Electromagnetic Time-Reversal Technique for Monitoring Skull Healing Stages

• This paper provides a novel non-resonant spectroscopy technique for monitoring of the healing process in skull defects created in cranial surgery. Currently, there is not such a method and information on the healing process which is vital to avoid further complications and for a better understanding of the process. During the healing, which can take several months, the thickness and the permittivity of the skull varies. The proposed approach makes use of the approximated Dyadic Green's function integral equation for deriving the variations in permittivity of the skull. The scattered electric field from the defect is collected using Time-Reversal Array (TRA) in order to produce the Multi-StaticData Matrix (MDM). The singular value decomposition on the MDM matrix based on time reversal operator (TRO) decomposition (known under French acronym DORT), provides information of the permittivity of the defect. The problem of the sensing the defect response after the cranial surgery is formulated for multi-layer medium and it will be shown that the dominant singular values are corresponding to the permittivity of the defect. Finally, the veracity of the theory is clarified by conducting a numerical simulation for a planar multi-layer medium consisting of skin, skull brain and a defect embedded in the skull. We show that the dominant singular values corresponding to the defect permittivity vary significantly which conclude that the dominant singular value can be a good criterion for monitoring the skull healing stages.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Keyword

Time-Reversal

Singular Value Decomposition

Dyadic Green's Function

Bone Mineral Density

Spectroscopy

Publication and Content Type

ref (subject category)

kon (subject category)