General Electric Company

Bipul Das

Ajay Narayanan

Roshni Bhagalia

Pratik Shah

Scott David Wollenweber

Souma Sengupta

Rahul Bhotika

Paulo Ricardo Dos Santos Mendonca

Andras Kriston

Laszlo Rusko

Vignesh Thirukazhukundram Subrahmaniam

Kosuke Sasaki

Bhushan Dayaram Patil

Ferenc Kovacs

Fei Zhao

Masayuki Kudo

Uday Damodar Patil

Qi Song

Srikrishnan V

Peter Lamb

Harshit Sureka

Peter Alexander John Lamb

Ziyue Xu

Naga Venkata Kartheek Medathati

The present approach relates, in some aspects, to a multi-level and a multi-channel frame work for segmentation using model-based or 'shallow' classification (i.e. learning processes such as linear regression, clustering, support vector machines, and so forth) followed by deep learning. This framework starts with a very low resolution version of the multi-channel data and constructs an shallow classifier with simple features to generate a coarser level tissue mask that in turn is used to crop patches from the high-resolution volume. The cropped volume is then processed using the trained convolution network to perform a deep learning based segmentation within the slices.

Material segmentation in image volumes

Identification of an optimal monochromatic energy for displaying monochromatic images is disclosed. In certain embodiments, determination of an optimal monochromatic energy may be performed by generating histograms for various monochromatic images generated based on a set of acquired multi-energy projections and by evaluating the histogram dispersion for the respective histograms.

Generation of monochromatic images

A method includes receiving a Computed Tomography (CT) image comprising a head of a subject from a CT scanner and detecting a first and a second petrous bone of the head in the CT image. The method further includes determining a nasion of the head in the CT image based on the first and the second petrous bone. The method also includes generating a reference plane of the head based on the nasion and the first and the second petrous bone.

System and method for generating a reference plane of a head

A method includes receiving a monochromatic image comprising a head of a subject from a Computed Tomography (CT) scanner and detecting a petrous bone of the head in the monochromatic image. The method further includes determining a linear attenuation coefficient of at least one petrous voxel representing the petrous bone and calculating a mass attenuation coefficient of the petrous voxel based on the linear attenuation coefficient and a density of the petrous bone. The method also includes computing a monochromatic energy level of the monochromatic image based on the mass attenuation coefficient of the petrous voxel and recalibrating the monochromatic image corresponding to the computed monochromatic energy level to the desired monochromatic energy level.

System and method for recalibrating a monochromatic image

A hierarchical multi-object active appearance model (AAM) framework is disclosed for processing image data, such as localizer or scout image data. In accordance with this approach, a hierarchical arrangement of models (e.g., a model pyramid) maybe employed where a global or parent model that encodes relationships across multiple co-located structures is used to obtain an initial, coarse fit. Subsequent processing by child sub-models add more detail and flexibility to the overall fit.

Automatic estimation of anatomical extents

The disclosed embodiments relate to determining an amount of a contrast agent in an image. For example, a computer-implemented method of image processing includes generating, from a first polychromatic contrast-enhanced X-ray image obtained at a first energy and a second polychromatic contrast-enhanced X-ray image obtained at a second energy, a simulated first monochromatic contrast-enhanced X-ray image and a simulated second monochromatic contrast-enhanced X-ray image. The simulated first monochromatic contrast-enhanced X-ray image includes first regions of enhanced contrast and the simulated second monochromatic contrast-enhanced includes second regions of enhanced contrast. The method also includes isolating the first and second regions of enhanced contrast from other regions of the image, and determining an amount of the contrast agent within the first and second regions of enhanced contrast based at least on a derived partial signal attributable to the contrast agent.

System and method for contrast agent estimation in X-ray imaging

An apparatus for multi-energy tissue quantification includes an x-ray imaging system comprises an x-ray source configured to emit a beam of x-rays toward an object to be imaged, a detector configured to receive the x-rays attenuated by the object, and a data acquisition system (DAS) operably coupled to the detector. A computer operably connected to the x-ray source and the DAS is programmed to cause the x-ray source to emit x-rays at each of a first kVp and a second kVp toward the detector, acquire x-ray data from x-rays emitted at the first and second kVp through a region of interest (ROI), and perform a first multi-material decomposition based on the acquired x-ray data. The computer is also programmed to quantify a volume fraction of a first material in the ROI based on the first multi-material decomposition and display the volume fraction of the first material to a user.

Apparatus and method for multi-energy tissue quantification

A computer-implemented method of post-processing medical image data is provided. The method includes receiving tracked image data representative of multiple blood vessels, generating a binary tree structure for the multiple blood vessels based on a parent-child relationship between branches of the multiple blood vessels, generating a likelihood model for determining a validity of the branches of the multiple blood vessels, and generating a likelihood score for each branch based on the respective branch's compatibility with the likelihood model. The method also includes generating a reconstructed tree for the multiple blood vessels. Compatible branches are included in the reconstructed tree, while invalid branches are not included in the reconstructed tree.

Methods for generating a reconstructed tree structure from medical image data

A method, system and apparatus for filtering multi-energy images using spectral filtering technique is described. In one embodiment, the method includes obtaining a first image of an anatomical object corresponding to a first radiation energy. In addition, the method includes obtaining at least one additional image, herein called a second image of the anatomical object corresponding to at least one second radiation energy. The at least one second radiation energy is distinct from the first radiation energy. The method also includes determining joint attenuation characteristics of each tissue at the first radiation energy and at the second radiation energy or their derivatives. The method also includes selectively filtering attenuation value in a multi-energy space due to at least one tissue to generate a filtered image from a reference image. The reference image is one of the first image or the second image or their derivatives.

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Bengaluru, India

Bipul Das