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About 4,647 papers for “Data diffusion machine”

AUGMENTATION AND ANALYSIS OF DIFFUSION MRI DATA VIA MACHINE LEARNING METHODS

Reagan Dugan · Civil War Book Review · 2024

Diffusion Magnetic Resonance Imaging (dMRI) measures the diffusion of water within the body and can be used for inspecting white matter tracts within the brain. dMRI data can be used for estimation of white matter fiber orientations as well as for estimating measures of tract integrity. In order to utilize state-of-the-art analysis techniques, large quantities of dMRI data are necessary, and scanning times have become a limiting factor in the implementation of dMRI clinically. In this dissertation, we propose machine learning methods to reduce the amount of data that needs to be acquired at the scanner. In chapter 2, we have proposed a model that estimates dMRI data with high levels of diffusion weighting from volumes with a lower level of diffusion weighting. In chapter 3, we applied this model to data collected from the Bogalusa Heart Study and analyzed lifespan effects of cardiometabolic health on white matter health in midlife. In chapter 4, we proposed a second machine learning model that improves the angular resolution of dMRI data by mapping the spherical harmonic representation of dMRI data acquired with a small number of diffusion weighting directions to the spherical harmonic representation of the same volume with a greater number of diffusion weighting directions. The proposed models enable data acquired in a short period of time to be used with advanced analysis methods which would normally require significantly more scan time and provide more sensitive measures of white matter health.

Cited by 0Open access

The Data Diffusion Machine with a Scalable Point-to-Point Network

Paul W. A. Stallard, David H. D. Warren, Henk L. Muller et al. · 1993

The Data Diffusion Machine (DDM) is a virtual shared memory architecture. Data items in the machine have no fixed home location but instead migrate to the processors that are actually using the data. Coherency is maintained with a hierarchical directory scheme. In an uncongested machine, all memory operations complete in a time bounded by the logarithm of the number of processors. As the machine load increases, the natural combining of the DDM hierarchy allows better network utilisation and helps maintain performance. This paper proposes an implementation of the DDMwith split directories interconnected with a point-to-point network. This is easier to construct and scales better than previous designs. The paper also defines the protocol that is needed to maintain consistency on this split tree (even though the network provides a number of paths between pairs of processors). Conflicting requests (writes on different processors to the same data item) are resolved in a time bounded by the number of processors, without chance of starvation. The protocol has been fully implemented, and we show some results for up to 72 processors.

Cited by 0

AUGMENTATION AND ANALYSIS OF DIFFUSION MRI DATA VIA MACHINE LEARNING METHODS

Dugan, Reagan T · Civil War Book Review · 2024

Diffusion Magnetic Resonance Imaging (dMRI) measures the diffusion of water within the body and can be used for inspecting white matter tracts within the brain. dMRI data can be used for estimation of white matter fiber orientations as well as for estimating measures of tract integrity. In order to utilize state-of-the-art analysis techniques, large quantities of dMRI data are necessary, and scanning times have become a limiting factor in the implementation of dMRI clinically. In this dissertation, we propose machine learning methods to reduce the amount of data that needs to be acquired at the scanner. In chapter 2, we have proposed a model that estimates dMRI data with high levels of diffusion weighting from volumes with a lower level of diffusion weighting. In chapter 3, we applied this model to data collected from the Bogalusa Heart Study and analyzed lifespan effects of cardiometabolic health on white matter health in midlife. In chapter 4, we proposed a second machine learning model that improves the angular resolution of dMRI data by mapping the spherical harmonic representation of dMRI data acquired with a small number of diffusion weighting directions to the spherical harmonic representation of the same volume with a greater number of diffusion weighting directions. The proposed models enable data acquired in a short period of time to be used with advanced analysis methods which would normally require significantly more scan time and provide more sensitive measures of white matter health.

Cited by 0Open access

A Machine Learning Model for Predicting Major Depressive Disorder Using Diffusion-Tensor Imaging Data

J. H. Lee, David Lee · European Psychiatry · 2024

Introduction Major Depressive Disorder (MDD) stands as a prevalent psychiatric condition within the general population. Despite extensive research efforts, the identification of definitive diagnostic biomarkers for depressive disorders remains elusive. Currently, machine learning methods are gaining prominence in the diagnosis of medical illnesses. Objectives This study aims to construct a machine learning-based prediction model for Major Depressive Disorder (MDD) by harnessing diffusion tensor imaging (DTI) data. Methods The DTI datasets comprising MDD (N=83) and Healthy Control (N=70) groups were procured from the cohort study of Anxiety and Depression conducted at the National Center for Mental Health in South Korea. A machine learning method using a decision tree algorithm was employed to select relevant brain regions and establish a robust diagnostic model. Features associated with white matter (WM) tracts were chosen through recursive feature elimination. Results Demographic characteristics, including age, sex, and handedness, displayed no significant differences between the MDD and Healthy Control groups. However, the total score of the Beck Depression Inventory was notably higher in individuals with MDD compared to Healthy Controls. A diagnostic model was crafted using the decision tree algorithms to distinguish between the two groups. The model demonstrated the following classification performance metrics: accuracy (65.6% ± 8.5), sensitivity (66.6% ± 12.5), and specificity (64.7% ± 13.6). Furthermore, through recursive feature elimination, specific neuroanatomical features tied to brain structures such as the inferior cerebellar peduncle, posterior thalamic radiation, cingulum (hippocampus), uncinate fasciculus, and tapetum were identified. Conclusions Despite of limited performance of classification, a machine learning-based approach could provide insights into the development of a diagnostic model for MDD using neuroimaging data. Furthermore, these features, derived from DTI-derived data, may have implications for understanding the neural underpinnings of major depressive disorder. Disclosure of Interest None Declared

Cited by 0Open access

Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology

Zezhong Ye, Richard L. Price, Xiran Liu et al. · 2023

<div>AbstractPurpose:<p>Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation remains an unmet need in the clinical management of GBMs.</p>Experimental Design:<p>We employ a novel diffusion histology imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM.</p>Results:<p>Gadolinium-enhanced T1-weighted or hyperintense fluid-attenuated inversion recovery failed to reflect the morphologic complexity underlying tumor in patients with GBM. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in GBM specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0%, and 93.4% accuracy, respectively.</p>Conclusions:<p>Our results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques in guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of GBM.</p></div>

Cited by 0Open access

Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology

Zezhong Ye, Richard L. Price, Xiran Liu et al. · 2023

<div>AbstractPurpose:<p>Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation remains an unmet need in the clinical management of GBMs.</p>Experimental Design:<p>We employ a novel diffusion histology imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM.</p>Results:<p>Gadolinium-enhanced T1-weighted or hyperintense fluid-attenuated inversion recovery failed to reflect the morphologic complexity underlying tumor in patients with GBM. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in GBM specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0%, and 93.4% accuracy, respectively.</p>Conclusions:<p>Our results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques in guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of GBM.</p></div>

Cited by 0Open access

Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning

Jason P. Lim, Stefano B. Blumberg, Neil Narayan et al. · arXiv (Cornell University) · 2022

Machine learning is a powerful approach for fitting microstructural models to diffusion MRI data. Early machine learning microstructure imaging implementations trained regressors to estimate model parameters in a supervised way, using synthetic training data with known ground truth. However, a drawback of this approach is that the choice of training data impacts fitted parameter values. Self-supervised learning is emerging as an attractive alternative to supervised learning in this context. Thus far, both supervised and self-supervised learning have typically been applied to isotropic models, such as intravoxel incoherent motion (IVIM), as opposed to models where the directionality of anisotropic structures is also estimated. In this paper, we demonstrate self-supervised machine learning model fitting for a directional microstructural model. In particular, we fit a combined T1-ball-stick model to the multidimensional diffusion (MUDI) challenge diffusion-relaxation dataset. Our self-supervised approach shows clear improvements in parameter estimation and computational time, for both simulated and in-vivo brain data, compared to standard non-linear least squares fitting. Code for the artificial neural net constructed for this study is available for public use from the following GitHub repository: https://github.com/jplte/deep-T1-ball-stick

Cited by 0Open access