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The images were reconstructed using the ordered subsets expectation maximisation (OSEM) method.
The model above is estimated using the ordered logistic regression model.
SPECT reconstructions were performed using the ordered subset expectation maximum (OSEM) algorithm with two iterations, 10 subsets and post-filtering.
Column (1) of Table 7 also presents the results of the estimation using the ordered probit model.
Tomographic images were then reconstructed using the ordered subset expectation maximization (OSEM) algorithm, four iterations, and eight subsets.
Factors associated with overall community perception of healthcare quality was determined using the ordered logistic regression (OLR) test at univariate and multivariate levels.
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The thermodynamic factor can readily be calculated using the ordering energy obtained by analysing activity data.
We used the ordered probit model because a match result was ternary.
The PET imaging data were reconstructed using the ordered-subsets expectation maximization 3D algorithm (OSEM3D) with attenuation correction based on transmission source measurement.
Reconstruction was performed using the Ordered-Subsets Expectation Maximisation (OSEM) with 15 subsets and with 4 iterations and displayed in sagittal, transverse and coronal planes.
Using the ordering (2.10) for eigenvalues, we complete the proof of Theorem 1.2.
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