Abstract
Background
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 parameters that lead to comparable contrast recovery curves across simultaneous PET/MRI systems.
Method:
The NEMA NU-2 image quality phantom was imaged on the GE Signa and on the Siemens mMR PET/MRI scanners. The phantom was imaged at 9.8:1 contrast with standard spheres (diameter 10, 13, 17, 22, 28, 37 mm) 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 data acquisition and on a subset of 5 minutes of data acquisition. 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. For each of these image reconstruction parameter sets, contrast recovery coefficients (CRCs) were determined for the SUVmean, SUVmax and SUVpeak for each sphere. The root mean squared error (RMSE) was computed and used to rank the similarity of image reconstruction combination pairs for the two scanners. The image reconstruction parameter set with the lowest RMSE was identified as the best candidate reconstruction for each vendor for harmonized PET image reconstruction.
Results
The range of clinically relevant image reconstruction parameters demonstrated widely different quantitative performance across devices. The best match of CRC curves were obtained with: for SUVmean, 2 iterations/ 16 subsets − 7 mm filter with PSF on the GE Signa and 4 iterations/21 subsets-5 mm filter on the Siemens mMR, for SUVmax, 2 iterations/16 subsets-7 mm filter on the GE Signa, 4 iterations/21 subsets- 6 mm filter on the Siemens mMR and for SUVpeak, 4 iterations/16subsets-7 mm filter with PSF on the GE Signa and 4 iterations/21 subsets-5 mm filter on the Siemens mMR. Over all reconstructions, the RMSE between CRCs for the scanners were 8.1%, 16.7% and 7.1% for mean, max and peak, respectively. These were reduced to less than 2% for harmonized reconstruction settings.
Conclusions
For two commercially-available PET/MRI scanners, user-selectable parameters that control iterative updates, image smoothing, and PSF-modeling provide a range of contrast recovery curves that allow harmonization. This work demonstrates that nearly identical CRC curves can be obtained on different commercially available scanners by selecting appropriate image reconstruction parameters.
Fast 3D MR elastography of the whole brain using spiral staircase: Data acquisition, image reconstruction, and joint deblurring
