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- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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- Tran-Gia, Johannes, et al.
(författare)
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On the use of solid 133Ba sources as surrogate for liquid 131I in SPECT/CT calibration : a European multi-centre evaluation
- 2023
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Ingår i: EJNMMI Physics. - 2197-7364. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Commissioning, calibration, and quality control procedures for nuclear medicine imaging systems are typically performed using hollow containers filled with radionuclide solutions. This leads to multiple sources of uncertainty, many of which can be overcome by using traceable, sealed, long-lived surrogate sources containing a radionuclide of comparable energies and emission probabilities. This study presents the results of a quantitative SPECT/CT imaging comparison exercise performed within the MRTDosimetry consortium to assess the feasibility of using 133Ba as a surrogate for 131I imaging. Materials and methods: Two sets of four traceable 133Ba sources were produced at two National Metrology Institutes and encapsulated in 3D-printed cylinders (volume range 1.68–107.4 mL). Corresponding hollow cylinders to be filled with liquid 131I and a mounting baseplate for repeatable positioning within a Jaszczak phantom were also produced. A quantitative SPECT/CT imaging comparison exercise was conducted between seven members of the consortium (eight SPECT/CT systems from two major vendors) based on a standardised protocol. Each site had to perform three measurements with the two sets of 133Ba sources and liquid 131I. Results: As anticipated, the 131I pseudo-image calibration factors (cps/MBq) were higher than those for 133Ba for all reconstructions and systems. A site-specific cross-calibration reduced the performance differences between both radionuclides with respect to a cross-calibration based on the ratio of emission probabilities from a median of 12–1.5%. The site-specific cross-calibration method also showed agreement between 133Ba and 131I for all cylinder volumes, which highlights the potential use of 133Ba sources to calculate recovery coefficients for partial volume correction. Conclusion: This comparison exercise demonstrated that traceable solid 133Ba sources can be used as surrogate for liquid 131I imaging. The use of solid surrogate sources could solve the radiation protection problem inherent in the preparation of phantoms with 131I liquid activity solutions as well as reduce the measurement uncertainties in the activity. This is particularly relevant for stability measurements, which have to be carried out at regular intervals.
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| 10. |
- Eriksson, Olivia, PhD, 1971-, et al.
(författare)
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Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows
- 2022
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Ingår i: eLIFE. - : eLife Sciences Publications, Ltd. - 2050-084X. ; 11
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Forskningsöversikt (refereegranskat)abstract
- Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.
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