7 papers · 1 filter
: learning to disentangle technical distortions from true biological change
Jingru Fu, Kathleen E. Larson, Douglas N. Greve +2
Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease. Deformable image registration is a key tool for estimati…
MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
Armina Fani, Mike Doan, Isabelle Le +4
Deployment complexity and specialized hardware requirements hinder the adoption of deep learning models in neuroimaging. We present MindGrab, a lightweight, fully convolutional mod…
Deep learning water-unsuppressed MRSI at ultra-high field for simultaneous quantitative metabolic, susceptibility and myelin water imaging
Paul J. Weiser, Jiye Kim, Jongho Lee +6
Purpose: Magnetic Resonance Spectroscopic Imaging (MRSI) maps endogenous brain metabolism while suppressing the overwhelming water signal. Water-unsuppressed MRSI (wu-MRSI) allows…
WALINET: A water and lipid identification convolutional Neural Network for nuisance signal removal in 1H MR Spectroscopic Imaging
Paul Weiser, Georg Langs, Stanislav Motyka +5
Purpose. Proton Magnetic Resonance Spectroscopic Imaging (1H-MRSI) provides non-invasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain 1H-M…
Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging
Paul Weiser, Georg Langs, Wolfgang Bogner +11
Introduction: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonanc…
Learning accurate rigid registration for longitudinal brain MRI from synthetic data
Jingru Fu, Adrian V. Dalca, Bruce Fischl +2
Rigid registration aims to determine the translations and rotations necessary to align features in a pair of images. While recent machine learning methods have become state-of-the-…