Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Commissariat &#xc0; L'&#xc9;nergie Atomique Et Aux &#xc9;nergies Alternatives

Siemens Healthcare Diagnostics Gmbh

Boris Mailhe

Mariappan S Nadar

Xiao Chen

Simon Arberet

Benjamin L Odry

Marcel Dominik Nickel

Qiu Wang

Mahmoud Mostapha

Pascal Ceccaldi

Dorin Comaniciu

Thomas Benkert

Hasan Ertan Cetingul

Peter Speier

Ali Kamen

Andreas Greiser

Stephan Kannengiesser

Youngjin Yoo

Venkata Veerendranadh Chebrolu

Sandro Braun

Tommaso Mansi

Vivek Kumar Singh

Ankur Kapoor

Mathias Nittka

Xiaoguang Lu

Bin Lou

Nirmal Janardhanan

Qiaoying Huang

Gregor Körzdörfer

Laszlo Lazar

Charles Millard

Johannes Flake

Katrin Mentl

Guillaume Daval Frerot

Bogdan Georgescu

Puneet Sharma

Shaohua Kevin Zhou

Yefeng Zheng

Benjamin S Pollack

Sasa Grbic

Yao-Jen Chang

David D Liu

Rui Liao

Steffen Kappler

Shun P Miao

Michael Zenge

Birgi Tamersoy

Robert Grimm

Ludwig Ritschl

Eli Gibson

Daniel Rinck

Esther Raithel

Siqi Liu

Jan Fritz

Alexandre Vignaud

Kelvin Chow

Sebastien Piat

Philippe Ciuciu

Awais Mansoor

Yi Guo

Florian Knoll

Jyotipriya Das

Sajan Goud Lingala

Michael Bush

Aurélien Stalder

Florin-Cristian Ghesu

Sebastian Vogt

Inge Brinkmann

Aurelien Massire

Hongki Lim

Mathilde Ripart

Guillaume Daval-Frerot

Christopher Cline

Anatole Louis Jerome Moreau

Ozgur Balkan

Jin Tan

Alexander Ruppel

Yevgen Matviychuk

Yuanhsi Chen

Julia Traechtler

Evan Schwab

Niklas Baumgarten

Florent Billy Romaric Gbelidji

Christian Alexander Braun

S Nadar Mariappan

For data analytics in magnetic resonance (MR) scanning, the scanning configuration information and the resulting raw data are directly used to determine the analytics or clinical decision. Artificial intelligence provides a value for a clinical finding characteristic of the patient based on the raw data from scanning and the controls used to scan, allowing the value to be based on all of the information content of the scan results. Reconstruction is not needed, allowing for simpler hardware, such as hardware with less homogeneous B0 and/or B1 fields than the norm and/or non-linear gradients.

Artificial intelligence for end-to-end analytics in magnetic resonance scanning

Various techniques of reconstructing multiple Magnetic Resonance Imaging, MRI, images for multiple slices based on an MRI measurement dataset that is acquired using a simultaneous multi-slice protocol and undersampling and K-space are disclosed. A convolutional neural network can be used to implement a regularization operation of an iterative optimization for the reconstruction, i.e., an unrolled neural network or variational neural network. A combination with Dixon imaging, i.e., separation of multiple chemical species, is disclosed.

Simultaneous multi-slice protocol and deep-learning magnetic resonance imaging reconstruction

A method of synthesizing an image of a tube assembly includes capturing an image of the tube assembly, wherein the capturing generates a captured image. The captured image is decomposed into a plurality of features in latent space using a trained image decomposition model. One or more of the features in the latent space is manipulated into one or more manipulated features. A synthesized tube assembly image is generated with at least one of the manipulated features using a trained image composition model. Other methods and systems are disclosed.

Diagnostic laboratory systems and methods of imaging tube assemblies

Systems and methods for generating augmented images are provided. One or more input medical images are received. At least one of noise and one or more transformations are applied to the one or more input medical images to generate one or more noisy augmented images. The one or more noisy augmented images are denoised using a diffusion-based denoising system to generate one or more denoised augmented images. The applying and the denoising are repeated for one or more iterations using the one or more denoised augmented images as the one or more input medical images to generate one or more final augmented images. The one or more final augmented images are output.

Machine learning data augmentation using diffusion-based generative models

A method of scanning an organ structure of a patient using magnetic resonance imaging, includes scanning, in a first scanning process, the patient to obtain first image data indicative of at least the organ structure of the patient. The method further includes determining, based on the first image data, one or more parameters obtain second image data indicative of at least the organ structure of the patient. The first scanning process includes a first quality of imaging scan, the second scanning process includes a second quality of imaging scan, and the first quality of imaging scan is higher than the second quality of imaging scan.

Magnetic resonance imaging of an organ structure

A computer-implemented method includes, based on an input dataset defining an input image, determining a reconstructed image using a reconstruction algorithm, and executing a data-consistency operation for enforcing consistency between the input image and the reconstructed image. The data-consistency operation determines, for multiple K-space positions at which the input dataset comprises respective source data, a contribution of respective K-space values associated with the input dataset to a K-space representation of the reconstructed image.

Data-consistency for image reconstruction

For reconstruction, a machine-learned model is adapted to allow for reconstruction based on the repetitions available in some scanning. The reconstruction for one or more subsets is performed during the scanning. The machine-learned model is trained to reconstruction separately or independently for each repetition or to use information from previous repetitions without requiring waiting for completion of scanning. The reconstructed image may be displayed much more rapidly after completion of the acquisition since the reconstruction begins during the reconstruction.

Deep learning-based realtime reconstruction

For reconstruction in medical imaging, user control of a characteristic (e.g., noise level) of the reconstructed image is provided. A machine-learned model alters the reconstructed image to enhance or reduce the characteristic. The user selected level of characteristic is then provided by combining the reconstructed image with the altered image based on the input level of the characteristic. Personalized or more controllable impression for medical imaging reconstruction is provided without requiring different reconstructions.

Reconstruction with user-defined characteristic strength

A magnetic resonance (MR) image may be created from MR data by receiving the MR data, applying a transform to the MR data, where a result of the applying is an image space representation of the MR data, determining a wrapped phase map of the image space representation of the MR data, obtaining an unwrapped phase map based on the wrapped phase map, scaling the unwrapped phase map into a B0 field map, reconstructing the MR image based on the MR data, correcting the MR image based on the B0 field map, and outputting the MR image. The scaling may be free of accounting for effects on the MR data by artifact sources secondary to B0 field inhomogeneities.

B0 field inhomogeneity estimation using internal phase maps from long single echo time MRI acquisition

For correction of an image from an imaging system, an inverse solution uses an imaging prior as a regularizer and a physics model of the imaging system. An invertible network is used as the deep-learnt generative model in the regularizer of the inverse solution with the physics model of the degradation behavior of the imaging system. The prior model based on the invertible network provides a closed-form expression of the prior probability, resulting in a more versatile or accurate probability prediction.

Image correction using an invertable network

For magnetic resonance (MR) reconstruction using artificial intelligence (AI), the AI-based reconstruction for MR imaging systems is offloaded to one or more servers. A remote server performs AI-based reconstruction. A library of recent, old, custom, and/or publicly available AI-based reconstruction processes may be rapidly deployed and available to the server, which has the memory and processing resources for AI-based reconstruction. Load balancing of the data and/or between servers may improve performance.

Architecture for artificial intelligence-based magnetic resonance reconstruction

For autonomous MR scanning for a given medical test, a simplified MR scanner may be used without or will little input or control by a technologist (e.g., by a physician, radiologist, or person trained in MR scanner operation). The MR scanner autonomously positions, scans, checks quality, analyzes, and/or outputs an answer to a diagnostic question with or without an MR image. Scan analysis, based on artificial intelligence, allows for on-going or on-the-fly alteration of the scanning configuration to acquire the data desired to answer the diagnostic question. By using a simplified MR scanner, both position of the patient relative to the MR scanner and localization of the scan by the MR scanner are jointly solved. Sensors may sense a patient in a scan position where the reduced radio frequency requirements allow for a more open bore.

Autonomous magnetic resonance scanning for a given medical test

In one approach, VDAMP is improved to allow multiple coils. The aliasing is modeled in the wavelet domain with spatial modulation for each of the frequency subbands. The spatial modulation uses the coil sensitivities. As a result of the spatial modulation, the aliasing modeling more closely models the variance allowing the regularization to use denoising operations. In another approach, the regularization computation may be simplified by using a machine-learned network in VDAMP. To account for the aliasing modeling of VDAMP, a convolutional neural network is trained with input of both the noisy image and the covariances of the aliasing model.

Reconstruction with magnetic resonance compressed sensing

For reconstruction in medical imaging using a scan protocol with repetition, a machine learning model is trained for reconstruction of an image for each repetition. Rather than using a loss for that repetition in training, the loss based on an aggregation of images reconstructed from multiple repetitions is used to train the machine learning model. This loss for reconstruction of one repetition based on aggregation of reconstructions for multiple repetitions is based on deep set-based deep learning. The resulting machine-learned model may better reconstruct an image from a given repetition and/or a combined image from multiple repetitions than a model learned from a loss per repetition.

Aggregation based on deep set learning in magnetic resonance reconstruction

Magnetic resonance imaging (MRI) image reconstruction using machine learning is described. A variational or unrolled deep neural network can be used in the context of an iterative optimization. In particular, a regularization operation can be based on a deep neural network. The deep neural network can take, as an input, an aliasing data structure being indicative of aliasing artifacts in one or prior images of the iterative optimization. The deep neural networks can be trained to suppress aliasing artifacts.

Magnetic resonance imaging reconstruction using machine learning

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Plainsboro, NJ, United States of America

Boris Mailhe