In vivo magnetic resonance imaging of glucose - initial experience

A new noninvasive, nonradioactive approach for glucose imaging using spin hyperpolarization technology and stable isotope labeling is presented. A glucose analog labeled with 13C at all six positions increased the overall hyperpolarized imaging signal; deuteration at all seven directly bonded proton positions prolonged the spin-lattice relaxation time. High-bandwidth 13C imaging overcame the large glucose carbon chemical shift dispersion. Hyperpolarized glucose images in the live rat showed time-dependent organ distribution patterns. At 8s after the start of bolus injection, the inferior vena cava was demonstrated at angiographic quality. Distribution of hyperpolarized glucose in the kidneys, vasculature, and heart was demonstrated at 12 and 20s. The heart-to-vasculature intensity ratio at 20s suggests myocardial uptake. Cancer imaging, currently performed with 18F-deoxyglucose positron emission tomography (FDG-PET), warrants further investigation, and glucose imaging could be useful in a vast range of clinical conditions and research fields where the radiation associated with the FDG-PET examination limits its use. © 2012 John Wiley & Sons, Ltd

Mutations in the Fatty Acid 2-Hydroxylase Gene Are Associated with Leukodystrophy with Spastic Paraparesis and Dystonia

Myelination is a complex, developmentally regulated process whereby myelin proteins and lipids are coordinately expressed by myelinating glial cells. Homozygosity mapping in nine patients with childhood onset spasticity, dystonia, cognitive dysfunction, and periventricular white matter disease revealed inactivating mutations in the FA2H gene. FA2H encodes the enzyme fatty acid 2-hydroxylase that catalyzes the 2-hydroxylation of myelin galactolipids, galactosylceramide, and its sulfated form, sulfatide. To our knowledge, this is the first identified deficiency of a lipid component of myelin and the clinical phenotype underscores the importance of the 2-hydroxylation of galactolipids for myelin maturation. In patients with autosomal-recessive unclassified leukodystrophy or complex spastic paraparesis, sequence analysis of the FA2H gene is warranted

Fibrous dysplasia in combination with aneurysmal bone cyst of the occipital bone and the clivus: case report and review of the literature

Fibrous dysplasia of the cranium is a relatively uncommon disorder that affects primarily the anterior cranial region; its occurrence in the cranial base in combination with aneurysmal bone cyst (ABC) constitutes an extremely rare condition, only two cases of which have been reported previously in the literature. It is important to recognize and treat these cases properly because of the special location in the cranial base and the possibility of neural structure impingement. We report the case of a 19-year-old man with a slowly enlarging mass of the occiput, with computed tomographic and magnetic resonance imaging revealing involvement of petrous and basisphenoid bone and growing ABC. Open biopsy confirmed the diagnosis of fibrous dysplasia. Partial excision of the lesion and removal of the ABC were performed in a second stage after embolization. ABC associated with fibrous dysplasia of the cranial base may enlarge rapidly after puberty and require excision. This is facilitated by preoperative embolization

Atlas Guided Identification . . .

This study presents a novel automatic approach for the identification of anatomical brain structures in magnetic resonance images (MRI). The method combines a fast multiscale multi-channel three dimensional (3D) segmentation algorithm providing a rich feature vocabulary together with a support vector machine (SVM) based classifier. The segmentation produces a full hierarchy of segments, expressed by an irregular pyramid with only linear time complexity. The pyramid provides a rich, adaptive representation of the image, enabling detection of various anatomical structures at different scales. A key aspect of the approach is the thorough set of multiscale measures employed throughout the segmentation process which are also provided at its end for clinical analysis. These features include in particular the prior probability knowledge of anatomic structures due to the use of an MRI probabilistic atlas. An SVM classifier is trained based on this set of features to identify the brain structures. We validated the approach using a gold standard real brain MRI data set. Comparison of the results with existing algorithms displays the promise of our approach