A Macroquantification Approach for Region-of-Interest Assessment in Emission Tomography

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Abstract

In this article, we propose a quantification methodology for estimating the statistical parameters of the activity inside regions of interest (ROIs). Macroquantification implies a rearrangement of the emission projection data into macroprojections and a redefinition of the system matrix based either on an image reconstruction involving iterative ROI-wise regularization or on an ROI uniformity assumption. The technique allows a very fast computation of the ROI activities and covariance matrix in the least squares sense using a low-dimensional model of the tomographic problem. The macroquantification approach is evaluated through Monte Carlo simulations using a numerical thorax phantom, without taking into account the measurement artifacts and assuming a perfect a priori ROI definition. Various tumor ROI configurations and count rates are considered to reflect clinical situations. The results show that our technique yields low-bias ROI estimations that turn out to be more accurate than classical estimates relying on pixel summation. Macroquantification also provides an approximation for the ROI variance that describes the effective variance obtained through the simulations fairly well. The technique is then validated using single photon emission computed tomography (SPECT) data from a physical phantom composed of cylinders filled with different 99mTc concentrations for the task of ROI comparison. Here again, the study shows excellent agreement between the measured and predicted values of the ROI variance resulting in efficient estimations of ROI ratios and highly accurate ROI comparisons. In its simplest formulation, macroquantification has a short computation time, making it an ideal technique for quantitative ROI assessment that is compatible with a wide range of routine clinical applications.

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