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.
KEYWORDS: Magnetic resonance imaging, Diffusion tensor imaging, Principal component analysis, Shape analysis, Surgery, Tissues, Anisotropy, Signal to noise ratio
Quantitative MRI (qMRI) has been shown to be crucial for assessing organ dysfunction in the body. Usually, in qMRI approaches, a few metrics are extracted to distinguish normal and abnormal tissues. In this study, we coupled four MRI protocols (mDIXON T1, T1 and T2 mapping and DTI) to obtain 34 complementary metrics including 20 shape metrics, 2 texture metrics and 12 water diffusivity metrics for thigh muscle analysis. These metrics were calculated on both thighs to detect a pathological difference between a pair of right and left muscles. The method is based on a dimension reduction method and a projection of shape and diffusivity metrics into a three-dimensional linear latent space, along with two texture metrics. 5 healthy individuals (10 thighs, each thigh 7 muscles, i.e., 4 flexors and 3 extensors) were scanned to provide the reference scores. The developed pipeline was used to analyse the thighs of 4 patients in order to suggest a specific muscle therapy before total knee arthroplasty (TKA) and for each of the 7 muscles studied. Preliminary results from the analysis of thigh muscle texture, shape and diffusivity showed that this qMRI protocol can help to suggest a targeted, patient-specific exercise plan to improve muscle recovery after TKA surgery. More healthy and pathological subjects are needed to confirm these encouraging results.
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