Automated contrast phase based medical image selection/exclusion
US-11263481-B1 · Mar 1, 2022 · US
US12137512B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12137512-B2 |
| Application number | US-202117165388-A |
| Country | US |
| Kind code | B2 |
| Filing date | Feb 2, 2021 |
| Priority date | Feb 2, 2021 |
| Publication date | Nov 5, 2024 |
| Grant date | Nov 5, 2024 |
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The present approach relates to generating one or both of a failure prediction indication for an X-ray tube or a remaining useful life estimate for the X-ray tube. In one implementation, a complexity of a regression model is selected based on the operating points utilized by an imaging system for the X-ray tube, where the regression model estimates coefficients utilized by a static tube model in estimating health (e.g., thickness) of the electron emitter of the X-ray tube, which in turn may be used in predicting remaining useful life of an electron emitter of the X-ray tube. In another implementation, replacement of an X-ray tube or a component of a filament drive circuit coupled to the X-ray tube may be detected.
Opening claim text (preview).
The invention claimed is: 1. A method for constructing an X-ray tube model for utilizing in assessing health of an X-ray tube, comprising: acquiring training data points for a respective X-ray tube after installation of the X-ray tube in an imaging system; determining a number of operating points utilized in the imaging system for the respective X-ray tube; and selecting a regression model from a plurality of regression models based on the number of operating points, wherein each regression model uses the training data points to derive respective values for a plurality of coefficients. 2. The method of claim 1 , wherein the operating points comprise mA-kV settings that characterize each respective training data point. 3. The method of claim 1 , wherein each regression model of the plurality of regression models comprises a different number of coefficients for the plurality of coefficients, or a different form of the regression model. 4. The method of claim 1 , comprising constructing the X-ray tube model using the plurality of coefficients estimated by the selected regression model. 5. The method of claim 4 , wherein the X-ray tube model returns an estimate of a variable related to emitter resistance in response to input data points. 6. The method of claim 4 , wherein the X-ray tube model comprises a model relating X-ray tube voltage and electron emitter current with X-ray tube current.
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