Improving 1H MRSI measurement of cerebral lactate for clinical applications
Introduction
Cerebral lactate, a metabolic product of glycolysis, plays an integral role in neuronal energy metabolism (Schurr, 2006). Lactate exists in the healthy brain at low basal concentrations, and elevations can indicate transient changes in physiological state (van Rijen et al., 1989, Dager et al., 1999b, Friedman et al., 2007) or neural activation (Prichard et al., 1991, Sappey-Marinier et al., 1992, Frahm et al., 1996), as well as altered metabolic regulation such as in bipolar disorder (Dager et al., 2004) and panic disorder (Dager et al., 1994, Maddock, 2001). Other brain pathological states also exhibit characteristic brain lactate elevations, including tumors (Sijens et al., 1996), ischemia (Behar et al., 1983, Mathews et al., 1995), traumatic brain injury (Makoroff et al., 2005), and metabolic compromise from severe mitochondrial dysfunction, such as in MELAS (Kaufmann et al., 2004) or Leigh syndrome (Sijens et al., 2008). There is also increasing interest in brain lactate as a biomarker for less severe mitochondrial dysfunction (Lin et al., 2003) (http://www.ninds.nih.gov/news_and_events/proceedings/20090629_mitochondrial.htm). Although proton magnetic resonance spectroscopy (MRS) provides a non-invasive means for lactate measurement in vivo, reliable quantification can be difficult, particularly at normal resting state. One known problem with measuring lactate and other chemicals at low natural abundance is systematic over-estimation when using linear combination fitting algorithms to estimate concentrations (Tkac et al., 2002, Kreis, 2004). A number of investigators have demonstrated that chemical estimation reliability can be increased when data signal-to-noise ratio (SNR) is improved by increasing the number of signal acquisitions or the static magnetic field strength (Tkac et al., 2002, Otazo et al., 2006, Posse et al., 2007).
At the same time, advances in magnetic resonance spectroscopic imaging (MRSI) have substantially increased the number of spectra that can be acquired in a given time. Two-dimensional proton MRSI techniques that can produce hundreds of usable spectra within a relatively short scan duration of 5–10 min are readily available on most clinical and research scanners, and three-dimensional MRSI techniques that can generate thousands of individual spectra in a single scanning session are rapidly coming into more common usage (Dager et al., 2008). Advances in MRSI data processing and analytic procedures have provided major improvements in the processing of large arrays of spectra (e.g. MIDAS (Maudsley et al., 2006), DSX (http://godzilla.kennedykrieger.org), and 3DiMRSI (http://mrs.cpmc.columbia.edu/3dicsi.html)); however, there is a persistent need for anatomically specific results generated with minimal operator bias.
One strategy for summarizing the information in MRSI data sets that has been applied by investigators is to average chemical information across voxels within specific regions of interest (ROIs). This strategy preserves regionally specific information while also reducing the number of final calculated chemical concentrations to a manageable number. The predominantly used practice in carrying out this strategy is to first calculate individual voxel chemical concentration estimates and to then average these values across all voxels within an ROI. An alternative approach is to average data across voxels within the ROI prior to spectral fitting and chemical concentration estimation.
In this work we investigate whether averaging free induction decays (FIDs) prior to spectral fitting (referred to here as the spectral enhanced averaging method, or SEAM), instead of averaging chemical concentrations after fitting individual voxel data (referred to here as the individual voxel averaging method, or IVAM), can capitalize upon the benefits of improved spectral SNR to yield improved estimates of cerebral lactate when used in conjunction with fitting with LCModel (Provencher, 1993), a widely used linear combination fitting program for spectroscopic data. We analyze in vivo brain lactate data in healthy subjects at baseline levels, then longitudinally in response to intravenous sodium lactate infusion, utilizing ROIs defined through automated coregistration of proton echo-planar spectroscopic imaging (PEPSI) volumes to an anatomical atlas. As a point of reference, we also present findings for the measurement of N-acetyl-aspartate (NAA), which has a robust 1H MRSI signal. We integrate this technique of averaging across ROIs into a systematic method that can be applied to 2D and 3D MRSI data acquired at any field strength.
Section snippets
Subjects
MR data from 18 healthy control adults (8 males and 10 females ranging from 18 to 53 years of age) acquired as part of an intravenous sodium lactate infusion study of panic disorder, as previously described (Dager et al., 1994, Dager et al., 1999a), were included in this analysis. All subjects were medication free and fasting at the time of MRS evaluation, had no history of Axis I DSM-IV defined psychiatric disorders, and were not taking psychotropic medications. All subjects provided written
Results
In the identification of usable spectra from the in vivo sodium lactate infusion experiment, the NAA CRLB threshold alone resulted in the inclusion of 96% of voxels in the left insular region, 60% of the voxels in the right frontal lobe region, and 61% of the voxels in the whole slab ROI. Addition of the FWHM and water signal quality threshold resulted in a much more conservative number of included voxels, with 63% of the spectra included for the left insular region, 27% of the spectra included
Discussion
Systematic handling of the large volumes on data contained in MRSI data sets can be achieved through automated coregistration to an anatomic atlas, and averaging of data within ROIs. Results from this study demonstrate that the approach used for combining data in ROIs can have a substantial effect on cerebral lactate concentration estimates. The sodium lactate infusion data set provided a valuable opportunity to investigate the effect of two different data averaging techniques on lactate
Acknowledgements
This study was supported by National Institutes of Health grants RO1-2MH50579 and 1P50HD55782.
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