Removing magnetocardiography noise using a deep learning network trained on synthetic magnetocardiography

A system comprising:

• one or more computers;
• and
• one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform a method comprising:
• obtaining, using a plurality of magnetometers of a magnetically unshielded device, noisy magnetic measurement data of a magnetic field at least partially caused by an organ of a subject and an ambient environment;
• determining one or more sources of magnetic noise caused by the ambient environment;
• determining cleaned magnetic field data associated with the organ based at least in part on the noisy magnetic measurement data and the one or more noises;
• and
• taking an action based at least in part on the cleaned magnetic field data.

A denoising method comprising:

• measuring noisy magnetic field data proximate to an organ of a subject using an unshielded measurement device, wherein the noisy magnetic field is formed by the magnetic field produced by the organ and by sources of magnetic noise;
• generating cleaned magnetic field data by applying a deep learning network (DLN) denoising model to the noisy magnetic field data, wherein the cleaned magnetic field data represents an estimation of the magnetic field data produced by the organ of the subject, the DLN denoising model having been trained using noisy training magnetic data and synthetic training magnetic data pairs;
• and
• taking an action based on the cleaned magnetic field data.