Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images

1 code implementation • 14 Jun 2022 • Alessandro Sciarra, Soumick Chatterjee, Max Dünnwald, Giuseppe Placidi, Andreas Nürnberger, Oliver Speck, Steffen Oeltze-Jafra

An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work.

Image Quality Assessment regression +1

StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder

1 code implementation • 31 Jan 2022 • Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Pavan Tummala, Shubham Kumar Agrawal, Aishwarya Jauhari, Aman Kalra, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger

Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology.

Unsupervised Anomaly Detection

ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning

no code implementations • 25 Feb 2021 • Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Raghava Vinaykanth Mushunuri, Ranadheer Podishetti, Rajatha Nagaraja Rao, Geetha Doddapaneni Gopinath, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger

In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI.

Image Super-Resolution SSIM

Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning

1 code implementation • 28 Nov 2020 • Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Steffen Oeltze-Jafra, Andreas Nürnberger, Oliver Speck

Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts.