activity
20172022
most citedTADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data

73 citations · 236 across the 13 of their papers we have counts for

collaborators
Showing eess.IVShow all

7 papers · 1 filter

eess.IV2022

Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning

Jason P. Lim, Stefano B. Blumberg, Neil Narayan +4

Machine learning is a powerful approach for fitting microstructural models to diffusion MRI data. Early machine learning microstructure imaging implementations trained regressors t…

eess.IV2020

Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast

Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre +6

Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently hav…

eess.IV202047 cited

DeepReg: a deep learning toolkit for medical image registration

Yunguan Fu, Nina Montaña Brown, Shaheer U. Saeed +14

DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.

eess.IV2020

Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients

Matteo Figini, Hongxiang Lin, Godwin Ogbole +10

1.5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robu…

eess.IV2019

Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator

Hongxiang Lin, Matteo Figini, Ryutaro Tanno +10

MR images scanned at low magnetic field (T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those fr…

eess.IV2019

Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression

Daniele Ravi, Daniel C. Alexander, Neil P. Oxtoby

Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This…