36 citations · 47 across the 4 of their papers we have counts for
4 papers
Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images
Alessandro Sciarra, Soumick Chatterjee, Max Dünnwald +4
Motion artefacts in magnetic resonance brain images can have a strong impact on diagnostic confidence. The assessment of MR image quality is fundamental before proceeding with the…
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald +7
Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the…
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning
Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald +7
Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a n…
Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning
Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald +3
In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods, such as prospec…