Cisco Technology, Inc.

Hyperfine Operations, Inc.

Bfly Operations, Inc.

Hyperfine Research, Inc.

Butterfly Network, Inc.

Siemens Medical Solutions USA, Inc.

Siemens Corporation

Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Product Lifecycle Management Software Inc.

Hyperfine, Inc.

Siemens Healthineers Ag

Michal Sofka

Jonathan Marc Rothberg

Jo Schlemper

Matthew De Jonge

Abraham Neben

Alex Rothberg

Shaohua Kevin Zhou

Karel Bartos

David Elgena

Tomer Gafner

Tyler S Ralston

Gregory L Charvat

Prantik Kundu

Karl E Thiele

Laura Sacolick

Hadrien A Dyvorne

Rafael O'Halloran

Robert Schneider

Carole Lazarus

Seyed Sadegh Mohseni Salehi

Neil Birkbeck

Jimmy Jia

Seyed Sadegh Moshen Salehi

Daniel Nouri

Jingdan Zhang

Lukas Machlica

Dijia Wu

Meizhu Liu

Ulf Schäfer

Seyed Sadegh Mosheni Salehi

Jan Jusko

Jens Wetzl

Clifford R Weiss

Jonathan M Rothberg

Ziyi Wang

Vojtech Franc

Dorin Comaniciu

Li Zhang

Martin Rehak

Mark Michalski

Leo John Grady

David A McGrew

Michael Stephen Poole

Atilla Peter Kiraly

Grzegorz Soza

Gianluca Paladini

Noha Youssry El-Zehiry

Jin-hyeong Park

Christian Tietjen

Jerome Declerck

Jens Kaftan

Khan Mohammad Siddiqui

Jiri Havelka

Kristof Ralovich

Jirí Havelka

Neel Dey

Martin Rehák

Tianrui Luo

Sunli Tang

Timo Kohlberger

Techniques for denoising a magnetic resonance (MR) image are provided, including: obtaining a noisy MR image; denoising the noisy MR image of the subject using a denoising neural network model, and outputting a denoised MR image. The denoising neural network model is trained by: generating first training data for training a first neural network model to denoise MR images by generating a first plurality of noisy MR images using clean MR data associated with a source domain and first MR noise data associated with the target domain; training the first neural network model using the first training data; generating training data for training the denoising neural network model by applying the first neural network model to a second plurality of noisy MR images and generating a plurality of denoised MR images; and training the denoising neural network model using the training data for training the denoising neural network model.

Deep learning methods for noise suppression in medical imaging

A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a Bmagnet configured to provide a Bfield for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.

Deep learning techniques for magnetic resonance image reconstruction

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Self ensembling techniques for generating magnetic resonance images from spatial frequency data

Some aspects include a method of determining change in size of an abnormality in a brain of a patient positioned within a low-field magnetic resonance imaging (MRI) device. The method comprises, while the patient remains positioned within the low-field MRI device, acquiring first and second magnetic resonance (MR) image data of the patient's brain; providing the first and second MR image data as input to a trained statistical classifier to obtain corresponding first and second output; identifying, using the first output, at least one initial value of at least one feature indicative of a size of the abnormality; identifying, using the second output, at least one updated value of the at least one feature; determining the change in the size of the abnormality using the at least one initial value of the at least one feature and the at least one updated value of the at least one feature.

Systems and methods for automated detection in magnetic resonance images

Aspects of the technology described herein relate to techniques for guiding an operator to use an ultrasound device. Thereby, operators with little or no experience operating ultrasound devices may capture medically relevant ultrasound images and/or interpret the contents of the obtained ultrasound images. For example, some of the techniques disclosed herein may be used to identify a particular anatomical view of a subject to image with an ultrasound device, guide an operator of the ultrasound device to capture an ultrasound image of the subject that contains the particular anatomical view, and/or analyze the captured ultrasound image to identify medical information about the subject.

Automated image analysis for diagnosing a medical condition

Augmented reality interface for assisting a user to operate an ultrasound device

Techniques are provided for imaging a subject. A magnetic resonance imaging (MRI) system may use at least one RF coil to generate an initial MR data set for an initial image of the subject. The MRI system may use the initial MR image to determine a difference in orientation between a current orientation of the subject in the initial MR image and a target orientation of the subject. The MRI system may use the determined difference in orientation to determine an adjustment to a gradient pulse sequence for controlling at least one gradient coil. The MRI system may apply the determined adjustment to the gradient pulse sequence to obtain an adjusted gradient pulse sequence. The MRI system may generate an adjusted MR data set using the adjusted gradient pulse sequence, and a second MR image of the subject using the adjusted MR data set.

Systems and methods for performing magnetic resonance imaging with reduced operator interaction

Techniques for removing artefacts, such as RF interference and/or noise, from magnetic resonance data. The techniques include: obtaining input magnetic resonance (MR) data using at least one radio-frequency (RF) coil of a magnetic resonance imaging (MRI) system; and generating an MR image from input MR data at least in part by using a neural network model to suppress at least one artefact in the input MR data.

Deep learning techniques for suppressing artefacts in magnetic resonance images

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques include: obtaining input MR spatial frequency data obtained by imaging the subject using the MRI system; generating an MR image of the subject from the input MR spatial frequency data using a neural network model comprising: a pre-reconstruction neural network configured to process the input MR spatial frequency data; a reconstruction neural network configured to generate at least one initial image of the subject from output of the pre-reconstruction neural network; and a post-reconstruction neural network configured to generate the MR image of the subject from the at least one initial image of the subject.

Deep learning techniques for generating magnetic resonance images from spatial frequency data

Generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system by: generating first and second sets of one or more MR images from first and second input MR data; aligning the first and second sets of MR images using a neural network model comprising first and second neural networks, the aligning comprising: estimating, using the first neural network, a first transformation between the first and second sets of MR images; generating a first updated set of MR images from the second set of MR images using the first transformation; estimating, using the second neural network, a second transformation between the first set and the first updated set of MR images; and aligning the first set of MR images and the second set of MR images at least in part by using the first transformation and the second transformation.

Deep learning techniques for alignment of magnetic resonance images

Growing community of inventors

Princeton, NJ, United States of America

Michal Sofka