[BibTex]

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.

[BibTex]

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.

[BibTex]

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.