AIMC Topic: Magnetic Resonance Imaging

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Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropa...

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases.

NeuroImage
Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network de...

Deep learning-based automatic delineation of the hippocampus by MRI: geometric and dosimetric evaluation.

Radiation oncology (London, England)
BACKGROUND: Whole brain radiotherapy (WBRT) can impair patients' cognitive function. Hippocampal avoidance during WBRT can potentially prevent this side effect. However, manually delineating the target area is time-consuming and difficult. Here, we p...

Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data - A systematic review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological con...

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.

Nature communications
Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advant...

Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture.

Physics in medicine and biology
Detection of brain metastases is a paramount task in cancer management due both to the number of high-risk patients and the difficulty of achieving consistent detection. In this study, we aim to improve the accuracy of automated brain metastasis (BM)...

A dual-task dual-domain model for blind MRI reconstruction.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
MRI reconstruction is the key technology to accelerate MR acquisition. Recent cascade models have gained satisfactory results, however, they deeply rely on the known sample mask, which we call it mask prior. To restore the MR image without mask prior...

A deep learning model for diagnosing dystrophinopathies on thigh muscle MRI images.

BMC neurology
BACKGROUND: Dystrophinopathies are the most common type of inherited muscular diseases. Muscle biopsy and genetic tests are effective to diagnose the disease but cost much more than primary hospitals can reach. The more available muscle MRI is promis...

A deep learning framework for F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.

European journal of nuclear medicine and molecular imaging
PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, F-fl...

Recent advances in medical image processing for the evaluation of chronic kidney disease.

Medical image analysis
Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (C...