The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses

Abstract

BackgroundProton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored.ResultsThis work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1st, can be easily used with any new case from any MR scanner manufacturer and 2nd, improves the initial analysis capabilities of the first version. The main improvements are an embedded database, user accounts, more diagnostic discrimination capabilities and the possibility to analyse data acquired under additional data acquisition conditions. Other improvements include a customisable graphical user interface (GUI). Most diagnostic problems included have been addressed through a pattern-recognition based approach, in which classifiers based on linear discriminant analysis (LDA) were trained and tested.ConclusionsThe INTERPRET DSS 3.0 allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1.5 T, and to analyse them. The system is expected to help in the categorisation of MR Spectra from abnormal brain masses.

Non-invasive grading of astrocytic tumours from the relative contents of myo-inositol and glycine measured by in vivo mrs

“…In vivo and phantom spectra were automatically processed with a software module derived from the INTERPRET data manipulation software (11,12). This module carried out the required functions such as Fourier transform, residual water filtering by HLSVD between 4.3 and 5.1 ppm, offset correction, zero order (Klose algorithm) phase correction, setting the 4.2 to 5.0 ppm range to zero, exponential apodisation result- The mI signal at LTE was attenuated when compared to the creatine signal, due to mI J-coupling induced phase modulation, whereas glycine remained practically isointense with respect to creatine at 3.03 ppm (Fig.…”

“…As MRS spectra of brain tumours and other focal lesions are often quite distinctive, 1 H-MRS is a very promising non-invasive method for brain tumour diagnosis, and it is becoming widely acknowledged as a useful complement to MRI. Several in vivo diagnostic approaches for MRS have been tested, such as spectroscopic imaging (10) or single voxel MRS (11,12). Previous studies with MRS have suggested correlations between metabolic features in vivo and the histopathological grade of astrocytic tumours (4,13,14), but they were generally concerned with the NAA, Cr and Cho resonances.…”

Pattern Recognition Analysis of MR Spectra

“…Some authors have also developed empirical alignment algorithms for cases in which the SNR for the potential reference resonances may vary strongly among samples. 14 …”

“…The possibility of seeing each spectrum as a symbol in a 2-D or 3-D space, where its position is determined by the PR algorithm, has been shown to be a successful approach, and it has even been implemented in a decision-support system ( Figure 7). 8,14,49 On the other hand, for MV data, the most successful representation has been the 'nosologic image' concept, where each pixel or voxel is colored according to the predicted class ( Figure 8) 5,50 . An interesting approach related to visualization of MV data was developed for classifying voxels in an MV study of brain tumor patients, as belonging to the investigated classes or to 'unknown' or 'undecided' (Figure 9), by applying a threshold based on Mahalanobis inter-and intraclass distances.…”

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

“…It was designed to provide the display of classification plots, which is useful for the automatic classification of tumor spectra [37] . The differentiation between different tumor groups was achieved by plotting the boundaries that were defined by the bisectors between the centroids of each class [38] . The users could enter their own spectrum, position it automatically among the tumor groups of the system and compare it with other spectra.…”