AIMC Topic: Echo-Planar Imaging

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Machine learning based compartment models with permeability for white matter microstructure imaging.

NeuroImage Feb 8, 2017
Some microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use computational models learned from simulations to est...
Middle Aged Models, Theoretical Permeability Multiple Sclerosis Image Interpretation, Computer-Assisted Monte Carlo Method Young Adult Adult Male Computer Simulation Female Humans White Matter Machine Learning Echo-Planar Imaging Brain
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Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

Psychiatry research Jul 5, 2015
Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to ad...
Reference Values Image Interpretation, Computer-Assisted Nerve Net Diagnosis, Computer-Assisted Magnetic Resonance Imaging Temporal Lobe Echo-Planar Imaging Adult Guilt Sensitivity and Specificity Algorithms Male Biomarkers Depressive Disorder, Major Female Case-Control Studies Humans Machine Learning
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Accelerating prostate rs-EPI DWI with deep learning: Halving scan time, enhancing image quality, and validating in vivo.

Magnetic resonance imaging Sep 1, 2025
OBJECTIVES: This study aims to evaluate the feasibility and effectiveness of deep learning-based super-resolution techniques to reduce scan time while preserving image quality in high-resolution prostate diffusion-weighted imaging (DWI) with readout-...
Middle Aged Deep Learning Image Processing, Computer-Assisted Reproducibility of Results Male Signal-To-Noise Ratio Diffusion Magnetic Resonance Imaging Echo-Planar Imaging Prostatic Neoplasms Prostate Humans Algorithms Prospective Studies Retrospective Studies Image Interpretation, Computer-Assisted
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NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.

Radiology. Artificial intelligence Mar 1, 2025
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computation...
Brain Retrospective Studies Female Humans Magnetic Resonance Spectroscopy Echo-Planar Imaging Depressive Disorder, Major Deep Learning Glioblastoma Brain Neoplasms Male
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Super-Resolution Diffusion Tensor Imaging using SRCNN: A Feasibility Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
High-resolution diffusion imaging with submillimeter isotropic voxels requires long scan times that are usually clinically impractical. Even with those long scans, the image quality can still suffer from low signal-to-noise ratio (SNR) and severe geo...
Image Processing, Computer-Assisted Feasibility Studies Diffusion Tensor Imaging Brain Deep Learning Diffusion Magnetic Resonance Imaging Echo-Planar Imaging Humans
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Intravoxel Incoherent Motion: Model-Free Determination of Tissue Type in Abdominal Organs Using Machine Learning.

Investigative radiology Dec 1, 2017
PURPOSE: For diffusion data sets including low and high b-values, the intravoxel incoherent motion model is commonly applied to characterize tissue. The aim of the present study was to show that machine learning allows a model-free approach to determ...
Motion Reference Values Spleen Diffusion Magnetic Resonance Imaging Echo-Planar Imaging Cluster Analysis Liver Principal Component Analysis Kidney Abdomen Female Reproducibility of Results Young Adult Humans Adult Machine Learning Male Algorithms Image Processing, Computer-Assisted
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