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Improved Image Quality in Brain F-18 FDG PET Using Penalized-Likelihood Image Reconstruction Via a Generalized Preconditioned Alternating Projection Algorithm: The First Patient Results

C Schmidtlein

CR Schmidtlein1*, S Li2, Z Wu2, J Zhang3, L Vogelsang4, L Shen3, Y Xu2, D Feiglin5, B Beattie1, J Humm1, A Krol5** , (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Sun Yat-sen University, Guangzhou, Guangdong, (3) Syracuse University, Syracuse, NY, (4) VirtualScopics, Rochester, NY, (5) SUNY Upstate Medical University, Syracuse, New York


MO-G-17A-7 Monday 4:30PM - 6:00PM Room: 17A

Purpose: To investigate the performance of a new penalized-likelihood PET image reconstruction algorithm using the l₁-norm total-variation (TV) sum of the 1st through 4th-order gradients as the penalty. Simulated and brain patient data sets were analyzed.

Methods: This work represents an extension of the preconditioned alternating projection algorithm (PAPA) for emission-computed tomography. In this new generalized algorithm (GPAPA), the penalty term is expanded to allow multiple components, in this case the sum of the 1st to 4th order gradients, to reduce artificial piece-wise constant regions ("staircase" artifacts typical for TV) seen in PAPA images penalized with only the 1st order gradient. Simulated data were used to test for "staircase" artifacts and to optimize the penalty hyper-parameter in the root-mean-squared error (RMSE) sense. Patient FDG brain scans were acquired on a GE D690 PET/CT (370 MBq at 1-hour post-injection for 10 minutes) in time-of-flight mode and in all cases were reconstructed using resolution recovery projectors. GPAPA images were compared PAPA and RMSE-optimally filtered OSEM (fully converged) in simulations and to clinical OSEM reconstructions (3 iterations, 32 subsets) with 2.6 mm XY-Gaussian and standard 3-point axial smoothing post-filters.

Results: The results from the simulated data show a significant reduction in the "staircase" artifact for GPAPA compared to PAPA and lower RMSE (up to 35%) compared to optimally filtered OSEM. A simple power-law relationship between the RMSE-optimal hyper-parameters and the noise equivalent counts (NEC) per voxel is revealed. Qualitatively, the patient images appear much sharper and with less noise than standard clinical images. The convergence rate is similar to OSEM.

Conclusions: GPAPA reconstructions using the l₁-norm total-variation sum of the 1st through 4th-order gradients as the penalty show great promise for the improvement of image quality over that currently achieved with clinical OSEM reconstructions.

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