Automatic failure detection in magnetic resonance apparatuses
AssigneeSiemens Healthcare GmbH
InventorsAndre de Oliveira, Georg Goertler, Atilla Peter Kiraly
Life of this patent
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Abstract
In a method, a computer and a medical computer for automatic failure analysis in order to provide a cause of failure of the medical imaging apparatus during operation, input data are read into the computer that include raw data or image data, acquired by the imaging apparatus. A set of performance indicators in the input data is calculated by the computer. A trained neural network system is accessed with the calculated performance indicators, in order to provide result data that, in the case of a failure, identify a failure source.
Claims10
A method for automatic failure analysis of a medical imaging apparatus during operation of the medical imaging apparatus, comprising:
- reading in input data, comprising raw data or image data, acquired by the medical imaging apparatus into a computer;
- in said computer, calculating a set of performance indicators in the input data, the set of performance indicators comprising:
- an average signal per channel of said medical imaging apparatus, a highest relative Fast Fourier Transform (FF1) peak, and an average signal-to-noise ratio (SNR);
- from said computer, accessing a trained neural network system with said calculated performance indicators in order to use said trained neural network system to produce result data such that, if a failure of said medical imaging apparatus exists, a cause of said failure of said medical imaging apparatus is identified, and otherwise an operational state of the medical imaging apparatus is identified;
- and from said computer, presenting said result data at a display screen.
An automatic failure analyzer for use with a medical imaging apparatus, the automatic failure analyzer comprising:
- an input interface configured to read in input data, comprising raw data or image data, acquired by the medical imaging apparatus;
- one or more processors configured to calculate a set of performance indicators in the input data, the set of performance indicators comprising an average signal per channel of said medical imaging apparatus, a highest relative Fast Fourier Transform (FF1′) peak, and an average signal-to-noise ratio (SNR);
- a trained neural network system configured to produce result data as a correlation between performance indicators and causes of failure of said medical imaging apparatus;
- and
- an output interface configured to present said result data at a display screen.
Dependencies are inferred from the claim text.