scholarly journals T 2 prep three-dimensional spiral imaging with efficient whole brain coverage for myelin water quantification at 1.5 tesla

2012 ◽  
Vol 67 (3) ◽  
pp. 614-621 ◽  
Author(s):  
Thanh D. Nguyen ◽  
Cynthia Wisnieff ◽  
Mitchell A. Cooper ◽  
Dushyant Kumar ◽  
Ashish Raj ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Whole Brain ◽  
Spiral Imaging ◽  
Water Quantification

Three-Dimensional Whole-Brain Mapping

1992 ◽  
Vol 58 (1-4) ◽  
pp. 141-143 ◽  
Author(s):  
T.L. Hardy ◽  
L.R.D. Brynildson ◽  
J.G. Gray ◽  
D. Spurlock
Keyword(s):  
Brain Mapping ◽  
Three Dimensional ◽  
Whole Brain

scholarly journals Whole-brain three-dimensional T2-weighted BOLD functional magnetic resonance imaging at 7 Tesla

2013 ◽  
Vol 72 (6) ◽  
pp. 1530-1540 ◽  
Author(s):  
Jun Hua ◽  
Qin Qin ◽  
Peter C.M. van Zijl ◽  
James J. Pekar ◽  
Craig K. Jones
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Three Dimensional ◽  
7 Tesla ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Whole Brain

scholarly journals Whole Brain 3D MR Fingerprinting in Multiple Sclerosis: A Pilot Investigation

2021 ◽  
Author(s):  
Thomaz R. Mostardeiro ◽  
Ananya Panda ◽  
Norbert G. Campeau ◽  
Robert J. Witte ◽  
Yi Sui ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Three Dimensional ◽  
Acquisition Time ◽  
Imaging Method ◽  
Whole Brain ◽  
Normal Appearing White Matter ◽  
Pilot Investigation ◽  
Quantitative Properties ◽  
Perfect Discrimination ◽  
3.0T Mri

Abstract Background: MR fingerprinting (MRF) is a novel imaging method proposed for the diagnosis of Multiple Sclerosis (MS). This study aims to determine if MR Fingerprinting (MRF) relaxometry can differentiate frontal normal appearing white matter (F-NAWM) and splenium in patients diagnosed with MS as compared to controls and to characterize the relaxometry of demyelinating plaques relative to the time of diagnosis.Methods: Three-dimensional (3D) MRF data were acquired on a 3.0T MRI system resulting in isotropic voxels (1x1x1mm3) and a total acquisition time of 4min 38s. Data were collected on 18 subjects paired with 18 controls. Regions of interested were drawn over MRF-derived T1 relaxometry maps encompassing selected MS lesions, F-NAWM and splenium. T1 and T2 relaxometry features from those segmented areas were used to classify MS lesions from F-NAWM and splenium with T-distributed stochastic neighbor embedding algorithms (T-SNE). Partial least squares discriminant analysis (PLS-DA) was performed to discriminate NAWM and Splenium in MS compared with controls. Results: Mean out-of-fold machine learning prediction accuracy for discriminant results between MS patients and controls for F-NAWM was 65% and approached 90% for the splenium. There was significant positive correlation between time since diagnosis and MS lesions mean T2 (p=0.015), minimum T1 (p=0.03) and negative correlation with splenium uniformity (p=0.04). Perfect discrimination (AUC=1) was achieved between selected features from MS lesions and F-NAWM.Conclusions: 3D-MRF has the ability to differentiate between MS and controls based on relaxometry properties from the F-NAWM and splenium. Whole brain coverage allows the assessment of quantitative properties within lesions that provide chronological assessment of the time from MS diagnosis.

Towards an Efficient Validation of Dynamical Whole-brain Models

2021 ◽  
Author(s):  
Kevin J. Wischnewski ◽  
Simon B. Eickhoff ◽  
Viktor K. Jirsa ◽  
Oleksandr V. Popovych
Keyword(s):  
Structural Connectivity ◽  
Three Dimensional ◽  
Bayesian Optimization ◽  
High Dimensional ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Phase Oscillators ◽  
Dimensional Parameter ◽  
Whole Brain ◽  
Brain Models

Abstract Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.

SU-F-T-401: Three-Dimensional Conformal Planning Using Low-Segment Multi-Criteria Optimization for Hippocampal Avoidance Whole Brain Radiotherapy

2016 ◽  
Vol 43 (6Part19) ◽  
pp. 3555-3555
Author(s):  
X Zhang ◽  
J Penagaricano ◽  
G Narayanasamy ◽  
R Griffin ◽  
S Maraboyina ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Whole Brain Radiotherapy ◽  
Whole Brain ◽  
Brain Radiotherapy ◽  
Hippocampal Avoidance

A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing

2018 ◽  
Vol 21 (4) ◽  
pp. 625-637 ◽  
Author(s):  
Tatsuya C. Murakami ◽  
Tomoyuki Mano ◽  
Shu Saikawa ◽  
Shuhei A. Horiguchi ◽  
Daichi Shigeta ◽  
...  
Keyword(s):  
Single Cell ◽  
Three Dimensional ◽  
Brain Atlas ◽  
Whole Brain ◽  
Tissue Clearing

The Lesion Analysis of Cholinergic Neurons in 5XFAD Mouse Model in the Three-Dimensional Level of Whole Brain

2017 ◽  
Author(s):  
Huanhuan Yan ◽  
Pei Pang ◽  
Wenqi Chen ◽  
Houze Zhu ◽  
Henok K.A. ◽  
...  
Keyword(s):  
Mouse Model ◽  
Three Dimensional ◽  
Cholinergic Neurons ◽  
Whole Brain ◽  
5Xfad Mouse ◽  
Lesion Analysis

scholarly journals The Metabolic Centroid Method for PET Brain Image Analysis

1989 ◽  
Vol 9 (3) ◽  
pp. 388-397 ◽  
Author(s):  
A. V. Levy ◽  
J. D. Brodie ◽  
JJ. A. G. Russell ◽  
N. D. Volkow ◽  
E. Laska ◽  
...  
Keyword(s):  
Random Noise ◽  
Three Dimensional ◽  
Normal Subjects ◽  
Subcortical Structures ◽  
Centroid Method ◽  
Whole Brain ◽  
Positron Emission ◽  
The Mean ◽  
Brain Image Analysis ◽  
The Brain

The method of centroids is an approach to the analysis of three-dimensional whole-brain positron emission tomography (PET) metabolic images. It utilizes the brain's geometric centroid and metabolic centroid so as to objectively characterize the central tendency of the distribution of metabolic activity in the brain. The method characterizes the three-dimensional PET metabolic image in terms of four parameters: the coordinates of the metabolic centroid and the mean metabolic rate of the whole brain. These parameters are not sensitive to spatially uniform random noise or to the position of the subject's head within a uniform PET camera field of view. The method has been applied to 40 normal subjects, 22 schizophrenics who were treated with neuroleptics, and 20 schizophrenics who were neuroleptic-free. The mean metabolic centroid of the normal subjects was found to be superior to the mean geometric centroid of the brain. The mean metabolic centroid of chronic schizophrenics is lower and more posterior to the mean geometric centroid than is that of normals. This difference is greater in medicated than in unmedicated schizophrenics. The posterior and downward displacement of the mean metabolic centroid is consistent with the concepts of hypofrontality, hyperactivity of subcortical structures, and neuroleptic effect in schizophrenics.

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