  Imaging the bio-impedance distribution of the brain can provide initial
diagnosis of acute stroke. This paper presents a compact and non-radiative
tomographic modality, i.e. multi-frequency Electromagnetic Tomography (mfEMT),
for the initial diagnosis of acute stroke. The mfEMT system consists of 12
channels of gradiometer coils with adjustable sensitivity and excitation
frequency. To solve the image reconstruction problem of mfEMT, we propose an
enhanced Frequency-Constrained Sparse Bayesian Learning (FC-SBL) to
simultaneously reconstruct the conductivity distribution at all frequencies.
Based on the Multiple Measurement Vector (MMV) model in the Sparse Bayesian
Learning (SBL) framework, FC-SBL can recover the underlying distribution
pattern of conductivity among multiple images by exploiting the frequency
constraint information. A realistic 3D head model was established to simulate
stroke detection scenarios, showing the capability of mfEMT to penetrate the
highly resistive skull and improved image quality with FC-SBL. Both simulations
and experiments showed that the proposed FC-SBL method is robust to noisy data
for image reconstruction problems of mfEMT compared to the single measurement
vector model, which is promising to detect acute strokes in the brain region
with enhanced spatial resolution and in a baseline-free manner.
