General Electric Company

Ge Medical Systems Global Technology Company, LLC

John Andrew Johnson

Piero Patrone Bonissone

Yu-To Chen

Rasiklal Punjalal Shah

Phillip Edward Steen

Vipin Kewal Ramani

Ramesh Ramachandran

Izaak Koller

Ianne Mae Howards Koritzinsky

Dan Apone

Deborah Ann Babula

Perry S Frederick

Henry John Hummel, Jr

Leo Michael Kucek

Cyrillus Tamsil Steven Kunta Hutabarat

Kevin James Jay

Ramesh Ramchandran

Steve Frankovich

Amanda Juris

Roland Horth

Marc Singer

Stephen Hoyt Hancock

Glenn Klecker

A system and method for diagnosing and validating a machine with waveform data generated therefrom. Historical waveform data are obtained from machines having known faults along with corresponding actions for repairing the machines and are used to develop fault classification rules. The fault classification rules are stored in a diagnostic knowledge database. The database of classification rules are used to diagnose new waveform data from a machine having an unknown fault.

System and method for diagnosing and validating a machine using waveform data

A system and method for diagnosing and validating a machine over a network using waveform data. Historical waveform data are obtained via the network from machines having known faults along with corresponding actions for repairing the machines and are used to develop fault classification rules. The fault classification rules are stored in a diagnostic knowledge database. The database of classification rules are used to diagnose new waveform data from a machine having an unknown fault, via the network.

System and method for diagnosing and validating a machine over a network using waveform data

A diagnostic system is provided for identifying faults in a machine (e.g., CT scanner, MRI system, x-ray apparatus) by analyzing a data file generated thereby. The diagnostic system includes a trained database containing a plurality of trained data, each trained data associated with one of plurality of known fault types. Each trained data is represented by a trained set of feature values and corresponding weight values. Once a data file is generated by the machine, a current set of feature values are extracted from the data file by performing various analyses (e.g., time domain analysis, frequency domain analysis, wavelet analysis). The current set of feature values extracted is analyzed by a fault detector which produces a candidate set of faults based on the trained set of feature values and corresponding weight values for each of the fault types. The candidate set of faults produced by the fault detector is presented to a user along with a recommend repair procedure. In cases where no fault is identified or in response to a misdiagnosis produced by the diagnostic system, the user may interactively input a faulty condition associated with the machine being diagnosed (e.g., based on his/her experience). The diagnostic system further includes a learning subsystem which automatically updates the plurality of trained data based on the faulty condition input by the user.

Diagnostic system with learning capabilities

A technique is provided for evaluation of an operative state of a medical diagnostic system. The technique is particularly adapted to evaluating image data from a variety of imaging modalities, including magnetic resonance imaging systems, computed tomography imaging systems, x-ray imaging systems, and so forth. A user interface permits a service request to be formulated at the diagnostic system. Data files, such as image files, log files, error files, and so forth, are identified and stored corresponding to the service request. The service request is then transmitted to a remote service facility for evaluation. The necessary data files may be transmitted with the request or subsequent thereto. The request is acknowledged by the remote service facility in an electronic message. The request uniquely identifies the diagnostic system, as well as the modality of the system.

Medical imaging system service evaluation method and apparatus

A system and method for providing remote viewing and analysis of diagnostic images is provided by the present invention. Electronic diagnostic imaging equipment receives raw data from an operator, and means responsive to the raw data translates the data into scanner commands to acquire a scan. A remote terminal is adapted to receive the raw data and scan data, and to reproduce and manipulate images represented by the received data. In accordance with the present invention, these steps and functions can be rapidly achieved, so that the maximum amount of existing data required for image quality issue analysis is available.

Method and apparatus for capturing and automatically transferring an x-ray image to a remote location

In an MR system employing a phased array surface coil, a method is provided for reducing artifacts in an MR image acquired from a region of an object lying within a specified field of view, wherein the field of view has a dimension extending between first and second boundary limits. The method includes the step of positioning the phased array surface coil, which comprises a linear array of coil elements, in selected spatial relationship with the object region. The method further comprises selecting a particular coil element for use in acquiring MR data only if the coil element lies at least partially in a range which extends along the phased array and has a length equal to the field of view dimension, the range lying between positions respectively corresponding to the first and second boundary limits. Each of the selected coil elements is operated to acquire MR data from respectively corresponding subregions of the object region, and the MR image is constructed only from MR data acquired by respective selected coil elements.

Method for reducing artifacts in MR image acquired with phased array

The present invention discloses a system and method for diagnosing and validating a machine with waveform data generated therefrom. In this invention, historical waveform data are obtained from machines having known faults along with corresponding actions for repairing the machines and are used to develop fault classification rules. The fault classification rules are stored in a diagnostic knowledge database. The database of classification rules are used to diagnose new waveform data from a machine having an unknown fault.

System and method for diagnosing and validating a machine using waveform

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Delafield, WI, United States of America

John Andrew Johnson