MR spectroscopy is used worldwide as an adjunct to MR imaging in several common neurologic diseases, including brain neoplasms, inherited metabolic disorders, demyelinating disorders, and infective... A large body of published work shows that proton (hydrogen 1 [1H]) magnetic resonance (MR) spectroscopy has evolved from a research tool into a clinical neuroimaging modality. Herein, the authors p...

Proton MRS (¹H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good‐quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi‐adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B₀) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use.

Proton spectroscopy can noninvasively provide useful information on brain tumor type and grade. Short‐ (30 ms) and long‐ (136 ms) echo time (TE) ¹H spectra were acquired from normal white matter (NWM), meningiomas, grade II astrocytomas, anaplastic astrocytomas, glioblastomas, and metastases. Very low myo‐Inositol ([mI]) and creatine ([Cr]) were characteristic of meningiomas, and high [mI] characteristic of grade II astrocytomas. Tumor choline ([Cho]) was greater than NWM and increased with grade for grade II and anaplastic astrocytomas, but was highly variable for glioblastomas. Higher [Cho] and [Cr] correlated with low lipid and lactate (P < 0.05), indicating a dilution of metabolite concentrations due to necrosis in high‐grade tumors. Metabolite peak area ratios showed no correlation with lipids and mI/Cho (at TE = 30 ms), and Cr/Cho (at TE = 136 ms) best correlated with tumor grade. The quantified lipid, macromolecule, and lactate levels increased with grade of tumor, consistent with progression from hypoxia to necrosis. Quantification of lipids and macromolecules at short TE provided a good marker for tumor grade, and a scatter plot of the sum of alanine, lactate, and δ1.3 lipid signals vs. mI/Cho provided a simple way to separate most tumors by type and grade. Magn Reson Med 49:223–232, 2003.

Background: Damage to white matter tracts, resulting in “cerebral disconnection,” may underlie age-related cognitive decline. Methods: Using diffusion tensor MRI (DTI) to investigate white matter damage, and magnetic resonance spectroscopy (MRS) to look at its underlying pathologic basis, the authors investigated the relationship between white matter structure and cognition in 106 healthy middle-aged and elderly adults. Fractional anisotropy (FA) and mean diffusivity (MD) values, whole brain white matter histograms, and regions of interest placed in the white matter of the centrum semiovale were analyzed. Correlations with executive function, working memory, and information-processing speed were performed. Results: There was a progressive reduction in FA and increase in diffusivity with age in both region of interest (r = 0.551, p < 0.001), and whole brain histograms (r = 0.625, p < 0.001). DTI values correlated with performance in all three cognitive domains. After controlling for age, DTI parameters correlated with working memory but not with the other two cognitive domains. MRS studies found a correlation of N-acetyl aspartate, a neuronal marker, with DTI parameters (r = 0.253, p < 0.05). Conclusion: The results are consistent with white matter damage due to axonal loss, causing age- related cognitive decline. Working memory may be particularly dependent on complex networks dependent on white matter connections.

Purpose We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). Methods The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. Results The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. Conclusions This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.