The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites...
Computer methods and programs in biomedicine
Mar 1, 2023
Magnetic resonance imaging (MRI) has become one of the most powerful imaging techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for application. Reconstruction methods based on compress sensing (CS) have made progre...
The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genet...
BACKGROUND: Mesh-reinforced ventral hernia repair is considered the gold standard treatment for all but the smallest of hernias. Human data on mesh shrinkage in the retrorectus mesh position is lacking. A prospective observational cohort study was pe...
There are considerable interests in automatic stroke lesion segmentation on magnetic resonance (MR) images in the medical imaging field, as stroke is an important cerebrovascular disease. Although deep learning-based models have been proposed for thi...
PURPOSE: To develop a truly calibrationless reconstruction method that derives An Eigenvalue Approach to Autocalibrating Parallel MRI (ESPIRiT) maps from uniformly-undersampled multi-channel MR data by deep learning.
INTRODUCTION: We aimed to develop an artificial intelligence (AI) deep learning algorithm to efficiently estimate placental and fetal volumes from magnetic resonance (MR) scans.
Neuroimaging clinics of North America
Feb 26, 2023
Susceptibility-weighted imaging (SWI) is a MR imaging technique suited to detect structural and microstructural abnormalities in traumatic brain injury (TBI). This review article provide an insight in to the physics principles of SWI and its clinical...
Mathematical operations have long been regarded as a sparse, symbolic process in neuroimaging studies. In contrast, advances in artificial neural networks (ANN) have enabled extracting distributed representations of mathematical operations. Recent ne...
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