Magnetic resonance insights最新文献

Carotid artery atherosclerosis is a major cause of ischemic stroke. For more than 30 years, future stroke risk and carotid stroke etiology have been determined using percent diameter stenosis based on clinical trials in the 1990s. In the past 10 years, magnetic resonance imaging (MRI) sequences have been developed to detect carotid intraplaque hemorrhage. By detecting carotid intraplaque hemorrhage, MRI identifies potential stroke sources that are often overlooked by lumen imaging. In addition, MRI can dramatically improve assessment of future stroke risk beyond lumen stenosis alone. In this review, we discuss the use of heavily T1-weighted MRI sequences used to detect carotid intraplaque hemorrhage. In addition, advances in ciné imaging, motion robust techniques, and specialized neck coils will be reviewed. Finally, the clinical use and future impact of MRI plaque hemorrhage imaging will be discussed.

Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.

Objectives: Lung cancer is one of the most lethal cancers. Currently, there are no biomarkers for early detection, monitoring treatment response, and detecting recurrent lung cancer. We undertook this study to determine if ¹H magnetic resonance spectroscopy (MRS) of sputum and exhaled breath condensate (EBC), as a noninvasive tool, can identify metabolic biomarkers of lung cancer.

Materials and methods: Sputum and EBC samples were collected from 20 patients, comprising patients with pathologically confirmed non-small cell lung cancer (n = 10) and patients with benign respiratory conditions (n = 10). Both sputum and EBC samples were collected from 18 patients; 2 patients provided EBC samples only. ¹H MR spectra were obtained on a Bruker Avance 400 MHz nuclear magnetic resonance (NMR) spectrometer. Sputum samples were further confirmed cytologically to distinguish between true sputum and saliva.

Results: In the EBC samples, median concentrations of propionate, ethanol, acetate, and acetone were higher in lung cancer patients compared to the patients with benign conditions. Median concentration of methanol was lower in lung cancer patients (0.028 mM) than in patients with benign conditions (0.067 mM; P = 0.028). In the combined sputum and saliva and the cytologically confirmed sputum samples, median concentrations of N-acetyl sugars, glycoprotein, propionate, lysine, acetate, and formate were lower in the lung cancer patients than in patients with benign conditions. Glucose was found to be consistently absent in the combined sputum and saliva samples (88%) as well as in the cytologically confirmed sputum samples (86%) of lung cancer patients.

Conclusion: Absence of glucose in sputum and lower concentrations of methanol in EBC of lung cancer patients discerned by ¹H MRS may serve as metabolic biomarkers of lung cancer for early detection, monitoring treatment response, and detecting recurrence.

Purpose: To assess the safety and efficacy of gadoxetate disodium-enhanced liver MR imaging in pediatric patients.

Material and methods: Retrospective, multicenter study including pediatric patients aged >2 months to <18 years who underwent contrast-enhanced liver MRI due to focal liver lesions. A single intravenous bolus injection of 0.025 to 0.05 mmol/kg body weight of gadoxetate disodium was administered. Adverse events (AEs) up to 24 hours after injection were recorded and a one-year follow-up was conducted for all serious and unexpected AEs. Efficacy was defined based on the additional diagnostic information obtained from the combined (pre- and postcontrast) image sets as compared with the precontrast image sets by blinded reading.

Results: A total of 52 patients for safety and 51 patients for efficacy analyses were evaluated. Twenty-two patients (42.3%) reported a total of 51 serious AEs (SAEs) and one AE after one year. No SAE or AE was related to gadoxetate disodium injection. Gadoxetate disodium-related effects on vital signs were not seen. Additional diagnostic information was obtained for 86.3% of patients. The three most improved efficacy variables were lesion-to-background contrast, lesion characterization, and improved border delineation in 78.4%, 76.5%, and 70.6% of patients, respectively.

Conclusion: Gadoxetate disodium in pediatric patients did not raise any clinically significant safety concern. Contrast enhancement provided additional clinically relevant information.

The aim is to investigate the relationship between microstructural white matter (WM) diffusivity indices and macrostructural WM volume (WMV) among healthy individuals (20-85 years). Whole-brain diffusion measures were calculated from diffusion tensor imaging using FMRIB software library while WMV was estimated through voxel-based morphometry, and voxel-based analysis was carried out using tract-based spatial statistics. Our results revealed that mean diffusivity, axial diffusivity, and radial diffusivity had shown good correlation with WMV but not for fractional anisotropy (FA). Voxel-wise tract-based spatial statistics analysis for FA showed a significant decrease in four regions for middle-aged group compared to young-aged group, in 22 regions for old-aged group compared to middle-aged group, and in 26 regions for old-aged group compared to young-aged group (P < 0.05). We found significantly lower WMV, FA, and mean diffusivity values in females than males and inverted-U trend for FA in males. We conclude differential age- and gender-related changes for structural WMV and WM diffusion indices.

With larger data sets and more sophisticated analyses, it is becoming increasingly common for neuroimaging researchers to push (or exceed) the limitations of standalone computer workstations. Nonetheless, although high-performance computing platforms such as clusters, grids and clouds are already in routine use by a small handful of neuroimaging researchers to increase their storage and/or computational power, the adoption of such resources by the broader neuroimaging community remains relatively uncommon. Therefore, the goal of the current manuscript is to: 1) inform prospective users about the similarities and differences between computing clusters, grids and clouds; 2) highlight their main advantages; 3) discuss when it may (and may not) be advisable to use them; 4) review some of their potential problems and barriers to access; and finally 5) give a few practical suggestions for how interested new users can start analyzing their neuroimaging data using cloud resources. Although the aim of cloud computing is to hide most of the complexity of the infrastructure management from end-users, we recognize that this can still be an intimidating area for cognitive neuroscientists, psychologists, neurologists, radiologists, and other neuroimaging researchers lacking a strong computational background. Therefore, with this in mind, we have aimed to provide a basic introduction to cloud computing in general (including some of the basic terminology, computer architectures, infrastructure and service models, etc.), a practical overview of the benefits and drawbacks, and a specific focus on how cloud resources can be used for various neuroimaging applications.

Partial volume effects have the potential to cause inaccuracies when quantifying metabolites using proton magnetic resonance spectroscopy (MRS). In order to correct for cerebrospinal fluid content, a spectroscopic voxel needs to be segmented according to different tissue contents. This article aims to detail how automated partial volume segmentation can be undertaken and provides a software framework for researchers to develop their own tools. While many studies have detailed the impact of partial volume correction on proton magnetic resonance spectroscopy quantification, there is a paucity of literature explaining how voxel segmentation can be achieved using freely available neuroimaging packages.