General Electric Company

Ge Precision Healthcare LLC

Sandeep Suryanarayana Kaushik

Dattesh Dayanand Shanbhag

Florian Wiesinger

Rakesh Mullick

Sheshadri Thiruvenkadam

Sangtae Ahn

Arathi Sreekumari

Hua Qian

Ravindra Mohan Manjeshwar

Krishna Seetharam Shriram

Hariharan Ravishankar

Rahul Venkataramani

Chandan Kumar Mallappa Aladahalli

Sai Hareesh Anamandra

Laura Irene Sacolick

Anne Menini

Dirk Bequé

Cristina Cozzini

Lishui Cheng

Various methods and systems are provided for translating magnetic resonance (MR) images to pseudo computed tomography (CT) images. In one embodiment, a method comprises acquiring an MR image, generating, with a multi-task neural network, a pseudo CT image corresponding to the MR image, and outputting the MR image and the pseudo CT image. In this way, the benefits of CT imaging with respect to accurate density information, especially in sparse regions of bone which exhibit with high dynamic range, may be obtained in an MR-only workflow, thereby achieving the benefits of enhanced soft-tissue contrast in MR images while eliminating CT dose exposure for a patient.

Methods and systems for translating magnetic resonance images to pseudo computed tomography images

A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.

System and method for memory augmented domain adaptation

Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

Systems and methods for generating normative imaging data for medical image processing using deep learning

A method for synchronization of a longitudinal data set from a subject includes receiving a first ensemble registration estimate having a first reference image corresponding to a first image ensemble and receiving a second image ensemble different from the first image ensemble. The method includes determining a second reference image based on the second image ensemble and the first reference image. Further, the method includes determining a second ensemble registration estimate based on the first ensemble registration estimate, the second reference image, the first image ensemble and the second image ensemble using an optimization technique. The method further includes generating a synchronized image ensemble corresponding to the first image ensemble and the second image ensemble based on the second ensemble registration estimate. The method also includes determining a medical condition of the subject by a medical practitioner based on the synchronized image ensemble.

System and method for longitudinal data processing

A system and method for estimating image intensity bias and segmentation tissues is presented. The system and method includes obtaining a first image data set and at least a second image data set, wherein the first and second image data sets are representative of an anatomical region in a subject of interest. Furthermore, the system and method includes generating a baseline bias map by processing the first image data set. The system and method also includes determining a baseline body mask by processing the second image data set. In addition, the system and method includes estimating a bias map corresponding to a sub-region in the anatomical region based on the baseline body mask. Moreover, the system and method includes segmenting one or more tissues in the anatomical region based on the bias map.

System and method for image intensity bias estimation and tissue segmentation

Aspects of the invention relate to generating an emission activity image as well as an emission attenuation map using an iterative updation based on both the raw emission projection data and the raw radiography projection data, and an optimization function. The outputs include an optimized emission activity image, and at least one of an optimized emission attenuation map or an optimized radiography image. In some aspects an attenuated corrected emission activity image is obtained using the optimized emission activity image, and the optimized emission attenuation map.

Systems and methods for reconstruction of emission activity image

Systems and methods of classifying component tissues of magnetic resonance images, where the method includes performing a proton density weighted, short echo-time magnetic resonance imaging measurement over a first volume field-of-view region of interest (ROI), repeating a series refining the first volume field-of-view ROI into a plurality of subsequent smaller ROI volumes having respective smaller resolutions, reconstructing a complex image from the plurality of magnetic resonance imaging measurements, performing a bias correction on at least one of the plurality of subsequent smaller ROI volumes, and classifying the ROI volumes by tissue type based on the bias-corrected image signal, wherein at least one tissue type is bone. A non-transitory medium containing processor instructions and a system are disclosed.

Systems and methods for magnetic resonance bone depiction

Imaging system and method are presented. Emission scan (ES) and anatomical scan (AS) data corresponding to a target volume in a subject are received. One or more at least partial AS images are reconstructed using AS data. An image-space certainty (IC) map representing a confidence level (CL) for attenuation coefficients of selected voxels in AS images and a preliminary attenuation (PA) map based on AS images are generated. One or more of selected attenuation factors (AF) in projection-space are initialized based on PA map. A projection-space certainty (PC) map representing CL for the selected AF is generated based on IC map. An emission image of the target volume is initialized. The selected AF and emission image are iteratively updated based on the ES data, PC map, initial AF, and/or initial emission image. A desired emission image and/or AF values are determined based on the iteratively updated AF and/or emission image.

System and method for joint estimation of attenuation and activity information

In one embodiment, a method includes performing a magnetic resonance (MR) imaging sequence to acquire MR image slices or volumes of a first station representative of a portion of a patient; applying a first phase field algorithm to the first station to determine a body contour of the patient in the first station; identifying a contour of a first anatomy of interest within the body contour of the first station using the first phase field algorithm or a second phase field algorithm; segmenting the first anatomy of interest based on the identified contour of the first anatomy of interest; correlating first attenuation information to the segmented first anatomy of interest; and modifying a positron emission tomography (PET) image based at least on the first correlated attenuation information.

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Karnataka, India

Sandeep Suryanarayana Kaushik