Search Results for author:
Found 17 papers, 6 papers with code
Learning accurate rigid registration for longitudinal brain MRI from synthetic data
Rigid registration aims to determine the translations and rotations necessary to align features in a pair of images.
Search Wide, Focus Deep: Automated Fetal Brain Extraction with Sparse Training Data
Automated fetal brain extraction from full-uterus MRI is a challenging task due to variable head sizes, orientations, complex anatomy, and prevalent artifacts.
Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging
Here, we propose a principled way to propagate uncertainties (epistemic or aleatoric) estimated at the level of spatial location by these methods, to the level of global transformation models, and further to downstream tasks.
WALINET: A water and lipid identification convolutional Neural Network for nuisance signal removal in 1H MR Spectroscopic Imaging
The WALINET (WAter and LIpid neural NETwork) was compared to conventional methods such as the state-of-the-art lipid L2 regularization and Hankel-Lanczos singular value decomposition (HLSVD) water suppression.
Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging
Methods: Fast high-resolution whole-brain metabolic imaging was performed at 3. 4 mm$^3$ isotropic resolution with acquisition times between 4:11-9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner.
Registration by Regression (RbR): a framework for interpretable and flexible atlas registration
In human neuroimaging studies, atlas registration enables mapping MRI scans to a common coordinate frame, which is necessary to aggregate data from multiple subjects.
Boosting Skull-Stripping Performance for Pediatric Brain Images
With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing.
SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI
Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking.
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image.
Data Consistent Deep Rigid MRI Motion Correction
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies.
Learning the Effect of Registration Hyperparameters with HyperMorph
We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values.
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.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment.
HyperMorph: Amortized Hyperparameter Learning for Image Registration
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training.
SynthMorph: learning contrast-invariant registration without acquired images
This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts.
Analyzing Hypersensitive AI: Instability in Corporate-Scale Machine Learning
Predictive geometric models deliver excellent results for many Machine Learning use cases.
