General Electric Company

Andras Kriston

Ferenc Kovacs

Tamas Blaskovics

Istvan Ubelhart

Robert John Johnsen

Pal Csongor Sprencz

Elizabeth Philps

Andrea Fazekas

Scott David Wollenweber

Souma Sengupta

Rahul Bhotika

Paulo Ricardo Dos Santos Mendonca

Bipul Das

Laszlo Rusko

Kosuke Sasaki

Masayuki Kudo

Gergely Nyiri

Peter Alexander John Lamb

Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

Methods and systems for evaluating bone lesions

Method of generating a multi-modality anatomical atlas. The method includes receiving first and second medical images of a region-of-interest (ROI) of a same individual. The first and second medical images are acquired by different first and second imaging modalities. The method includes generating first and second feature images based on the first and second medical images. The first and second feature images include a same designated anatomical feature of the ROI. The method includes determining a transformation function by registering the first and second feature images and applying the transformation function to the first and second medical images to register the medical images. The method includes generating a multi-modality anatomical atlas. The multi-modality atlas has the first and second medical images. The first and second medical images are first and second reference images. The multi-modality anatomical atlas includes an organ model that corresponds to an organ in the ROI.

Method of generating a multi-modality anatomical atlas

A method that includes receiving an input image of a region of interest (ROI) of an individual. The input image is a medical image acquired by a first imaging modality. The method also includes generating a first feature image based on the input image. The first feature image includes a designated anatomical feature of the ROI. The method also includes obtaining an anatomical atlas. The atlas has a reference image of the ROI of at least one other individual and an organ model. The reference image is a medical image that is acquired by a second imaging modality that is different from the first imaging modality. The method also includes determining a transformation function by registering the first feature image with a second feature image that is based on the reference image and includes the designated anatomical feature.

Methods and systems for determining a transformation function to automatically register different modality medical images

A method for automatic segmentation of a medical image is provided. The method comprises registering a reference image associated with an object to the medical image, determining a transformation function on the basis of the registration, applying the transformation function to a probability map associated with the object; carrying out a probability thresholding on the transformed probability map by selecting a first area of the medical image in which the probability of the object is within a probability range, carrying out an intensity thresholding on the medical image by selecting a second area of the medical image in which the intensity is within an intensity range, selecting a common part of the first and second areas and carrying out on the common part a morphological opening resulting in separate sub-areas, selecting the largest sub-area as a seed, and segmenting the medical image on the basis of the seed.

Method, system and computer readable medium for automatic segmentation of a medical image

A method for automatically displaying an organ of interest includes accessing a series of medical images acquired from a first imaging modality, receiving an input that indicates an organ of interest, automatically detecting the organ of interest within at least one image in the series of medical images, automatically placing a visual indicator in the region of interest, and automatically propagating the visual indicator to at least one image that is acquired using a second different imaging modality. A medical imaging system and a non-transitory computer readable medium are also described herein.

Systems and methods for performing image background selection

A method for determining image information for an image of an object includes obtaining at least one image of an object of interest, automatically determining an image type and a presence or absence of a contrast agent, automatically generating a label that indicates the image type and the presence or absence of the contrast agent, and modifying the at least one image to include the label. A system and non-transitory computer readable medium are also described herein.

Systems and methods for performing image type recognition

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.

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Mako, Hungary

Andras Kriston