2-Deoxy-D-glucose (2DG) is a known surrogate molecule that is useful for inferring glucose uptake and metabolism. Although ¹³C-labeled 2DG can be detected by nuclear magnetic resonance (NMR), its low sensitivity for detection prohibits imaging to be performed. Using chemical exchange saturation transfer (CEST) as a signal-amplification mechanism, 2DG and the phosphorylated 2DG-6-phosphate (2DG6P) can be indirectly detected in ¹H magnetic resonance imaging (MRI). We showed that the CEST signal changed with 2DG concentration, and was reduced by suppressing cerebral metabolism with increased general anesthetic. The signal changes were not affected by cerebral or plasma pH, and were not correlated with altered cerebral blood flow as demonstrated by hypercapnia; neither were they related to the extracellular glucose amounts as compared with injection of D- and L-glucose. In vivo³¹P NMR revealed similar changes in 2DG6P concentration, suggesting that the CEST signal reflected the rate of glucose assimilation. This method provides a new way to use widely available MRI techniques to image deoxyglucose/glucose uptake and metabolism in vivo without the need for isotopic labeling of the molecules.

Brain extraction is an important preprocessing step for further processing (e.g., registration and morphometric analysis) of brain MRI data. Due to the operator-dependent and time-consuming nature of manual extraction, automated or semi-automated methods are essential for large-scale studies. Automatic methods are widely available for human brain imaging, but they are not optimized for rodent brains and hence may not perform well. To date, little work has been done on rodent brain extraction. We present an extended pulse-coupled neural network algorithm that operates in 3-D on the entire image volume. We evaluated its performance under varying SNR and resolution and tested this method against the brain-surface extractor (BSE) and a level-set algorithm proposed for mouse brain. The results show that this method outperforms existing methods and is robust under low SNR and with partial volume effects at lower resolutions. Together with the advantage of minimal user intervention, this method will facilitate automatic processing of large-scale rodent brain studies.

Conventional model-based or statistical analysis methods for functional MRI (fMRI) suffer from the limitation of the assumed paradigm and biased results. Temporal clustering methods, such as fuzzy clustering, can eliminate these problems but are difficult to find activation occupying a small area, sensitive to noise and initial values, and computationally demanding. To overcome these adversities, a cascade clustering method combining a Kohonen clustering network and fuzzy, means is developed. Receiver operating characteristic (ROC) analysis is used to compare this method with correlation coefficient analysis and t test on a series of testing phantoms. Results shown that this method can efficiently and stably identify the actual functional response with typical signal change to noise ratio, from a small activation area occupying only 0.2% of head size, with phase delay, and from other noise sources such as head motion. With the ability of finding activities of small sizes stably this method can not only identify the functional responses and the active regions more precisely, but also discriminate responses from different signal sources, such as large venous vessels or different types of activation patterns in human studies involving motor cortex activation. Even when the experimental paradigm is unknown in a blind test such that model-based methods are inapplicable, this method can identify the activation patterns and regions correctly.