AIMC Topic: Neuroimaging

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Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis.

IEEE journal of biomedical and health informatics
Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities t...

Deep learning only by normal brain PET identify unheralded brain anomalies.

EBioMedicine
BACKGROUND: Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a mo...

Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network.

Magnetic resonance imaging
For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T-weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE fo...

Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain F-FDG PET.

Physics in medicine and biology
Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diag...

3D whole brain segmentation using spatially localized atlas network tiles.

NeuroImage
Detailed whole brain segmentation is an essential quantitative technique in medical image analysis, which provides a non-invasive way of measuring brain regions from a clinical acquired structural magnetic resonance imaging (MRI). Recently, deep conv...

Hippocampus Segmentation Based on Iterative Local Linear Mapping With Representative and Local Structure-Preserved Feature Embedding.

IEEE transactions on medical imaging
Hippocampus segmentation plays a significant role in mental disease diagnoses, such as Alzheimer's disease, epilepsy, and so on. Patch-based multi-atlas segmentation (PBMAS) approach is a popular method for hippocampus segmentation and has achieved a...

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.

NeuroImage
Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources ...

Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges.

IEEE reviews in biomedical engineering
Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever-continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented...

Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI.

NeuroImage. Clinical
Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Curre...