AIMC Topic: Diffusion Magnetic Resonance Imaging
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Artificial Neural Network Computer Tomography Perfusion Prediction of Ischemic Core.
Stroke
May 16, 2019
Background and Purpose- Computed tomography perfusion (CTP) is a useful tool in the evaluation of acute ischemic stroke, where it can provide an estimate of the ischemic core and the ischemic penumbra. The optimal CTP parameters to identify the ische...
Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.
Magnetic resonance imaging
May 3, 2019
PURPOSE: To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and deep learning based on pre-treatment MRI and a mid-radiation follow-up MRI taken 3-4 weeks after the ...
High-field mr diffusion-weighted image denoising using a joint denoising convolutional neural network.
Journal of magnetic resonance imaging : JMRI
Apr 22, 2019
BACKGROUND: Low signal-to-noise ratio (SNR) has been a major limiting factor for the application of higher-resolution diffusion-weighted imaging (DWI). Most of the conventional denoising models suffer from the drawbacks of shallow feature extraction ...
Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.
IEEE transactions on medical imaging
Apr 15, 2019
Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampl...
A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.
Scientific reports
Apr 11, 2019
This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the f...
Diagnosis, Computer-Assisted
Adolescent
Graft Survival
Glomerular Filtration Rate
Adult
Male
Middle Aged
Postoperative Complications
Diffusion Magnetic Resonance Imaging
Kidney Function Tests
Follow-Up Studies
Graft Rejection
Image Interpretation, Computer-Assisted
Neural Networks, Computer
Algorithms
Humans
Kidney Transplantation
Prognosis
Female
Risk Factors
Aged
Young Adult
Prostate cancer detection using residual networks.
International journal of computer assisted radiology and surgery
Apr 10, 2019
PURPOSE: To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI).
A deep learning approach to estimation of subject-level bias and variance in high angular resolution diffusion imaging.
Magnetic resonance imaging
Mar 26, 2019
The ability to evaluate empirical diffusion MRI acquisitions for quality and to correct the resulting imaging metrics allows for improved inference and increased replicability. Previous work has shown promise for estimation of bias and variance of ge...
MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features.
IEEE transactions on medical imaging
Mar 18, 2019
In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature...
Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks.
Medical physics
Feb 14, 2019
PURPOSE: The purpose of this study was to develop a neural network that accurately performs diffusion tensor imaging (DTI) reconstruction from highly accelerated scans.
Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features.
Academic radiology
Jan 17, 2019
RATIONALE AND OBJECTIVE: Uterine leiomyomas with high signal intensity on T2-weighted imaging (T2WI) can be difficult to distinguish from sarcomas. This study assessed the feasibility of using machine learning to differentiate uterine sarcomas from l...
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