Combining the boundary shift integral and tensor-based morphometry for brain atrophy estimation

Mateusz Michalkiewicz; Akshay Pai; Kelvin K. Leung; Stefan Sommer; Sune Darkner; Lauge Sørensen; Jon Sporring; Mads Nielsen

doi:10.1117/12.2217089

21 March 2016 Combining the boundary shift integral and tensor-based morphometry for brain atrophy estimation

Mateusz Michalkiewicz, Akshay Pai, Kelvin K. Leung, Stefan Sommer, Sune Darkner, Lauge Sørensen, Jon Sporring, Mads Nielsen

Author Affiliations +

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978406 (2016) https://doi.org/10.1117/12.2217089
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Brain atrophy from structural magnetic resonance images (MRIs) is widely used as an imaging surrogate marker for Alzheimers disease. Their utility has been limited due to the large degree of variance and subsequently high sample size estimates. The only consistent and reasonably powerful atrophy estimation methods has been the boundary shift integral (BSI). In this paper, we first propose a tensor-based morphometry (TBM) method to measure voxel-wise atrophy that we combine with BSI. The combined model decreases the sample size estimates significantly when compared to BSI and TBM alone.

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Mateusz Michalkiewicz, Akshay Pai, Kelvin K. Leung, Stefan Sommer, Sune Darkner, Lauge Sørensen, Jon Sporring, and Mads Nielsen "Combining the boundary shift integral and tensor-based morphometry for brain atrophy estimation", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978406 (21 March 2016); https://doi.org/10.1117/12.2217089

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