26 citations · 40 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge
Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf +3
Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial reso…
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…
Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
Dhanunjaya Mitta, Soumick Chatterjee, Oliver Speck +1
Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual…
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
A. Emre Kavur, N. Sinem Gezer, Mustafa Barış +24
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have intr…
Breathing deformation model -- application to multi-resolution abdominal MRI
Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf +3
Dynamic MRI is a technique of acquiring a series of images continuously to follow the physiological changes over time. However, such fast imaging results in low resolution images.…