Low rank off-resonance correction for double half-echo k-space acquisitions.

The present study describes a model-based approach for correcting off-resonance in the context of double half-echo k-space acquisitions. This technique employs center-out readouts in forward and reverse directions to reduce echo-times but is sensitive to off-resonance, which manifests as pixel shifts in both directions. Demodulating the k-space signal with a constant off-resonance term per slice removes pixel shifts and results in a marked reduction in blurring. Phantom and in vivo datasets are demonstrated from low bandwidth sodium imaging.

The epilepsy bioinformatics study for anti-epileptogenic therapy (EpiBioS4Rx) clinical biomarker: Study design and protocol.

The Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy (EpiBioS4Rx) is a longitudinal prospective observational study funded by the National Institute of Health (NIH) to discover and validate observational biomarkers of epileptogenesis after traumatic brain injury (TBI). A multidisciplinary approach has been incorporated to investigate acute electrical, neuroanatomical, and blood biomarkers after TBI that may predict the development of post-traumatic epilepsy (PTE). We plan to enroll 300 moderate-severe TBI patients with a frontal and/or temporal lobe hemorrhagic contusion. Acute evaluation with blood, imaging and electroencephalographic monitoring will be performed and then patients will be tracked for 2 years to determine the incidence of PTE. Validation of selected biomarkers that are discovered in planned animal models will be a principal feature of this work. Specific hypotheses regarding the discovery of biomarkers have been set forth in this study. An international cohort of 13 centers spanning 2 continents will be developed to facilitate this study, and for future interventional studies.

Response Assessment in Neuro-Oncology working group and European Association for Neuro-Oncology recommendations for the clinical use of PET imaging in gliomas.

This guideline provides recommendations for the use of PET imaging in gliomas. The review examines established clinical benefit in glioma patients of PET using glucose ((18)F-FDG) and amino acid tracers ((11)C-MET, (18)F-FET, and (18)F-FDOPA). An increasing number of studies have been published on PET imaging in the setting of diagnosis, biopsy, and resection as well radiotherapy planning, treatment monitoring, and response assessment. Recommendations are based on evidence generated from studies which validated PET findings by histology or clinical course. This guideline emphasizes the clinical value of PET imaging with superiority of amino acid PET over glucose PET and provides a framework for the use of PET to assist in the management of patients with gliomas.

Metabolomic analysis of cerebral spinal fluid from patients with severe brain injury.

Proton nuclear magnetic resonance (H-NMR) spectroscopic analysis of cerebral spinal fluid provides a quick, non-invasive modality for evaluating the metabolic activity of brain-injured patients. In a prospective study, we compared the CSF of 44 TBI patients and 13 non-injured control subjects. CSF was screened for ten parameters: ß-glucose (Glu), lactate (Lac), propylene glycol (PG), glutamine (Gln), alanine (Ala), α-glucose (A-Glu), pyruvate (PYR), creatine (Cr), creatinine (Crt), and acetate (Ace). Using mixed effects measures, we discovered statistically significant differences between control and trauma concentrations (mM). TBI patients had significantly higher concentrations of PG, while statistical trends existed for lactate, glutamine, and creatine. TBI patients had a significantly decreased concentration of total creatinine. There were no significant differences between TBI patients and non-injured controls regarding ß- or α-glucose, alanine, pyruvate or acetate. Correlational analysis between metabolites revealed that the strongest significant correlations in non-injured subjects were between ß- and α-glucose (r = 0.74), creatinine and pyruvate (r = 0.74), alanine and creatine (r = 0.62), and glutamine and α-glucose (r = 0.60). For TBI patients, the strongest significant correlations were between lactate and α-glucose (r = 0.54), lactate and alanine (r = 0.53), and α-glucose and alanine (r = 0.48). The GLM and multimodel inference indicated that the combined metabolites of PG, glutamine, α-glucose, and creatinine were the strongest predictors for CMRO2, ICP, and GOSe. By analyzing the CSF of patients with TBI, our goal was to create a metabolomic fingerprint for brain injury.