Dr. Benjamin Conrad
Profil
Zusammenfassung
Dr. Benjamin Conrad entwickelt und optimiert bildgebende Verfahren für die Magnetresonanztomographie (MRT), insbesondere zur Untersuchung von Rückenmark und Gehirn. Seine Expertise liegt in der Verarbeitung komplexer MRT-Daten, der Qualitätssicherung von Bildern und der Analyse funktionaler Netzwerke bei neurologischen Erkrankungen wie Multipler Sklerose. Diese Kompetenzen sind für die Medizintechnik und klinische Bildgebung relevant, wo zuverlässige, standardisierte Verfahren zur Diagnose und Verlaufskontrolle erforderlich sind.
Skills
Stammdaten
Identität, Organisation und Kontakt aus HU-FIS.
Forschungsthemen1
Schulden, Reformen, Kriege. Russlands Staatsfinanzen in der Zeit der Napoleonischen Kriege 1796-1816
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 05/2018 - 12/2023 Projektleitung: Dr. Benjamin Conrad
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Publikationen25
Top 25 nach Zitationen — Quelle: OpenAlex (BAAI/bge-m3 embedded für Matching).
Journal of Neurophysiology · 153 Zitationen · DOI
NeuroImage · 152 Zitationen · DOI
An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.
Magnetic Resonance in Medicine · 118 Zitationen · DOI
PURPOSE: Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document. METHODS: The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses. RESULTS: Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets. CONCLUSIONS: The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.
Kooperationen0
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