11,020 research outputs found
Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases
Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine
Advanced Magnetic Resonance Imaging in Glioblastoma: A Review
INTRODUCTION
In 2017, it is estimated that 26,070 patients will be diagnosed with a malignant primary brain tumor in the United States, with more than half having the diagnosis of glioblas- toma (GBM).1 Magnetic resonance imaging (MRI) is a widely utilized examination in the diagnosis and post-treatment management of patients with glioblastoma; standard modalities available from any clinical MRI scanner, including T1, T2, T2-FLAIR, and T1-contrast-enhanced (T1CE) sequences, provide critical clinical information. In the last decade, advanced imaging modalities are increasingly utilized to further charac- terize glioblastomas. These include multi-parametric MRI sequences, such as dynamic contrast enhancement (DCE), dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI), functional imaging, and spectroscopy (MRS), to further characterize glioblastomas, and significant efforts are ongoing to implement these advanced imaging modalities into improved clinical workflows and personalized therapy approaches. A contemporary review of standard and advanced MR imaging in clinical neuro-oncologic practice is presented
Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training
Multiple sclerosis (MS) is a demyelinating disease of the central nervous
system (CNS). A reliable measure of the tissue myelin content is therefore
essential for the understanding of the physiopathology of MS, tracking
progression and assessing treatment efficacy. Positron emission tomography
(PET) with [^{11} \mbox{C}] \mbox{PIB} has been proposed as a promising
biomarker for measuring myelin content changes in-vivo in MS. However, PET
imaging is expensive and invasive due to the injection of a radioactive tracer.
On the contrary, magnetic resonance imaging (MRI) is a non-invasive, widely
available technique, but existing MRI sequences do not provide, to date, a
reliable, specific, or direct marker of either demyelination or remyelination.
In this work, we therefore propose Sketcher-Refiner Generative Adversarial
Networks (GANs) with specifically designed adversarial loss functions to
predict the PET-derived myelin content map from a combination of MRI
modalities. The prediction problem is solved by a sketch-refinement process in
which the sketcher generates the preliminary anatomical and physiological
information and the refiner refines and generates images reflecting the tissue
myelin content in the human brain. We evaluated the ability of our method to
predict myelin content at both global and voxel-wise levels. The evaluation
results show that the demyelination in lesion regions and myelin content in
normal-appearing white matter (NAWM) can be well predicted by our method. The
method has the potential to become a useful tool for clinical management of
patients with MS.Comment: Accepted by MICCAI201
- …