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  • 1. Berthon, B.
    et al.
    Häggström, Ida
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Apte, A.
    Beattie, B.
    Kirov, A.
    Humm, J.
    Marshall, C.
    Spezi, E.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Schmidtlein, C.
    A Fast Positron Emission Tomography Simulator for Synthetic Lesion Simulation2014In: European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, E-ISSN 1619-7089, Vol. 41, no 2, p. S367-S367Article in journal (Other academic)
  • 2.
    Berthon, Beatrice
    et al.
    Wales Research and Diagnostic PET Imaging Centre, Cardiff University, Cardiff, UK.
    Häggström, Ida
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Apte, Aditya
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Beattie, Bradley J.
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Kirov, Assen S.
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Humm, John L.
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Marshall, Christopher
    Wales Research and Diagnostic PET Imaging Centre, Cardiff University, Cardiff, UK.
    Spezi, Emiliano
    School of Engineering, Cardiff University, Cardiff, Wales, UK.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Schmidtlein, C. Ross
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    PETSTEP: generation of synthetic PET lesions for fast evaluation of segmentation methods2015In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 31, no 8, p. 969-980Article in journal (Refereed)
    Abstract [en]

    Purpose: This work describes PETSTEP (PET Simulator of Tracers via Emission Projection): a faster and more accessible alternative to Monte Carlo (MC) simulation generating realistic PET images, for studies assessing image features and segmentation techniques.

    Methods: PETSTEP was implemented within Matlab as open source software. It allows generating threedimensional PET images from PET/CT data or synthetic CT and PET maps, with user-drawn lesions and user-set acquisition and reconstruction parameters. PETSTEP was used to reproduce images of the NEMA body phantom acquired on a GE Discovery 690 PET/CT scanner, and simulated with MC for the GE Discovery LS scanner, and to generate realistic Head and Neck scans. Finally the sensitivity (S) and Positive Predictive Value (PPV) of three automatic segmentation methods were compared when applied to the scanner-acquired and PETSTEP-simulated NEMA images.

    Results: PETSTEP produced 3D phantom and clinical images within 4 and 6 min respectively on a single core 2.7 GHz computer. PETSTEP images of the NEMA phantom had mean intensities within 2% of the scanner-acquired image for both background and largest insert, and 16% larger background Full Width at Half Maximum. Similar results were obtained when comparing PETSTEP images to MC simulated data. The S and PPV obtained with simulated phantom images were statistically significantly lower than for the original images, but led to the same conclusions with respect to the evaluated segmentation methods.

    Conclusions: PETSTEP allows fast simulation of synthetic images reproducing scanner-acquired PET data and shows great promise for the evaluation of PET segmentation methods.

  • 3.
    Häggström, Ida
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Quantitative methods for tumor imaging with dynamic PET2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    There is always a need and drive to improve modern cancer care. Dynamic positron emission tomography (PET) offers the advantage of in vivo functional imaging, combined with the ability to follow the physiological processes over time. In addition, by applying tracer kinetic modeling to the dynamic PET data, thus estimating pharmacokinetic parameters associated to e.g. glucose metabolism, cell proliferation etc., more information about the tissue's underlying biology and physiology can be determined. This supplementary information can potentially be a considerable aid when it comes to the segmentation, diagnosis, staging, treatment planning, early treatment response monitoring and follow-up of cancerous tumors.

    We have found it feasible to use kinetic parameters for semi-automatic tumor segmentation, and found parametric images to have higher contrast compared to static PET uptake images. There are however many possible sources of errors and uncertainties in kinetic parameters obtained through compartment modeling of dynamic PET data. The variation in the number of detected photons caused by the random nature of radioactive decay, is of course always a major source. Other sources may include: the choice of an appropriate model that is suitable for the radiotracer in question, camera detectors and electronics, image acquisition protocol, image reconstruction algorithm with corrections (attenuation, random and scattered coincidences, detector uniformity, decay) and so on. We have found the early frame sampling scheme in dynamic PET to affect the bias and uncertainty in calculated kinetic parameters, and that scatter corrections are necessary for most but not all parameter estimates. Furthermore, analytical image reconstruction algorithms seem more suited for compartment modeling applications compared to iterative algorithms.

    This thesis and included papers show potential applications and tools for quantitative pharmacokinetic parameters in oncology, and help understand errors and uncertainties associated with them. The aim is to contribute to the long-term goal of enabling the use of dynamic PET and pharmacokinetic parameters for improvements of today's cancer care.

  • 4.
    Häggström, Ida
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Schmidtlein, Ross
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, USA.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Johansson, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Sörensen, Jens
    Medical Sciences, Nuclear Medicine, Uppsala University Hospital, Uppsala, Sweden.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET2015In: Journal of Nuclear Medicine Technology, ISSN 0091-4916, E-ISSN 1535-5675, Vol. 43, no 1, p. 53-60Article in journal (Refereed)
    Abstract [en]

    Compartmental modeling of dynamic PET data enables quantifi- cation of tracer kinetics in vivo, through the calculated model parameters. In this study, we aimed to investigate the effect of early frame sampling and reconstruction method on pharmacokinetic parameters obtained from a 2-tissue model, in terms of bias and uncertainty (SD). Methods: The GATE Monte Carlo software was used to simulate 2 · 15 dynamic 3′-deoxy-3′-18F-fluorothymidine (18F-FLT) brain PET studies, typical in terms of noise level and kinetic parameters. The data were reconstructed by both 3- dimensional (3D) filtered backprojection with reprojection (3DRP) and 3D ordered-subset expectation maximization (OSEM) into 6 dynamic image sets with different early frame durations of 1, 2, 4, 6, 10, and 15 s. Bias and SD were evaluated for fitted parameter estimates, calculated from regions of interest. Results: The 2-tissue-model parameter estimates K1, k2, and fraction of arterial blood in tissue depended on early frame sampling, and a sampling of 6–15 s generally minimized bias and SD. The shortest sampling of 1 s yielded a 25% and 42% larger bias than the other schemes, for 3DRP and OSEM, respectively, and a parameter uncertainty that was 10%–70% higher. The schemes from 4 to 15 s were generally not significantly different in regards to bias and SD. Typically, the reconstruction method 3DRP yielded less framesampling dependence and less uncertain results, compared with OSEM, but was on average more biased. Conclusion: Of the 6 sampling schemes investigated in this study, an early frame duration of 6–15 s generally kept both bias and uncertainty to a minimum, for both 3DRP and OSEM reconstructions. Veryshort frames of 1 s should be avoided because they typically resulted in the largest parameter bias and uncertainty. Furthermore, 3DRP may be p

  • 5.
    Häggström, Ida
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Beattie, Bradley J
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Schmidtlein, C Ross
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies2016In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 43, no 6, p. 3104-3116Article in journal (Refereed)
    Abstract [en]

    Purpose: To develop and evaluate a fast and simple tool called dPETSTEP (Dynamic PET Simulator ofTracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment,postprocessing choices, etc., on dynamic and parametric images.

    Methods: The tool was developed in PETSTEP using both new and previously reported modules of PETSTEP (PET Simulator of Tracers via Emission Projection). Time activity curves are generated foreach voxel of the input parametric image, whereby effects of imaging system blurring, counting noise,scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed intoimages according to the user specified method, settings, and corrections. Reconstructed images werecompared to MC data, and simple Gaussian noised time activity curves (GAUSS).

    Results: dPETSTEP was 8000 times faster than MC. Dynamic images from dPETSTEP had a root meansquare error that was within 4% on average of that of MC images, whereas the GAUSS images werewithin 11%. The average bias in dPETSTEP and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dPETSTEP images conformed well to MC images, confirmed visually by scatterplot histograms, and statistically by tumor region of interest histogram comparisons that showed nosignificant differences (p < 0.01). Compared to GAUSS, dPETSTEP images and noise properties agreedbetter with MC.

    Conclusions: The authors have developed a fast and easy one-stop solution for simulationsof dynamic PET and parametric images, and demonstrated that it generates both images andsubsequent parametric images with very similar noise properties to those of MC images, in afraction of the time. They believe dPETSTEP to be very useful for generating fast, simple, andrealistic results, however since it uses simple scatter and random models it may not be suitablefor studies investigating these phenomena. dPETSTEP can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.

  • 6.
    Häggström, Ida
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Johansson, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Östlund, Nils
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Sörensen, Jens
    Medical Sciences, Nuclear Medicine, Uppsala University Hospital, Uppsala, Sweden.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Semi-automatic tumour segmentation by selective navigation in a three-parameter volume, obtained by voxel-wise kinetic modelling of 11C-acetate2010In: Radiation Protection Dosimetry, ISSN 0144-8420, E-ISSN 1742-3406, Vol. 139, no 1-3, p. 214-218Article in journal (Refereed)
    Abstract [en]

    Positron emission tomography (PET) is increasingly used for delineation of tumour tissue in, for example, radiotherapy treatment planning. The most common method used is to outline volumes with a certain per cent uptake over background in a static image. However, PET data can also be collected dynamically and analysed by kinetic models, which potentially represent the underlying biology better. In the present study, a three-parameter kinetic model was used for voxel-wise evaluation of (11)C-acetate data of head/neck tumours. These parameters which represent the tumour blood volume, the uptake rate and the clearance rate of the tissue were derived for each voxel using a linear regression method and used for segmentation of active tumour tissue. This feasibility study shows that it is possible to segment images based on derived model parameters. There is, however, room for improvements concerning the PET data acquisition, noise reduction and the kinetic modelling. In conclusion, this early study indicates a strong potential of the method even though no 'true' tumour volume was available for validation.

  • 7.
    Häggström, Ida
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Johansson, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Schmidtlein, C Ross
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Sörensen, Jens
    Medical Sciences, Nuclear Medicine, Uppsala University Hospital, Uppsala, Sweden.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    The influence of time sampling scheme on kinetic parameters obtained from compartmental modeling of a dynamic PET study: a Monte Carlo study2012In: IEEE Nuclear Science Symposium Conference Record / [ed] B. Yu, Anaheim: IEEE conference proceedings, 2012, p. 3101-3107Conference paper (Refereed)
    Abstract [en]

    Compartmental modeling of dynamic PET data enables quantification of tracer kinetics in vivo, through the obtained model parameters. The dynamic data is sorted into frames during or after the acquisition, with a sampling interval usually ranging from 10 s to 300 s. In this study we wanted to investigate the effect of the chosen sampling interval on kinetic parameters obtained from a 2-tissue model, in terms of bias and standard deviation, using a complete Monte Carlo simulated dynamic F-18-FLT PET study. The results show that the bias and standard deviation in parameter K-1 is small regardless of sampling scheme or noise in the time-activity curves (TACs), and that the bias and standard deviation in k(4) is large for all cases. The bias in V-a is clearly dependent on sampling scheme, increasing for increased sampling interval. In general, a too short sampling interval results in very noisy images and a large bias of the parameter estimate, and a too long sampling interval also increases bias. Noise in the TACs is the largest source of bias.

  • 8.
    Häggström, Ida
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Schmidtlein, C Ross
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Compartment Modeling of Dynamic Brain PET: The Effect of Scatter Corrections on Parameter Errors2014Conference paper (Other academic)
    Abstract [en]

    Purpose: To investigate the effects of corrections for random and scattered coincidences on kinetic parameters in brain tumors, by using ten Monte Carlo (MC) simulated dynamic FLT-PET brain scans.

     

    Methods: The GATE MC software was used to simulate ten repetitions of a 1 hour dynamic FLT-PET scan of a voxelized head phantom. The phantom comprised six normal head tissues, plus inserted regions for blood and tumor tissue. Different time-activity-curves (TACs) for all eight tissue types were used in the simulation and were generated in Matlab using a 2-tissue model with preset parameter values (K1,k2,k3,k4,Va,Ki). The PET data was reconstructed into 28 frames by both ordered-subset expectation maximization (OSEM) and 3D filtered back-projection (3DFBP). Five image sets were reconstructed, all with normalization and different additional corrections C (A=attenuation, R=random, S=scatter): Trues (AC), trues+randoms (ARC), trues+scatters (ASC), total counts (ARSC) and total counts (AC). Corrections for randoms and scatters were based on real random and scatter sinograms that were back-projected, blurred and then forward projected and scaled to match the real counts. Weighted non-linear-least-squares fitting of TACs from the blood and tumor regions was used to obtain parameter estimates.

     

    Results: The bias was not significantly different for trues (AC), trues+randoms (ARC), trues+scatters (ASC) and total counts (ARSC) for either 3DFBP or OSEM (p<0.05). Total counts with only AC stood out however, with an up to 160% larger bias. In general, there was no difference in bias found between 3DFBP and OSEM, except in parameter Va and Ki.

     

    Conclusion: According to our results, the methodology of correcting the PET data for randoms and scatters performed well for the dynamic images where frames have much lower counts compared to static images. Generally, no bias was introduced by the corrections and their importance was emphasized since omitting them increased bias extensively.

  • 9.
    Häggström, Ida
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Schmidtlein, C Ross
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Compartment modeling of dynamic brain PET: the impact of scatter corrections on parameter errors2014In: Medical physics, ISSN 0094-2405, Vol. 41, no 11, p. 111907-Article in journal (Refereed)
    Abstract [en]

    Purpose: The aim of this study was to investigate the effect of scatter and its correction on kinetic parameters in dynamic brain positron emission tomography (PET) tumor imaging. The 2-tissue compartment model was used, and two different reconstruction methods and two scatter correction (SC) schemes were investigated.

    Methods: The gate Monte Carlo (MC) softwarewas used to perform 2×15 full PET scan simulations of a voxelized head phantom with inserted tumor regions. The two sets of kinetic parameters of all tissues were chosen to represent the 2-tissue compartment model for the tracer 3′-deoxy- 3′-(18F)fluorothymidine (FLT), and were denoted FLT1 and FLT2. PET data were reconstructed with both 3D filtered back-projection with reprojection (3DRP) and 3D ordered-subset expectation maximization (OSEM). Images including true coincidences with attenuation correction (AC) and true+scattered coincidences with AC and with and without one of two applied SC schemes were reconstructed. Kinetic parameters were estimated by weighted nonlinear least squares fitting of image derived time–activity curves. Calculated parameters were compared to the true input to the MC simulations.

    Results: The relative parameter biases for scatter-eliminated data were 15%, 16%, 4%, 30%, 9%, and 7% (FLT1) and 13%, 6%, 1%, 46%, 12%, and 8% (FLT2) for K1, k2, k3, k4,Va, and Ki, respectively. As expected, SC was essential for most parameters since omitting it increased biases by 10 percentage points on average. SC was not found necessary for the estimation of Ki and k3, however. There was no significant difference in parameter biases between the two investigated SC schemes or from parameter biases from scatter-eliminated PET data. Furthermore, neither 3DRP nor OSEM yielded the smallest parameter biases consistently although therewas a slight favor for 3DRP which produced less biased k3 and Ki estimates while OSEM resulted in a less biased Va. The uncertainty in OSEM parameterswas about 26% (FLT1) and 12% (FLT2) larger than for 3DRP although identical postfilters were applied.

    Conclusions: SC was important for good parameter estimations. Both investigated SC schemes performed equally well on average and properly corrected for the scattered radiation, without introducing further bias. Furthermore, 3DRP was slightly favorable over OSEM in terms of kinetic parameter biases and SDs.

  • 10.
    Häggström, Ida
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Schmidtlein, C Ross
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Do scatter and random corrections affect the errors in kinetic parameters in dynamic PET?: a Monte Carlo study2013In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), IEEE conference proceedings, 2013, , p. 4Conference paper (Refereed)
    Abstract [en]

    Dynamic positron emission tomography (PET) data can be evaluated by compartmental models, yielding model specific kinetic parameters. For the parameters to be of quantitative use however, understanding and estimation of errors and uncertainties associated with them are crucial.

    The aim in this study was to investigate the effects of the inclusion of scattered and random counts and their respective corrections on kinetic parameter errors.

    The MC software GATE was used to simulate two dynamic PET scans of a phantom containing three regions; blood, tissue and a static background. The two sets of time-activity-curves (TACs) used were generated for a 2-tissue compartment model with preset parameter values (K1, k2, k3, k4 and Va). The PET data was reconstructed into 19 frames by both ordered-subset expectation maximization (OSEM) and 3D filtered back-projection with reprojection (3DFBPRP) with normalization and additional corrections (A=attenuation, R=random, S=scatter, C=correction): True counts (AC), true+random counts (ARC), true+scattered counts (ASC) and total counts (ARSC).

    The results show that parameter estimates from true counts (AC), true+random counts (ARC), true+scattered counts (ASC) and total counts (ARSC) were not significantly different, with the exception of Va where the bias increased with added corrections. Thus, the inclusion of and correction for scattered and random counts did not affect the bias in parameter estimates K1, k2, k3, k4 and Ki. Uncorrected total counts (only AC) resulted in biases of hundreds or even thousands of percent, emphasizing the need for proper corrections. Reconstructions with 3DFBPRP resulted in overall 20-40% less biased estimates compared to OSEM.

  • 11.
    Schmidtlein, Charles R.
    et al.
    Memorial Sloan Kettering Cancer Center.
    Turner, James N.
    State University of New York at Binghamton.
    Thompson, Michael O.
    Cornell University.
    Mandal, Krishna C.
    University of South Carolina.
    Häggström, Ida
    Memorial Sloan Kettering Cancer Center.
    Zhang, Jiahan
    State University of New York Upstate Medical University.
    Humm, John L.
    Memorial Sloan Kettering Cancer Center.
    Feiglin, David H.
    State University of New York Upstate Medical University.
    Krol, Andrzej
    State University of New York Upstate Medical University.
    Initial performance studies of a wearable brain positron emission tomography camera based on autonomous thin-film digital Geiger avalanche photodiode arrays2017In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 4, no 1, article id 011003Article in journal (Refereed)
    Abstract [en]

    Using analytical and Monte Carlo modeling, we explored performance of a lightweight wearable helmet-shaped brain positron emission tomography (PET), or BET camera, based on thin-film digital Geiger avalanche photodiode arrays with Lutetium-yttrium oxyorthosilicate (LYSO) or LaBr3 scintillators for imaging in vivo human brain function of freely moving and acting subjects. We investigated a spherical cap BET and cylindrical brain PET (CYL) geometries with 250-mm diameter. We also considered a clinical whole-body (WB) LYSO PET/CT scanner. The simulated energy resolutions were 10.8% (LYSO) and 3.3% (LaBr3), and the coincidence window was set at 2 ns. The brain was simulated as a water sphere of uniform F-18 activity with a radius of 100 mm. We found that BET achieved >40% better noise equivalent count (NEC) performance relative to the CYL and >800% than WB. For 10-mm-thick LaBr3 equivalent mass systems, LYSO (7-mm thick) had similar to 40% higher NEC than LaBr3. We found that 1 x 1 x 3 mm scintillator crystals achieved similar to 1.1 mm full-width-half-maximum spatial resolution without parallax errors. Additionally, our simulations showed that LYSO generally outperformed LaBr3 for NEC unless the timing resolution for LaBr3 was considerably smaller than that presently used for LYSO, i.e., well below 300 ps.

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