Publications by authors named "M Scheel"
To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities.
View Article and Find Full Text PDFPurpose: To identify the impact of endovascular simulator training and shadowing in interventional radiology on medical students' self-assessed IR knowledge. Moreover, the sequence of the teaching methods and its influence on the self-assessed IR knowledge is investigated.
Materials And Methods: A total of 19 fourth-year medical students participated in this study.
Article Synopsis
- In NMDA receptor encephalitis, most patients have normal MRIs during acute episodes, but subtle lesions can occur; these lesions may signal overlaps with conditions like multiple sclerosis or neuromyelitis optica.
- Other types, such as limbic encephalitis related to LGI1, CASPR2, and GAD antibodies, show specific hyperintensities in the medial temporal lobe on MRI, while GABA receptor encephalitides present different patterns of lesions, underscoring the importance of MRI in diagnosing autoimmune encephalitis and differentiating it from other disorders.
Purpose: This observational study aims to provide a detailed clinical and imaging characterization/workup of acute intracerebral hemorrhage (ICH) due to either an underlying metastasis (mICH) or brain tumor (tICH) lesion.
Methods: We conducted a retrospective, single-center study, evaluating patients presenting with occult ICH on initial CT imaging, classified as tICH or mICH on follow-up MRI imaging according to the H-Atomic classification. Demographic, clinical and radiological data were reviewed.
Article Synopsis
- A study evaluated the effectiveness of open-source large language models (LLMs) in extracting clinical data from unstructured mechanical thrombectomy reports for ischemic stroke patients.
- Three models (Mixtral, Qwen, BioMistral) were tested using data from two institutions, showing varying performance in precision and recall for clinical data categories.
- The findings suggest that LLMs, especially when combined with a human-in-the-loop approach, can significantly improve the efficiency and accuracy of clinical data extraction, with time savings of around 65.6% per case.