AIMC Topic: Magnetic Resonance Imaging
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Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model.
Radiology
May 25, 2021
Background Group comparison results associating cortical thinning and Parkinson disease (PD) dementia (PDD) are limited in their application to clinical settings. Purpose To investigate whether cortical thickness from MRI can help predict conversion ...
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation.
Medical physics
May 25, 2021
PURPOSE: Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reli...
Representational Content of Oscillatory Brain Activity during Object Recognition: Contrasting Cortical and Deep Neural Network Hierarchies.
eNeuro
May 25, 2021
Numerous theories propose a key role for brain oscillations in visual perception. Most of these theories postulate that sensory information is encoded in specific oscillatory components (e.g., power or phase) of specific frequency bands. These theori...
Robot-Assisted Stereotactic Shunting as a Novel Treatment for Pontine Glioependymal Cysts.
Journal of neurological surgery. Part A, Central European neurosurgery
May 24, 2021
In this case report, the authors describe the first case of a glioependymal cyst of the brainstem managed by robot-assisted, stereotactic, cysto-ventricular shunting. Glioependymal cysts are rare congenital cystic lesions that are thought to form by ...
Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
May 24, 2021
Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This...
Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning.
Nature communications
May 24, 2021
Commonly used for Parkinson's disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here,...
Deep learning for whole-body medical image generation.
European journal of nuclear medicine and molecular imaging
May 22, 2021
BACKGROUND: Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and tr...
Suppression of artifact-generating echoes in cine DENSE using deep learning.
Magnetic resonance in medicine
May 22, 2021
PURPOSE: To use deep learning for suppression of the artifact-generating T -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time.
Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging.
European radiology
May 20, 2021
OBJECTIVES: Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether RE has brain developmental delay. This study employed deep learning-based neuroanatomic biomarker t...
MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer.
Computational biology and chemistry
May 20, 2021
Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example...
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