Abstract
In this paper, we propose a framework for synthesising 3D brain T1-weighted (T1-w) MRI images from Partial Volume (PV) maps for the purpose of generating synthetic MRI volumes with more accurate tissue borders. Synthetic MRIs are required to enlarge and enrich very limited data sets available for training of brain segmentation and related models. In comparison to current state-of-the-art methods, our framework exploits PV-map properties in order to guide a Generative Adversarial Network (GAN) towards the generation of more accurate and realistic synthetic MRI volumes. We demonstrate that conditioning a GAN on PV-maps instead of Binary-maps results in 58.96% more accurate tissue borders in synthetic MRIs. Furthermore, our results indicate an improvement in the representation of the Deep Gray Matter region in synthetic MRI volumes. Finally, we show that fine changes introduced into PV-maps are reflected in the synthetic images, while preserving accurate tissue borders, thus enabling better control during the data synthesis of novel synthetic MRI volumes.
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Notes
- 1.
Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). For up-to-date information, see www.adni-info.org.
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Rusak, F. et al. (2020). 3D Brain MRI GAN-Based Synthesis Conditioned on Partial Volume Maps. In: Burgos, N., Svoboda, D., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2020. Lecture Notes in Computer Science(), vol 12417. Springer, Cham. https://doi.org/10.1007/978-3-030-59520-3_2
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