Computer-Assisted

Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T (1) H-MR spectroscopy-A multi-center study.

3T magnetic resonance scanners have boosted clinical application of H-MR spectroscopy (MRS) by offering an improved signal-to-noise ratio and increased spectral resolution, thereby identifying more metabolites and extending the range of metabolic …

Multiclass imbalance learning: Improving classification of pediatric brain tumors from magnetic resonance spectroscopy.

Classification of pediatric brain tumors from H-magnetic resonance spectroscopy (MRS) can aid diagnosis and management of brain tumors. However, varied incidence of the different tumor types leads to imbalanced class sizes and introduces difficulties …

Influence of macromolecule baseline on (1) H MR spectroscopic imaging reproducibility.

Poorly characterized macromolecular (MM) and baseline artefacts are known to reduce metabolite quantitation accuracy in H MR spectroscopic imaging (MRSI). Increasing echo time (TE) and improvements in MM analysis schemes have both been proposed as …

Sensitivity encoding for fast (1) H MR spectroscopic imaging water reference acquisition.

Accurate and fast (1) H MR spectroscopic imaging (MRSI) water reference scans are important for absolute quantification of metabolites. However, the additional acquisition time required often precludes the water reference quantitation method for MRSI …

Classification of single-voxel 1H spectra of childhood cerebellar tumors using LCModel and whole tissue representations.

In this study, mean tumor spectra are used as the basis functions in LCModel to create a direct classification tool for short echo time (1)H magnetic resonance spectroscopy of pediatric brain tumors. LCModel is a widely used analysis tool designed to …

A hybrid method of application of independent component analysis to in vivo 1H MR spectra of childhood brain tumours.

Independent component analysis (ICA) can automatically extract individual metabolite, macromolecular and lipid (MMLip) components from a series of in vivo MR spectra. The traditional feature extraction (FE)-based ICA approach is limited, in that a …

A comparative study of feature extraction and blind source separation of independent component analysis (ICA) on childhood brain tumour 1H magnetic resonance spectra.

Independent component analysis (ICA) has the potential of determining automatically the metabolite signals which make up MR spectra. However, the reliability with which this is accomplished and the optimal approach for investigating in vivo MRS have …