Abstract: A method for performing a scan of a subject includes receiving a selected protocol for the scan and triggering, upon receiving a start signal, automatic landmarking of the subject on a table of a magnetic resonance imaging (MRI) scanner utilizing a three-dimensional (3D) camera. The method includes obtaining landmark positioning data from the 3D camera and utilizing the landmark positioning data for localization of the region of interest. The method includes, subsequent to the automatic landmarking, triggering a calibration scan of the subject with the MRI scanner and obtaining calibration data from the MRI scanner and utilizing the calibration data for refining the localization of the region of interest. The method includes generating a geometry plan for subsequent scans utilizing both the landmark positioning data and the calibration data and triggering at least one subsequent scan of the subject with the MRI scanner based on the geometry plan.

Abstract: A method for performing one-shot anatomy localization includes obtaining a medical image of a subject. The method includes receiving a selection of both a template image and a region of interest within the template image, wherein the template image includes one or more anatomical landmarks assigned a respective anatomical label. The method includes inputting both the medical image and the template image into a trained vision transformer model. The method includes outputting from the trained vision transformer model both patch level features and image level features for both the medical image and the template image. The method still further includes interpolating pixel level features from the patch level features for both the medical image and the template image. The method includes utilizing the pixel level features within the region of interest of the template image to locate and label corresponding pixel level features in the medical image.

Abstract: A computer-implemented method for performing a scan of a subject utilizing a magnetic resonance imaging (MRI) system includes initiating, via a processor, a prescan of the subject by an MRI scanner of the MRI system without a priori knowledge as to whether the subject has a metal implant. The computer-implemented method also includes executing, via the processor, a metal detection algorithm during a prescan entry point of the prescan to detect whether the metal implant is present in the subject. The computer-implemented method further includes determining, via the processor, to proceed with a calibration scan and the scan utilizing predetermined scan parameters when no metal implant is detected in the subject. The computer-implemented method even further includes switching, via the processor, into a metal implant scan mode when one or more metal implants are detected in the subject.

Abstract: A computer-implemented method for performing a scan of a subject utilizing a magnetic resonance imaging (MRI) system includes initiating, via a processor, a prescan of the subject by an MRI scanner of the MRI system without a priori knowledge as to whether the subject has a metal implant. The computer-implemented method also includes executing, via the processor, a metal detection algorithm during a prescan entry point of the prescan to detect whether the metal implant is present in the subject. The computer-implemented method further includes determining, via the processor, to proceed with a calibration scan and the scan utilizing predetermined scan parameters when no metal implant is detected in the subject. The computer-implemented method even further includes switching, via the processor, into a metal implant scan mode when one or more metal implants are detected in the subject.

Abstract: A method for generating an image of a subject with a magnetic resonance imaging (MRI) system is presented. The method includes first performing a localizer scan of the subject to acquire localizer scan data. A machine learning (ML) module is then used to detect the presence of metal regions in the localizer scan data based on magnitude and phase information of the localizer scan data. Based on the detected metal regions in the localizer scan data, the MRI workflow is adjusted for diagnostic scan of the subject. The image of the subject is then generated using the adjusted workflow.

Abstract: A method for performing a scan of a subject utilizing a magnetic resonance imaging (MRI) system includes triggering a prescan by an MRI scanner of the MRI system upon the subject being setup on a table of the MRI scanner and the table reaching an iso-center of the MRI scanner. The method includes subsequent to the prescan, triggering a calibration scan of the subject with the MRI scanner, wherein the calibration scan is an acoustic noise suppressed MRI scan. The method includes obtaining calibration data from the calibration scan. The method includes obtaining prescription parameters for subsequent scans of the subject with the MRI scanner from the calibration data. The method includes triggering at least one scan of the subject with the MRI scanner based on the prescription parameters.

Abstract: A method for generating an image of a subject with a magnetic resonance imaging (MRI) system is presented. The method includes first performing a localizer scan of the subject to acquire localizer scan data. A machine learning (ML) module is then used to detect the presence of metal regions in the localizer scan data based on magnitude and phase information of the localizer scan data. Based on the detected metal regions in the localizer scan data, the MRI workflow is adjusted for diagnostic scan of the subject. The image of the subject is then generated using the adjusted workflow.

Abstract: Methods and systems are provided for cardiac triggering of an imaging system. a method for an imaging system comprises acquiring, during a scan of a subject, an electrical signal indicating a periodic physiological motion of an organ of the subject, inputting a sample of the electrical signal into a trained neural network to detect whether a peak is present in the sample, triggering acquisition of image data responsive to detecting the peak in the sample, and not triggering the acquisition of image data responsive to not detecting the peak in the sample. In this way, the timing of data acquisition may be optimally and robustly synchronized with a cardiac cycle.

Abstract: Methods and systems are provided for determining scan settings for a localizer scan based on a magnetic resonance (MR) calibration image. In one example, a method for magnetic resonance imaging (MRI) includes acquiring an MR calibration image of an imaging subject, mapping, by a trained deep neural network, the MR calibration image to a corresponding anatomical region of interest (ROI) attribute map for an anatomical ROI of the imaging subject, adjusting one or more localizer scan parameters based on the anatomical ROI attribute map, and acquiring one or more localizer images of the anatomical ROI according to the one or more localizer scan parameters.

Abstract: Methods and systems are provided for automated graphical prescriptions with deep learning systems. In one embodiment, a method for a medical imaging system comprises acquiring, by the medical imaging system, localizer images of a subject, generating, by a trained neural network system, a graphical prescription using the localizer images, and performing, by the medical imaging system, a scan of the subject according to the graphical prescription. In this way, a desired region of interest of the subject may be accurately scanned with minimal input from an operator of the medical imaging system.

Abstract: A method of controlling magnetic resonance (MR) image data acquisition includes generating a plurality of one-dimensional (1D) navigator profiles reflecting motion of an anatomic boundary region of an imaging subject over time at a measurement interval, and then generating a plurality of navigator image segments each for a corresponding 1D navigator profile of the plurality of 1D navigator profiles. A navigator image is then generated based on the plurality of navigator image segments, and a determination is made whether to acquire MR image data based on the navigator image.

Abstract: Methods and systems are provided for determining diagnostic-scan parameters for a magnetic resonance (MR) diagnostic-scan, from MR calibration images, enabling acquisition of high-resolution diagnostic images of one or more anatomical regions of interest, while bypassing acquisition of localizer images, increasing a speed and efficiency of MR diagnostic-scanning. In one embodiment, a method for a magnetic resonance imaging (MRI) system comprises, acquiring a magnetic resonance (MR) calibration image of an imaging subject, mapping the MR calibration image to a landmark map using a trained deep neural network, determining one or more diagnostic-scan parameters based on the landmark map, acquiring an MR diagnostic image according to the diagnostic-scan parameters, and displaying the MR diagnostic image via a display device.

Abstract: Methods and systems are provided for predicting B1+ field maps from magnetic resonance calibration images using deep neural networks. In an exemplary embodiment a method for magnetic resonance imaging comprises, acquiring a magnetic resonance (MR) calibration image of an anatomical region, mapping the MR calibration image to a transmit field map (B1+ field map) with a trained deep neural network, acquiring a diagnostic MR image of the anatomical region, and correcting inhomogeneities of a transmit field in the diagnostic MR image with the B1+ field map. Further, methods and systems are provided for collecting and processing training data, as well as utilizing the training data to train a deep learning network to predict B1+ field maps from MR calibration images.

Abstract: Methods and systems are provided for determining scan settings for a localizer scan based on a magnetic resonance (MR) calibration image. In one example, a method for magnetic resonance imaging (MRI) includes acquiring an MR calibration image of an imaging subject, mapping, by a trained deep neural network, the MR calibration image to a corresponding anatomical region of interest (ROI) attribute map for an anatomical ROI of the imaging subject, adjusting one or more localizer scan parameters based on the anatomical ROI attribute map, and acquiring one or more localizer images of the anatomical ROI according to the one or more localizer scan parameters.

Abstract: A method of controlling magnetic resonance (MR) image data acquisition includes generating a plurality of one-dimensional (1D) navigator profiles reflecting motion of an anatomic boundary region of an imaging subject over time at a measurement interval, and then generating a plurality of navigator image segments each for a corresponding 1D navigator profile of the plurality of 1D navigator profiles. A navigator image is then generated based on the plurality of navigator image segments, and a determination is made whether to acquire MR image data based on the navigator image.

Abstract: Methods and systems are provided for predicting B1+ field maps from magnetic resonance calibration images using deep neural networks. In an exemplary embodiment a method for magnetic resonance imaging comprises, acquiring a magnetic resonance (MR) calibration image of an anatomical region, mapping the MR calibration image to a transmit field map (B1+ field map) with a trained deep neural network, acquiring a diagnostic MR image of the anatomical region, and correcting inhomogeneities of a transmit field in the diagnostic MR image with the B1+ field map. Further, methods and systems are provided for collecting and processing training data, as well as utilizing the training data to train a deep learning network to predict B1+ field maps from MR calibration images.

Abstract: Systems and methods for accelerated multi-contrast PROPELLER are disclosed herein. K-space is sampled in a rotating fashion using a plurality of radially directed blades around a center of k-space. A first subset of blades is acquired for a first contrast and a second subset of blades is acquired for a second contrasts. The first subset of blades is combined with high frequency components of the second subset of blades to produce an image of the first contrast. And the second subset of blades are combined with high frequency components of the first subset of blades to produce an image of the second contrast.

Abstract: Various methods and systems are provided for selecting radio frequency (RF) coil array for magnetic resonance imaging (MRI). In one embodiment, the method comprises grouping the plurality of coil elements into receiving elements groups (REGs) according to REGs information, generating channel sensitivity maps for the plurality of coil elements, generating REG sensitivity maps based on the REGs information and the channel sensitivity maps, selecting one or more REGs based on the REG sensitivity maps and a region of interest (ROI), and scanning the ROI with the coil elements of the one or more selected REGs being activated and the coil elements not in any selected REGs being deactivated. In this way, coil arrays may be automatically selected for improved image quality of the MRI.

Abstract: Various methods and systems are provided for selecting radio frequency coil array comprising a plurality of coil elements for magnetic resonance imaging. In one embodiment, the method includes grouping the plurality of coil elements into receive elements groups (REGs) according to REGs information; generating REG sensitivity maps; determining, for each REG, signal in a region of interest (ROI) and signal in an annefact source region based on the REG sensitivity maps; selecting one or more REGs based on the signal in the ROI and the signal in the annefact source region; and scanning the ROI with the coil elements in the one or more selected REGs being activated and the coil elements not in any selected REGs being deactivated. In this way, annefact artifacts in the reconstructed image may be reduced.

Abstract: In one example, an RF coil array includes a first RF coil configured to generate a magnetic field along a first axis, the first RF coil having a first surface, a second RF coil configured to generate a magnetic field along a second axis, orthogonal to the first axis, the second RF coil having a second surface, and a first foldable interconnect coupling the first RF coil to the second RF coil. The first foldable interconnect may be adjusted to couple the first RF coil to the second RF coil with a first amount of overlap and with the first surface and second surface facing a common direction, or couple the first RF coil to the second RF coil with a second amount of overlap, larger than the first amount of overlap, and with the first surface in face to face position with the second surface.