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Magic-angle spinning (MAS) has recently been shown to enhance spectral resolution in NMR examinations of intact biological tissue ex vivo. This work demonstrates that freezing certain tissue samples before examination by 1H MAS NMR can have a marked effect on their spectra. Spectra of rat kidney after freezing in liquid nitrogen, compared with spectra before freezing, showed a significant increase in signal intensities from alanine (>100%), glutamine (>40%), and glycine (>100%), and a decrease in signals assigned to lipids and other macromolecules. Some resonances--such as from leucine, valine, isoleucine, and aspartate--only became visible after freezing the tissue. These observations suggest that low temperature storage of tissue necropsies or biopsies might affect the results of a MAS NMR analysis, possibly resulting in the misinterpretation of metabolite changes to pathogen or disease effects.

When using tracer kinetic modeling to analyze dynamic contrast-enhanced MRI (DCE-MRI) it is necessary to identify an appropriate arterial input function (AIF). The measured AIF is often poorly sampled in both clinical and preclinical MR systems due to the initial rapid increase in contrast agent concentration and the subsequent large-scale signal change that occurs in the arteries. However, little work has been carried out to quantify the sensitivity of tracer kinetic modeling parameters to the form of AIF. Using a preclinical experimental data set, we sought to measure the effect of varying model forms of AIF on the extended Kety compartmental model parameters (K(trans), v(e), and v(p)) through comparison with the results of experimentally acquired high temporal resolution AIFs. The AIF models examined have the potential to be parameterized on lower temporal resolution data to predict the form of the true, higher temporal resolution AIF. The models were also evaluated through application to the population average AIF. It was concluded that, in the instance of low temporal resolution or noisy data, it may be preferable to use a bi-exponential model applied to the raw data AIF, or when individual measurements are not available a bi-exponential model of the average AIF.