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 for producing an image of a subject with a magnetic resonance imaging (MRI) comprises acquiring a first set of partial k-space data from the subject and generating a phase corrected image based on a phase correction factor and the first set of the partial k-space data. The method further includes transforming the phase corrected image into a second set of partial k-space data and reconstructing the image of the subject from the second set of the partial k-space data and a weighting function.

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 independently removing streak artifacts and noise from medical images, using trained deep neural networks. In one embodiment, streak artifacts and noise may be selectively and independently removed from a medical image by receiving the medical image comprising streak artifacts and noise, mapping the medical image to a streak residual and a noise residual using the trained deep neural network, subtracting the streak residual from the medical image to a first extent, and subtracting the noise residual from the medical image to a second extent, to produce a de-noised medical image, and displaying the de-noised medical image via a display device.

Abstract: A computer-implemented method of removing truncation artifacts in magnetic resonance (MR) images is provided. The method includes receiving a crude image that is based on partial k-space data from a partial k-space that is asymmetrically truncated in at least one k-space dimension. The method also includes analyzing the crude image using a neural network model trained with a pair of pristine images and corrupted images. The corrupted images are based on partial k-space data from partial k-spaces truncated in one or more partial sampling patterns. The pristine images are based on full k-space data corresponding to the partial k-space data of the corrupted images, and target output images of the neural network model are the pristine images. The method further includes deriving an improved image of the crude image based on the analysis, wherein the derived improved image includes reduced truncation artifacts and increased high spatial frequency data.

Abstract: A computer-implemented method of correcting phase and reducing noise in magnetic resonance (MR) phase images is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images, wherein the corrupted images include corrupted phase information, the pristine images are the corrupted images with the corrupted phase information reduced, and target output images of the neural network model are the pristine images. The method further includes receiving MR images including corrupted phase information, and analyzing the received MR images using the neural network model. The method also includes deriving pristine phase images of the received MR images based on the analysis, wherein the derived pristine phase images include reduced corrupted phase information, compared to the received MR images, and outputting MR images based on the derived pristine phase images.

Abstract: Methods and systems are provided for independently removing streak artifacts and noise from medical images, using trained deep neural networks. In one embodiment, streak artifacts and noise may be selectively and independently removed from a medical image by receiving the medical image comprising streak artifacts and noise, mapping the medical image to a streak residual and a noise residual using the trained deep neural network, subtracting the streak residual from the medical image to a first extent, and subtracting the noise residual from the medical image to a second extent, to produce a de-noised medical image, and displaying the de-noised medical image via a display device.

Abstract: A computer-implemented method of correcting phase and reducing noise in magnetic resonance (MR) phase images is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images, wherein the corrupted images include corrupted phase information, the pristine images are the corrupted images with the corrupted phase information reduced, and target output images of the neural network model are the pristine images. The method further includes receiving MR images including corrupted phase information, and analyzing the received MR images using the neural network model. The method also includes deriving pristine phase images of the received MR images based on the analysis, wherein the derived pristine phase images include reduced corrupted phase information, compared to the received MR images, and outputting MR images based on the derived pristine phase images.

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 independently removing streak artifacts and noise from medical images, using trained deep neural networks. In one embodiment, streak artifacts and noise may be selectively and independently removed from a medical image by receiving the medical image comprising streak artifacts and noise, mapping the medical image to a streak residual and a noise residual using the trained deep neural network, subtracting the streak residual from the medical image to a first extent, and subtracting the noise residual from the medical image to a second extent, to produce a de-noised medical image, and displaying the de-noised medical image via a display device.

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: An MRI system for performing time resolved MR imaging of an object with grouped data acquisition is provided. The MRI system includes an MRI controller in electronic communication with a magnet assembly and operative to sample a group of data points within a first region of a k-space. The first region includes a central sub-region and a first peripheral sub-region. The MRI controller is further operative to sample a group of data points within a second region of the k-space. The second region includes the central sub-region and a second peripheral sub-region different from the first peripheral sub-region.

Abstract: An MRI system for performing time resolved MR imaging of an object with grouped data acquisition is provided. The MRI system includes an MRI controller in electronic communication with a magnet assembly and operative to sample a group of data points within a first region of a k-space. The first region includes a central sub-region and a first peripheral sub-region. The MRI controller is further operative to sample a group of data points within a second region of the k-space. The second region includes the central sub-region and a second peripheral sub-region different from the first peripheral sub-region.

Abstract: The present disclosure relates to a method for assessing biological features, the method that includes rendering a graphical user interface allowing a user to select patient cohort information defining one or more characteristics of a patient cohort and allowing a user to select an analysis technique from a plurality of analysis techniques, wherein the analysis technique operates on the patient data from both the first data acquisition modality and the second data acquisition modality to generate a derived variable. The method also includes allowing a user to define a threshold for the derived variable to define a first patient group above the threshold and a second patient group below the threshold for each patient of a patient cohort.

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.

Abstract: A method of magnetic resonance (MR) imaging includes segmenting a ky-kz plane comprising a plurality of encoding points into a plurality of annular segments. For each annular segment, a view ordering is defined based on a polar angle associated with each encoding point contained within the annular segment. MR data is acquired for the plurality of encoding points based on the view ordering for each annular segment and at least one MR image is generated using the acquired MR data.

Abstract: Methods and systems are provided for modifying a pulse sequence. In one embodiment, a determination is made whether an estimated peripheral nerve stimulation (PNS) associated with a pulse sequence exceeds a PNS limit. If the estimated PNS exceeds the PNS limit, a slew rate associated with one or more axes of the pulse sequence may be reduced and the maximum gradient amplitudes for each axis of the pulse sequence may be adjusted. In one embodiment, adjustment of the maximum gradient amplitudes or local slew rate may be based upon a cost analysis performed on the pulse sequence.

Abstract: A method for acquiring magnetic resonance (MR) data for a three-dimensional (3D) dynamic study includes partitioning a ky-kz plane with a plurality of views into an inner region and a plurality of outer regions. The inner region includes a set of views in a central region of the ky-kz plane and each outer region includes a plurality of views outside of the central region of the ky-kz plane. The method also includes partitioning each outer region into a plurality of radial fan beam segments, defining a first view ordering for the inner region and defining a second view ordering for each outer region. Once the ky-kz plane is partitioned and the view orderings are defined, MR data is acquired for the set of views in the inner region and for all of the views in each of the outer regions in an alternating acquisition order where the set of views in the inner region are acquired more frequently than the views in each of the outer regions. At least one MR image is generated based on the acquired MR data.

Abstract: Methods and systems are provided for modifying a pulse sequence. In one embodiment, a determination is made whether an estimated peripheral nerve stimulation (PNS) associated with a pulse sequence exceeds a PNS limit. If the estimated PNS exceeds the PNS limit, a slew rate associated with one or more axes of the pulse sequence may be reduced and the maximum gradient amplitudes for each axis of the pulse sequence may be adjusted. In one embodiment, adjustment of the maximum gradient amplitudes or local slew rate may be based upon a cost analysis performed on the pulse sequence.