Combining sodium MRI, proton MR spectroscopic imaging, and intracerebral EEG in epilepsy.

Whole brain ionic and metabolic imaging has potential as a powerful tool for the characterization of brain diseases. We combined sodium MRI (23 Na MRI) and 1 H-MR Spectroscopic Imaging (1 H-MRSI), assessing changes within epileptogenic networks in comparison with electrophysiologically normal networks as defined by stereotactic EEG (SEEG) recordings analysis. We applied a multi-echo density adapted 3D projection reconstruction pulse sequence at 7 T (23 Na-MRI) and a 3D echo-planar spectroscopic imaging sequence at 3 T (1 H-MRSI) in 19 patients suffering from drug-resistant focal epilepsy who underwent presurgical SEEG. We investigated 23 Na MRI parameters including total sodium concentration (TSC) and the sodium signal fraction associated with the short component of T2 * decay (f), alongside the level of metabolites N-acetyl aspartate (NAA), choline compounds (Cho), and total creatine (tCr). All measures were extracted from spherical regions of interest (ROIs) centered between two adjacent SEEG electrode contacts and z-scored against the same ROI in controls. Group comparison showed a significant increase in f only in the epileptogenic zone (EZ) compared to controls and compared to patients' propagation zone (PZ) and non-involved zone (NIZ). TSC was significantly increased in all patients' regions compared to controls. Conversely, NAA levels were significantly lower in patients compared to controls, and lower in the EZ compared to PZ and NIZ. Multiple regression analyzing the relationship between sodium and metabolites levels revealed significant relations in PZ and in NIZ but not in EZ. Our results are in agreement with the energetic failure hypothesis in epileptic regions associated with widespread tissue reorganization.

Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model.

PURPOSE: To remove nuisance signals (e.g., water and lipid signals) for (1) H MRSI data collected from the brain with limited and/or sparse (k, t)-space coverage. METHODS: A union-of-subspace model is proposed for removing nuisance signals. The model exploits the partial separability of both the nuisance signals and the metabolite signal, and decomposes an MRSI dataset into several sets of generalized voxels that share the same spectral distributions. This model enables the estimation of the nuisance signals from an MRSI dataset that has limited and/or sparse (k, t)-space coverage. RESULTS: The proposed method has been evaluated using in vivo MRSI data. For conventional chemical shift imaging data with limited k-space coverage, the proposed method produced "lipid-free" spectra without lipid suppression during data acquisition at 130 ms echo time. For sparse (k, t)-space data acquired with conventional pulses for water and lipid suppression, the proposed method was also able to remove the remaining water and lipid signals with negligible residuals. CONCLUSION: Nuisance signals in (1) H MRSI data reside in low-dimensional subspaces. This property can be utilized for estimation and removal of nuisance signals from (1) H MRSI data even when they have limited and/or sparse coverage of (k, t)-space. The proposed method should prove useful especially for accelerated high-resolution (1) H MRSI of the brain.