Compressive sensing approach for microwave imaging application

Early detection is one of the prevention from tumor and cancer disease. This is usually done by scanning using Computed Tomography (CT) scan or Magnetic Resonance Imaging (MRI). However, those modalities are costly, bulky and not portable. Microwave Imaging is one of the emerging modalities that may overcome the aforementioned problems. Up till now, we have been developing microwave imaging, but still with large measurement data. Basically, in imaging system, good reconstruction result usually requires large measurement data. In order to reduce the measurement data, in this research, we investigate Compressive Sensing (CS) approach to be applied in microwave imaging application. CS allows reconstruction of signal with fewer measurement than required in Shannon-Nyquist theorem. This study provides simulation using transmission method on acquisition data which is based on first generation of CT. To meet the concept of CS, Discrete Radon Transform (DRT) is used as projection matrices on data acquisition scheme. This scheme is varied to 18 angles, 36 angles, and 51 angles to test the performance of CS in reconstructing the signal with few number of measurements. Dictionary matrix that is used as a sparse basis is Discrete Cosine Transform, and algorithm that is used to reconstruct the sparse signal is Basis Pursuit. Simulation results show that reference image can be well reconstructedusing CS approach by applying only few measurement data. Reconstructed image using CS is also compared withthe Algebraic Reconstruction Technique (ART) and the Filtered Back Projection (FBP) algorithms to show the relevance and advantage of using CS approach in microwave imaging applications.