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Found 5 papers, 3 papers with code
Lossy compression of multidimensional medical images using sinusoidal activation networks: an evaluation study
In this work, we evaluate how neural networks with periodic activation functions can be leveraged to reliably compress large multidimensional medical image datasets, with proof-of-concept application to 4D diffusion-weighted MRI (dMRI).
Q-space quantitative diffusion MRI measures using a stretched-exponential representation
Diffusion magnetic resonance imaging (dMRI) is a relatively modern technique used to study tissue microstructure in a non-invasive way.
Tractometry-based Anomaly Detection for Single-subject White Matter Analysis
There is an urgent need for a paradigm shift from group-wise comparisons to individual diagnosis in diffusion MRI (dMRI) to enable the analysis of rare cases and clinically-heterogeneous groups.
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues.
q-Space Novelty Detection with Variational Autoencoders
Since abnormal samples are not used during training, we define novelty metrics based on the (partially complementary) assumptions that the VAE is less capable of reconstructing abnormal samples well; that abnormal samples more strongly violate the VAE regularizer; and that abnormal samples differ from normal samples not only in input-feature space, but also in the VAE latent space and VAE output.
