Abstract: A system and method include determination of multi-modal data associated with a subject, the multi-modal data including image data of the subject, input of the multi-modal data to a first trained clustering model to determine a cluster for the subject, determination of a proposed treatment for the subject, and input of the multi-modal data, the cluster and the treatment to a second trained model, where the second trained model outputs a probability associated with a treatment outcome in response to the input multi-modal data, cluster and treatment.

Abstract: A method of scanning an organ structure of a patient using magnetic resonance imaging, includes scanning, in a first scanning process, the patient to obtain first image data indicative of at least the organ structure of the patient. The method further includes determining, based on the first image data, one or more parameters obtain second image data indicative of at least the organ structure of the patient. The first scanning process includes a first quality of imaging scan, the second scanning process includes a second quality of imaging scan, and the first quality of imaging scan is higher than the second quality of imaging scan.

Abstract: For autonomous MR scanning for a given medical test, a simplified MR scanner may be used without or will little input or control by a technologist (e.g., by a physician, radiologist, or person trained in MR scanner operation). The MR scanner autonomously positions, scans, checks quality, analyzes, and/or outputs an answer to a diagnostic question with or without an MR image. Scan analysis, based on artificial intelligence, allows for on-going or on-the-fly alteration of the scanning configuration to acquire the data desired to answer the diagnostic question. By using a simplified MR scanner, both position of the patient relative to the MR scanner and localization of the scan by the MR scanner are jointly solved. Sensors may sense a patient in a scan position where the reduced radio frequency requirements allow for a more open bore.

Abstract: For data analytics in magnetic resonance (MR) scanning, the scanning configuration information and the resulting raw data are directly used to determine the analytics or clinical decision. Artificial intelligence provides a value for a clinical finding characteristic of the patient based on the raw data from scanning and the controls used to scan, allowing the value to be based on all of the information content of the scan results. Reconstruction is not needed, allowing for simpler hardware, such as hardware with less homogeneous B0 and/or B1 fields than the norm and/or non-linear gradients.

Abstract: Systems and methods for generating images with a magnetic resonance imaging (“MRI”) system, in which the images have been corrected for receive coil nonuniformities are described. Improved data acquisition schemes for fat saturation are also described.

Abstract: The present disclosure is directed to systems and methods for visualizing low-intensity pathologies or anatomical structures. The method can include applying, via a MRI machine, an RF pulse configured to suppress the imaging of fat in the subject and acquiring, via the MRI machine, an image of the subject. A subtle intensity graduating homomorphic transform is applied to the acquired image to provide improved visualized of the low-intensity pathology or anatomical structure. The resulting transformed image can be output to the user.

Abstract: Systems and methods for determining proton spectral characteristics associated with a pair of targets from an MRI data volume are provided. The methods can include identifying spectral widths and peak-to-peak distance associated with the targets from the MRI data volume. The targets could include water and fat. The identified proton spectral characteristics can be useful for accurate spectral fat saturation, improving dynamic shim routines, and optimizing bandwidth of radiofrequency pulses used in multi-slice or multi-band excitation.

Abstract: The present disclosure is directed to systems and methods for visualizing low-intensity pathologies or anatomical structures. The method can include applying, via a MRI machine, an RF pulse configured to suppress the imaging of fat in the subject and acquiring, via the MRI machine, an image of the subject. A subtle intensity graduating homomorphic transform is applied to the acquired image to provide improved visualized of the low-intensity pathology or anatomical structure. The resulting transformed image can be output to the user.

Abstract: Systems and methods for generating images with a magnetic resonance imaging (“MRI”) system, in which the images have been corrected for receive coil nonuniformities are described. Improved data acquisition schemes for fat saturation are also described.

Abstract: A method for automated evaluation of motion correction is presented. The method includes identifying one or more regions of interest in each of a plurality of images corresponding to a subject of interest. Furthermore, the method includes selecting valid voxels in each of the one or more regions of interest in each of the plurality of images. The method also includes computing a similarity metric, a dispersion metric, or both the similarity metric and the dispersion metric for each region of interest in each of the plurality of images. Additionally, the method includes generating a similarity map, a dispersion map, or both the similarity map and the dispersion map based on the similarity metrics and the dispersion metrics corresponding to the one or more regions of interest.

Abstract: A method for automated evaluation of motion correction is presented. The method includes identifying one or more regions of interest in each of a plurality of images corresponding to a subject of interest. Furthermore, the method includes selecting valid voxels in each of the one or more regions of interest in each of the plurality of images. The method also includes computing a similarity metric, a dispersion metric, or both the similarity metric and the dispersion metric for each region of interest in each of the plurality of images. Additionally, the method includes generating a similarity map, a dispersion map, or both the similarity map and the dispersion map based on the similarity metrics and the dispersion metrics corresponding to the one or more regions of interest.

Abstract: A system for automatic segmentation of tumor tissue, useful for motion correction during radiotherapy using real-time imaging, identifies multiple regions based on the values of data and then identifies the tumors within the regions based on a priori knowledge about tumor size and/or location. The regions may be refined with robust and fast morphological operations, providing segmentation at speeds commensurate with the motion to be corrected.

Abstract: A system for automatic segmentation of tumor tissue, useful for motion correction during radiotherapy using real-time imaging, identifies multiple regions based on the values of data and then identifies the tumors within the regions based on a priori knowledge about tumor size and/or location. The regions may be refined with robust and fast morphological operations, providing segmentation at speeds commensurate with the motion to be corrected.