For both virtual tube current reduction as well as sparse sampling the same in-house developed SIR algorithm that was based on ordered-subset separable paraboloidal surrogate combining a momentum accelerating approach was used (29,30). In detail, a Gaussian noise model was applied and the likelihood term for SIR was computed with log-converted projection data. To enhance convergence and to further depress image noise while achieving adequate bone/soft tissue contrast, a regularization term based on a Huber penalty was applied. The calibration data served for calculating linear attenuation coefficients of resulting imaging data, which were then translated to Hounsfield units by using air and water information.

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