Abstract: Methods and systems are provided for de-noising medical images using deep neural networks. In one embodiment, a method comprises receiving a medical image acquired by an imaging system, wherein the medical image comprises colored noise; mapping the medical image to a de-noised medical image using a trained convolutional neural network (CNN); and displaying the de-noised medical image via a display device. The deep neural network may thereby reduce colored noise in the acquired noisy medical image, increasing a clarity and diagnostic quality of the image.

Abstract: A computer-implemented method of reducing noise and artifacts in medical images is provided. The method includes receiving a series of medical images along a first dimension, wherein the signals in the medical images having a higher correlation in the first dimension than the noise and the artifacts in the medical images. The method further includes, for each of a plurality of pixels in the medical images, deriving a series of data points along the first dimension based on the series of medical images, inputting the series of data points into a neural network model, and outputting the component of signals in the series of data points. The neural network model is configured to separate a component of signals from a component of noise and artifacts in the series of data points. The method further includes generating a series of corrected medical images based on the outputted component of signals.

Abstract: A computer-implemented method of reducing noise and artifacts in medical images is provided. The method includes receiving a series of medical images along a first dimension, wherein the signals in the medical images having a higher correlation in the first dimension than the noise and the artifacts in the medical images. The method further includes, for each of a plurality of pixels in the medical images, deriving a series of data points along the first dimension based on the series of medical images, inputting the series of data points into a neural network model, and outputting the component of signals in the series of data points. The neural network model is configured to separate a component of signals from a component of noise and artifacts in the series of data points. The method further includes generating a series of corrected medical images based on the outputted component of signals.

Abstract: Methods and systems are provided for de-noising medical images using deep neural networks. In one embodiment, a method comprises receiving a medical image acquired by an imaging system, wherein the medical image comprises colored noise; mapping the medical image to a de-noised medical image using a trained convolutional neural network (CNN); and displaying the de-noised medical image via a display device. The deep neural network may thereby reduce colored noise in the acquired noisy medical image, increasing a clarity and diagnostic quality of the image.

Abstract: A method that includes obtaining an MRI gradient echo train of at least three echo data sets at differing phase angles; producing a plurality of phase error maps among the at least three echo data sets; and imaging at least three distinct chemical species based on the plurality of phase error maps.

Abstract: A method that includes obtaining an MRI gradient echo train of at least three echo data sets at differing phase angles; producing a plurality of phase error maps among the at least three echo data sets; and imaging at least three distinct chemical species based on the plurality of phase error maps.