Fluorescence molecular tomographic image reconstruction based on reduced measurement data

Abstract. The analysis of fluorescence molecular tomography is important for medical diagnosis and treatment. Although the quality of reconstructed results can be improved with the increasing number of measurement data, the scale of the matrices involved in the reconstruction of fluorescence molecular tomography will also become larger, which may slow down the reconstruction process. A new method is proposed where measurement data are reduced according to the rows of the Jacobian matrix and the projection residual error. To further accelerate the reconstruction process, the global inverse problem is solved with level-by-level Schur complement decomposition. Simulation results demonstrate that the speed of the reconstruction process can be improved with the proposed algorithm.

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