Abstract: Systems and methods for simulating structures and images are disclosed. The techniques described herein can include obtaining a first image of a subject. The techniques can include determining a location for simulating a structure within the first image. The techniques can include simulating, according to the location, a shape for the structure. The techniques can include generating a mask according to the location and the shape for the structure. The techniques can include applying the mask to the first image to generate a second image simulating the structure.

Abstract: Systems and methods for training and deploying machine learning segmentation models to produce foreground masks of images are provided. The model may be used to generate foreground masks. A first method includes receiving images and annotations indicative of which pixels are in the foregrounds of the images, and generating, based on the images and the annotations, a model configured to receive, as input, a subject image and provide, as output, one or more probability maps indicative of foreground probabilities for pixels in the subject image. A foreground probability is indicative of a likelihood that the pixel is part of a foreground object or artifact in the image. Another method includes receiving a subject image, and generating a foreground mask for the subject image at least in part by applying a machine learning model to the subject image, the model having been generated based on disclosed training methods.

Abstract: Techniques for denoising a magnetic resonance (MR) image are provided, including: obtaining a noisy MR image; denoising the noisy MR image of the subject using a denoising neural network model, and outputting a denoised MR image. The denoising neural network model is trained by: generating first training data for training a first neural network model to denoise MR images by generating a first plurality of noisy MR images using clean MR data associated with a source domain and first MR noise data associated with the target domain; training the first neural network model using the first training data; generating training data for training the denoising neural network model by applying the first neural network model to a second plurality of noisy MR images and generating a plurality of denoised MR images; and training the denoising neural network model using the training data for training the denoising neural network model.

Abstract: A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.

Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Abstract: Systems and methods for dual-domain self-supervised learning for accelerated non-Cartesian magnetic resonance imaging reconstruction are provided. The present techniques provide a method for training a machine-learning model that receives magnetic resonance (MR) data and generates a reconstruction of the MR data. The machine-learning model can be trained based on a set of losses comprising a first loss value corresponding to a frequency-domain and a second loss value corresponding to an image-based domain. The training process can be a self-supervised training process that can utilize under-sampled and non-Cartesian MR data. The machine-learning model is trained by optimizing both data consistency in the frequency domain and appearance consistency in the image-based domain.

Abstract: A computer-implemented method that includes providing as input to the neural network, a first image and a second image. The method further includes obtaining, using the neural network, a first transformed image based on the first image that may be aligned with the second image. The method further includes computing a first loss value based on a comparison of the first transformed image and the second image. The method further includes obtaining, using the neural network, a second transformed image based on the second image that may be aligned with the first image. The method further includes computing a second loss value based on a comparison of the second transformed image and the first image. The method further includes adjusting one or more parameters of the neural network based on the first loss value and the second loss value.

Abstract: Some aspects include a method of determining change in size of an abnormality in a brain of a patient positioned within a low-field magnetic resonance imaging (MRI) device. The method comprises, while the patient remains positioned within the low-field MRI device, acquiring first and second magnetic resonance (MR) image data of the patient's brain; providing the first and second MR image data as input to a trained statistical classifier to obtain corresponding first and second output; identifying, using the first output, at least one initial value of at least one feature indicative of a size of the abnormality; identifying, using the second output, at least one updated value of the at least one feature; determining the change in the size of the abnormality using the at least one initial value of the at least one feature and the at least one updated value of the at least one feature.

Abstract: A computer-implemented method that includes providing as input to the neural network, a first image and a second image. The method further includes obtaining, using the neural network, a transformed image based on the first image that may be aligned with the second image. The method further includes obtaining a plurality of first patches from the transformed image by encoding the transformed image using a first encoder that has a first plurality of encoding layers. The method further includes obtaining a plurality of second patches from the second image by encoding the second image using a second encoder that has a second plurality of encoding layers. The method further includes computing a loss value based on comparison of respective first patches and second patches. The method further includes adjusting one or more parameters of the neural network based on the loss value.

Abstract: A method includes: obtaining an ultrasound image of an anatomical area from an ultrasound imaging device; inputting the ultrasound image into a first stage of a convolutional neural network, the first stage configured to determine key-point locations of the anatomical area; generating, for each of the key-point locations, a cropped region of the ultrasound image; inputting each of the cropped regions into a second stage of the convolutional neural network, the second stage configured to locate an anatomical landmark of the anatomical area; and outputting a location of the anatomical landmark.

Abstract: Techniques are provided for imaging a subject. The method may comprise receiving an indication to image the subject using an magnetic resonance imaging (MRI) system, and in response to receiving the indication, with at least one controller: generating, using at least one RF coil, an initial MR data set for generating an initial image of the subject; determining, using the initial MR image, a difference in orientation between a current orientation of the subject in the initial MR image and a target orientation of the subject; determining, using the determined difference in orientation, an adjustment to a gradient pulse sequence for controlling at least one gradient coil; applying the determined adjustment to the gradient pulse sequence to obtain an adjusted gradient pulse sequence; generating an adjusted MR data set using the adjusted gradient pulse sequence; and generating a second MR image of the subject using the adjusted MR data set.

Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Abstract: Aspects of the technology described herein relate to techniques for guiding an operator to use an ultrasound device. Thereby, operators with little or no experience operating ultrasound devices may capture medically relevant ultrasound images and/or interpret the contents of the obtained ultrasound images. For example, some of the techniques disclosed herein may be used to identify a particular anatomical view of a subject to image with an ultrasound device, guide an operator of the ultrasound device to capture an ultrasound image of the subject that contains the particular anatomical view, and/or analyze the captured ultrasound image to identify medical information about the subject.

Abstract: Aspects of the technology described herein relate to techniques for guiding an operator to use an ultrasound device. Thereby, operators with little or no experience operating ultrasound devices may capture medically relevant ultrasound images and/or interpret the contents of the obtained ultrasound images. For example, some of the techniques disclosed herein may be used to identify a particular anatomical view of a subject to image with an ultrasound device, guide an operator of the ultrasound device to capture an ultrasound image of the subject that contains the particular anatomical view, and/or analyze the captured ultrasound image to identify medical information about the subject.

Abstract: Techniques for removing artefacts, such as RF interference and/or noise, from magnetic resonance data. The techniques include: obtaining input magnetic resonance (MR) data using at least one radio-frequency (RF) coil of a magnetic resonance imaging (MRI) system; and generating an MR image from input MR data at least in part by using a neural network model to suppress at least one artefact in the input MR data.

Abstract: Techniques are provided for imaging a subject. A magnetic resonance imaging (MRI) system may use at least one RF coil to generate an initial MR data set for an initial image of the subject. The MRI system may use the initial MR image to determine a difference in orientation between a current orientation of the subject in the initial MR image and a target orientation of the subject. The MRI system may use the determined difference in orientation to determine an adjustment to a gradient pulse sequence for controlling at least one gradient coil. The MRI system may apply the determined adjustment to the gradient pulse sequence to obtain an adjusted gradient pulse sequence. The MRI system may generate an adjusted MR data set using the adjusted gradient pulse sequence, and a second MR image of the subject using the adjusted MR data set.

Abstract: Techniques for removing artefacts, such as RF interference and/or noise, from magnetic resonance data. The techniques include: obtaining input magnetic resonance (MR) data using at least one radio-frequency (RF) coil of a magnetic resonance imaging (MRI) system; and generating an MR image from input MR data at least in part by using a neural network model to suppress at least one artefact in the input MR data.

Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Abstract: Aspects of the technology described herein relate to techniques for guiding an operator to use an ultrasound device. Thereby, operators with little or no experience operating ultrasound devices may capture medically relevant ultrasound images and/or interpret the contents of the obtained ultrasound images. For example, some of the techniques disclosed herein may be used to identify a particular anatomical view of a subject to image with an ultrasound device, guide an operator of the ultrasound device to capture an ultrasound image of the subject that contains the particular anatomical view, and/or analyze the captured ultrasound image to identify medical information about the subject.

Abstract: Aspects of the technology described herein relate to techniques for guiding an operator to use an ultrasound device. Thereby, operators with little or no experience operating ultrasound devices may capture medically relevant ultrasound images and/or interpret the contents of the obtained ultrasound images. For example, some of the techniques disclosed herein may be used to identify a particular anatomical view of a subject to image with an ultrasound device, guide an operator of the ultrasound device to capture an ultrasound image of the subject that contains the particular anatomical view, and/or analyze the captured ultrasound image to identify medical information about the subject.