Simulating Longitudinal Brain MRIs with known Volume Changes and Realistic Variations in Image Intensity

Bishesh Khanal 1 Nicholas Ayache 1 Xavier Pennec 1
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and 18 atrophy generation.
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Frontiers in Neuroscience, Frontiers, 2017, 11 (Article 132), pp.18. 〈10.3389/fnins.2017.00132〉
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Bishesh Khanal, Nicholas Ayache, Xavier Pennec. Simulating Longitudinal Brain MRIs with known Volume Changes and Realistic Variations in Image Intensity. Frontiers in Neuroscience, Frontiers, 2017, 11 (Article 132), pp.18. 〈10.3389/fnins.2017.00132〉. 〈hal-01348959v3〉

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