AIMC Topic: Magnetic Resonance Imaging

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Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning.

NeuroImage
Reconstructing perceived stimulus (image) only from human brain activity measured with functional Magnetic Resonance Imaging (fMRI) is a significant task in brain decoding. However, the inconsistent distribution and representation between fMRI signal...

Small Steatotic HCC: A Radiological Variant Associated With Improved Outcome After Ablation.

Hepatology communications
Percutaneous thermal ablation is a validated treatment option for small hepatocellular carcinoma (HCC). Steatotic HCC can be reliably detected by magnetic resonance imaging. To determine the clinical relevance of this radiological variant, we include...

Computer-Aided Diagnosis of Alzheimer's Disease through Weak Supervision Deep Learning Framework with Attention Mechanism.

Sensors (Basel, Switzerland)
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. H...

Machine learning-based multimodal prediction of language outcomes in chronic aphasia.

Human brain mapping
Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following this line of work, the current study uses machine learning approaches to predict aphasia severity and ...

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

NeuroImage
Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-site multi-scanner datasets. Combining the data from the different scanners is vital for increased statistical power; however, this leads to an increase ...

Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Brain extraction is a fundamental prerequisite step in neuroimage analysis for fetus. Due to surrounding maternal tissues and unpredictable movement, brain extraction from fetal Magnetic Resonance (MR) images is a challenging task. In this paper, we ...

Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution.

IEEE transactions on medical imaging
Time-resolved MR angiography (tMRA) has been widely used for dynamic contrast enhanced MRI (DCE-MRI) due to its highly accelerated acquisition. In tMRA, the periphery of the k -space data are sparsely sampled so that neighbouring frames can be merged...

Wasserstein GANs for MR Imaging: From Paired to Unpaired Training.

IEEE transactions on medical imaging
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are und...

Ultrashort echo time time-spatial labeling inversion pulse magnetic resonance angiography with denoising deep learning reconstruction for the assessment of abdominal visceral arteries.

Journal of magnetic resonance imaging : JMRI
Current contrast-enhanced magnetic resonance angiography (MRA) and non-contrast-enhanced balanced steady-state free precession (bSSFP) MRA cause susceptibility artifacts from metallic devices in assessing endovascular visceral-artery interventions. T...