Model-based motion correction of reduced field of view diffusion MRI data

Jan Hering; Ivo Wolf; Hans-Peter Meinzer; Klaus H. Maier-Hein

doi:10.1117/12.2043711

13 March 2014 Model-based motion correction of reduced field of view diffusion MRI data

Jan Hering, Ivo Wolf, Hans-Peter Meinzer, Klaus H. Maier-Hein

Proceedings Volume 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging; 90381L (2014) https://doi.org/10.1117/12.2043711
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

In clinical settings, application of the most recent modelling techniques is usually unfeasible due to the limited acquisition time. Localised acquisitions enclosing only the object of interest by reducing the field-of-view (FOV) counteract the time limitation but pose new challenges to the subsequent processing steps like motion correction. We use datasets from the Human Connectome Project (HCP) to simulate head motion distorted reduced FOV acquisitions and present an evaluation of head motion correction approaches: the commonly used affine regis- tration onto an unweighted reference image guided by the mutual information (MI) metric and a model-based approach, which uses reference images computed from approximated tensor data to improve the performance of the MI metric. While the standard approach using the MI metric yields up to 15% outliers (error>5 mm) and a mean spatial error above 1.5 mm, the model-based approach reduces the number of outliers (1%) and the spatial error significantly (p<;0.01). The behavior is also reflected by the visual analysis of the MI metric. The evaluation shows that the MI metric is of very limited use for reduced FOV data post-processing. The model-based approach has proven more suitable in this context.

Citation Download Citation

Jan Hering, Ivo Wolf, Hans-Peter Meinzer, and Klaus H. Maier-Hein "Model-based motion correction of reduced field of view diffusion MRI data", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 90381L (13 March 2014); https://doi.org/10.1117/12.2043711

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