Java-based framework for processing and displaying short-echo-time magnetic resonance spectroscopy signals.

Magnetic resonance spectroscopy (MRS) can be used to determine in a non-invasive way the concentrations of certain chemical substances, also called metabolites. The spectra of MRS signals contain peaks that correspond to the metabolites of interest. Short-echo-time signals are characterized by heavily overlapping metabolite peaks and require sophisticated processing methods. To be useful in a clinical environment tools are needed that can process those signals in an accurate and fast way. Therefore, we developed novel processing methods and we designed a freely available and open-source framework (http://www.esat.kuleuven.ac.be/sista/members/biomed) in which the processing methods can be integrated. The framework has a set of abstract classes, called hot spots, and its goal is to provide a general structure and determine the control flow of the program. It provides building blocks or components in order to help developers with integrating their methods in the framework via a plug-in system. The framework is designed with the unified modeling language (UML) and implemented in Java. When a developer implements the framework he gets an application that acts like a simple and user-friendly graphical user interface (GUI) for processing MRS data. This article describes in detail the structure and implementation of the framework and the integration of our processing methods in it.

Detection of fast neuronal signals in the motor cortex from functional near infrared spectroscopy measurements using independent component analysis.

Fast changes, in the range of milliseconds, in the optical properties of cerebral tissue are associated with brain activity and can be detected using non-invasive near-infrared spectroscopy (NIRS). These changes are assumed to be caused by changes in the light scattering properties of the neuronal tissue. The aim of this study was to develop highly sensitive data analysis algorithms to detect this fast signal, which is small compared with other physiological signals. A frequency-domain tissue oximeter, whose laser diodes were intensity modulated at 110 MHz, was used. The amplitude, mean intensity and phase of the modulated optical signal were measured at a sample rate of 96 Hz. The probe, consisting of four crossed source detector pairs was placed above the motor cortex, contralateral to the hand performing a tapping exercise consisting of alternating rest and tapping periods of 20 s each. An adaptive filter was used to remove the arterial pulsatility from the optical signals. Independent component analysis allowed further separation of a signal component containing the fast signal. In nine out of 14 subjects, a significant fast neuronal signal related to the finger tapping was found in the intensity signals. In the phase signals, indications of the fast signal were found in only two subjects.