Quantization of reconstruction error with an interval-based algorithm: an experimental comparison

SPECT∗ image based diagnosis generally consists in comparing the reconstructed activities within two regions of interest. Due to noise in the measured activities, this comparison is subject to instability, mainly because both statistical nature and level of the noise in the reconstructed activities is unknown. In this paper, we experimentally show that an interval valued extension of the classical MLEM algorithm is efficient to estimate this noise level. The experimental settings consist in simulating the acquisition of a phantom composed of three zones having the same shape but different levels of activity. The levels are chosen to simulate usual medical image conditions. We evaluate the ability of the interval-valued reconstruction to quantify the noise level by testing whether or not it allows the association of two zones having the same activity and the differentiation between two zones having different activities. Our experiment shows that the error quantification truly reflects the difficulty in differentiating two zones having very close activity level. Indeed, the method allows a reliable association of two zones having the same activity level, whatever the noise conditions. However, the possibility of differentiating two zones having different levels of activity depends on the signal-to-noise ratio.