Siemens Healthcare Gmbh

Duke University

Other

Mustafa R Bashir

Marcel Dominik Nickel

Berthold Kiefer

Stephan Kannengiesser

Xiaodong Zhong

Vibhas S Deshpande

Brian Dale

Daniel T Boll

Elmar M Merkle

Claudia Fellner

A computer-implemented method for using machine learning to suppress fat in acquired MR images includes receiving multi-echo images from an anatomical area of interest acquired using an MRI system. A first subset of the multi-echo images is acquired prior to application of contrast to the anatomical area of interest and a second subset of the multi-echo images is acquired after application of contrast to the anatomical area of interest. Next, data is generated including water images, fat images, and effective R*maps from the multi-echo images. The water images, the fat images, and the effective R*maps are used to create synthetic fat suppressed images. A neural network is trained to use the multi-echo images as input and the synthetic fat suppressed images as ground truth. A plurality of components of the neural network are saved to allow later deployment of the neural network on a computing system.

Artificial intelligence based suppression of chemical species in magnetic resonance imaging

A magnetic resonance imaging system and method are provided for improved determination of noise bias effects in calculating fitted parameters for quantitative MRI procedures. The system and method includes selecting a range for the SNR and fitted parameter values, and for each of a plurality of base pairs of these values and for a plurality of b values, adding a random noise term to the real and imaginary components of a plurality of corresponding signal terms, fitting magnitudes of the resulting 'noisy' signals to determine a 'noisy' fitted parameter value, and compare the &#x201c;noisy&#x201d; and base fitted parameter values to determine a noise-based error for each pair of base values. The noise-based errors can be used to generate an error map, modify imaging parameters to reduce such errors, or correct fitted parameters directly.

Systems and methods for predicting errors and optimizing protocols in quantitative magnetic resonance imaging

In a magnetic resonance (MR) apparatus and a method for operating such an apparatus, a T1 parameter map is generated with fat fraction correction, by using a model in which the fat fraction of acquired MR data is used as a known parameter. The T1 values from the acquired MR data are fat fraction-corrected in such a manner, so as to generate fat fraction-corrected entries for the T1 parameter map according to the model.

Method and magnetic resonance apparatus for generating a fat fraction-corrected T1 parameter map

Disclosed herein are systems and methods for automated MRI. According to an aspect, a method for MRI includes receiving a plurality of MRI data signals representative of a region including a volume of interest. The method also includes determining at least one subvolume within the VOI. Further, the method includes determining a state of the at least one subvolume. The method also includes implementing a predetermined action based on the predetermined state.

Systems and methods for automated magnetic resonance imaging

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Cary, NC, United States of America

Mustafa R Bashir