Systems and methods for in body microwave imaging of a subject

A system, comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• obtain an initial coarse microwave (MW) data pertaining to a set of predefined uniform locations scanned around a specific portion of a body of a subject;
• sequentially recommend, by using a trained reinforcement learning (RL) agent, one or more subsequent locations pertaining to the specific portion of the body of the subject using the initial coarse microwave data to obtain a set of subsequent MW data;
• vectorize and stack the initial coarse microwave data and the set of subsequent MW data to obtain a measurement matrix;
• estimate a Kernel K using a reference point scatterer of dielectric value ε0 and an area Ar at a set of reference coordinate placed in a region of interest, wherein the Kernel K estimated at the set of reference coordinate is used to estimate the Kernel K at remaining coordinates in the region of interest based on an Euclidean distance between the set of reference coordinate and the remaining coordinates;
• and
• estimate an unknown dielectric constant of the region of interest in the specific portion using an inverse model formulation obtained from the measurement matrix and the Kernel K, wherein the unknown dielectric constant indicates a degree of severity of a disease associated with the region of interest in the specific portion of the body tissue;
• wherein the MW data comprises MW radiation.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• obtaining an initial coarse microwave (MW) data pertaining to a set of predefined uniform locations scanned around a specific portion of a body of a subject;
• sequentially recommending, by using a trained reinforcement learning (RL) agent, one or more subsequent locations pertaining to the specific portion of the body of the subject using the initial coarse microwave data to obtain a set of subsequent MW data;
• vectorizing and stacking the initial coarse microwave data and the set of subsequent MW data to obtain a measurement matrix;
• estimating a Kernel K using a reference point scatterer of dielectric value ε0 and an area Ar at a set of reference coordinate placed in a region of interest, wherein the Kernel K estimated at the set of reference coordinate is used to estimate the Kernel K at remaining coordinates in the region of interest based on an Euclidean distance between the set of reference coordinate and the remaining coordinates;
• and
• estimating an unknown dielectric constant of the region of interest in the specific portion using an inverse model formulation obtained from the measurement matrix and the Kernel K, wherein the unknown dielectric constant indicates a degree of severity of a disease associated with the region of interest in the specific portion of body tissue;
• wherein the MW data comprises MW radiation.