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Abstract #0728

Improved contextual fibre growth for generating white matter numerical phantoms with realistic microstructure 

Ross Callaghan1, Daniel C Alexander1, Marco Palombo1, and Hui Zhang1
1Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom

We present an improved version of the ConFiG white matter numerical phantom generator to create realistic white matter microstructure. Building on ConFiG’s novel fibre growth algorithm, the enhancement incorporates a dynamic growth network and global optimisation of fibre positions. Resulting phantoms represent a significant improvement over those from the original ConFiG algorithm, with realistic morphology and an increase in packing density of up to 30%. These improved phantoms result in much more realistic simulated diffusion MRI signals, reducing RMSE to real data by ten times. This improvement demonstrates the potential of ConFiG as a computational model of white matter microstructure.

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