Abstract
Objective: Simultaneous PET/MRIs vary in their quantitative PET performance due to inherent differences in the physical systems and differences in the image reconstruction implementation. This variability in quantitative accuracy confounds the ability to meaningfully combine and compare data across scanners. In this work, we define image reconstruction hyperparameters that lead to comparable contrast recovery curves across simultaneous PET/MRI systems.Method: The NEMA NU-2 image quality phantom was imaged on one GE Signa and on one Siemens mMR PET/MRI scanner. The phantom was imaged at 9.8:1 contrast with standard spheres (diameter 10, 13, 17, 22, 28, 37mm) and with custom spheres (diameter: 8.5, 11.5, 15, 25, 32.5, 44 mm) using a standardized methodology. Analysis was performed on a 30 minute listmode data acquisition and on 6 realizations of 5 minutes from the listmode data. Images were reconstructed with the manufacturer provided iterative image reconstruction algorithms with and without point spread function (PSF) modeling. For both scanners, a post-reconstruction Gaussian filter of 3 to 7 mm in steps of 1 mm were applied. Attenuation correction was provided from a scaled Computed Tomography (CT) image of the phantom registered to the MR-based attenuation images and verified to align on the non-attenuated corrected PET images. For each of these image reconstruction parameter sets, contrast recovery coefficients (CRCs) were determined for the SUVmean, SUVmax and SUVpeak for each sphere. A hybrid metric combining the root mean squared discrepancy (RMSD) and the absolute CRC values were used to simultaneously optimize for best match in CRC between the two scanners while simultaneously weighting towards higher resolution reconstructions. The image reconstruction hyperparameter set were identified as the best candidate reconstruction for each vendor for harmonized PET image reconstruction. Results: The range of clinically relevant image reconstruction hyperparameters demonstrated widely different quantitative performance across cameras. The best match of CRC curves were obtained at the lowest RMSD values with: for CRCmean, 2 iterations -7mm filter with PSF on the GE Signa and 4 iterations -6mm filter on the Siemens mMR, for CRCmax, 2 iterations -7mm filter on the GE Signa, 4 iterations - 6mm filter on the Siemens mMR and for CRCeak, 4 iterations-7mm filter with PSF on the GE Signa and 3 iterations-6mm filter on the Siemens mMR. Over all reconstructions, the RMSD between CRCs were 2.4%, 3.1% and 2.3% for CRC mean, max and peak, respectively. The solution of 2 iterations-3mm on the GE Signa and 4 iterations-3mm on Siemens mMR, both with PSF, led to simultaneous harmonization and with high CRC and low RMSD for CRC mean, max and peak with RMSD values of 3.4%, 5.5% and 3.0 %, respectively.Conclusions: For two commercially-available PET/MRI scanners, user-selectable hyperparameters that control iterative updates, image smoothing, and PSF-modeling provide a range of contrast recovery curves that allow harmonization in harmonization strategies of optimal match in CRC or high CRC values. This work demonstrates that nearly identical CRC curves can be obtained on different commercially available scanners by selecting appropriate image reconstruction hyperparameters.
Harmonization of PET image reconstruction parameters in simultaneous PET/MRI
