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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

MMORF-FSL's MultiMOdal Registration Framework.

Journal article

Lange FJ. et al, (2024), Imaging Neurosci (Camb), 2, 1 - 30

Preprocessing for fMRI (and a little bit for diffusion MRI)

Chapter

Andersson JLR. and Lange FJ., (2024)

Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes

Conference paper

Roibu AC. et al, (2023), Proceedings - 2023 10th IEEE Swiss Conference on Data Science, SDS 2023, 17 - 25

Estimating axial diffusivity in the NODDI model.

Journal article

Howard AF. et al, (2022), Neuroimage, 262

More publications