Ge Precision Healthcare LLC

General Electric Company

Dattesh Dayanand Shanbhag

Chitresh Bhushan

Rakesh Mullick

Florian Wiesinger

Sandeep Suryanarayana Kaushik

Thomas Kwok-Fah Foo

Deepa Anand

Sheshadri Thiruvenkadam

Sudhanya Chatterjee

Soumya Ghose

Arathi Sreekumari

Hariharan Ravishankar

Dawei Gui

Radhika Madhavan

Ravindra Mohan Manjeshwar

Kavitha Manickam

Christopher Judson Hardy

Krishna Seetharam Shriram

Sangtae Ahn

Desmond Teck Beng Yeo

Hua Qian

Albert Henry Roger Lonn

Scott David Wollenweber

Charles William Stearns

Sandeep Narendra Gupta

Mahendra Madhukar Patil

Marc Thomas Edgar

Kevin Matthew Koch

Ileana Hancu

Rahul Venkataramani

André De Almeida Maximo

Chandan Kumar Mallappa Aladahalli

Laura Irene Sacolick

Dirk Bequé

Anne Menini

Kumar Rajamani

Harsh Kumar Agarwal

James Chapman Ross

Florintina C

Dirk Wim Jos Beque

Amod Suhas Jog

Syed Asad Hashmi

Suresh Emmanuel Devadoss Joel

Cristina Cozzini

Suresh Joel

Desmond Teck-Beng Yeo

Siddharth Vikal

Rekha Venkatesan Tranquebar

Venkata Veerendranadh Chebrolu

Lishui Cheng

Systems and methods are provided for an image processing system. In an example, a method includes acquiring a pathology dataset, acquiring a reference dataset, generating a deformation field by mapping points of a reference case of the reference dataset to points of a patient image of the pathology dataset, manipulating the deformation field, applying the deformation field to the reference case to generate a simulated pathology image including a simulated deformation pathology, and outputting the simulated pathology image.

Simulating pathology images based on anatomy data

A computer implemented method is provided. The method includes establishing, via multiple processors, a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites. The method also includes retraining or retuning, via the multiple processors, the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets.

System and method for deep learning techniques utilizing continuous federated learning with a distributed data generative model

Systems/techniques that facilitate multi-modal image registration via modality-neutral machine learning transformation are provided. In various embodiments, a system can access a first image and a second image, where the first image can depict an anatomical structure according to a first imaging modality, and where the second image can depict the anatomical structure according to a second imaging modality that is different from the first imaging modality. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a modality-neutral version of the first image and a modality-neutral version of the second image. In various instances, the system can register the first image with the second image, based on the modality-neutral version of the first image and the modality-neutral version of the second image.

Multi-modal image registration via modality-neutral machine learning transformation

Methods and systems are provided for automatic placement of at least one saturation band on a medical image, which may direct saturation pulses during a MRI scan. A method may include acquiring a localizer image of an imaging subject, determining a plane mask for the localizer image by entering the localizer image as input to a deep neural network trained to output the plane mask based on the localizer image, generating a saturation band based on the plane mask by positioning the saturation band at a position and an angulation of the plane mask, and outputting a graphical prescription for display on a display device, the graphical prescription including the saturation band overlaid on the medical image.

Methods and systems for automated saturation band placement

Systems and methods for workflow management for labeling the subject anatomy are provided. The method comprises obtaining at least one localizer image of a subject anatomy using a low-resolution medical imaging device. The method further comprises labeling at least one anatomical point within the at least one localizer image. The method further comprises extracting using a machine learning module a mask of the at least one localizer image comprising the at least one anatomical point label. The method further comprises using the mask to label at least one anatomical point on a high-resolution image of the subject anatomy based on the at least one anatomical point within the localizer image.

Workflow management for labeling the subject anatomy

A method for performing a scan of a subject utilizing a magnetic resonance imaging (MRI) system includes triggering a prescan by an MRI scanner of the MRI system upon the subject being setup on a table of the MRI scanner and the table reaching an iso-center of the MRI scanner. The method includes subsequent to the prescan, triggering a calibration scan of the subject with the MRI scanner, wherein the calibration scan is an acoustic noise suppressed MRI scan. The method includes obtaining calibration data from the calibration scan. The method includes obtaining prescription parameters for subsequent scans of the subject with the MRI scanner from the calibration data. The method includes triggering at least one scan of the subject with the MRI scanner based on the prescription parameters.

System and method for improving magnetic resonance scanning workflow

Techniques are described for generating high quality training data collections for training artificial intelligence (AI) models in the medical imaging domain. A method embodiment comprises receiving, by a system comprising processor, input indicating a clinical context associated with usage of a medical image dataset, and selecting, by the system, one or more data scrutiny metrics for filtering the medical image dataset based on the clinical context. The method further comprises applying, by the system, one or more image processing functions to the medical image dataset to generate metric values of the one or more data scrutiny metrics for respective medical images included in the medical image dataset, filtering, by the system, the medical image dataset into one or more subsets based on one or more acceptability criteria for the metric values.

Generating high quality training data collections for training artificial intelligence models

Systems and techniques that facilitate data diversity visualization and/or quantification for machine learning models are provided. In various embodiments, a processor can access a first dataset and a second dataset, where a machine learning (ML) model is trained on the first dataset. In various instances, the processor can obtain a first set of latent activations generated by the ML model based on the first dataset, and a second set of latent activations generated by the ML model based on the second dataset. In various aspects, the processor can generate a first set of compressed data points based on the first set of latent activations, and a second set of compressed data points based on the second set of latent activations, via dimensionality reduction. In various instances, a diversity component can compute a diversity score based on the first set of compressed data points and second set of compressed data points.

Data diversity visualization and quantification for machine learning models

The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

Plane selection using localizer images

A medical imaging system includes at least one medical imaging device providing image data of a subject and a processing system programmed to generate a plurality of training images having simulated medical conditions by blending a pathology region from a plurality of template source images to a plurality of target images. The processing system is further programmed to train a deep learning network model using the plurality of training images and input the image data of the subject to the deep learning network model. The processing system is further programmed to generate a medical image of the subject based on the output of the deep learning network model.

Deep learning based medical system and method for image acquisition

Methods and systems are provided for reconstructing images from measurement data using one or more deep neural networks according to a decimation strategy. In one embodiment, a method for reconstructing an image using measurement data comprises, receiving measurement data acquired by an imaging device, selecting a decimation strategy, producing a reconstructed image from the measurement data using the decimation strategy and one or more deep neural networks, and displaying the reconstructed image via a display device. By decimating measurement data to form one or more decimated measurement data arrays, a computational complexity of mapping the measurement data to image data may be reduced from O(N), where N is the size of the measurement data, to O(M), where M is the size of an individual decimated measurement data array, wherein M<N.

System and methods for reconstructing medical images using deep neural networks and recursive decimation of measurement data

A computer-implemented method for performing a scan of a subject utilizing a magnetic resonance imaging (MRI) system includes initiating, via a processor, a prescan of the subject by an MRI scanner of the MRI system without a priori knowledge as to whether the subject has a metal implant. The computer-implemented method also includes executing, via the processor, a metal detection algorithm during a prescan entry point of the prescan to detect whether the metal implant is present in the subject. The computer-implemented method further includes determining, via the processor, to proceed with a calibration scan and the scan utilizing predetermined scan parameters when no metal implant is detected in the subject. The computer-implemented method even further includes switching, via the processor, into a metal implant scan mode when one or more metal implants are detected in the subject.

System and method for adaptive magnetic resonance imaging workflows from prescan data for subjects with metal

A method for generating an image of a subject with a magnetic resonance imaging (MRI) system is presented. The method includes first performing a localizer scan of the subject to acquire localizer scan data. A machine learning (ML) module is then used to detect the presence of metal regions in the localizer scan data based on magnitude and phase information of the localizer scan data. Based on the detected metal regions in the localizer scan data, the MRI workflow is adjusted for diagnostic scan of the subject. The image of the subject is then generated using the adjusted workflow.

System and method for improved metal detection in magnetic resonance imaging

A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates. In addition, the method includes updating the training dataset using the test image and the optimal label, retraining the neural network model, generating a label of an unlabeled image using the retrained neural network model, and outputting the generated label.

Systems and methods of automatic medical image labeling

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

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Bengaluru, India

Dattesh Dayanand Shanbhag