High-fidelity meshes from tissue samples for diffusion MRI simulations

Authors:
Eleftheria Panagiotaki;Matt G. Hall;Hui Zhang;Bernard Siow;Mark F. Lythgoe;Daniel C. Alexander
Affiliations:
Centre for Medical Image Computing, Department of Computer Science, University College London, UK;Centre for Medical Image Computing, Department of Computer Science, University College London, UK;Centre for Medical Image Computing, Department of Computer Science, University College London, UK;Centre for Medical Image Computing, Department of Computer Science, University College London, UK and Centre for Advanced Biomedical Imaging , University College London, UK;Centre for Advanced Biomedical Imaging , University College London, UK;Centre for Medical Image Computing, Department of Computer Science, University College London, UK
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

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Abstract

This paper presents a method for constructing detailed geometric models of tissue microstructure for synthesizing realistic diffusion MRI data. We construct three-dimensional mesh models from confocal microscopy image stacks using the marching cubes algorithm. Randomwalk simulations within the resulting meshes provide synthetic diffusion MRI measurements. Experiments optimise simulation parameters and complexity of the meshes to achieve accuracy and reproducibility while minimizing computation time. Finally we assess the quality of the synthesized data from the mesh models by comparison with scanner data as well as synthetic data from simple geometric models and simplified meshes that vary only in two dimensions. The results support the extra complexity of the three-dimensional mesh compared to simpler models although sensitivity to the mesh resolution is quite robust.