AIMC Topic: Magnetic Resonance Imaging

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Deep Learning Enables 60% Accelerated Volumetric Brain MRI While Preserving Quantitative Performance: A Prospective, Multicenter, Multireader Trial.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: In this prospective, multicenter, multireader study, we evaluated the impact on both image quality and quantitative image-analysis consistency of 60% accelerated volumetric MR imaging sequences processed with a commercially av...

Domain- and task-specific transfer learning for medical segmentation tasks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Transfer learning is a valuable approach to perform medical image segmentation in settings with limited cases available for training convolutional neural networks (CNN). Both the source task and the source domain influence ...

Left ventricular non-compaction cardiomyopathy automatic diagnosis using a deep learning approach.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy characterised by a thick and spongy left ventricle wall caused by the high presence of trabeculae (hyper-trabeculation). Recently, the percentage of the tr...

Implementation and prospective clinical validation of AI-based planning and shimming techniques in cardiac MRI.

Medical physics
PURPOSE: Cardiovascular magnetic resonance (CMR) is a vital diagnostic tool in the management of cardiovascular diseases. The advent of advanced CMR technologies combined with artificial intelligence (AI) has the potential to simplify imaging, reduce...

Comparison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation.

BMC medical informatics and decision making
INTRODUCTION AND GOAL TO BACKGROUND: Due to the importance of segmentation of MRI images in identifying brain tumors, various methods including deep learning have been introduced for automatic brain tumor segmentation. On the other hand, using a comb...

A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer's disease.

Computers in biology and medicine
Ageing is associated with various ailments including Alzheimer 's disease (AD), which is a progressive form of dementia. AD symptoms develop over a period of years and, unfortunately, there is no cure. Existing AD treatments can only slow down the pr...

Automatic quadriceps and patellae segmentation of MRI with cascaded U -Net and SASSNet deep learning model.

Medical physics
PURPOSE: Automatic muscle segmentation is critical for advancing our understanding of human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely exploration of large multi-dimensional image sets. Segmentation models are ...

Deep learning based pipelines for Alzheimer's disease diagnosis: A comparative study and a novel deep-ensemble method.

Computers in biology and medicine
BACKGROUND: Alzheimer's disease is a chronic neurodegenerative disease that destroys brain cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are not yet fully understood, and there is no curative treatment. Howe...

Deep learning-based diagnosis of temporal lobe epilepsy associated with hippocampal sclerosis: An MRI study.

Epilepsy research
PURPOSE: The currently available indicators-sensitivity and specificity of expert radiological evaluation of MRIs-to identify mesial temporal lobe epilepsy (MTLE) associated with hippocampal sclerosis (HS) are deficient, as they cannot be easily asse...