Impact of Point-Spread Function Modeling on PET Image Quality in Integrated PET/MR Hybrid Imaging

The aim of this study was to systematically assess the quantitative and qualitative impact of including point-spread function (PSF) modeling into the process of iterative PET image reconstruction in integrated PET/MR imaging.

All measurements were performed on an integrated whole-body PET/MR system. Three substudies were performed: an (18)F-filled Jaszczak phantom was measured, and the impact of including PSF modeling in ordinary Poisson ordered-subset expectation maximization reconstruction on quantitative accuracy and image noise was evaluated for a range of radial phantom positions, iteration numbers, and postreconstruction smoothing settings; 5 representative datasets from a patient population (total n = 20, all oncologic (18)F-FDG PET/MR) were selected, and the impact of PSF on lesion activity concentration and image noise for various iteration numbers and postsmoothing settings was evaluated; and for all 20 patients, the influence of PSF modeling was investigated on visual image quality and number of detected lesions, both assessed by clinical experts. Additionally, the influence on objective metrics such as changes in SUVmean, SUVpeak, SUVmax, and lesion volume was assessed using the manufacturer-recommended reconstruction settings.

In the phantom study, PSF modeling significantly improved activity recovery and reduced the image noise at all radial positions. This effect was measurable only at a high number of iterations (>10 iterations, 21 subsets). In the patient study, again, PSF increased the detected activity in the patient's lesions at concurrently reduced image noise. Contrary to the phantom results, the effect was notable already at a lower number of iterations (>1 iteration, 21 subsets). Lastly, for all 20 patients, when PSF and no-PSF reconstructions were compared, an identical number of congruent lesions was found. The overall image quality of the PSF reconstructions was rated better when compared with no-PSF data. The SUVs of the detected lesions with PSF were substantially increased in the range of 6%-75%, 5%-131%, and 5%-148% for SUVmean, SUVpeak, and SUVmax, respectively. A regression analysis showed that the relative increase in SUVmean/peak/max decreases with increasing lesion size, whereas it increases with the distance from the center of the PET field of view.

In whole-body PET/MR hybrid imaging, PSF-based PET reconstructions can improve activity recovery and image noise, especially at lateral positions of the PET field of view. This has been demonstrated quantitatively in phantom experiments as well as in patient imaging, for which additionally an improvement of image quality could be observed.