A method for optimizing reconstruction algorithms is presented that is based on how well a specified task can be performed using the reconstructed images. task performance is numerically assessed by a Monte Carlo simulation, of the complete imaging process including the generation of scenes appropriate to the desired application, subsequent data taking, reconstruction, and performance of the stated task based on the final image. The use of this method is demonstrated through the optimization of the Algebraic Reconstruction Technique (ART), which reconstructs images from their projections by an iterative procedure. The optimization is accomplished by varying the relaxation factor employed in the updating procedure. In some of the imaging situations studied, it is found that the optimization of constrained ART, in which a nonnegativity constraint is invoked, can vastly increase the detectability of objects. There is little improvement attained for unconstrained ART.. The general method presented may be applied to the problem of designing neutron-diffraction spectrometers.
Keywords: task performance, evaluation of image-processing algorithms, algebraic reconstruction technique (ART), update relaxation, reconstruction optimization, nonnegativity constraint, deletectability, Monte Carlo technique
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