AIMC Topic: Magnetic Resonance Imaging

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CS-MRI reconstruction based on analysis dictionary learning and manifold structure regularization.

Neural networks : the official journal of the International Neural Network Society
Compressed sensing (CS) significantly accelerates magnetic resonance imaging (MRI) by allowing the exact reconstruction of image from highly undersampling k-space data. In this process, the high sparsity obtained by the learned dictionary and exploit...

BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model.

Medical hypotheses
A brain tumor is a mass that grows unevenly in the brain and directly affects human life. This mass occurs spontaneously because of the tissues surrounding the brain or the skull. Surgical methods are generally preferred for the treatment of the brai...

Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography.

NMR in biomedicine
Magnetic resonance electrical properties tomography (MR-EPT) is a technique used to estimate the conductivity and permittivity of tissues from MR measurements of the transmit magnetic field. Different reconstruction methods are available; however, al...

An Effective MR-Guided CT Network Training for Segmenting Prostate in CT Images.

IEEE journal of biomedical and health informatics
Segmentation of prostate in medical imaging data (e.g., CT, MRI, TRUS) is often considered as a critical yet challenging task for radiotherapy treatment. It is relatively easier to segment prostate from MR images than from CT images, due to better so...

Artificial intelligence in medical imaging.

Magnetic resonance imaging
The medical specialty radiology has experienced a number of extremely important and influential technical developments in the past that have affected how medical imaging is deployed. Artificial intelligence (AI) is potentially another such developmen...

CAD and AI for breast cancer-recent development and challenges.

The British journal of radiology
Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results ca...

A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease.

NeuroImage
Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can delay its progression, no effective cures are available for AD. Accura...

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation.

IEEE transactions on medical imaging
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive archite...

Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning.

Neural plasticity
According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphologi...