Siemens Healthcare Gmbh

Siemens Healthineers Ag

Mayo Foundation for Medical Education and Research

Venkata Veerendranadh Chebrolu

Peter Kollasch

Mariappan S Nadar

Boris Mailhe

Andreas Greiser

Dorin Comaniciu

Ali Kamen

Johann Sukkau

Bin Lou

Joel P Felmlee

Vibhas S Deshpande

Michael Wullenweber

Daniel Rinck

John William Grinstead

Eric G Stinson

Matthew A Frick

Kimberly K Amrami

Andreas Schäfer

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.

Magnetic resonance imaging of an organ structure

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.

Autonomous magnetic resonance scanning for a given medical test

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.

Method for uniform reconstruction of multi-channel surface-coil magnetic resonance data without use of a reference scan

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.

Subtle intensity graduating homomorphic transform to improve conspicuity of low-intensity pathologies

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.

System and method for automated identification of spectral characteristics

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Rochester, MN, United States of America

Venkata Veerendranadh Chebrolu