The arterial input function is crucial in pharmacokinetic analysis of dynamic contrast-enhanced MRI data. Among other artifacts in arterial input function quantification, the blood inflow effect and nonideal radiofrequency spoiling can induce large measurement errors with subsequent reduction of accuracy in the pharmacokinetic parameters. These errors were investigated for a 3D spoiled gradient-echo sequence using a pulsatile flow phantom and a total of 144 typical imaging settings. In the presence of large inflow effects, results showed poor average accuracy and large spread between imaging settings, when the standard spoiled gradient-echo signal equation was used in the analysis. For example, one of the investigated inflow conditions resulted in a mean error of about 40% and a spread, given by the coefficient of variation, of 20% for K trans . Minimizing inflow effects by appropriate slice placement, combined with compensation for nonideal radiofrequency spoiling, significantly improved the results, but they remained poorer than without flow (e.g., 3-4 times larger coefficient of variation for K trans ). It was concluded that the 3D spoiled gradient-echo sequence is not optimal for accurate arterial input function quantification and that correction for nonideal radiofrequency spoiling in combination with inflow minimizing slice placement should be used to reduce the errors. Magn Reson Med 65:1670-1679, 2011. V C 2011 Wiley-Liss, Inc.Key words: dynamic contrast-enhanced MRI; arterial input function; blood flow effects; RF spoiling Dynamic contrast-enhanced MRI (DCE-MRI) is a technique based on the acquisition of a series of images before, during, and after intravenous administration of contrast agent (CA). CA concentration curves can be derived from the images, and tissue specific quantitative pharmacokinetic parameters can subsequently be obtained by appropriate modeling (1).This technique has many applications, for example, in clinical oncology. Biomarkers for drug efficacy and clinical outcome, derived from DCE-MRI, can potentially increase cost efficiency and thus facilitate early phase clinical trials of antiangiogenic and vascular disrupting agents. In the clinical setting, DCE-MRI can provide noninvasive tumor grading as well as predict treatment outcome (2,3).In general, the data acquisition of quantitative DCE-MRI consists of two steps. First, baseline T 1 is quantified, typically by using a gradient echo (GRE) variable flip angle (FA) method (4). After that, a dynamic scan is performed in which the first few images are acquired before the injection of CA (i.e., the baseline signal), and the remaining images are acquired during and up to several minutes after the injection. Then, the T 1 relaxation time is estimated from baseline and dynamic scan images. Under the assumption of the fast exchange limit (5), one can estimate the concentration of CA from a linear relationship between the concentration and the T 1 relaxation rate provided that the relaxivity of the CA is known. The relaxivity is typically ...

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Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a promising tool in the evaluation of tumor physiology. From rapidly acquired images and a model for contrast agent pharmacokinetics, physiological parameters are derived. One pharmacokinetic model, the tissue homogeneity model, enables estimation of both blood flow and vessel permeability together with parameters that describe blood volume and extracellular extravascular volume fraction. However, studies have shown that parameter estimation with this model is unstable. Therefore, several initial guesses are needed for accurate estimates, which makes the estimation slow. In this study a new estimation algorithm for the tissue homogeneity model, based on Fourier domain calculations, was derived and implemented as a Matlab program. The algorithm was tested with Monte-Carlo simulations and the results were compared to an existing method that uses the adiabatic approximation. The algorithm was also tested on data from a metastasis in the brain. The comparison showed that the new algorithm gave more accurate results on the 2.5th and 97.5th percentile levels, for instance the error in blood volume was reduced by 21%. In addition, the time needed for the computations was reduced with a factor 25. It was concluded that the new algorithm can be used to speed up parameter estimation while accuracy can be gained at the same time.

Intermittent disturbances are common in ECG signals recorded with smart clothing: this is mainly because of displacement of the electrodes over the skin. We evaluated a novel adaptive method for spatio-temporal filtering for heartbeat detection in noisy multi-channel ECGs including short signal interruptions in single channels. Using multi-channel database recordings (12-channel ECGs from 10 healthy subjects), the results showed that multi-channel spatio-temporal filtering outperformed regular independent component analysis. We also recorded seven channels of ECG using a T-shirt with textile electrodes. Ten healthy subjects performed different sequences during a 10-min recording: resting, standing, flexing breast muscles, walking and pushups. Using adaptive multi-channel filtering, the sensitivity and precision was above 97% in nine subjects. Adaptive multi-channel spatio-temporal filtering can be used to detect heartbeats in ECGs with high noise levels. One application is heartbeat detection in noisy ECG recordings obtained by integrated textile electrodes in smart clothing.