Low‐field MRI: clinical promise and challenges
Modern MRI scanners have trended toward higher field strengths to maximize signal and
resolution while minimizing scan time. However, high‐field devices remain expensive to …
resolution while minimizing scan time. However, high‐field devices remain expensive to …
[HTML][HTML] SynthStrip: skull-stripping for any brain image
The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as
skull-stripping, is an integral component of many neuroimage analysis streams. Despite their …
skull-stripping, is an integral component of many neuroimage analysis streams. Despite their …
[HTML][HTML] TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Background and ObjectiveProcessing of medical images such as MRI or CT presents
different challenges compared to RGB images typically used in computer vision. These …
different challenges compared to RGB images typically used in computer vision. These …
SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry
Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in
hospitals across the world. These have the potential to revolutionize our understanding of …
hospitals across the world. These have the potential to revolutionize our understanding of …
[HTML][HTML] SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …
How machine learning is powering neuroimaging to improve brain health
This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
Quantitative brain morphometry of portable low-field-strength MRI using super-resolution machine learning
Background Portable, low-field-strength (0.064-T) MRI has the potential to transform
neuroimaging but is limited by low spatial resolution and low signal-to-noise ratio. Purpose …
neuroimaging but is limited by low spatial resolution and low signal-to-noise ratio. Purpose …
Measuring consciousness in the intensive care unit
Early reemergence of consciousness predicts long-term functional recovery for patients with
severe brain injury. However, tools to reliably detect consciousness in the intensive care unit …
severe brain injury. However, tools to reliably detect consciousness in the intensive care unit …
A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI
JE Iglesias - Scientific Reports, 2023 - nature.com
Volumetric registration of brain MRI is routinely used in human neuroimaging, eg, to align
different MRI modalities, to measure change in longitudinal analysis, to map an individual to …
different MRI modalities, to measure change in longitudinal analysis, to map an individual to …
Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
Like neocortical structures, the archicortical hippocampus differs in its folding patterns
across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for …
across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for …