21 citations · 32 across the 5 of their papers we have counts for
5 papers
An explainability framework for cortical surface-based deep learning
Fernanda L. Ribeiro, Steffen Bollmann, Ross Cunnington +1
The emergence of explainability methods has enabled a better comprehension of how deep neural networks operate through concepts that are easily understood and implemented by the en…
NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data
Francesco Cognolato, Kieran O'Brien, Jin Jin +4
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many curr…
DeepRetinotopy: Predicting the Functional Organization of Human Visual Cortex from Structural MRI Data using Geometric Deep Learning
Fernanda L. Ribeiro, Steffen Bollmann, Alexander M. Puckett
Whether it be in a man-made machine or a biological system, form and function are often directly related. In the latter, however, this particular relationship is often unclear due…
Overview of quantitative susceptibility mapping using deep learning -- Current status, challenges and opportunities
Woojin Jung, Steffen Bollmann, Jongho Lee
Quantitative susceptibility mapping (QSM) has gained broad interests in the field by extracting biological tissue properties, predominantly myelin, iron and calcium from magnetic r…
Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks
Shahrokh Abbasi-Rad, Kieran O'Brien, Samuel Kelly +6
Purpose: The purpose of this study is to demonstrate a method for Specific Absorption Rate (SAR) reduction for T2-FLAIR MRI sequences at 7T by predicting the required adiabatic pul…