Yee Kai Tee

Identifying the ischaemic penumbra using pH-weighted magnetic resonance imaging

Harston et al. establish proof of principle for clinical use of pH-weighted MRI in patients with acute ischaemic stroke. Detailed tissue-level analysis reveals that cerebral intracellular pH, a marker of metabolic stress, is associated with eventual tissue outcome, and complements established imaging modalities.

Comparing different analysis methods for quantifying the MRI amide proton transfer (APT) effect in hyperacute stroke patients.

Amide proton transfer (APT) imaging is a pH mapping method based on the chemical exchange saturation transfer phenomenon that has potential for penumbra identification following stroke. The majority of the literature thus far has focused on generating pH‐weighted contrast using magnetization transfer ratio asymmetry analysis instead of quantitative pH mapping. In this study, the widely used asymmetry analysis and a model‐based analysis were both assessed on APT data collected from healthy subjects (n = 2) and hyperacute stroke patients (n = 6, median imaging time after onset = 2 hours 59 minutes). It was found that the model‐based approach was able to quantify the APT effect with the lowest variation in grey and white matter (≤ 13.8 %) and the smallest average contrast between these two tissue types (3.48 %) in the healthy volunteers. The model‐based approach also performed quantitatively better than the other measures in the hyperacute stroke patient APT data, where the quantified APT effect in the infarct core was consistently lower than in the contralateral normal appearing tissue for all the patients recruited, with the group average of the quantified APT effect being 1.5 ± 0.3 % (infarct core) and 1.9 ± 0.4 % (contralateral). Based on the fitted parameters from the model‐based analysis and a previously published pH and amide proton exchange rate relationship, quantitative pH maps for hyperacute stroke patients were generated, for the first time, using APT imaging.

Quantitative Bayesian model-based analysis of amide proton transfer MRI

Amide Proton Transfer (APT) reports on contrast derived from the exchange of protons between amide groups and water. Commonly, APT contrast is quantified by asymmetry analysis, providing an ensemble contrast of both amide proton concentration and exchange rate. An alternative is to sample the off‐resonant spectrum and fit an exchange model, permitting the APT effect to be quantified, correcting automatically for confounding effects of spillover, field inhomogeneity, and magnetization transfer. Additionally, it should permit amide concentration and exchange rate to be independently quantified. Here, a Bayesian method is applied to this problem allowing pertinent prior information to be specified. A three‐pool model was used incorporating water protons, amide protons, and magnetization transfer effect. The method is demonstrated in simulations, creatine phantoms with varying pH and in vivo (n = 7). The Bayesian model‐based approach was able to quantify the APT effect accurately (root‐mean‐square error < 2%) even when subject to confounding field variation and magnetization transfer effect, unlike traditional asymmetry analysis. The in vivo results gave approximate APT concentration (relative to water) and exchange rate values of 3 × 10−3 and 15 s−1. A degree of correlation was observed between these parameter making the latter difficult to quantify with absolute accuracy, suggesting that more optimal sampling strategies might be required. Magn Reson Med 70:556–567, 2013.

Optimal sampling schedule for chemical exchange saturation transfer

The sampling schedule for chemical exchange saturation transfer imaging is normally uniformly distributed across the saturation frequency offsets. When this kind of evenly distributed sampling schedule is used to quantify the chemical exchange saturation transfer effect using model‐based analysis, some of the collected data are minimally informative to the parameters of interest. For example, changes in labile proton exchange rate and concentration mainly affect the magnetization near the resonance frequency of the labile pool. In this study, an optimal sampling schedule was designed for a more accurate quantification of amine proton exchange rate and concentration, and water center frequency shift based on an algorithm previously applied to magnetization transfer and arterial spin labeling. The resulting optimal sampling schedule samples repeatedly around the resonance frequency of the amine pool and also near to the water resonance to maximize the information present within the data for quantitative model‐based analysis. Simulation and experimental results on tissue‐like phantoms showed that greater accuracy and precision (>30% and >46%, respectively, for some cases) were achieved in the parameters of interest when using optimal sampling schedule compared with evenly distributed sampling schedule. Hence, the proposed optimal sampling schedule could replace evenly distributed sampling schedule in chemical exchange saturation transfer imaging to improve the quantification of the chemical exchange saturation transfer effect and parameter estimation. Magn Reson Med 70:1251–1262, 2013.

Quantification of amide proton transfer effect pre- and post-gadolinium contrast agent administration.

To compare quantification of the amide proton transfer (APT) effect pre‐ and post‐gadolinium contrast agent (Gd) administration in order to establish to what extent Gd alters quantification of the APT effect.

Determination of an optimally sensitive and specific chemical exchange saturation transfer MRI quantification metric in relevant biological phantoms

The purpose of this study was to develop realistic phantom models of the intracellular environment of metastatic breast tumour and naïve brain, and using these models determine an analysis metric for quantification of CEST MRI data that is sensitive to only labile proton exchange rate and concentration. The ability of the optimal metric to quantify pH differences in the phantoms was also evaluated.

An Introduction to Brain Tumor Imaging

Brain tumors are among the leading causes of tumor-related deaths globally; hence considerable research effort is being expended to improve the patient outcome. There are now multiple imaging techniques available for the diagnosis and management of brain tumors in clinical practice, as well as different contrast agents. All these coupled with amino acid tracers newly available in positron emission tomography can offer more accurate tumor diagnosis. The evaluation of tumors using multiple imaging modalities is now one of the trends in neuroradiology, where computed tomography (CT), magnetic resonance imaging (MRI) and molecular imaging all play a vital role in the brain tumor assessment.

Ventricular extension of intracerebral hemorrhage during intravenous thrombolysis.

Acknowledgements and Sources of Funding The research was supported by the National Institute for Health Research Oxford Biomedical Research Centre Programme, the Dunhill Medical Trust (grant No. OSRP1/1006) and the Centre of Excellence for Personalized Healthcare funded by the Wellcome Trust and Engineering and Physical Sciences Research Council under grant No. WT 088877/Z/09/Z. We wish to acknowledge the facilities provided by the Oxford Acute Vascular Imaging Centre.

Hybrid Artificial Intelligent Algorithm for Call Admission Control in WCDMA Mobile Network

In wideband code division multiple access (WCDMA) mobile network, total transmission power of node B depends on diverse factors such as accommodation of new service request, termination of active user equipment (UE) and movement of UE. This makes power prediction a complicated task. In this paper, support vector regression (SVR) has been implemented successfully to forecast next interval power consumption at node B with different type of antenna system. The predicted output is used by WCDMA mobile network to make decision on new service request admission. Genetic algorithm is then applied to form beams with minimum power to cover all UEs in a macro cell. The proposed algorithm, support vector regression assists genetic algorithm (SVRaGA) was tested in a dynamic WCDMA mobile network simulator. Simulation results have shown SVR can predict next cycle power usage at node B with excellent accuracy and improve the quality of service (QoS) by minimizing dropped calls in the system.