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

Abstract

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. Methods: 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 orderedsubset 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 5 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 SUV mean , SUV peak , SUV max , and lesion volume was assessed using the manufacturer-recommended reconstruction settings. Results: 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 SUV mean , SUV peak , and SUV max , respectively. A regression analysis showed that the relative increase in SUV mean/peak/max decreases with increasing lesion size, whereas it increases with the distance from the center of the PET field of view. Conclusion: 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. Si multaneous hybrid imaging with PET and MR (PET/MR) combines MR's excellent soft-tissue contrast with the high sensitivity and quantitative information of radiotracer metabolism imaged in PET (1-5). It was shown that PET/MR may provide early detection of metastases and guidance in therapy selection and can also improve the process of clinical patient man...

Preparing data for multiparametric PET/MR imaging: Influence of PET point spread function modelling and EPI distortion correction on the spatial correlation of [18F]FDG-PET and diffusion-weighted MRI in head and neck cancer

“…The effect of PSF modelling was, as expected, larger for smaller tumors, as edge effects are more dominant. Only one other study by Aklan, et al [15] has investigated the quantitative effect of PSF modelling in PET/MR and reported an average increase in SUV mean and SUV max of 21% and 37%, respectively. However, they reported results from generally smaller lesions located at a larger radial distance from the center of transverse FOV, compared to the present study, which may explain the greater impact of the PET PSF modelling.…”

“…PET images was also reconstructed using PSF modelling (PSF OP-OSEM) with 3 iterations, 21 subsets and 2 mm Gaussian post-filter. The vendor recommends a decrease in Gaussian post filter size from 4 mm to 2 mm when using PSF modelling [15]. All images were reconstructed on 344 × 344 matrices with a pixel size of 2 × 2 mm 2 and slice thickness of 2 mm.…”

“…PSF modelling is widely used in PET/CT and has proven useful for oncologic purposes [13]. PSF modelling is now also available for integrated PET/MR, but so far has been evaluated in only two studies [14,15].…”

Coronary Artery PET/MR Imaging

“…The initial PET image reconstruction used an iterative ordinary Poisson ordered-subsets expectation maximization algorithm with 21 subsets and 3 iterations incorporating point-spread function resolution modeling (16), a 344 × 344 × 127 matrix, and a 2-mm full-width-at-half-maximum Gaussian post-reconstruction filter. Attenuation correction for the body in the PET reconstruction was estimated using MR imaging (see “Technological Development” section).…”

Quantitative analysis of image metrics for reduced and standard dose pediatric 18F-FDG PET/MRI examinations

“…(1) Lesions SUV measurements: SUVmax, SUVmean, SUVpeak inside the VOIs, that are the most commonly used semiquantitative parameters for evaluation of tracer uptake; (2) Images Normalized Noise (NN): in the liver ROI, image noise was calculated as the standard deviation divided by the SUVmean in the ROI, as a quantitative evaluation of image quality; 34 (3) Lesions Contrast-to-noise ratio (CNR) was computed for all images as an objective metric of lesion detectability considering image noise around the lesion as defined by Yan et al 23 as…”