Abstract: A method includes acquiring an MRI complex signal having a plurality of complex echoes during an SWI sequence. The method includes phase filtering each complex echo of the plurality of complex echoes. The method also includes generating a respective phase image and a respective magnitude image from each phase filtered complex echo. The method further includes combining separately the respective magnitude images of the plurality of complex echoes with each other to generate a combined magnitude image and the respective phase images of the plurality of complex echoes with each other to generate a combined phase image. The method includes generating a complex image from both the combined magnitude image and the combined phase image. The method includes utilizing a deep learning-based denoising network to denoise the complex image to generate a denoised complex image.

Abstract: A method for generating magnetic resonance images includes obtaining, via a processing system, a single complex magnetic resonance image per slice from magnetic resonance scan data acquired of a subject with a magnetic resonance scanner utilizing a chemical shifted sequence where signal contributions from different pre-defined components to acquired echo signals are separated, wherein the single complex magnetic resonance image comprises a non-zero phase angle between the different pre-defined components. The method also includes inputting, via the processing system, the single complex magnetic resonance image for each slice into a decomposition model trained to predict chemical shifted-like images from the single complex magnetic resonance image for each slice.

Abstract: A method includes obtaining k-space data of a subject acquired from a plurality of receiving channels of a magnetic resonance imaging scanner during a scan, wherein at least some receiving channels of the plurality of receiving channels are located in different spatial locations from other receiving channels of the plurality of receiving channels. The method includes obtaining channel sensitivity data of the plurality of receiving channels acquired with magnetic resonance imaging scanner prior to acquiring the k-space data. The method includes obtaining noise data for the plurality of receiving channels acquired with magnetic resonance imaging scanner prior to acquiring the k-space data. The method includes linearly combining the k-space data from the plurality of receiving channels based on both the channel sensitivity data and the noise data to estimate virtual channels having virtual k-space data that maximize signal to radio frequency interference ratio while simultaneously isolating zipper artifacts.

Abstract: A method includes obtaining k-space data, wherein a plurality of navigator like echoes of the k-space data including an additional navigator like echo are acquired for each shot or a group of shots. The k-space data is motion corrupted. The method includes identifying any shots where a subject moved during acquisition based on the respective additional navigator like echoes. The method includes generating dominant pose k-space data based on identification of any shots where the subject moved during acquisition, the dominant pose k-space data includes only shots not affected by movement, wherein the dominant pose k-space data is missing k-space data due to rejecting the shots where the subject moved. The method includes utilizing a deep learning-based reconstruction model on the motion-corrupted k-space data to modify motion-corrupted k-space data with k-space data that is consistent with the dominant pose k-space data to generate a reconstructed image.

Abstract: A method includes obtaining k-space data, wherein a plurality of navigator like echoes of the k-space data including an additional navigator like echo are acquired for each shot or a group of shots. The k-space data is motion corrupted. The method includes identifying any shots where a subject moved during acquisition based on the respective additional navigator like echoes. The method includes generating dominant pose k-space data based on identification of any shots where the subject moved during acquisition, the dominant pose k-space data includes only shots not affected by movement, wherein the dominant pose k-space data is missing k-space data due to rejecting the shots where the subject moved. The method includes utilizing a deep learning-based reconstruction model on the motion-corrupted k-space data to modify motion-corrupted k-space data with k-space data that is consistent with the dominant pose k-space data to generate a reconstructed image.

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 includes obtaining k-space data acquired by an MRI scanner from a single channel body coil utilizing a multi-shot EP-DWI pulse sequence and sampling the k-space data for a plurality of shots so that for each shot both a central k-space is fully sampled to form a central calibration region and an outer k-space is partially sampled by a factor equal to a number of shots. The method includes reconstructing an initial fully sampled k-space estimate for each shot utilizing both partial Fourier constant sampling and projection on convex sets reconstruction, wherein the plurality of shots is treated as a plurality of channels for filling in missing k-space for a respective shot. The method includes utilizing a low-rank regularization algorithm in an iterative manner to generate a reconstructed image for each shot, wherein the initial fully sampled k-space estimate for each shot is utilized as an initial guess.

Abstract: A method includes receiving a selection of a scan protocol for the scan of a subject and obtaining localizer images including an anatomic landmark of interest of the subject acquired with the MRI system. The method includes automatically detecting the anatomic landmark of interest in localizer images and determining a geometry plan of the scan including extents of the anatomic landmark of interest. The method includes automatically determining a coverage of the scan to include the anatomic landmark of interest and to match the extents of the anatomic landmark of interest. The method includes obtaining limits on adjustments scan time and one or more image quality parameters for the scan protocol. The method includes generating an updated scan protocol by automatically adjusting one or more parameters of the scan protocol based on the scan protocol, the limits on adjustments, and the coverage of the scan.

Abstract: A method includes obtaining k-space data acquired by an MRI scanner from a single channel body coil utilizing a multi-shot EP-DWI pulse sequence and sampling the k-space data for a plurality of shots so that for each shot both a central k-space is fully sampled to form a central calibration region and an outer k-space is partially sampled by a factor equal to a number of shots. The method includes reconstructing an initial fully sampled k-space estimate for each shot utilizing both partial Fourier constant sampling and projection on convex sets reconstruction, wherein the plurality of shots is treated as a plurality of channels for filling in missing k-space for a respective shot. The method includes utilizing a low-rank regularization algorithm in an iterative manner to generate a reconstructed image for each shot, wherein the initial fully sampled k-space estimate for each shot is utilized as an initial guess.

Abstract: A method for magnetic resonance imaging (MRI) includes determining a Partial Fourier (PF) factor and an acceleration factor for acquiring k-space data from a subject. The method also includes acquiring a set of k-space data from the subject using the PF factor along with an under-sampling technique, wherein the under-sampling technique is dependent on the acceleration factor. The image of the subject is reconstructed by processing the set of k-space data using a deep learning (DL) network.

Abstract: A method includes acquiring an MRI complex signal having a plurality of complex echoes during an SWI sequence. The method includes phase filtering each complex echo of the plurality of complex echoes. The method also includes generating a respective phase image and a respective magnitude image from each phase filtered complex echo. The method further includes combining separately the respective magnitude images of the plurality of complex echoes with each other to generate a combined magnitude image and the respective phase images of the plurality of complex echoes with each other to generate a combined phase image. The method includes generating a complex image from both the combined magnitude image and the combined phase image. The method includes utilizing a deep learning-based denoising network to denoise the complex image to generate a denoised complex image.

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.