AIMC Topic: Magnetic Resonance Imaging

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Neural Mechanisms for Accepting and Rejecting Artificial Social Partners in the Uncanny Valley.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Artificial agents are becoming prevalent across human life domains. However, the neural mechanisms underlying human responses to these new, artificial social partners remain unclear. The uncanny valley (UV) hypothesis predicts that humans prefer anth...

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain.

Journal of visualized experiments : JoVE
Mapping the motor cortex with transcranial magnetic stimulation (TMS) has potential to interrogate motor cortex physiology and plasticity but carries unique challenges in children. Similarly, transcranial direct current stimulation (tDCS) can improve...

SmartPulse, a machine learning approach for calibration-free dynamic RF shimming: Preliminary study in a clinical environment.

Magnetic resonance in medicine
PURPOSE: A calibration-free pulse design method is introduced to alleviate artifacts in clinical routine with parallel transmission at high field, dealing with significant inter-subject variability, found for instance in the abdomen.

Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning.

European journal of nuclear medicine and molecular imaging
INTRODUCTION: Recently there have been significant advances in the field of machine learning and artificial intelligence (AI) centered around imaging-based applications such as computer vision. In particular, the tremendous power of deep learning alg...

Brain tumor classification using deep CNN features via transfer learning.

Computers in biology and medicine
Brain tumor classification is an important problem in computer-aided diagnosis (CAD) for medical applications. This paper focuses on a 3-class classification problem to differentiate among glioma, meningioma and pituitary tumors, which form three pro...

A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging.

Journal of neuroscience methods
BACKGROUND: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve high prediction accuracy on neural signals, visual encoding models shoul...

Anatomical context improves deep learning on the brain age estimation task.

Magnetic resonance imaging
Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network desig...

A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm.

Human brain mapping
Imaging studies have characterized functional and structural brain abnormalities in adults after premature birth, but these investigations have mostly used univariate methods that do not account for hypothesized interdependencies between brain region...

MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

European journal of radiology
PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with ...

3D-CNN based discrimination of schizophrenia using resting-state fMRI.

Artificial intelligence in medicine
MOTIVATION: This study reports a framework to discriminate patients with schizophrenia and normal healthy control subjects, based on magnetic resonance imaging (MRI) of the brain. Resting-state functional MRI data from a total of 144 subjects (72 pat...