KEYWORDS: Anatomy, Magnetic resonance imaging, Medical imaging, Image restoration, Image processing, Super resolution, Network architectures, Deep learning, Medical image reconstruction
Unpaired image synthesis is a particularly active area of research, especially in medical imaging, where paired datasets are rare. Disentangled representations are an important part of the techniques used, following those based on GAN. However, by relying on the factorization of an image into independent variation latent codes, these methods can offer greater control over the synthesis result than GANs. This work investigates the use of disentangled representation learning for high-resolution dynamic MRI synthesis.
Cerebral palsy is a common physical disability in childhood that results in aberrant movement and postural patterns. To better understand the pathology and improve the rehabilitation of patients, this article comparatively studies the ankle short bones morphology with one of the state-of-the-art tools, ShapeWorks, and a deformation-based method. Experiments on a clinical dataset reveal calcaneus and talus deformity patterns, providing a guideline for clinical decisions.
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