Smart healthcare is a framework that utilizes technologies such as wearable devices, the
Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless …

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

Owing to the limitations of imaging sensors, it is challenging to obtain a medical image that
simultaneously contains functional metabolic information and structural tissue details …

For the last decade, it has been shown that neuroimaging can be a potential tool for the
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …

Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing
diagnostic quality by using deep learning methods. Materials and Methods Forty data sets …

After a century of false hopes, recent studies have placed the concept of diaschisis at the
centre of the understanding of brain function. Originally, the term 'diaschisis' was coined by …

Over the past three decades, neuroimaging studies—including structural, functional and
molecular modalities—have provided invaluable insights into the mechanisms underlying …

Alzheimer's disease (AD) is a neurodegenerative disease causing progressive cognitive
decline until eventual death. AD affects millions of individuals worldwide in the absence of …

Background ¹⁸F‐FDFG uptake by brain tissue as measured by positron emission
tomography (PET) is a well‐established method for assessment of brain function in people …

Multimodality imaging has made great strides in the imaging evaluation of patients with a
variety of diseases. Positron emission tomography/computed tomography (PET/CT) is now …