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To do this we threshold our reconstructed images at 10%to90%0% of the maximum value of each reconstructed image and inspect D S,G) for each threshold.

To quantitatively assess the performance of the proposed method, the spatial errors of the reconstructed target signatures and the Signal-to-Noise Ratio (SNR) of each reconstructed image were calculated.

Each reconstructed sequence significantly minimized genetic distances to extant sequences throughout the first 5 years of infection of an individual.

Hence, no additional calibration factor is needed to obtain absolute values of the activity in each reconstructed source voxel.

The difference between the baseline image, without artifact, and each reconstructed image with an artificial artifact was calculated.

Assuming a Laplacian model for the correlation noise,, the coding distortion between each reconstructed and original DCT band can be measured as (11).

On each reconstructed PET dataset, we calculated mean and maximum activity concentration recovery coefficients (RCmean and RCmax) for all phantom spheres, according to EANM guidelines [7].

The locations errors of the target signatures and SNR of each reconstructed image are shown in Table 2. Finally, the computational cost of the proposed technique was evaluated.

Then, the Fourier transform of each reconstructed range profile is taken to recover the full frequency data measurements at each antenna location.

A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image.

In order to achieve this goal a content adaptive function (CAF) that utilizes the normalized sum of absolute difference (SAD) between each reconstructed frame and its predicted counterpart is used.

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