System and method for predictive maintenance of medical diagnostic machine components

1. A method of reducing downtime of an MRI or CT scanner due to component or image quality failure, the method comprising:

deriving training matrices using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with various components of MRI or CT machines and image quality parameters;

obtaining digital sensing data and QA-DICOM data from the MRI or CT scanner;

projecting the obtained digital sensing data and QA-DICOM data onto the training matrices to provide a health matrix of the various components;

comparing elements in the health matrix to predefined threshold values associated with each of the elements;

classifying the performance of the components or the image quality into following categories: “normal”, “warning” or “critical” based at least on the compared elements; and

calculating a time remaining to take a service action as a function of the obtained digital sensing data, QA-DICOM data and the training matrix, wherein the time remaining to take a service action is associated with predicted failure of the component or image quality.

2. The method of claim 1 , wherein the plurality of input variables comprises helium level, helium gas pressure, cold head temperature, re-condenser temperature, thermal shield temperature, helium gas low pressure, helium gas high pressure, water low temperature, water high temperature, MRI acoustic noise, duty cycle, magnet center frequency, signal to noise ratio, geometric accuracy in −X direction, geometric accuracy in −Y direction, geometric accuracy in −Z direction, magnitude stability, and phase stability.

3. The method of claim 1 further comprising sending an alert to a user if the component or image quality is classified as warning or critical, wherein the alert enables the user to access analysis results using API calls.

4. The method of claim 1 , wherein:

the MRI or CT scanner is an MRI scanner;

the component or image quality failure is coldhead failure; and

the step of deriving training matrices using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with various components of MRI or CT machines and image quality parameters comprises deriving a coldhead training matrix using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with the coldhead of the MRI.

5. The method of claim 4 , wherein the plurality of input variables comprises helium level, helium gas pressure, cold head temperature, re-condenser temperature, thermal shield temperature, helium gas low pressure, helium gas high pressure, water low temperature, water high temperature.

6. The method of claim 4 further comprising sending an alert to a user if the coldhead performance is classified as warning or critical, wherein the alert enables the user to access analysis results using API calls.

7. The method of claim 1 , wherein:

the MRI or CT scanner is a CT scanner;

the component or image quality failure is CT tube failure; and

the step of deriving training matrices using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with various components of MRI or CT machines and image quality parameters comprises deriving a component training matrix using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with the tube of the CT.

8. The method of claim 7 , wherein the plurality of input variables comprises X-ray tube acoustic noise and X-ray tube vibration.

9. The method of claim 7 further comprising sending an alert to a user if the CT tube performance is classified as warning or critical, wherein the alert enables the user to access analysis results using API calls.

10. The method of claim 1 , wherein:

the MRI or CT scanner is an MRI scanner;

the component or image quality failure is unacceptable MRI field homogeneity; and

the step of deriving training matrices using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with various components of MRI or CT machines and image quality parameters comprises deriving an MRI field homogeneity training matrix using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with MRI field homogeneity.

11. The method of claim 10 , wherein the plurality of input variables comprises magnet center frequency, signal to noise ratio, geometric accuracy in −X direction, geometric accuracy in −Y direction, and geometric accuracy in −Z direction.

12. The method of claim 10 further comprising sending an alert to a user if the MRI field homogeneity performance is classified as warning or critical, wherein the alert enables the user to access analysis results using API calls.

13. The method of claim 1 , wherein:

the MRI or CT scanner is an MRI scanner;

the component or image quality failure is MRI image quality failure; and

the step of deriving training matrices using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with various components of MRI or CT machines and image quality parameters comprises deriving an MRI image quality training matrix using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with MRI image quality.

14. The method of claim 13 , wherein the plurality of input variables comprises signal to noise ratio, geometric accuracy in −X direction, geometric accuracy in −Y direction, geometric accuracy in −Z direction, magnitude stability, and phase stability.

15. The method of claim 13 further comprising sending an alert to a user if the MRI image quality is classified as warning or critical, wherein the alert enables the user to access analysis results using API calls.

16. The method of claim 1 , wherein:

the MRI or CT scanner is a CT scanner;

the component or image quality failure is CT image quality failure; and

the step of deriving training matrices using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with various components of MRI or CT machines and image quality parameters comprises deriving an CT image quality training matrix using robust singular value decomposition or matrix inversion techniques on a plurality of input variables associated with CT image quality.

17. The method of claim 16 , wherein the plurality of input variables comprises water CT number, and air CT number.

18. The method of claim 16 further comprising sending an alert to a user if the CT image quality is classified as warning or critical, wherein the alert enables the user to access analysis results using API calls.

19. The method of claim 1 , wherein the plurality of input variables comprises X-ray tube acoustic noise, X-ray tube vibration, water CT number, and air CT number.