Justin A. Blaber
YOU?
Author Swipe
View article: Generalizing deep whole brain segmentation for pediatric and post-contrast MRI with augmented transfer learning
Generalizing deep whole brain segmentation for pediatric and post-contrast MRI with augmented transfer learning Open
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles (SLANT) …
View article: MRI correlates of chronic symptoms in mild traumatic brain injury
MRI correlates of chronic symptoms in mild traumatic brain injury Open
Veterans with mild traumatic brain injury (mTBI) have reported auditory and visual dysfunction that persists beyond the acute incident. The etiology behind these symptoms is difficult to characterize with current clinical imaging. These fu…
View article: Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps
Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps Open
Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstr…
View article: Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution
Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution Open
Confocal histology provides an opportunity to establish intra-voxel fiber orientation distributions that can be used to quantitatively assess the biological relevance of diffusion weighted MRI models, e.g., constrained spherical deconvolut…
View article: Generalizing Deep Whole Brain Segmentation for Pediatric and\n Post-Contrast MRI with Augmented Transfer Learning
Generalizing Deep Whole Brain Segmentation for Pediatric and\n Post-Contrast MRI with Augmented Transfer Learning Open
Generalizability is an important problem in deep neural networks, especially\nin the context of the variability of data acquisition in clinical magnetic\nresonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles\n(SLAN…
View article: Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge
Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge Open
Background Fiber tracking with diffusion‐weighted MRI has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. Purpo…
View article: TractEM: Fast Protocols for Whole Brain Deterministic Tractography-Based White Matter Atlas
TractEM: Fast Protocols for Whole Brain Deterministic Tractography-Based White Matter Atlas Open
Reproducible identification of white matter tracts across subjects is essential for the study of structural connectivity of the human brain. The key challenges are anatomical differences between subjects and human rater subjectivity in lab…
View article: Learning 3D White Matter Microstructure from 2D Histology
Learning 3D White Matter Microstructure from 2D Histology Open
Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due…
View article: Harmonizing 1.5T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators
Harmonizing 1.5T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators Open
Diffusion weighted magnetic resonance imaging (DW-MRI) is interpreted as a quantitative method that is sensitive to tissue microarchitecture at a millimeter scale. However, the sensitization is dependent on acquisition sequences (e.g., dif…
View article: Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury Open
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient anonymity it is frequently impractical or prohibited to tra…
View article: Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge
Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge Open
Purpose: Fiber tracking with diffusion weighted magnetic resonance imaging has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tra…
View article: Limits to anatomical accuracy of diffusion tractography using modern approaches
Limits to anatomical accuracy of diffusion tractography using modern approaches Open
Diffusion MRI fiber tractography is widely used to probe the structural connectivity of thebrain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limit…
View article: Issue Information
Issue Information Open
of mathematics, computing, engineering, physics, biophysics, chemistry, biochemistry, and physiology directly relevant to magnetic resonance will be accepted, as well as methodology-oriented clinical studies.
View article: Phantom-based field maps for gradient nonlinearity correction in diffusion imaging
Phantom-based field maps for gradient nonlinearity correction in diffusion imaging Open
Gradient coils in magnetic resonance imaging do not produce perfectly linear gradient fields. For diffusion imaging, the field nonlinearities cause the amplitude and direction of the applied diffusion gradients to vary over the field of vi…
View article: Empirical estimation of intravoxel structure with persistent angular structure and Q-ball models of diffusion weighted MRI
Empirical estimation of intravoxel structure with persistent angular structure and Q-ball models of diffusion weighted MRI Open
The diffusion tensor model is nonspecific in regions where micrometer structural patterns are inconsistent at the millimeter scale (i.e., brain regions with pathways that cross, bend, branch, fan, etc.). Numerous models have been proposed …
View article: SHARD: spherical harmonic-based robust outlier detection for HARDI methods
SHARD: spherical harmonic-based robust outlier detection for HARDI methods Open
High Angular Resolution Diffusion Imaging (HARDI) models are used to capture complex intra-voxel microarchitectures. The magnetic resonance imaging sequences that are sensitized to diffusion are often highly accelerated and prone to motion…
View article: Empirical single sample quantification of bias and variance in Q‐ball imaging
Empirical single sample quantification of bias and variance in Q‐ball imaging Open
Purpose The bias and variance of high angular resolution diffusion imaging methods have not been thoroughly explored in the literature and may benefit from the simulation extrapolation (SIMEX) and bootstrap techniques to estimate bias and …
View article: Effects of b-value and number of gradient directions on diffusion MRI measures obtained with Q-ball imaging
Effects of b-value and number of gradient directions on diffusion MRI measures obtained with Q-ball imaging Open
High-angular-resolution diffusion-weighted imaging (HARDI) MRI acquisitions have become common for use with higher order models of diffusion. Despite successes in resolving complex fiber configurations and probing microstructural propertie…