Abstract
Diffusion Tensor Imaging (DTI), the Fractional Anisotropy (FA) is used to measure the integrity of the white matter (WM); it is considered as a biomarker for stroke recovery. This measure is highly sensitive to applied pre-processing steps; in particular, the presence of a lesion may result into severe misregistration. In this paper, it is proposed to quantitatively assess the impact of large stroke lesions onto the registration process. To reduce this impact, a new registration algorithm, that localizes the lesion via Bayesian estimation, is proposed.
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Notes
- 1.
See BRATS (http://braintumorsegmentation.org) and ISLES (http://www.isles-challenge.org): 2015’ medical imaging challenges on lesion segmentation.
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Acknowledgments
This study was partially supported by PHRC-HERMES, and by French ANR projects e-SwallHome (ANR-13-TECS-0011) and ERATRANIRMA (ANR-12-EMMA-0056).
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Renard, F., Urvoy, M., Jaillard, A. (2016). Bayesian Stroke Lesion Estimation for Automatic Registration of DTI Images. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_9
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DOI: https://doi.org/10.1007/978-3-319-30858-6_9
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