Three-dimensional optical tomographic brain imaging in small animals, part 1: hypercapnia

2004 ◽  
Vol 9 (5) ◽  
pp. 1046 ◽  
Author(s):  
A. Y. Bluestone ◽  
M. Stewart ◽  
J. Lasker ◽  
G. S. Abdoulaev ◽  
A. H. Hielscher
Keyword(s):  
Brain Imaging ◽  
Three Dimensional ◽  
Small Animals

Three-dimensional optical tomographic brain imaging in small animals

2002 ◽  
Author(s):  
A.Y. Bluestone ◽  
G.S. Abdoulaev ◽  
J.M. Lasker ◽  
M. Stewart ◽  
A.H. Hielscher
Keyword(s):  
Brain Imaging ◽  
Three Dimensional ◽  
Small Animals

Three-dimensional optical tomographic brain imaging in small animals, part 2: Unilateral carotid occlusion

2004 ◽  
Vol 9 (5) ◽  
pp. 1063 ◽  
Author(s):  
A. Y. Bluestone ◽  
M. Stewart ◽  
B. Lei ◽  
I. S. Kass ◽  
J. Lasker ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Three Dimensional ◽  
Carotid Occlusion ◽  
Small Animals

scholarly journals A Mesh-Based Monte Carlo Study for Investigating Structural and Functional Imaging of Brain Tissue Using Optical Coherence Tomography

2019 ◽  
Vol 9 (19) ◽  
pp. 4008
Author(s):  
Luying Yi ◽  
Liqun Sun ◽  
Mingli Zou ◽  
Bo Hou
Keyword(s):  
Optical Coherence Tomography ◽  
Monte Carlo ◽  
Brain Imaging ◽  
Brain Tissue ◽  
Three Dimensional ◽  
Hemoglobin Concentration ◽  
Monte Carlo Study ◽  
Biological Tissues ◽  
Optical Coherence ◽  
Quantitative Accuracy

Optical coherence tomography (OCT) can obtain high-resolution three-dimensional (3D) structural images of biological tissues, and spectroscopic OCT, which is one of the functional extensions of OCT, can also quantify chromophores of tissues. Due to its unique features, OCT has been increasingly used for brain imaging. To support the development of the simulation and analysis tools on which OCT-based brain imaging depends, a model of mesh-based Monte Carlo for OCT (MMC-OCT) is presented in this work to study OCT signals reflecting the structural and functional activities of brain tissue. In addition, an approach to improve the quantitative accuracy of chromophores in tissue is proposed and validated by MMC-OCT simulations. Specifically, the OCT-based brain structural imaging was first simulated to illustrate and validate the MMC-OCT strategy. We then focused on the influences of different wavelengths on the measurement of hemoglobin concentration C, oxygen saturation Y, and scattering coefficient S in brain tissue. Finally, it is proposed and verified here that the measurement accuracy of C, Y, and S can be improved by selecting appropriate wavelengths for calculation, which contributes to the experimental study of brain functional sensing.

Three-dimensional optical tomographic brain imaging during hypercapnia in rodents

2003 ◽  
Author(s):  
Avraham Y. Bluestone ◽  
Mark Stewart ◽  
Joseph Lasker ◽  
Gassan S. Abdoulaev ◽  
Andreas H. Hielscher
Keyword(s):  
Brain Imaging ◽  
Three Dimensional

Three-Dimensional Radial VIBE Sequence for Contrast-Enhanced Brain Imaging: An Alternative for Reducing Motion Artifacts in Restless Children

2018 ◽  
Vol 210 (4) ◽  
pp. 876-882 ◽  
Author(s):  
Ji Eun Park ◽  
Young Hun Choi ◽  
Jung-Eun Cheon ◽  
Woo Sun Kim ◽  
In-One Kim ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Three Dimensional ◽  
Motion Artifacts ◽  
Radial Vibe ◽  
Contrast Enhanced

scholarly journals Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a “Diagnostic Label-Free” Approach: Application to Schizophrenia Datasets

2021 ◽  
Vol 15 ◽  
Author(s):  
Hiroyuki Yamaguchi ◽  
Yuki Hashimoto ◽  
Genichi Sugihara ◽  
Jun Miyata ◽  
Toshiya Murai ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Brain Imaging ◽  
Spatial Information ◽  
Three Dimensional ◽  
Clinical Information ◽  
Label Free ◽  
Brain Images ◽  
Convolutional Autoencoder ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (3D-CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS) and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets, including 71 SZ patients and 71 HS. We created 16 3D-CAE models with different channels and convolutions to explore the effective range of hyperparameters for psychiatric brain imaging. The number of blocks containing two convolutional layers and one pooling layer was set, ranging from 1 block to 4 blocks. The number of channels in the extraction layer varied from 1, 4, 16, and 32 channels. The proposed 3D-CAEs were successfully reproduced into 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relation to clinical information. We explored the appropriate hyperparameter range of 3D-CAE, and it was suggested that a model with 3 blocks may be related to extracting features for predicting the dose of medication and symptom severity in schizophrenia.

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