חדר 861, בניין מאייר, הפקולטה להנדסת חשמל
Mobile sensing is an emerging research area with many applications, including environmental monitoring and medical imaging. For example, tomography, which is used extensively in medical imaging, can be re-formulated by means of sampling along trajectories in the sinogram domain.
We propose a model that accounts for finite-energy functions and present continuous-domain analysis accordingly. We then consider the problem of reconstructing CT scans within the mobile sensing framework. We initially focused on 1D signals and found minimum norm approximation and its associated conditions for perfect reconstruction. We then introduced a data-driven approach to estimate the model parameters in order to make the method adaptive. The proposed approach is tested on synthesized and on real noisy signals. A 2D method is also developed and tested on CT imaging.
Our results show that the proposed method can achieve higher quality images using lower radiation doses compared to presently available results.
*M.Sc. research under the supervision of Prof. Moshe Porat (EE, Technion) and Dr. Hagai Kirshner.