to match with a geometry of the x-ray image. 
     
     
         6 . The at least one computer-readable storage medium of  claim 1 , wherein the instructions, when executed, further cause the at least one processor to:
 map each pixel of the synthetic 2D image to at least one voxel in the reconstructed 3D volume;   present the synthetic 2D image in a graphical user interface (GUI) generated on a graphical display;   receive a user selection of an object of interest in the x-ray image;   identify at least one plane through the 3D volume; and   present the at least one identified plane on the graphical display.   
     
     
         7 . The at least one computer-readable storage medium of  claim 1 , wherein the instructions, when executed, further cause the at least one processor to:
 extract areas of the reconstructed 3D volume; and   enhance the synthetic 2D image with the areas extracted from the reconstructed 3D volume; and   enrich a mapping of the Synthetic 2D image to the reconstructed 3D volume with the location of the extracted areas.   
     
     
         8 . The at least one computer-readable storage medium of  claim 1 , wherein the instructions, when executed, further cause the at least one processor to:
 extract areas of the reconstructed 3D volume; and   enhance the x-ray image with the areas extracted from the reconstructed 3D volume,   wherein the synthetic 2D image generation algorithm is generated from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the enhanced x-ray image.   
     
     
         9 . The at least one computer-readable storage medium of  claim 1 , wherein an energy to obtain the x-ray image is higher than an energy to obtain the plurality of two-dimensional (2D) tomosynthesis projection images 
     
     
         10 . The at least one computer-readable storage medium of  claim 1 , wherein generating the synthetic 2D image generation algorithm includes determining the synthetic 2D image generation algorithm using a training model such that the synthetic 2D image generation algorithm tends to minimize the similarity metric between the synthetic 2D image and the x-ray image. 
     
     
         11 . The at least one computer-readable storage medium of  claim 1 , wherein the model includes an artificial neural network model. 
     
     
         12 . At least one computer-readable storage medium including 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;   obtain an x-ray image of the organ with a second level of x-ray energization;   generate a volume reconstruction algorithm from the plurality of 2D tomosynthesis projection images based on a similarity metric between at least the volume reprojection and the x-ray image; and   deploy a model instantiating the volume reconstruction algorithm.   
     
     
         13 . The at least one computer-readable storage medium of  claim 12 , wherein generating the volume reconstruction algorithm includes determining the volume reconstruction algorithm using a training model such that the volume reconstruction algorithm tends to minimize the similarity metric between a volume reprojection formed using the volume reconstruction algorithm and the x-ray image. 
     
     
         14 . The at least one computer-readable storage medium of  claim 12 , wherein the x-ray image is to be registered to fit in a geometry of the plurality of 2D tomosynthesis projection images. 
     
     
         15 . The at least one computer-readable storage medium of  claim 12 , wherein the plurality of 2D tomosynthesis projection images of the organ and the x-ray image of the organ are to be obtained during a same compression of the organ. 
     
     
         16 . The at least one computer-readable storage medium of  claim 12 , wherein the plurality of 2D tomosynthesis projection images and the x-ray image are further to be obtained with a detector that receives x-rays emitted from the x-ray emitter, the instructions, when executed, to further cause the at least one processor to:
 applying a dynamic range correction factor to at least one of the plurality of 2D projection images or the x-ray image.   
     
     
         17 . The at least one computer-readable storage medium of  claim 12 , wherein each plane of the volume reprojection is to match with a geometry of the x-ray image. 
     
     
         18 . The at least one computer-readable storage medium of  claim 12 , wherein the model includes an artificial neural network model. 
     
     
         19 . At least one computer-readable storage medium including 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 projection images;   obtain an x-ray image of the organ with a second level of x-ray energization;   generate an image enhancement algorithm from a central tomosynthesis projection from the plurality of 2D tomosynthesis projection images based on a similarity metric between an output of the image enhancement algorithm and the x-ray image; and   deploy a model instantiating the image enhancement algorithm.   
     
     
         20 . The at least one computer-readable storage medium of  claim 19 , wherein generating the image enhancement algorithm includes generating the image enhancement algorithm from the central tomosynthesis projection that tends to minimize the similarity metric between the output of the image enhancement algorithm and the x-ray image. 
     
     
         21 . At least one computer-readable storage medium including 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 projection images;   degrade the plurality of 2D projection images to form a set of degraded tomosynthesis projection images that appear to have been acquired with lower level of x-ray energization than the first level of x-ray energization;   generate an image enhancement algorithm from the set of degraded tomosynthesis projection images that tends to minimize a similarity metric between an output of the image enhancement algorithm and the original projections; and   deploy a model instantiating the image enhancement algorithm.   
     
     
         22 . At least one computer-readable storage medium including 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 projection images;   reconstruct an original three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images;   degrade the plurality of 2D projection images to form a set of degraded tomosynthesis projection images that appear to have been acquired with lower level of x-ray energization than the first level of x-ray energization;   generate a volume reconstruction algorithm from the set of degraded tomosynthesis projection images that tends to minimize a similarity metric between an output of the volume reconstruction algorithm and the original 3D volume; and   deploy a model instantiating the volume reconstruction algorithm.