Rejecting spurious magnetic fields during magnetic marker localization

Patent metadata

Field	Value
Publication number	US-2025387041-A1
Application number	US-202519236768-A
Country	US
Kind code	A1
Filing date	Jun 12, 2025
Priority date	Jun 20, 2024
Publication date	Dec 25, 2025
Grant date	—

Abstract

An exemplary method for detecting magnetic noise comprises: receiving a set of magnetic field gradient values obtained using a probe device; providing the set of magnetic field gradient values to a neural network to predict a marker state of a magnetic marker; providing the predicted marker state from the neural network to a magnetic field gradient prediction model to generate a predicted set of magnetic field gradient values; comparing the set of magnetic field gradient values obtained using the probe device with the set of predicted magnetic field gradient values; and providing a magnetic noise indicator depending on the comparison between the set of magnetic field gradient values obtained using the probe device and the set of predicted magnetic field gradient values.

First claim

What is claimed is:

1 . A method for detecting magnetic noise by a magnetic marker localization system, the method comprising:
receiving a set of magnetic field gradient values obtained using a probe device of the magnetic marker localization system; providing the set of magnetic field gradient values to a neural network, wherein the neural network is trained to receive the set of magnetic field gradient values and predict a marker state of a magnetic marker; providing the predicted marker state from the neural network to a magnetic field gradient prediction model, wherein the magnetic field gradient prediction model is configured to receive the predicted marker state and generate a predicted set of magnetic field gradient values; comparing the set of magnetic field gradient values obtained using the probe device with the set of predicted magnetic field gradient values; and providing a magnetic noise indicator depending on the comparison between the set of magnetic field gradient values obtained using the probe device and the set of predicted magnetic field gradient values.

2 . The method of claim 1 , wherein comparing the set of magnetic field gradient values obtained using the probe device with the set of predicted magnetic field gradient values comprises calculating one or more similarity measures.

3 . The method of claim 2 , wherein the one or more similarity measures comprise a normalized error vector magnitude, a cosine similarity, or a combination thereof.

4 . The method of claim 3 , wherein the magnetic noise indicator is provided if the normalized error vector is greater than a first predetermined threshold or when the cosine similarity is greater than a second predetermined threshold.

5 . The method of claim 2 , wherein the one or more similarity measures comprise one or more magnitude differences, one or more angular differences, or any combination thereof.

6 . The method of claim 1 , further comprising providing one or more historical pose measurements of the probe device to the neural network.

7 . The method of claim 6 , wherein the one or more historical pose measurements comprise one or more position measurements, one or more orientation measurements, or a combination thereof.

8 . The method of claim 1 , wherein the magnetic field gradient prediction model comprises a neural network model.

9 . The method of claim 1 , wherein the magnetic field gradient prediction model comprises an equation.

10 . The method of claim 1 , wherein the magnetic noise indicator comprises a visual indicator, an audible indicator, and/or a tactile indicator.

11 . The method of claim 10 , further comprising: changing a color or a shape for the visual indicator based on the comparison.

12 . The method of claim 1 , wherein the neural network or the magnetic field gradient prediction model is selected based on a probe device type associated with the probe device.

13 . The method of claim 1 , wherein the neural network or the magnetic field gradient prediction model is selected based on a magnetic marker type associated with the magnetic marker.

14 . The method of claim 1 , wherein the comparison between the set of magnetic field gradient values obtained using the probe device and the set of predicted magnetic field gradient values is based on one or more predetermined criteria.

15 . The method of claim 14 , further comprising:
calculating a distance between the magnetic marker and the probe device; and updating the predetermined one or more criteria for detecting magnetic noise for the next iteration.

16 . A non-transitory computer-readable storage medium storing one or more programs for detecting magnetic noise by a magnetic marker localization system, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to:
receive a set of magnetic field gradient values obtained using a probe device of the magnetic marker localization system; provide the set of magnetic field gradient values to a neural network, wherein the neural network is trained to receive the set of magnetic field gradient values and predict a marker state of a magnetic marker; provide the predicted marker state from the neural network to a magnetic field gradient prediction model, wherein the magnetic field gradient prediction model is configured to receive the predicted marker state and generate a predicted set of magnetic field gradient values; compare the set of magnetic field gradient values obtained using the probe device with the set of predicted magnetic field gradient values; and provide a magnetic noise indicator depending on the comparison between the set of magnetic field gradient values obtained using the probe device and the set of predicted magnetic field gradient values.

17 . A magnetic marker localization system, comprising:
a probe device having one or more magnetic sensors; and a processor in electronic communication with the probe device, the processor programmed to:
receive a set of magnetic field gradient values obtained using a probe device of the magnetic marker localization system;
provide the set of magnetic field gradient values to a neural network, wherein the neural network is trained to receive the set of magnetic field gradient values and predict a marker state of a magnetic marker;
provide the predicted marker state from the neural network to a magnetic field gradient prediction model, wherein the magnetic field gradient prediction model is configured to receive the predicted marker state and generate a predicted set of magnetic field gradient values;
compare the set of magnetic field gradient values obtained using the probe device with the set of predicted magnetic field gradient values; and
provide a magnetic noise indicator depending on the comparison between the set of magnetic field gradient values obtained using the probe device and the set of predicted magnetic field gradient values.

Assignees

• Stryker Corp

Inventors

• Pandey Prashant
• Dillon John
• Ravi Ananth
• Semple Mark

Classifications

• A61B5/742

  using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339) · CPC title

• A61B5/7264

  Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems · CPC title

• A61B5/7203

  for noise prevention, reduction or removal · CPC title

• A61B2090/3954

  magnetic, e.g. NMR or MRI · CPC title

• A61B5/062 Primary

  using magnetic field · CPC title