| 1. |
- Weigelt, G., et al.
(author)
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VLTI-MATISSE chromatic aperture-synthesis imaging of eta Carinae's stellar wind across the Br alpha line Periastron passage observations in February 2020
- 2021
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 652
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Journal article (peer-reviewed)abstract
- Context. Eta Carinae is a highly eccentric, massive binary system (semimajor axis similar to 15.5 au) with powerful stellar winds and a phase-dependent wind-wind collision (WWC) zone. The primary star, eta Car A, is a luminous blue variable (LBV); the secondary, eta Car B, is a Wolf-Rayet or O star with a faster but less dense wind. Aperture-synthesis imaging allows us to study the mass loss from the enigmatic LBV eta Car. Understanding LBVs is a crucial step toward improving our knowledge about massive stars and their evolution. Aims. Our aim is to study the intensity distribution and kinematics of eta Car's WWC zone. Methods. Using the VLTI-MATISSE mid-infrared interferometry instrument, we perform Br alpha imaging of eta Car's distorted wind. Results. We present the first VLTI-MATISSE aperture-synthesis images of eta Car A's stellar windin several spectral channels distributed across the Br alpha 4.052 mu m line (spectral resolving power R similar to 960). Our observations were performed close to periastron passage in February 2020 (orbital phase similar to 14.0022). The reconstructed iso-velocity images show the dependence of the primary stellar wind on wavelength or line-of-sight (LOS) velocity with a spatial resolution of 6 mas (similar to 14 au). The radius of the faintest outer wind regions is similar to 26 mas (similar to 60 au). At several negative LOS velocities, the primary stellar wind is less extended to the northwest than in other directions. This asymmetry is most likely caused by the WWC. Therefore, we see both the velocity field of the undisturbed primary wind and the WWC cavity. In continuum spectral channels, the primary star wind is more compact than in line channels. A fit of the observed continuum visibilities with the visibilities of a stellar wind CMFGEN model (CMFGEN is an atmosphere code developed to model the spectra of a variety of objects) provides a full width at half maximum fit diameter of the primary stellar wind of 2.84 +/- 0.06 mas (6.54 +/- 0.14 au). We comparethe derived intensity distributions with the CMFGEN stellar wind model and hydrodynamic WWC models.
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| 2. |
- Chiavassa, A., et al.
(author)
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The extended atmosphere and circumstellar environment of the cool evolved star VX Sagittarii as seen by MATISSE star
- 2022
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 658
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Journal article (peer-reviewed)abstract
- Context. VX Sgr is a cool, evolved, and luminous red star whose stellar parameters are difficult to determine, which affects its classification.Aims. We aim to spatially resolve the photospheric extent as well as the circumstellar environment.Methods. We used interferometric observations obtained with the MATISSE instrument in the L (3-4 mu m), M (4.5-5 mu m), and N (8-13 mu m) bands. We reconstructed monochromatic images using the MIRA software. We used 3D radiation-hydrodynamics simulations carried out with (COBOLD)-B-5 and a uniform disc model to estimate the apparent diameter and interpret the stellar surface structures. Moreover, we employed the radiative transfer codes OPTIM3D and RADMC3D to compute the spectral energy distribution for the L, M, and N bands, respectively.Results. MATISSE observations unveil, for the first time, the morphology of VX Sgr across the L, M, and N bands. The reconstructed images show a complex morphology with brighter areas whose characteristics depend on the wavelength probed. We measured the angular diameter as a function of the wavelength and showed that the photospheric extent in the L and M bands depends on the opacity through the atmosphere. In addition to this, we also concluded that the observed photospheric inhomogeneities can be interpreted as convection-related surface structures. The comparison in the N band yielded a qualitative agreement between the N-band spectrum and simple dust radiative transfer simulations. However, it is not possible to firmly conclude on the interpretation of the current data because of the difficulty in constraing the model parameters using the limited accuracy of our absolute flux calibration.Conclusions. MATISSE observations and the derived reconstructed images unveil the appearance of VX Sgr's stellar surface and circumstellar environment across a very large spectral domain for the first time.
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| 3. |
- Opheim, G., et al.
(author)
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7T Epilepsy Task Force Consensus Recommendations on the Use of 7T MRI in Clinical Practice
- 2021
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In: Neurology. - : Ovid Technologies (Wolters Kluwer Health). - 0028-3878 .- 1526-632X. ; 96:7, s. 327-341
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Journal article (peer-reviewed)abstract
- Identifying a structural brain lesion on MRI has important implications in epilepsy and is the most important factor that correlates with seizure freedom after surgery in patients with drug-resistant focal onset epilepsy. However, at conventional magnetic field strengths (1.5 and 3T), only approximately 60%-85% of MRI examinations reveal such lesions. Over the last decade, studies have demonstrated the added value of 7T MRI in patients with and without known epileptogenic lesions from 1.5 and/or 3T. However, translation of 7T MRI to clinical practice is still challenging, particularly in centers new to 7T, and there is a need for practical recommendations on targeted use of 7T MRI in the clinical management of patients with epilepsy. The 7T Epilepsy Task Force-an international group representing 21 7T MRI centers with experience from scanning over 2,000 patients with epilepsy-would hereby like to share its experience with the neurology community regarding the appropriate clinical indications, patient selection and preparation, acquisition protocols and setup, technical challenges, and radiologic guidelines for 7T MRI in patients with epilepsy. This article mainly addresses structural imaging; in addition, it presents multiple nonstructural MRI techniques that benefit from 7T and hold promise as future directions in epilepsy. Answering to the increased availability of 7T MRI as an approved tool for diagnostic purposes, this article aims to provide guidance on clinical 7T MRI epilepsy management by giving recommendations on referral, suitable 7T MRI protocols, and image interpretation.
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| 4. |
- Bergquist, Magnus, 1960-, et al.
(author)
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Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology
- 2024
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In: European Radiology. - Heidelberg : Springer. - 0938-7994 .- 1432-1084. ; 34:1, s. 338-347
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Journal article (peer-reviewed)abstract
- Objectives: To define requirements that condition trust in artificial intelligence (AI) as clinical decision support in radiology from the perspective of various stakeholders and to explore ways to fulfil these requirements.Methods: Semi-structured interviews were conducted with twenty-five respondents—nineteen directly involved in the development, implementation, or use of AI applications in radiology and six working with AI in other areas of healthcare. We designed the questions to explore three themes: development and use of AI, professional decision-making, and management and organizational procedures connected to AI. The transcribed interviews were analysed in an iterative coding process from open coding to theoretically informed thematic coding.Results: We identified four aspects of trust that relate to reliability, transparency, quality verification, and inter-organizational compatibility. These aspects fall under the categories of substantial and procedural requirements.Conclusions: Development of appropriate levels of trust in AI in healthcare is complex and encompasses multiple dimensions of requirements. Various stakeholders will have to be involved in developing AI solutions for healthcare and radiology to fulfil these requirements. Clinical relevance statement: For AI to achieve advances in radiology, it must be given the opportunity to support, rather than replace, human expertise. Support requires trust. Identification of aspects and conditions for trust allows developing AI implementation strategies that facilitate advancing the field.Key Points:• Dimensions of procedural and substantial demands that need to be fulfilled to foster appropriate levels of trust in AI in healthcare are conditioned on aspects related to reliability, transparency, quality verification, and inter-organizational compatibility. • Creating the conditions for trust to emerge requires the involvement of various stakeholders, who will have to compensate the problem’s inherent complexity by finding and promoting well-defined solutions. © 2023, The Author(s).
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| 5. |
- Düzel, E., et al.
(author)
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European Ultrahigh-Field Imaging Network for Neurodegenerative Diseases (EUFIND)
- 2019
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In: Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring. - : Wiley. - 2352-8729. ; 11, s. 538-549
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Journal article (peer-reviewed)abstract
- Introduction: The goal of European Ultrahigh-Field Imaging Network in Neurodegenerative Diseases (EUFIND) is to identify opportunities and challenges of 7 Tesla (7T) MRI for clinical and research applications in neurodegeneration. EUFIND comprises 22 European and one US site, including over 50 MRI and dementia experts as well as neuroscientists. Methods: EUFIND combined consensus workshops and data sharing for multisite analysis, focusing on 7 core topics: clinical applications/clinical research, highest resolution anatomy, functional imaging, vascular systems/vascular pathology, iron mapping and neuropathology detection, spectroscopy, and quality assurance. Across these topics, EUFIND considered standard operating procedures, safety, and multivendor harmonization. Results: The clinical and research opportunities and challenges of 7T MRI in each subtopic are set out as a roadmap. Specific MRI sequences for each subtopic were implemented in a pilot study presented in this report. Results show that a large multisite 7T imaging network with highly advanced and harmonized imaging sequences is feasible and may enable future multicentre ultrahigh-field MRI studies and clinical trials. Discussion: The EUFIND network can be a major driver for advancing clinical neuroimaging research using 7T and for identifying use-cases for clinical applications in neurodegeneration. © 2018 The Authors
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| 6. |
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| 7. |
- F., Durmo, et al.
(author)
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Multivoxel 1H MR spectroscopy biometrics for preoprerative differentiation between brain tumors
- 2018
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In: Neuroradiology. - : Springer Science and Business Media LLC. - 1432-1920 .- 0028-3940. ; 60:S2, s. 444-444
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Conference paper (peer-reviewed)abstract
- Purpose To investigate multivoxel proton Magnetic Resonance Spectroscopy (1HMRS) biometrics for preoperative differentiation and prognosis of patients with brain metastases (MET), low-(LGG) and high grade glioma (HGG). Methods Thirty-five patients (15 HGG, 9 LGG and 11 MET) were included. Proton Magnetic Resonance Spectroscopy Imaging(1H-MRSI) data was assessed and neurochemical profiles for metabolites (NAA+NAAG, Cr+PCr, Glu+Gln (Glx), Lac, Ins, GPC+PCho) and total Lipids (tLip) and macromolecule (tMM) signals were estimated. Concentrations were reported as either absolute or ratios to total choline (tCho=GPC+PCho) and creatine (tCr=Cr+PCr) levels. Voxels of interest (VOIs) in a MRSI matrix were labelled accordingly to contrast-enhancing/nonenhancing lesional, edema, ipsi- or contralateral healthy appearing tissue and the metabolite averages were reported for each tissue type. Multi-biometric analysis with logistic regression, ROC- and Kaplan-Meier survival analysis was performed in SPSS v.24 and postprocessing with LC Model. Results Across HGG/LGG/MET; the average Ins/tCho was shown to be prognostic for overall survival (OS): with low values (≤1.29) in affected hemisphere predicting worse OS than high values (>1.29), (Log Rank
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| 8. |
- Gryska, Emilia, 1992, et al.
(author)
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Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.
- 2021
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In: BMJ open. - : BMJ. - 2044-6055. ; 11:1
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Journal article (peer-reviewed)abstract
- Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.Scoping review.Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison.Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity.The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
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| 9. |
- Gryska, Emilia, 1992, et al.
(author)
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Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.
- 2022
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In: BMJ open. - : BMJ. - 2044-6055. ; 12:7
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Journal article (peer-reviewed)abstract
- To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines are sufficient.Two eligible validation studies of distinct deep learning (DL) methods were identified. We implemented the methods using published information and retraced the reported validation steps. We evaluated to what extent the description of the methods enabled reproduction of the results. We further attempted to replicate reported findings on a clinical set of images acquired at our institute consisting of high-grade and low-grade glioma (HGG, LGG), and meningioma (MNG) cases.We successfully reproduced one of the two tumour segmentation methods. Insufficient description of the preprocessing pipeline and our inability to replicate the pipeline resulted in failure to reproduce the second method. The replication of the first method showed promising results in terms of Dice similarity coefficient (DSC) and sensitivity (Sen) on HGG cases (DSC=0.77, Sen=0.88) and LGG cases (DSC=0.73, Sen=0.83), however, poorer performance was observed for MNG cases (DSC=0.61, Sen=0.71). Preprocessing errors were identified that contributed to low quantitative scores in some cases.Established reproducibility criteria do not sufficiently emphasise description of the preprocessing pipeline. Discrepancies in preprocessing as a result of insufficient reporting are likely to influence segmentation outcomes and hinder clinical utilisation. A detailed description of the whole processing chain, including preprocessing, is thus necessary to obtain stronger evidence of the generalisability of DL-based brain tumour segmentation methods and to facilitate translation of the methods into clinical practice.
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| 10. |
- Mavrina, E., et al.
(author)
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Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE)
- 2022
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In: Frontiers in Neurology. - : Frontiers Media SA. - 1664-2295. ; 13
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Journal article (peer-reviewed)abstract
- Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development.
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