Statistical Detection of Longitudinal Changes between Apparent Diffusion Coefficient Images: Application to Multiple Sclerosis

Authors:
Hervé Boisgontier;Vincent Noblet;Félix Renard;Fabrice Heitz;Lucien Rumbach;Jean-Paul Armspach
Affiliations:
Université de Strasbourg, CNRS, UMR 7005, LSIIT, France and Université de Strasbourg, CNRS, UMR 7191, LINC-IPB, France;Université de Strasbourg, CNRS, UMR 7005, LSIIT, France;Université de Strasbourg, CNRS, UMR 7005, LSIIT, France and Université de Strasbourg, CNRS, UMR 7191, LINC-IPB, France;Université de Strasbourg, CNRS, UMR 7005, LSIIT, France;Université de Strasbourg, CNRS, UMR 7191, LINC-IPB, France and Service de Neurologie, CHU Minjoz, France;Université de Strasbourg, CNRS, UMR 7191, LINC-IPB, France
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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Abstract

The automatic analysis of longitudinal changes between Diffusion Tensor Imaging (DTI) acquisitions is a promising tool for monitoring disease evolution. However, few works address this issue and existing methods are generally limited to the detection of changes between scalar images characterizing diffusion properties, such as Fractional Anisotropy or Mean Diffusivity, while richer information can be exploited from the whole set of Apparent Diffusion Coefficient (ADC) images that can be derived from a DTI acquisition. In this paper, we present a general framework for detecting changes between two sets of ADC images and we investigate the performance of four statistical tests. Results are presented on both simulated and real data in the context of the follow-up of multiple sclerosis lesion evolution.