Frederik Lange
Post-doctoral Research Assistant
Research Summary
My research focuses on developing and implementing algorithms for automated medical imaging analysis.
Currently I am developing a multi-model, non-linear registration tool which leverages the data from each modality to inform the transformation between subject space and reference space. The overall aim of this tool is to provide a single, unified framework for bringing multi-modal datasets into a common space.
Additionally, I am working towards addressing and overcoming computational bottlenecks in image analysis through leveraging the massively parallel architecture of general purpose graphics processing units (GPGPUs). The goal of this work is to reduce the runtime of commonly used analysis tools, particularly for use in the pre-processing pipelines of large imaging studies.
Recent publications
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Internally-consistent and fully-unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM
Journal article
LANGE F. et al, (2024), Imaging Neuroscience
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MMORF—FSL’s MultiMOdal Registration Framework
Journal article
Lange FJ. et al, (2024), Imaging Neuroscience, 2, 1 - 30
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Preprocessing for fMRI (and a little bit for diffusion MRI)
Chapter
Andersson JLR. and Lange FJ., (2024), Encyclopedia of the Human Brain, Second Edition: Volumes 1-5
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Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes
Conference paper
Roibu A-C. et al, (2023), 2023 10TH IEEE SWISS CONFERENCE ON DATA SCIENCE, SDS, 17 - 25
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Estimating axial diffusivity in the NODDI model
Journal article
Howard AFD. et al, (2022), NeuroImage, 262, 119535 - 119535