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Purpose: Hyperpolarized carbon-13 magnetic resonance spectroscopy is a novel and powerful tool for exploring the metabolic state of tissue, but a number of technological problems still limit this technology and need innovative solutions. In particular, the low molar concentration of derivate metabolites give rise to low signal-to-noise ratio (SNR), which makes the design and development of dedicated RF coils a task of fundamental importance. In this article, the authors describe the simulation and the design of a dedicated 13C surface coil for cardiac metabolism assessment in pig models. Methods: A SNR model for a circular loop is presented and applied to the design of a 13C coil which guarantees the desired field-of-view and provides high SNR with a good penetration in deep sample regions. The coil resistance was calculated from Ohm's law and the magnetic field pattern was calculated using Biot-Savart law, while the sample induced resistance was calculated using a numerical finite-difference time-domain algorithm. Successively, a prototype of the coil was built and tested on the workbench and by acquisition of MR data. Results: The comparison of SNR-vs-depth profiles between the theoretical SNR model and the experimental SNR extracted from the phantom chemical shift image (CSI) showed the accuracy of the authors' model. Moreover, the authors demonstrated the use of the coil for the acquisition of a CSI of a hyperpolarized [1-13C] pyruvate phantom. Conclusions: The results demonstrated the design trade-offs to successfully design a dedicated coil for cardiac imaging in the pig with hyperpolarized 13C by developing a SNR model which allows the prediction of the coil performance. This approach can be employed for deriving SNR formulations for coil with more complex geometries. [ABSTRACT FROM AUTHOR]

Brain activities are often investigated through Electroencephalographic (EEG) data analysis using time-domain Independent Component Analysis (ICA). Nevertheless, the instantaneous mixing model of ICA cannot properly describe spatio-temporal dynamics, such as those related to traveling waves of neural activity. In this work, we exploit the application of the Complex ICA (cICA) to describe the underlying brain activities in time and frequency domain. In particular, we show how to effectively extract the most significant time-frequency structure of cortical activity in order to solve a compelling EEG-based pattern classification problem. The crucial step of independent component selection among frequencies is performed using an objective computational method based on template matching techniques with physiologically-plausible activations. Experimental results are obtained using on-line EEG data from the BCI Competition 2003 and are expressed in terms of confusion matrix after leave-one-out validation procedure. A comparative analysis between ICA and cICA models reveals that cICA estimation gives powerful information and allows to achieve a higher classification accuracy with respect to instantaneous ICA.

Cardiac resynchronization therapy (CRT) is a therapeutic option with proven efficacy in improving symptoms and reducing both hospitalization and mortality in patients with refractory heart failure. However, a significant number of patients do not respond to CRT and this may be due to incomplete or inappropriate selection and characterization of patients before pacemaker implant. Cardiac magnetic resonance imaging (CMRI) is an imaging technique that may assist cardiologists in this regard. This technique has the potential to improve the success rate of CRT, due to pre-interventional evaluation of left ventricular function, mechanical dyssynchrony, and characterization and quantification of scar tissue. Recently, venous coronary anatomy has also been successfully evaluated by CMRI. In this review the role of CMRI in patients with heart failure who are candidates for CRT is discussed and potential future developments are indicated.

Purpose: Hyperpolarized carbon-13 magnetic resonance spectroscopy is a novel and powerful tool for exploring the metabolic state of tissue, but a number of technological problems still limit this technology and need innovative solutions. In particular, the low molar concentration of derivate metabolites give rise to low signal-to-noise ratio (SNR), which makes the design and development of dedicated RF coils a task of fundamental importance. In this article, the authors describe the simulation and the design of a dedicated 13C surface coil for cardiac metabolism assessment in pig models., Methods: A SNR model for a circular loop is presented and applied to the design of a 13C coil which guarantees the desired field-of-view and provides high SNR with a good penetration in deep sample regions. The coil resistance was calculated from Ohm's law and the magnetic field pattern was calculated using Biot-Savart law, while the sample induced resistance was calculated using a numerical finite-difference time-domain algorithm. Successively, a prototype of the coil was built and tested on the workbench and by acquisition of MR data., Results: The comparison of SNR-vs-depth profiles between the theoretical SNR model and the experimental SNR extracted from the phantom chemical shift image (CSI) showed the accuracy of the authors' model. Moreover, the authors demonstrated the use of the coil for the acquisition of a CSI of a hyperpolarized [1-13C] pyruvate phantom., Conclusions: The results demonstrated the design trade-offs to successfully design a dedicated coil for cardiac imaging in the pig with hyperpolarized 13C by developing a SNR model which allows the prediction of the coil performance. This approach can be employed for deriving SNR formulations for coil with more complex geometries.