Mayo Foundation for Medical Education and Research

Vanderbilt University

Edward Brian Welch

Armando Manduca

Jonathan Darrel Coker

David R Holmes, Iii

Timothy A Schaefer

Rick Allen Philpott

Clifford R Jack, Jr

Roger C Grimm

Timothy M Schaefer

David S Smith

David R Iii Holmes

Richard A Philpott

Systems and methods for processing magnetic resonance imaging (MRI) data are provided. A method includes receiving MRI data comprising a plurality of k-space points and deriving a plurality of image data sets based on the MRI data, each of the plurality of MRI image sets obtained by zeroing a different one of the plurality of k-space points. The method further includes computing image space metric values for each of the plurality of image data sets and adjusting a portion of the MRI data associated with ones of the image space metric values that fail to meet a threshold value to yield adjusted MRI data.

Automatic detection and correction of magnetic resonance imaging data

A system provides for remote monitoring using asynchronous code division multiple access (CDMA) communication techniques between a base station and one or more transponders. Each transponder is attached or otherwise associated with an object or an environment to be monitored. Upon receipt of an interrogation signal, a transponder generates and transmits a coded transponder signal containing the monitored data. The coded transponder signal is generated using a spreading code. Each transponder is associated with a unique, mutually exclusive set of spreading codes. Each spreading code is based on a unique transponder address and the monitored data to be sent. A detector asynchronously monitors received signals for any of the available spreading codes. Once the detector detects a particular spreading code, the base station can identify the source transponder and extract the monitored data.

Asynchronous communication system for remote monitoring of objects or an environment

Asynchronus communication system for remote monitoring of objects or an environment

Magnetic resonance images reconstructed from a radial/projection acquisition are corrected for motion corruption caused by in-plane translational and in-plane rotational motion of an imaged subject using only the projection data itself. The method is based on the consistency properties of the 0, 1, and 2order moments of the spatial domain projections. In-plane translational motion is corrected by shifting/aligning the spatial projections according to the center of mass of each projection, which is calculated using the 0and 1moments. In-plane rotational motion is accounted for by determining the rotational motion time record using the 2moment information. The determination of the rotational motion time record using the 2moments is made possible by acquiring the successive MR projections at a view angle spacing that is substantially 45&#xb0; and thus achieves sufficient linear independence. The translation-corrected spatial projections are reconstructed using the actual projection view angles, which are sum of the intended projection view angles plus the detected in-plane rotational motion time record. The calculation may also be performed in real-time during data acquisition in order to reacquire projections found to be too inconsistent due to through plane motion or other changes that are not in-plane rigid body motions or to acquire projections determined to be missing from the data set due to rotational motion.

Motion correction of magnetic resonance images using moments of spatial projections

A series of fMRI image frames are acquired along with interleaved navigator signals. The navigator signals are acquired while three orthogonal readout gradients are applied such that a spherical surface is sampled in k-space. The navigator signals are analyzed to measure subject rotational and translational motion during the scan.

Reduction of motion artifact in NMR images using spherical navigator signals

Two magnetic resonance frequency domain data sets are acquired with the phase and frequency encoding gradient axes swapped. A k-space phase difference data set is computed from the two acquisitions and corrections for interview in-plane, rigid body translational motion are calculated from the phase difference information and a large set of simultaneous linear equations. The two acquired data sets are corrected with phase changes to compensate for the subject motion and then combined to form a single image.

Motion correction of magnetic resonance images using phase difference of two orthogonal acquisitions

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Rochester, MN, United States of America

Edward Brian Welch