Abstract: A micelle solution for use in MRI phantoms (10) comprises a non-hydrogen bearing oil or oil-like fluid (26), a surfactant (29) and water (28) mixed to form a micelle solution and held in a non-ferromagnetic, non-conductive container (12) sized to be received by the MRI machine.

Abstract: Magnetic Resonance Imaging (MRI) systems including a coil system and methods for controlling the coil system are described. A described system includes a coil system for MRI. The coil system includes a local volume coil configured to transmit and receive MRI signals and an array of surface coils configured to receive MRI signals.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.