Abstract: 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*2 maps from the multi-echo images. The water images, the fat images, and the effective R*2 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.

Abstract: 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*2 maps from the multi-echo images. The water images, the fat images, and the effective R*2 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.

Abstract: 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 “noisy” 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.

Abstract: 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 “noisy” 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.