Abstract
We present a methodology which allows us to experimentally optimize an image reconstruction method for a specific medical task and to evaluate the relative efficacy of two reconstruction methods for a particular task in a manner which meets the high standards set by the methodology of statistical hypothesis testing. We illustrate this by comparing, in the area of Positron Emission Tomography (PET), a Maximum A posteriori Probability (MAP) algorithm with a method which maximizes likelihood and with two variants of the filtered backprojection method. We find that the relative performance of techniques is extremely task dependent, with the MAP method superior to the others from the point of view of pointwise accuracy, but not from the points of view of two other PET-related figures of merit. In particular, we find that, in spite of the very noisy appearance of the reconstructed images, the maximum likelihood method outperforms the others from the point of view of estimating average activity in individual neurological structures of interest.
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Herman, G.T., Odhner, D. (1991). Evaluation of reconstruction algorithms. In: Herman, G.T., Louis, A.K., Natterer, F. (eds) Mathematical Methods in Tomography. Lecture Notes in Mathematics, vol 1497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0084520
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DOI: https://doi.org/10.1007/BFb0084520
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