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All images were reconstructed using an iterative reconstruction algorithm (ADMIRE, Siemens Healthcare, Forchheim, Germany) and a medium sharp kernel (J40).
CT data were reconstructed using an iterative reconstruction algorithm (iDose, Philips Healthcare, Cleveland, OH, USA) with iDose level 4 and a slice thickness of 3 mm.
FDG-PET data were acquired as six frames of 5 min and reconstructed using an iterative reconstruction algorithm including corrections for randoms, dead time, scatter, and attenuation, and taking into account the finite point spread function of the PET system [31, 32].
PET images were reconstructed using an iterative reconstruction algorithm with segmented attenuation correction.
Subsequently, the microPET images were reconstructed using an iterative reconstruction technique.
The images were reconstructed using an iterative reconstruction algorithm and both attenuation-corrected and non-attenuation-corrected images were interpreted visually.
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The resulting images for each channel were then aligned with imageJ software[ 48] and reconstructed using an iterative Algebraic Reconstruction Technique (i-ART) algorithm with TXM-Wizard.[ 49] 3 D datasets were visualized using the Avizo Fire software and further analysis was performed using MATLAB.
Whole-body scans were acquired in four bed positions, and were reconstructed using an iterative median root reconstruction algorithm.
CT image sets were reconstructed using an iterative algorithm [Adaptive Statistical Iterative Reconstruction (ASIR), GE Healthcare].
Further, a technique called iterative multiview side information (IMSI) is introduced, where the final SI is used in an iterative reconstruction process.
Clinical image reconstruction is performed using an iterative Bayesian ('one-step-late') algorithm [42, 43].
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