AIMC Topic: Diffusion Magnetic Resonance Imaging
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Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI.
NeuroImage
Jun 26, 2021
Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the d...
Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models.
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Jun 23, 2021
Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perf...
Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study.
Scientific reports
Jun 14, 2021
There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively inc...
Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients.
Magnetic resonance in medicine
Jun 9, 2021
PURPOSE: Earlier work showed that IVIM-NET , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). Th...
Magnetic resonance imaging reconstruction algorithm under complex convolutional neural network in diagnosis and prognosis of cerebral infarction.
PloS one
May 17, 2021
This study was to explore the application value of magnetic resonance imaging (MRI) image reconstruction model based on complex convolutional neural network (CCNN) in the diagnosis and prognosis of cerebral infarction. Two image reconstruction method...
Prediction of brain age from routine T2-weighted spin-echo brain magnetic resonance images with a deep convolutional neural network.
Neurobiology of aging
Apr 28, 2021
Our study investigated the feasibility and clinical relevance of brain age prediction using axial T2-weighted images (T2-WIs) with a deep convolutional neural network (CNN) algorithm. The CNN model was trained by 1,530 scans in our institution. The p...
Multimodal super-resolved q-space deep learning.
Medical image analysis
Apr 21, 2021
Super-resolvedq-space deep learning (SR-q-DL) has been developed to estimate high-resolution (HR) tissue microstructure maps from low-quality diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients and ...
Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks.
Sensors (Basel, Switzerland)
Apr 12, 2021
The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI). Biparametric magnetic resonance imaging (bpMRI) was found to have a diagnostic accuracy comparable to mpMRI in ...
Longitudinal diffusion MRI analysis using Segis-Net: A single-step deep-learning framework for simultaneous segmentation and registration.
NeuroImage
Mar 29, 2021
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear regi...
Detecting the Early Infarct Core on Non-Contrast CT Images with a Deep Learning Residual Network.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Mar 27, 2021
PURPOSE: To explore a new approach mainly based on deep learning residual network (ResNet) to detect infarct cores on non-contrast CT images and improve the accuracy of acute ischemic stroke diagnosis.
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