Convolutional neural networks (CNNs) constitute a widely used deep learning approach that
has frequently been applied to the problem of brain tumor diagnosis. Such techniques still …

Management of brain tumors is based on clinical and radiological information with
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …

The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25
years. In this review, we dwell on the various challenges along the journey of bringing a …

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in
clinical practice and in clinical trials, to diagnose and monitor disease activity, support …

Humans are exposed to a complex mix of man-made electric and magnetic fields (MFs) at
many different frequencies, at home and at work. Epidemiological studies indicate that there …

Objective Summarize evidence for use of advanced MRI techniques as monitoring
biomarkers in the clinic, and highlight the latest bench-to-bedside developments. Methods …

Cancer cells differ in size from those of their host tissue and are known to change in size
during the processes of cell death. A noninvasive method for monitoring cell size would be …

Glioblastoma is the most common malignant primary brain tumor with poor overall survival.
Magnetic resonance imaging (MRI) is the main imaging modality for glioblastoma but has …

Simple Summary Advanced neuroimaging is gaining increasing relevance for the
characterization and the molecular profiling of brain tumor tissue. On one hand, for some …

Purpose We hypothesized that the time‐dependent diffusivity at short diffusion times, as
measured by oscillating gradient spin echo (OGSE) diffusion MRI, can characterize tissue …