Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

University of Arizona

Other

Siemens Corporation

Commissariat a L'energie Atomique Et Aux Energies Alternatives

North Carolina State University

National Institutes of Health

Seimens Medical Solutions Usa, Inc.

Arizona Board of Regents on Behalf of the Universtiy of Arizona

Mariappan S Nadar

Boris Mailhe

Xiao Chen

Simon Arberet

Qiu Wang

Benjamin L Odry

Marcel Dominik Nickel

Mahmoud Mostapha

Michael Zenge

Jun Liu

Pascal Ceccaldi

Hasan Ertan Cetingul

Alban Lefebvre

Peter Speier

Edgar Mueller

Ti-chiun Chang

Thomas Benkert

Youngjin Yoo

Nirmal Janardhanan

Dorin Comaniciu

Ali Bilgin

Edgar Müller

Bogdan Georgescu

Xiaoguang Lu

Michaela Schmidt

Zhili Yang

Sandro Braun

Yookyung Kim

Puneet Sharma

Shaohua Kevin Zhou

Andreas Greiser

Bernhard Geiger

Mathias Nittka

Jens Gühring

Hui Xue

Michael Wesley Marcellin

Kai Tobias Block

Jeremy Rapin

Qiaoying Huang

Venkata Veerendranadh Chebrolu

Silvia Bettina Arroyo Camejo

Gregor Körzdörfer

Laszlo Lazar

Yuanhsi (Tony) Chen

Axel Loewe

Katrin Mentl

Guillaume Daval Frerot

Johannes Flake

Charles Millard

Aurelien Flipo

Sajan Goud Lingala

Ali Kamen

Shanhui Sun

Yefeng Zheng

Frank Sauer

Vivek Kumar Singh

Sasa Grbic

Li Zhang

Stephan Kannengiesser

Ankur Kapoor

Arun Krishnan

Marie-Pierre Jolly

Daphne Yu

Peter Schmitt

Christophe Chefd'hotel

Birgi Tamersoy

Bin Lou

Sandra Sudarsky

Robert Grimm

Christoph Guetter

Eli Gibson

Arne Littmann

Daniel Rinck

Jeffrey Paul Johnson

Carmel Hayes

Esther Raithel

Thomas Re

Alexandre Vignaud

John Patrick Collins

Kelvin Chow

Philippe Ciuciu

Tim McGraw

John Scott Nafziger

Yi Guo

Jyotipriya Das

Zhizhou Wang

Abdelaziz Chihoub

Aurélien Stalder

Michael Bush

Xiao Bian

Hamid Krim

Thomas Stingl

Hariharan G Lalgudi

Rajagopalan Sundaresan

LuoLuo Liu

Niklas Baumgarten

Evan Schwab

Christopher Cline

Julia Traechtler

Systems and methods for reconstruction for a medical imaging system. An adapter is used to adapt scan data so that different quantities of repetitions or directions may be used to train and implement a single multichannel backbone network.

Multichannel deep learning reconstruction of multiple repetitions

For reconstruction in medical imaging, such as reconstruction in MR imaging, scanning is accelerated by under-sampling. In iterative reconstruction, the input to the regularizer is altered provide for correlation of non-local aliasing artifacts. Duplicates of the input image are shifted by different amounts based on the level of acceleration. The resulting shifted images are used to form the input to the regularizer. Providing an input based on shifts allows the regularization to suppress non-local as well as local aliasing artifacts.

Accelerated medical image reconstruction with improved aliasing artifact reduction

For reconstruction in medical imaging, such as reconstruction in MR imaging, an iterative, hierarchal network for regularization may decrease computational complexity. To further maintain computational complexity while improving robustness, auxiliary information is used in the regularization and corresponding reconstruction. The auxiliary information is in put to the machine-learned network.

Iterative hierarchal network for regulating medical image reconstruction

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 in medical imaging using phase correction, a machine learning model is trained for reconstruction of an image. The reconstruction may be for a sequence without repetitions or may be for a sequence with repetitions. Where repetitions are used, rather than using just a loss for that repetition in training, the loss based on an aggregation of images reconstructed from multiple repetitions may used to train the machine learning model. In either approach, a phase correction is applied in machine training. A phase map is extracted from output of the model in training or extracted from the ground truth of the training data. The phase correction, based on the phase map, is applied to the ground truth and/or the output of the model in training. The resulting machine-learned model may better reconstruct an image as a result of having been trained using phase correction.

Machine learning for medical image reconstruction with phase correction

For reconstruction in medical imaging, self-consistency using data augmentation is improved by including data consistency. Artificial intelligence is trained based on self-consistency and data consistency, allowing training without supervision. Fully sampled data and/or ground truth is not needed but may be used. The machine-trained model is less likely to reconstruct images with motion artifacts, and/or the training data may be more easily gathered by not requiring full sampling.

Self-supervised machine learning for medical image 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

Mariappan S Nadar