AIMC Topic: Magnetic Resonance Imaging

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Ensemble based on static classifier selection for automated diagnosis of Mild Cognitive Impairment.

Journal of neuroscience methods
BACKGROUND: Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Scientific research is very active in the challenge of designing automated approaches to achieve an early and certain diagnosis. Re...

Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias.

NeuroImage
OBJECTIVE: Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identifica...

Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in which bone ...

MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.

NeuroImage. Clinical
BACKGROUND: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images.

Neurobiological support to the diagnosis of ADHD in stimulant-naïve adults: pattern recognition analyses of MRI data.

Acta psychiatrica Scandinavica
OBJECTIVE: In adulthood, the diagnosis of attention-deficit/hyperactivity disorder (ADHD) has been subject of recent controversy. We searched for a neuroanatomical signature associated with ADHD spectrum symptoms in adults by applying, for the first ...

A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field.

Computer assisted surgery (Abingdon, England)
BACKGROUND AND OBJECTIVE: Image segmentation is a preliminary and fundamental step in computer aided magnetic resonance imaging (MRI) images analysis. But the performance of most current image segmentation methods is easily depreciated by noise in MR...

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

NeuroImage
UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound an...

Automatic labeling of MR brain images through extensible learning and atlas forests.

Medical physics
PURPOSE: Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about n...

A parallel MR imaging method using multilayer perceptron.

Medical physics
PURPOSE: To reconstruct MR images from subsampled data, we propose a fast reconstruction method using the multilayer perceptron (MLP) algorithm.

Learning-based deformable registration for infant MRI by integrating random forest with auto-context model.

Medical physics
PURPOSE: Accurately analyzing the rapid structural evolution of human brain in the first year of life is a key step in early brain development studies, which requires accurate deformable image registration. However, due to (a) dynamic appearance and ...