AIMC Topic: Neuroimaging

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DeepTOFSino: A deep learning model for synthesizing full-dose time-of-flight bin sinograms from their corresponding low-dose sinograms.

NeuroImage
PURPOSE: Reducing the injected activity and/or the scanning time is a desirable goal to minimize radiation exposure and maximize patients' comfort. To achieve this goal, we developed a deep neural network (DNN) model for synthesizing full-dose (FD) t...

Recycling diagnostic MRI for empowering brain morphometric research - Critical & practical assessment on learning-based image super-resolution.

NeuroImage
Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution (SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution isotropic data, which would be of great significance for brain res...

3D hemisphere-based convolutional neural network for whole-brain MRI segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Whole-brain segmentation is a crucial pre-processing step for many neuroimaging analyses pipelines. Accurate and efficient whole-brain segmentations are important for many neuroimage analysis tasks to provide clinically relevant information. Several ...

Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease.

Neuroscience letters
Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI) based CAD methods can help reveal structural changes in brain. Classifier is a key component in CAD...

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Computers in biology and medicine
Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation pro...

White matter hyperintensities segmentation using an ensemble of neural networks.

Human brain mapping
White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network ...

A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease.

Human brain mapping
Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer's disease (AD) and incipient dementia. Recently, many sMRI-based deep learning methods have been developed for AD diagnosis. Some of these met...

Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review.

World neurosurgery
OBJECTIVE: Artificial intelligence (AI) has facilitated the analysis of medical imaging given increased computational capacity and medical data availability in recent years. Although many applications for AI in the imaging of brain tumors have been p...

Deep learning based multiplexed sensitivity-encoding (DL-MUSE) for high-resolution multi-shot DWI.

NeuroImage
PURPOSE: A phase correction method for high-resolution multi-shot (MSH) diffusion weighted imaging (DWI) is proposed. The efficacy and generalization capability of the method were validated on both healthy volunteers and patients.

Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.

Computational and mathematical methods in medicine
Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine lear...