Microwave imaging for breast cancer detection is becoming a promising alternative technique to current imaging
modalities. The significant contrast between dielectric properties of normal and malignant breast tissues makes
microwave imaging a useful technique to provide important functional information for diagnoses. However, one of its
limitations is that it intrinsically cannot produce high resolution images as other conventional techniques such as MRI or
X-ray CT do. Those modalities are capable of producing high quality anatomical images, but unlike microwave imaging,
they often cannot provide the necessary functional information about tissue health. In order to refine the resolution of the
microwave images while also preserving the functional information, we have recently developed a new strategy, called
soft prior regularization. In this new approach, the prior anatomical information of the tissue from either x-ray, MR or
other sources is incorporated into our microwave imaging reconstruction algorithm through the following steps: First, the
anatomical information is used to create a reconstruction mesh which defines the boundaries of different internal regions.
Second, based on location of each mesh node, an associated weighting matrix is defined, such that all nodes within each
region are grouped with each other. Finally, the soft prior matrix is used as a regularizing term for our original Gauss-
Newton reconstruction algorithm. Results from initial phantom experiments show a significant improvement in the
recovered dielectric properties.