AIMC Topic: Neuroimaging

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Characterization of clot composition in acute cerebral infarct using machine learning techniques.

Annals of clinical and translational neurology
OBJECTIVE: Clot characteristics can provide information on the cause of cerebral artery occlusion and may guide acute revascularization and secondary prevention strategies. We developed a rapid automated clot analysis system using machine learning (M...

Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a tran...

Random forest prediction of Alzheimer's disease using pairwise selection from time series data.

PloS one
Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing...

RNN-based longitudinal analysis for diagnosis of Alzheimer's disease.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and other mental functions. Magnetic resonance images (MRI) have been widely used as an important imaging modality of brain for AD diagnosis ...

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.

NeuroImage
Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to pr...

Harnessing networks and machine learning in neuropsychiatric care.

Current opinion in neurobiology
The development of next-generation therapies for neuropsychiatric illness will likely rely on a precise and accurate understanding of human brain dynamics. Toward this end, researchers have focused on collecting large quantities of neuroimaging data....

Using neuroimaging to predict relapse in stimulant dependence: A comparison of linear and machine learning models.

NeuroImage. Clinical
OBJECTIVE: Relapse rates are consistently high for stimulant user disorders. In order to obtain prognostic information about individuals in treatment, machine learning models have been applied to neuroimaging and clinical data. Yet few efforts have b...

Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversa...

Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data.

NeuroImage
Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality ...

A Deep Learning-Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: MR imaging rescans and recalls can create large hospital revenue loss. The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series.