AIMC Topic: Diffusion Magnetic Resonance Imaging
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High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach.
European radiology
Apr 23, 2020
OBJECTIVE: To assess if adding perfusion information from dynamic contrast-enhanced (DCE MRI) acquisition schemes with high spatiotemporal resolution to T2w/DWI sequences as input features for a gradient boosting machine (GBM) machine learning (ML) c...
Machine learning volumetry of ischemic brain lesions on CT after thrombectomy-prospective diagnostic accuracy study in ischemic stroke patients.
Neuroradiology
Apr 22, 2020
PURPOSE: Ischemic lesion volume (ILV) is an important radiological predictor of functional outcome in patients with anterior circulation stroke. Our aim was to assess the agreement between automated ILV measurements on NCCT using the Brainomix softwa...
Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions.
Annals of clinical and translational neurology
Apr 18, 2020
OBJECTIVE: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterog...
Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model.
NeuroImage
Apr 9, 2020
BACKGROUND AND AIMS: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 ...
Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.
Physics in medicine and biology
Apr 2, 2020
Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the handcrafted (explicitly designed) and deep learning (DL)-based radiomic features extracted from pre-tr...
Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Mar 6, 2020
To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring...
Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram.
European radiology
Feb 17, 2020
PURPOSE: To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC).
Adult
Uterine Cervical Neoplasms
Middle Aged
Nomograms
Support Vector Machine
Retrospective Studies
Hysterectomy
Humans
Neoplasm Invasiveness
Age Factors
Female
Algorithms
Neoplasm Grading
Informatics
Magnetic Resonance Imaging
Reproducibility of Results
Peritoneum
Diffusion Magnetic Resonance Imaging
Aged
Towards deep learning for connectome mapping: A block decomposition framework.
NeuroImage
Feb 15, 2020
We propose a new framework to map structural connectomes using deep learning and diffusion MRI. We show that our framework not only enables connectome mapping with a convolutional neural network (CNN), but can also be straightforwardly incorporated i...
Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method.
NeuroImage
Jan 29, 2020
Diffusion Magnetic Resonance Imaging (dMRI) has shown great potential in probing tissue microstructure and structural connectivity in the brain but is often limited by the lengthy scan time needed to sample the diffusion profile by acquiring multiple...
Machine Learning Approach to Identify Stroke Within 4.5 Hours.
Stroke
Jan 28, 2020
Background and Purpose- We aimed to investigate the ability of machine learning (ML) techniques analyzing diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging to identify patients within the reco...
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