Convex-Envelope Based Automated Quantitative Approach to Multi-Voxel 1H-MRS Applied to Brain Tumor Analysis

Comparison of baseline correction algorithms for in vivo 1H-MRS

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data. In short-echo-time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long-echo-time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi-LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm-QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm-QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting.

MR spectroscopy in pediatric neuroradiology

Magnetic resonance spectroscopy (MRS), being able to identify and measure some brain components (metabolites) in pathologic lesions and in normal-appearing tissue, offers a valuable additional diagnostic tool to assess several pediatric neurological diseases. In this review we will illustrate the basic principles and clinical applications of brain proton (H¹; hydrogen) MRS (H¹MRS), by now the only MRS method widely available in clinical practice. Performing H¹MRS in the brain is inherently less complicated than in other tissues (e.g., liver, muscle), in which spectra are heavily affected by magnetic field inhomogeneities, respiration artifacts, and dominating signals from the surrounding adipose tissues. H¹MRS in pediatric neuroradiology has some advantages over acquisitions in adults (lack of motion due to children sedation and lack of brain iron deposition allow optimal results), but it requires a deep knowledge of pediatric pathologies and familiarity with the developmental changes in spectral patterns, particularly occurring in the first two years of life. Examples from our database, obtained mainly from a 1.5 Tesla clinical scanner in a time span of 15 years, will demonstrate the efficacy of H¹MRS in the diagnosis of a wide range of selected pediatric pathologies, like brain tumors, infections, neonatal hypoxic-ischemic encephalopathy, metabolic and white matter disorders.

A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas

Objectives

To investigate the association between proton magnetic resonance spectroscopy (¹H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade.

Methods

This study included 112 glioma patients who were divided into the training (n = 74) and validation (n = 38) sets based on the time of hospitalization. Twenty-six metabolic features were extracted from the preoperative ¹H-MRS image. The Student's t-test was conducted to screen for differentially expressed features between low- and high-grade gliomas (WHO grades II and III/IV, respectively). Next, the minimum Redundancy Maximum Relevance (mRMR) algorithm was performed to further select features for a support vector machine (SVM) classifier building. Performance of the predictive model was evaluated both in the training and validation sets using ROC curve analysis.

Results

Among the extracted ¹H-MRS metabolic features, thirteen features were differentially expressed. Four features were further selected as grade-predictive imaging signatures using the mRMR algorithm. The predictive performance of the machine-learning model measured by the AUC was 0.825 and 0.820 in the training and validation sets, respectively. This was better than the predictive performances of individual metabolic features, the best of which was 0.812.

Conclusions

¹H-MRS metabolic features could help in predicting the grade of gliomas. The machine-learning model achieved a better prediction performance in grading gliomas than individual features, indicating that it could complement the traditionally used metabolic features.

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¹H-MRS metabolic features could help in predicting the grades of gliomas.

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The machine-learning model performed better than individual metabolic features.

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The use of machine-learning model can complement the metabolic features.

Measuring the lactate-to-creatine ratio via 1H NMR spectroscopy can be used to noninvasively evaluate apoptosis in glioma cells after X-ray irradiation

BACKGROUND

Radiotherapy is among the commonly applied treatment options for glioma, which is one of the most common types of primary brain tumor. To evaluate the effect of radiotherapy noninvasively, it is vital for oncologists to monitor the effects of X-ray irradiation on glioma cells. Preliminary research had showed that PKC-ι expression correlates with tumor cell apoptosis induced by X-ray irradiation. It is also believed that the lactate-to-creatine (Lac/Cr) ratio can be used as a biomarker to evaluate apoptosis in glioma cells after X-ray irradiation. In this study, we evaluated the relationships between the Lac/Cr ratio, apoptotic rate, and protein kinase C iota (PKC-ι) expression in glioma cells.

METHODS

Cells of the glioma cell lines C6 and U251 were randomly divided into 4 groups, with every group exposed to X-ray irradiation at 0, 1, 5, 10 and 15 Gy. Single cell gel electrophoresis (SCGE) was conducted to evaluate the DNA damage. Flow cytometry was performed to measure the cell cycle blockage and apoptotic rates. Western blot analysis was used to detect the phosphorylated PKC-ι (p-PKC-ι) level. 1H NMR spectroscopy was employed to determine the Lac/Cr ratio.

RESULTS

The DNA damage increased in a radiation dose-dependent manner (p < 0.05). With the increase in X-ray irradiation, the apoptotic rate also increased (C6, p < 0.01; U251, p < 0.05), and the p-PKC-ι level decreased (C6, p < 0.01; U251, p < 0.05). The p-PKC-ι level negatively correlated with apoptosis, whereas the Lac/Cr ratio positively correlated with the p-PKC-ι level.

CONCLUSION

The Lac/Cr ratio decreases with an increase in X-ray irradiation and thus can be used as a biomarker to reflect the effects of X-ray irradiation in glioma cells.

Assessment of alterations in X-ray irradiation-induced DNA damage of glioma cells by using proton nuclear magnetic resonance spectroscopy

Glioma is one of the most common types of brain tumors. DNA damage is closely associated with glioma cell apoptosis induced by X-ray irradiation. Alterations of metabolites in glioma can be detected noninvasively by proton nuclear magnetic resonance (1H NMR) spectroscopy. To noninvasively explore the micro mechanism in X-ray irradiation-induced apoptosis, the relationship between metabolites and DNA damage in glioma cells was investigated. Three glioma cell lines (C6, U87 and U251) were randomly designated as control (0Gy) and treatment groups (1, 5, 10, 15Gy). After X-ray exposure, each group was separated into four parts: (i) to detect metabolites by 1H NMR spectroscopy; (ii) to make cell colonies; (iii) to detect cell cycle distribution and apoptosis rate by flow cytometry; and (iv) to measure DNA damage by comet assay. The metabolite ratios of lactate/creatine and succinate/creatine decreased (lactate/creatine: C6, 22.17-66.27%; U87, 15.93-44.56%; U251, 26.27-74.48%. succinate/creatine: C6, 14.41-48.35%; U87, 22.03-70.62%; U251, 17.33-60.06%) and choline/creatine increased (C6, 52.22-389.68%; U87, 56.15-82.36%; U251, 31.87-278.62%) in the treatment groups compared with the control group (each P<0.05), which linearly depended on DNA damage. An increasing dose of X-ray irradiation increased numbers of apoptotic cells (P<0.01), and the DNA damage parameters were dose-dependent (P<0.05). The colony-forming rate declined (P<0.01) and the percentage of cells at G1 stage increased when exposed to 1Gy X-ray (three cell lines, P<0.05). Metabolite alterations detected by 1H NMR spectroscopy can be used to determine DNA damage induced by X-ray irradiation. 1H NMR spectroscopy is a noninvasive method to predict DNA damage of glioma cell at the micro level.

Multiparametric Imaging Analysis: Magnetic Resonance Spectroscopy

Magnetic resonance spectroscopy (MRS) is a magnetic resonance-based imaging modality that allows noninvasive sampling of metabolic changes in normal and abnormal brain parenchyma. MRS is particularly useful in the differentiation of developmental or non-neoplastic disorders from neoplastic processes. MRS is also useful during routine imaging follow-up after radiation treatment or during antiangiogenic treatment and for predicting outcomes and treatment response. The objective of this article is to provide a concise but thorough review of the basic physical principles, important applications of MRS in brain tumor imaging, and future directions.