| 1. |
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| 2. |
- Berthon, Beatrice, et al.
(författare)
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PETSTEP : generation of synthetic PET lesions for fast evaluation of segmentation methods
- 2015
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Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 31:8, s. 969-980
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Boras, Dominik, et al.
(författare)
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Determining internal porosity in Prussian blue analogue cathode materials using positron annihilation lifetime spectroscopy
- 2023
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Ingår i: Journal of Materials Science. - : Springer Nature. - 0022-2461 .- 1573-4803. ; 58:42, s. 16344-16356
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Tidskriftsartikel (refereegranskat)abstract
- Prussian blue analogues (PBAs), AxM[M’(CN)6]1–y·zH2O, are a highly functional class of materials with use in a broad range of applications, such as energy storage, due to their porous structure and tunable composition. The porosity is particularly important for the properties and is deeply coupled to the cation, water, and [M’(CN)6]n– vacancy content. Determining internal porosity is especially challenging because the three compositional parameters are dependent on each other. In this work, we apply a new method, positron annihilation lifetime spectroscopy (PALS), which can be employed for the characterization of defects and structural changes in crystalline materials. Four samples were prepared to evaluate the method’s ability to detect changes in internal porosity as a function of the cation, water, and [M’(CN)6]n– vacancy content. Three of the samples have identical [M’(CN)6]n– vacancy content and gradually decreasing sodium and water content, while one sample has no sodium and 25% [M’(CN)6]n– vacancies. The samples were thoroughly characterized using inductively coupled plasma-optical emission spectroscopy (ICP-OES), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and Mössbauer spectroscopy as well as applying the PALS method. Mössbauer spectroscopy, XRD, and TGA analysis revealed the sample compositions Na1.8(2)Fe2+0.64(6)Fe2.6+0.36(10)[Fe2+(CN)6]·2.09(2)H2O, Na1.1(2)Fe2+0.24(6)Fe2.8+0.76(6)[Fe2.3+(CN)6]·1.57(1)H2O, Fe[Fe(CN)6]·0.807(9)H2O, and Fe[Fe(CN)6]0.75·1.5H2O, confirming the absence of vacancies in the three main samples. It was shown that the final composition of PBAs could only be unambiguously confirmed through the combination of ICP, XRD, TGA, and Mössbauer spectroscopy. Two positron lifetimes of 205 and 405 ps were observed with the 205 ps lifetime being independent of the sodium, water, and/or [Fe(CN)6]n– vacancy content, while the lifetime around 405 ps changes with varying sodium and water content. However, the origin and nature of the 405 ps lifetime yet remains unclear. The method shows promise for characterizing changes in the internal porosity in PBAs as a function of the composition and further development work needs to be carried out to ensure the applicability to PBAs generally.
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| 4. |
- Häggström, Ida, 1982-, et al.
(författare)
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A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET
- 2015
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Ingår i: Journal of Nuclear Medicine Technology. - : Society of Nuclear Medicine. - 0091-4916 .- 1535-5675. ; 43:1, s. 53-60
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Tidskriftsartikel (refereegranskat)abstract
- 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
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| 5. |
- Häggström, Ida, et al.
(författare)
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Compartment Modeling of Dynamic Brain PET : The Effect of Scatter Corrections on Parameter Errors
- 2014
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Ingår i: Medical physics (Lancaster). - : American Association of Physicists in Medicine (AAPM). - 0094-2405.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Häggström, Ida, 1982-, et al.
(författare)
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Compartment modeling of dynamic brain PET : the impact of scatter corrections on parameter errors
- 2014
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Ingår i: Medical physics. - : American Association of Physicists in Medicine. - 0094-2405. ; 41:11, s. 111907-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Häggström, Ida, 1982, et al.
(författare)
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Deep learning for [ 18 F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis
- 2024
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Ingår i: The Lancet Digital Health. - 2589-7500. ; 6:2, s. e114-e125
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Tidskriftsartikel (refereegranskat)abstract
- Background : The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. Methods : In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1–3 vs 4–5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. Findings : In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942–0·956), accuracy of 0·890 (0·879–0·901), sensitivity of 0·868 (0·851–0·885), and specificity of 0·913 (0·899–0·925); LARS-max achieved an AUC of 0·949 (0·942–0·956), accuracy of 0·868 (0·858–0·879), sensitivity of 0·909 (0·896–0·924), and specificity of 0·826 (0·808–0·843); and LARS-ptct achieved an AUC of 0·939 (0·930–0·948), accuracy of 0·875 (0·864–0·887), sensitivity of 0·836 (0·817–0·855), and specificity of 0·915 (0·901–0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938–0·966), accuracy of 0·907 (0·888–0·925), sensitivity of 0·874 (0·843–0·904), and specificity of 0·949 (0·921–0·960); LARS-max achieved an AUC of 0·952 (0·937–0·965), accuracy of 0·898 (0·878–0·916), sensitivity of 0·899 (0·871–0·926), and specificity of 0·897 (0·871–0·922); and LARS-ptct achieved an AUC of 0·932 (0·915–0·948), accuracy of 0·870 (0·850–0·891), sensitivity of 0·827 (0·793–0·863), and specificity of 0·913 (0·889–0·937). Interpretation : Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. Funding: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.
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| 8. |
- Häggström, Ida, 1982-, et al.
(författare)
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Do scatter and random corrections affect the errors in kinetic parameters in dynamic PET? : a Monte Carlo study
- 2013
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Ingår i: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC). - : IEEE conference proceedings. - 9781479905348
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 9. |
- Häggström, Ida, et al.
(författare)
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Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies
- 2016
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Ingår i: Medical physics (Lancaster). - : American Association of Physicists in Medicine. - 0094-2405. ; 43:6, s. 3104-3116
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 10. |
- Häggström, Ida, et al.
(författare)
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Monolithic Bragg-locked Nd:GdVO4 laser
- 2007
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Ingår i: Optics Express. - 1094-4087. ; 15:18, s. 11589-11594
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Tidskriftsartikel (refereegranskat)abstract
- We present a monolithic single-longitudinal-mode laser based on Nd:GdVO4 and a volume Bragg grating. The laser at 1066 nm had a bandwidth below 40 MHz at a power of 30 mW. With temperature, the laser frequency could be continuously tuned without mode hops over a range of 80 GHz. The demonstrated laser design is very compact and simple and can be used to lock the laser wavelength anywhere in the gain spectrum.
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