The PET acquisition was obtained in caudal-cranial direction; PET was reconstructed with a matrix of 128 × 128, ordered subset expectation maximum iterative reconstruction algorithm (two iterations, 28 subsets), 8 mm Gaussian filter, and 50 cm field of view.
The images obtained by the Reveal PET/CT and Discovery PET/CT scanners were reconstructed with a 128 × 128 matrix, an ordered-subset expectation maximum iterative reconstruction algorithm (4 iterations, 8 subsets), a Gaussian filter of 5.0 mm, and a slice thickness of either 3.0 mm (Reveal PET/CT) or 3.27 mm (Discovery PET/CT).
The images were reconstructed using ordered subsets expectation maximum iterative reconstruction.
Data were reconstructed using a vendor provided 3-dimensional row action maximum likelihood iterative reconstruction algorithm (3D RAMLA) with 3 iterations and 33 subsets, a relaxation factor of 1.0, a matrix size of 144 × 144 resulting in voxel sizes of 4 × 4 × 4 mm3.
Acquisition data were Fourier re-binned in 24 time frames (4 × 15 s, 4 × 60 s, 5 × 180 s, 8 × 5 min, 3 × 10 min) and reconstructed using maximum a posteriori iterative reconstruction (MAP; 18 iterations, 9 subsets, fixed resolution: 1.5 mm).
PET images were reconstructed using 3D row action maximum likelihood algorithm iterative reconstruction using two iterations.
The PET/CT data sets were reconstructed using iterative reconstruction (ordered subset expectation maximum [OSEM]) with attenuation correction applied.
Specifically, iterative algorithms such as Arithmetic Reconstruction Technique (ART), Maximum Likelihood Expectation Maximization (MLEM), Simultaneous Iterative Reconstruction Technique (SIRT), and Penalized Maximum Likelihood (PML) [3] use statistical models and cost functions to iteratively converge to a refined solution consistent with the measured data.
To determine the optimal percentage of iterative reconstruction for reconstructing LD-CT images by assessing changes in image quality when 40%% adaptive statistical iterative reconstruction (ASiR-40), 70%% adaptive statistical iterative reconstruction (ASiR-70) and 90%% adaptive statistical iterative reconstruction (ASiR-90) are used compared with traditional filtered back reconstruction (FBP).
An important contributor to the efficacy of tomographic imaging was the development of iterative reconstruction based on maximum likelihood (MLEM) [33, 34], especially the accelerated form based on ordered subsets (OSEM) [35], which permitted computationally demanding algorithms to perform in a clinically acceptable time.
Image series of 0.75 mm section width, reconstruction increment 0.4 mm, and convolution filter for vessels (I20f) were reconstructed using iterative reconstruction algorithm (SAFIRE, Siemens Healthcare, Forchheim, Germany).
