Reproducibility of Standardized Uptake Values Including Volume Metrics Between TOF-PET-MR and TOF-PET-CT.

Purpose To investigate the reproducibility of tracer uptake measurements, including volume metrics, such as metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG) obtained by TOF-PET-CT and TOF-PET-MR. Materials and Methods Eighty consecutive patients with different oncologic diagnoses underwent TOF-PET-CT (Discovery 690; GE Healthcare) and TOF-PET-MR (SIGNA PET-MR; GE Healthcare) on the same day with single dose-18F-FDG injection. The scan order, PET-CT following or followed by PET-MR, was randomly assigned. A spherical volume of interest (VOI) of 30 mm was placed on the liver in accordance with the PERCIST criteria. For liver, the maximum and mean standard uptake value for body weight (SUV) and lean body mass (SUL) were obtained. For tumor delineation, VOI with a threshold of 40 and 50% of SUVmax was used (VOI40 and VOI50). The SUVmax, SUVmean, SUVpeak, MTV and TLG were calculated. The measurements were compared between the two scanners. Results In total, 80 tumor lesions from 35 patients were evaluated. There was no statistical difference observed in liver regions, whereas in tumor lesions, SUVmax, SUV mean, and SUVpeak of PET-MR were significantly underestimated (p < 0.001) in both VOI40 and VOI50. Among volume metrics, there was no statistical difference observed except TLG on VOI50 (p = 0.03). Correlation between PET-CT and PET-MR of each metrics were calculated. There was a moderate correlation of the liver SUV and SUL metrics (r = 0.63-0.78). In tumor lesions, SUVmax and SUVmean had a stronger correlation with underestimation in PET-MR on VOI 40 (SUVmax and SUVmean; r = 0.92 and 0.91 with slope = 0.71 and 0.72, respectively). In the evaluation of MTV and TLG, the stronger correlations were observed both on VOI40 (MTV and TLG; r = 0.75 and 0.92) and VOI50 (MTV and TLG; r = 0.88 and 0.95) between PET-CT and PET-MR. Conclusion PET metrics on TOF-PET-MR showed a good correlation with that of TOF-PET-CT. SUVmax and SUVpeak of tumor lesions were underestimated by 16% on PET-MRI. MTV with % threshold can be regarded as identical volumetric markers for both TOF-PET-CT and TOF-PET-MR

Implementation of Image Reconstruction for GE SIGNA PET/MR PET Data in the STIR Library

Software for Tomographic Image Reconstruction (STIR: http://stir.sf.net) is an open source C++ library available for reconstruction of emission tomography data. This work aims at the incorporation of the GE SIGNA PET/MR scanner in STIR and enables PET image reconstruction with data corrections. The data extracted from the scanner after an acquisition includes a list of raw data files (emission, normalisation, geometric and well counter calibration (wcc) factors), magnetic resonance attenuation correction (MRAC) images and the scanner-based reconstructions. The listmode (LM) file stores a list of 'prompt' events and the singles per crystal per second. MRAC images from the scanner are used for attenuation correction. The modifications to STIR that allow accurate histogramming of this LM data in the same sinogram organisation as the scanner are also described. This allows reconstruction of acquisition data with all data corrections using STIR, and independent of any software supplied by the manufacturer. The implementations were validated by comparing the histogrammed data, data corrections and final reconstruction using the ordered subset expectation maximisation (OSEM) algorithm with the equivalents from the GE-toolbox, supplied by the manufacturer for the scanner. There is no difference in the histogrammed counts whereas an overall relative difference of 6.7 × 10 -8 % and from 0.01% to 0.86% is seen in the normalisation and randoms correction sinograms respectively. The STIR reconstructed images have similar resolution and quantification but have some residual differences due to wcc factors, decay and deadtime corrections, as well as the offset between PET and MR gantries that will be addressed in future work. This work will enable the use of all current and future STIR algorithms, including penalized image reconstruction, motion correction and direct parametric image estimation, on data from GE SIGNA PET/MR scanners

Introduction to Different PET Radiopharmaceuticals and Hybrid Modalities (PET/CT and PET/MRI)

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Quantitative performance and optimal regularization parameter in block sequential regularized expectation maximization reconstructions in clinical 68Ga-PSMA PET/MR.

BACKGROUND: In contrast to ordered subset expectation maximization (OSEM), block sequential regularized expectation maximization (BSREM) positron emission tomography (PET) reconstruction algorithms can run until full convergence while controlling image quality and noise. Recent studies with BSREM and F-FDG PET reported higher signal-to-noise ratios and higher standardized uptake values (SUV). In this study, we investigate the optimal regularization parameter (β) for clinical Ga-PSMA PET/MR reconstructions in the pelvic region applying time-of-flight (TOF) BSREM in comparison to TOF OSEM. Two-minute emission data from the pelvic region of 25 patients who underwent Ga-PSMA PET/MR were retrospectively reconstructed. Reference OSEM reconstructions had 28 subsets and 2 iterations. BSREM reconstructions were performed with 15 β values between 150 and 1200. Regions of interest (ROIs) were drawn around lesions and in uniform background. Background SUVmean (average) and SUVstd (standard deviation), and lesion SUVmax (average of 5 hottest voxels) were calculated. Differences were analyzed using the Wilcoxon matched pairs signed-rank test. RESULTS: A total of 40 lesions were identified in the pelvic region. Background noise (SUVstd) and lesions SUVmax decreased with increasing β. Image reconstructions with β values lower than 400 have higher (p < 0.01) background noise, compared to the reference OSEM reconstructions, and are therefore less useful. Lesions with low activity on images reconstructed with β values higher than 600 have a lower (p < 0.05) SUVmax compared to the reference. These reconstructions are likely visually appealing due to the lower background noise, but the lower SUVmax could possibly render small low-uptake lesions invisible. CONCLUSIONS: In our study, we showed that PET images reconstructed with TOF BSREM in combination with the Ga-PSMA tracer result in lower background noise and higher SUVmax values in lesions compared to TOF OSEM. Our study indicates that a β value between 400 and 550 might be the optimal compromise between high SUVmax and low background noise

Evaluation of Seamless Bridges

0-7011The research study included experimental testing and numerical modeling to obtain and develop much needed experimental data and analytical tools to study the performance of seamless systems, identify design issues, and propose design guidelines for the U.S. practice