Diagnostic Accuracy of MR Spectroscopic Imaging and 18F-FET PET for Identifying Glioma: A Biopsy-Controlled Hybrid PET/MRI Study.

Contrast-enhanced MRI is the method of choice for brain tumor diagnostics, despite its low specificity for tumor tissue. This study compared the contribution of MR spectroscopic imaging (MRSI) and amino acid PET to improve the detection of tumor tissue. Methods: In 30 untreated patients with suspected glioma, O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) PET; 3-T MRSI with a short echo time; and fluid-attenuated inversion recovery, T2-weighted, and contrast-enhanced T1-weighted MRI were performed for stereotactic biopsy planning. Serial samples were taken along the needle trajectory, and their masks were projected to the preoperative imaging data. Each sample was individually evaluated neuropathologically. 18F-FET uptake and the MRSI signals choline (Cho), N-acetyl-aspartate (NAA), creatine, myoinositol, and derived ratios were evaluated for each sample and classified using logistic regression. The diagnostic accuracy was evaluated by receiver operating characteristic analysis. Results: On the basis of the neuropathologic evaluation of tissue from 88 stereotactic biopsies, supplemented with 18F-FET PET and MRSI metrics from 20 areas on the healthy-appearing contralateral hemisphere to balance the glioma/nonglioma groups, 18F-FET PET identified glioma with the highest accuracy (area under the receiver operating characteristic curve, 0.89; 95% CI, 0.81-0.93; threshold, 1.4 × background uptake). Among the MR spectroscopic metabolites, Cho/NAA normalized to normal brain tissue showed the highest diagnostic accuracy (area under the receiver operating characteristic curve, 0.81; 95% CI, 0.71-0.88; threshold, 2.2). The combination of 18F-FET PET and normalized Cho/NAA did not improve the diagnostic performance. Conclusion: MRI-based delineation of gliomas should preferably be supplemented by 18F-FET PET.

Mapping early tumor response to radiotherapy using diffusion kurtosis imaging*.

PURPOSE: In this pilot study, DKI measures of diffusivity and kurtosis were compared in active tumor regions and correlated to radiologic response to radiotherapy after completion of 2 weeks of treatment to derive potential early measures of tumor response. METHODS: MRI and Magnetic Resonance Spectroscopic Imaging (MRSI) data were acquired before the beginning of RT (pre-RT) and 2 weeks after the initiation of treatment (during-RT) in 14 glioblastoma patients. The active tumor region was outlined as the union of the residual contrast-enhancing region and metabolically active tumor region. Average and standard deviation of mean, axial, and radial diffusivity (MD, AD, RD) and mean, axial, and radial kurtosis (MK, AK, RK) values were calculated for the active tumor VOI from images acquired pre-RT and during-RT and paired t-tests were executed to estimate pairwise differences. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the predictive capabilities of changes in diffusion metrics for progression-free survival (PFS). RESULTS: Analysis showed significant pairwise differences for AD (p = 0.035; Cohen's d of 0.659) and AK (p = 0.019; Cohen's d of 0.753) in diffusion measures after 2 weeks of RT. ROC curve analysis showed that percentage change differences in AD and AK between pre-RT and during-RT scans provided an Area Under the Curve (AUC) of 0.524 and 0.762, respectively, in discriminating responders (PFS>180 days) and non-responders (PFS<180 days). CONCLUSION: This pilot study, although preliminary in nature, showed significant changes in AD and AK maps, with kurtosis derived AK maps showing an increased sensitivity in mapping early changes in the active tumor regions.

A multi-institutional pilot clinical trial of spectroscopic MRI-guided radiation dose escalation for newly diagnosed glioblastoma.

Background: Glioblastomas (GBMs) are aggressive brain tumors despite radiation therapy (RT) to 60 Gy and temozolomide (TMZ). Spectroscopic magnetic resonance imaging (sMRI), which measures levels of specific brain metabolites, can delineate regions at high risk for GBM recurrence not visualized on contrast-enhanced (CE) MRI. We conducted a clinical trial to assess the feasibility, safety, and efficacy of sMRI-guided RT dose escalation to 75 Gy for newly diagnosed GBMs. Methods: Our pilot trial (NCT03137888) enrolled patients at 3 institutions (Emory University, University of Miami, Johns Hopkins University) from September 2017 to June 2019. For RT, standard tumor volumes based on T2-FLAIR and T1w-CE MRIs with margins were treated in 30 fractions to 50.1 and 60 Gy, respectively. An additional high-risk volume based on residual CE tumor and Cho/NAA (on sMRI) ≥2× normal was treated to 75 Gy. Survival curves were generated by the Kaplan-Meier method. Toxicities were assessed according to CTCAE v4.0. Results: Thirty patients were treated in the study. The median age was 59 years. 30% were MGMT promoter hypermethylated; 7% harbored IDH1 mutation. With a median follow-up of 21.4 months for censored patients, median overall survival (OS) and progression-free survival were 23.0 and 16.6 months, respectively. This regimen appeared well-tolerated with 70% of grade 3 or greater toxicity ascribed to TMZ and 23% occurring at least 1 year after RT. Conclusion: Dose-escalated RT to 75 Gy guided by sMRI appears feasible and safe for patients with newly diagnosed GBMs. OS outcome is promising and warrants additional testing. Based on these results, a randomized phase II trial is in development.

T1-weighted and T2-weighted Subtraction MR Images for Glioma Visualization and Grading.

BACKGROUND AND PURPOSE: To evaluate the performance of multiparametric MR images in differentiation of different regions of the gross tumor area and for assessment of glioma grade. METHODS: Forty-six glioma subjects (18 grade II, 11 grade III, and 17 grade IV) underwent a comprehensive MR and spectroscopic imaging procedure. Maps were generated by subtraction of T1-weighted images from contrast-enhanced T1-weighted images (ΔT1 map) and T1-weighted images from T2-weighted images (ΔT2 map). Regions of interest (ROIs) were positioned in normal-appearing white matter (NAWM), enhancing tumor, hyperintense T2, necrotic region, and immediate and distal peritumoral regions (IPR and DPR). Relative signal contrast was estimated as difference between mean intensities in ROIs and NAWM. Classification using support vector machines was applied to all image series to determine the efficacy of regional contrast measures for differentiation of low- and high-grade lesions and grade III and IV lesions. RESULTS: ΔT1 and ΔT2 maps offered higher contrast as compared to other parametric maps in differentiating enhancing tumor and edematous regions, respectively, and provided the highest classification accuracy for differentiating low- and high-grade tumors, of 91% and 90.4%. Choline/N-acetylaspartate maps provided significant contrast for delineating IPR and DPR. For differentiating high-grade gliomas, ΔT2 and ΔT1 maps provided a mean accuracy of 90.9% and 88.2%, which was lower than that obtained using cerebral blood volume (93.7%) and choline/creatine (93.3%) maps. CONCLUSION: This study showed that subtraction maps provided significant contrast in differentiating several regions of the gross tumor area and are of benefit for accurate tumor grading.

Evidence of widespread metabolite abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: assessment with whole-brain magnetic resonance spectroscopy.

Previous neuroimaging studies have detected markers of neuroinflammation in patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Magnetic Resonance Spectroscopy (MRS) is suitable for measuring brain metabolites linked to inflammation, but has only been applied to discrete regions of interest in ME/CFS. We extended the MRS analysis of ME/CFS by capturing multi-voxel information across the entire brain. Additionally, we tested whether MRS-derived brain temperature is elevated in ME/CFS patients. Fifteen women with ME/CFS and 15 age- and gender-matched healthy controls completed fatigue and mood symptom questionnaires and whole-brain echo-planar spectroscopic imaging (EPSI). Choline (CHO), myo-inositol (MI), lactate (LAC), and N-acetylaspartate (NAA) were quantified in 47 regions, expressed as ratios over creatine (CR), and compared between ME/CFS patients and controls using independent-samples t-tests. Brain temperature was similarly tested between groups. Significant between-group differences were detected in several regions, most notably elevated CHO/CR in the left anterior cingulate (p < 0.001). Metabolite ratios in seven regions were correlated with fatigue (p < 0.05). ME/CFS patients had increased temperature in the right insula, putamen, frontal cortex, thalamus, and the cerebellum (all p < 0.05), which was not attributable to increased body temperature or differences in cerebral perfusion. Brain temperature increases converged with elevated LAC/CR in the right insula, right thalamus, and cerebellum (all p < 0.05). We report metabolite and temperature abnormalities in ME/CFS patients in widely distributed regions. Our findings may indicate that ME/CFS involves neuroinflammation.

The Association between Whole-Brain MR Spectroscopy and IDH Mutation Status in Gliomas.

BACKGROUND AND PURPOSE: Mutations in isocitrate dehydrogenase (IDH) have a direct effect on gliomagenesis. The purpose of this study is to quantify differences in brain metabolites due to IDH mutations. METHODS: Magnetic Resonance Spectroscopic Imaging (MRSI) was performed in 35 patients with gliomas of different grade and varied IDH mutation status. Volumes of interest (VOIs) for active tumor (tVOI), peritumoral area (pVOI), and contralateral normal-appearing white matter (cVOI) were created. Metabolite ratios of Choline (Cho) to both N-acetylaspartate (NAA) and Creatine (Cr) were estimated. Ratios of Glutamate/Glutamine complex (Glx) and myoinositol (mIno) to Cr were also quantified. General linear models (GLMs) were used to estimate the effects of IDH mutation on metabolite measures, with age, gender, and tumor grade used as covariates. RESULTS: GLM analysis showed that maximum Cho/NAA and Cho/Cr in the tVOI were significantly (P < .05) higher in IDH mutant lesions as compared to wild-type. In the pVOI, mean Cho/Cr was found to be significantly different among IDH mutant and wild-type gliomas. Mean Cho/NAA (P = .306) and Cho/Cr (P = .292) within the tVOI were not significantly different. Ratios of Glx/Cr and mIno/Cr in any region showed no significant differences between IDH mutant and wild-type gliomas. No significant differences in metabolite ratios were seen in the cVOI between IDH mutants and wild-types. CONCLUSION: IDH mutation's effect in gliomas show an increase in Cho in the tumor and perilesional regions as compared to wild-type lesions but do not show widespread changes across the brain.

The Brain Imaging Collaboration Suite (BrICS): A Cloud Platform for Integrating Whole-Brain Spectroscopic MRI into the Radiation Therapy Planning Workflow.

Glioblastoma has poor prognosis with inevitable local recurrence despite aggressive treatment with surgery and chemoradiation. Radiation therapy (RT) is typically guided by contrast-enhanced T1-weighted magnetic resonance imaging (MRI) for defining the high-dose target and T2-weighted fluid-attenuation inversion recovery MRI for defining the moderate-dose target. There is an urgent need for improved imaging methods to better delineate tumors for focal RT. Spectroscopic MRI (sMRI) is a quantitative imaging technique that enables whole-brain analysis of endogenous metabolite levels, such as the ratio of choline-to-N-acetylaspartate. Previous work has shown that choline-to-N-acetylaspartate ratio accurately identifies tissue with high tumor burden beyond what is seen on standard imaging and can predict regions of metabolic abnormality that are at high risk for recurrence. To facilitate efficient clinical implementation of sMRI for RT planning, we developed the Brain Imaging Collaboration Suite (BrICS; https://brainimaging.emory.edu/brics-demo), a cloud platform that integrates sMRI with standard imaging and enables team members from multiple departments and institutions to work together in delineating RT targets. BrICS is being used in a multisite pilot study to assess feasibility and safety of dose-escalated RT based on metabolic abnormalities in patients with glioblastoma (Clinicaltrials.gov NCT03137888). The workflow of analyzing sMRI volumes and preparing RT plans is described. The pipeline achieved rapid turnaround time by enabling team members to perform their delegated tasks independently in BrICS when their clinical schedules allowed. To date, 18 patients have been treated using targets created in BrICS and no severe toxicities have been observed.

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectrum to the data. For high-resolution volumetric MRSI of the brain, which may have ~10,000 spectra, significant processing time is required for spectral analysis and generation of metabolite maps. METHODS: A novel unsupervised deep learning architecture that combines a convolutional neural network with a priori models of the spectrum is presented. This architecture, a convolutional encoder-model decoder (CEMD), combines the strengths of adaptive and unbiased convolutional networks with models of magnetic resonance and is readily interpretable. RESULTS: The CEMD architecture performs accurate spectral fitting for volumetric MRSI in patients with glioblastoma, provides whole-brain fitting in 1 min on a standard computer, and handles a variety of spectral artifacts. CONCLUSION: A new architecture combining physics domain knowledge with convolutional neural networks has been developed and is able to perform rapid spectral fitting of whole-brain data. Rapid processing is a critical step toward routine clinical practice.

Lesion segmentation for MR spectroscopic imaging using the convolution difference method.

PURPOSE: Delineation of lesion boundaries from volumetric MRSI metabolite ratio maps using a method that accounts for the spatial response function of the acquisition and variable spectral quality and is robust to signal heterogeneity within the lesion. METHODS: A novel method for lesion segmentation, termed convolution difference, has been developed that is robust to signal heterogeneity within the lesion and to differences in the spatial response function. Procedures are described for processing metabolite ratio maps and to exclude regions of inadequate spectral quality. This method was evaluated using computer simulations, and the results were compared with an iterative thresholding technique that determines an optimal amplitude threshold, and with the use of a fixed amplitude threshold. These methods were evaluated for segmentation of volumetric MRSI studies of gliomas using maps of the choline to N-acetylaspartate ratio, and a qualitative comparison of lesion volumes carried out. RESULTS: Simulation studies indicated improved performance for the convolution difference method when applied to ratio maps. Variations in tumor volume were observed for the in vivo studies between the convolution difference and the iterative thresholding methods; however, visual analysis indicates that both showed improved accuracy in comparison to using a fixed amplitude threshold. CONCLUSION: This study reinforces previous reports indicating that the use of fixed threshold values for segmentation of maps with broad spatial response functions can result in errors in lesion volume definition. A novel segmentation method, termed the convolution difference, has been introduced and demonstrated to be robust for segmentation of volumetric MRSI metabolite data.

Three-dimensional echo planar spectroscopic imaging for differentiation of true progression from pseudoprogression in patients with glioblastoma.

Accurate differentiation of true progression (TP) from pseudoprogression (PsP) in patients with glioblastomas (GBMs) is essential for planning adequate treatment and for estimating clinical outcome measures and future prognosis. The purpose of this study was to investigate the utility of three-dimensional echo planar spectroscopic imaging (3D-EPSI) in distinguishing TP from PsP in GBM patients. For this institutional review board approved and HIPAA compliant retrospective study, 27 patients with GBM demonstrating enhancing lesions within six months of completion of concurrent chemo-radiation therapy were included. Of these, 18 were subsequently classified as TP and 9 as PsP based on histological features or follow-up MRI studies. Parametric maps of choline/creatine (Cho/Cr) and choline/N-acetylaspartate (Cho/NAA) were computed and co-registered with post-contrast T1 -weighted and FLAIR images. All lesions were segmented into contrast enhancing (CER), immediate peritumoral (IPR), and distal peritumoral (DPR) regions. For each region, Cho/Cr and Cho/NAA ratios were normalized to corresponding metabolite ratios from contralateral normal parenchyma and compared between TP and PsP groups. Logistic regression analyses were performed to obtain the best model to distinguish TP from PsP. Significantly higher Cho/NAA was observed from CER (2.69 ± 1.00 versus 1.56 ± 0.51, p = 0.003), IPR (2.31 ± 0.92 versus 1.53 ± 0.56, p = 0.030), and DPR (1.80 ± 0.68 versus 1.19 ± 0.28, p = 0.035) regions in TP patients compared with those with PsP. Additionally, significantly elevated Cho/Cr (1.74 ± 0.44 versus 1.34 ± 0.26, p = 0.023) from CER was observed in TP compared with PsP. When these parameters were incorporated in multivariate regression analyses, a discriminatory model with a sensitivity of 94% and a specificity of 87% was observed in distinguishing TP from PsP. These results indicate the utility of 3D-EPSI in differentiating TP from PsP with high sensitivity and specificity.

Value of diffusion kurtosis imaging in assessing low-grade gliomas.

BACKGROUND: Diffusion kurtosis imaging (DKI) measures have been shown to provide increased sensitivity relative to diffusion tensor imaging (DTI) in detecting pathologies. PURPOSE: To compare the sensitivity of DKI-derived kurtosis and diffusion maps for assessment of low-grade gliomas (LGG). STUDY TYPE: Prospective study. POPULATION: In all, 19 LGG patients and 26 healthy control subjects were recruited. FIELD STRENGTH/SEQUENCE: Echo-planar-imaging diffusion-weighted MR images (b-values = 0, 1000, and 2000 with 30 diffusion gradient directions) were acquired on a 3T scanner. ASSESSMENT: Maps for mean, axial, and radial diffusivity (MD, AD, and RD) and kurtosis (MK, AK, and RK), and fractional anisotropy (FA) were evaluated in the tumor, perilesional white matter, and contralateral normal-appearing white matter regions. STATISTICAL TESTING: General linear models (GLM), Cohen's d for effect size estimates, false discovery rate (FDR) for multiple corrections, Cochran Q-test. RESULTS: Pairwise differences were observed for all diffusion and kurtosis measures between the studied regions (FDR P < 0.001), except an FA map that failed to show significant differences between the lesion and perilesional white matter (FDR P = 0.373). Effect size analysis showed that kurtosis metrics were found to be 18.8% (RK, P = 0.144) to 29.1% (AK, P < 0.05) more sensitive in discriminating perilesional regions from the lesion than corresponding diffusion metrics, whereas AK provided a 25.0% (P < 0.05) increase in sensitivity in discriminating perilesional and contralateral white matter. RK was found to be the most sensitive to contralateral white matter differences between low-grade gliomas and controls, with MK and RK providing a significantly greater sensitivity of 587.2% (P < 0.001) and 320.7% (P < 0.001) than MD and RD, respectively. DATA CONCLUSION: Kurtosis maps showed increased sensitivity, as compared to counterpart diffusion maps, for evaluation of microstructural changes in gliomas with a 3-6-fold increment in assessing changes in contralateral white matter. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:1551-1558.

A convolutional neural network to filter artifacts in spectroscopic MRI.

PURPOSE: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. METHODS: A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. RESULTS: When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. CONCLUSION: The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning.

Spatial Relationship of Glioma Volume Derived from 18F-FET PET and Volumetric MR Spectroscopy Imaging: A Hybrid PET/MRI Study.

PET imaging of amino acid transport using O-(2-18F-fluoroethyl)-l-tyrosine (18F-FET) and proton MR spectroscopy (MRS) imaging of cell turnover measured by the ratio of choline to N-acetyl-aspartate (Cho/NAA) may provide additional information on tumor extent of cerebral gliomas compared with anatomic imaging; however, comparative studies are rare. Methods: In this prospective study, 41 patients (16 women, 25 men; mean age ± SD, 48 ± 14 y) with cerebral gliomas (World Health Organization [WHO] grade II: 10 [including 1 patient with 2 lesions], WHO III: 17, WHO IV: 13, without biopsy low-grade: 1, high-grade: 1) were investigated with a hybrid PET/MR scanner. Tumor extent, spatial overlap, and the distance between the corresponding centers of mass in 18F-FET PET and MRS imaging of Cho/NAA, determined by simultaneously acquired, 3-dimensional spatially resolved MRS imaging data, were compared. Results: The average tumor volumes for 18F-FET uptake and increased Cho/NAA were 19 ± 20 cm3 (mean ± SD) and 22 ± 24 cm3, respectively, with an overlap of 40% ± 25% and separation of the centers of mass by 9 ± 8 mm. None of the parameters showed a significant correlation with tumor grade. Conclusion:18F-FET uptake and increased Cho/NAA ratio are not always congruent and may represent different properties of glioma metabolism. The relationship to histologic tumor extent needs to be further analyzed.

Association of Radiomics and Metabolic Tumor Volumes in Radiation Treatment of Glioblastoma Multiforme.

PURPOSE: To build a framework for investigation of the associations between imaging, clinical target volumes (CTVs), and metabolic tumor volumes (MTVs) features for better understanding of the underlying information in the CTVs and dependencies between these volumes. High-throughput extraction of imaging and metabolomic quantitative features from magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging of glioblastoma multiforme (GBM) results in tens of variables per patient. In radiation therapy of GBM the relevant metabolic tumor volumes (MTVs) are related to aberrant levels of N-acetyl aspartate (NAA) and choline (Cho). The corresponding clinical target volumes (CTVs) for radiation therapy are based on contrast-enhanced T1-weighted (CE-T1w) and T2-weighted (T2w)/fluid-attenuated inversion recovery MRI. METHODS AND MATERIALS: Necrotic portions, enhancing lesion, and edema were manually contoured on CE-T1w/T2w images for 17 GBM patients. Clinical target volumes and MTVs for NAA (MTVNAA) and Cho (MTVCho) were constructed. Imaging and metabolic features related to size, shape, and signal intensities of the volumes were extracted. Tumors were also scored categorically for 10 semantic imaging traits by a neuroradiologist. All features were investigated for redundancy. Two-way correlations between imaging and CTVs/MTVs features were visualized as heatmaps. Associations between MTVNAA and MTVCho and imaging features were studied using Spearman correlation. RESULTS: Forty-eight imaging features were extracted per patient. Half of the imaging traits were replaced with automatically extracted continuous variables. Twenty features were extracted from CTVs and MTVs. A series of semantic imaging traits were replaced with automatically extracted continuous variables. There were multiple (22) significant correlations of imaging measures with CTVs/MTVNAA, whereas there were only 6 with CTVs/MTVCho. CONCLUSIONS: A framework for investigation of codependencies between MRI and magnetic resonance spectroscopic imaging radiomic features and CTVs/MTVs has been established. The MTV for NAA was found to be closely associated with MRI volumes, whereas very few imaging features were related to MTVCho, indicating that Cho provides additional information to imaging.

Denoising of MR spectroscopic imaging data using statistical selection of principal components.

OBJECTIVES: To evaluate a new denoising method for MR spectroscopic imaging (MRSI) data based on selection of signal-related principal components (SSPCs) from principal components analysis (PCA). MATERIALS AND METHODS: A PCA-based method was implemented for selection of signal-related PCs and denoising achieved by reconstructing the original data set utilizing only these PCs. Performance was evaluated using simulated MRSI data and two volumetric in vivo MRSIs of human brain, from a normal subject and a patient with a brain tumor, using variable signal-to-noise ratios (SNRs), metabolite peak areas, Cramer-Rao bounds (CRBs) of fitted metabolite peak areas and metabolite linewidth. RESULTS: In simulated data, SSPC determined the correct number of signal-related PCs. For in vivo studies, the SSPC denoising resulted in improved SNRs and reduced metabolite quantification uncertainty compared to the original data and two other methods for denoising. The method also performed very well in preserving the spectral linewidth and peak areas. However, this method performs better for regions that have larger numbers of similar spectra. CONCLUSION: The proposed SSPC denoising improved the SNR and metabolite quantification uncertainty in MRSI, with minimal compromise of the spectral information, and can result in increased accuracy.

Radiation injury to the normal brain measured by 3D-echo-planar spectroscopic imaging and diffusion tensor imaging: initial experience.

BACKGROUND AND PURPOSE: Whole brain radiation therapy (WBRT) may cause cognitive and neuropsychological impairment and hence objective assessment of adverse effects of radiation may be valuable to plan therapy. The purpose of our study was to determine the potential of echo planar spectroscopic imaging (EPSI) and diffusion tensor imaging (DTI) in detecting subacute radiation induced injury to the normal brain. MATERIALS AND METHODS: Four patients with brain metastases and three patients with lung cancer underwent cranial irradiation. These patients were subjected to 3D-EPSI and DTI at two time points (pre-radiation, and 1 month post-irradiation). Parametric maps of N-acetyl aspartate (NAA), creatine (Cr), choline (Cho), mean diffusivity (MD), and fractional anisotropy (FA) were generated and co-registered to post-contrast T1-weighted images. Normal appearing gray-matter and white-matter regions were compared between the two time points to assess sub-acute effects of radiation using independent sample t-tests. RESULTS: Significantly increased MD (P = .02), Cho/Cr (P = .02) and a trend towards a decrease in NAA/Cr (P = .06) was observed from the hippocampus. Significant decrease in FA (P = .02) from the centrum-semiovale and a significant increase in MD (P = .04) and Cho/Cr (P = .02) from genu of corpus-callosum was also observed. CONCLUSIONS: Our preliminary findings suggest that 3D-EPSI and DTI may provide quantitative measures of radiation induced injury to the normal brain.

Volumetric spectroscopic imaging of glioblastoma multiforme radiation treatment volumes.

PURPOSE: Magnetic resonance (MR) imaging and computed tomography (CT) are used almost exclusively in radiation therapy planning of glioblastoma multiforme (GBM), despite their well-recognized limitations. MR spectroscopic imaging (MRSI) can identify biochemical patterns associated with normal brain and tumor, predominantly by observation of choline (Cho) and N-acetylaspartate (NAA) distributions. In this study, volumetric 3-dimensional MRSI was used to map these compounds over a wide region of the brain and to evaluate metabolite-defined treatment targets (metabolic tumor volumes [MTV]). METHODS AND MATERIALS: Volumetric MRSI with effective voxel size of ∼1.0 mL and standard clinical MR images were obtained from 19 GBM patients. Gross tumor volumes and edema were manually outlined, and clinical target volumes (CTVs) receiving 46 and 60 Gy were defined (CTV46 and CTV60, respectively). MTVCho and MTVNAA were constructed based on volumes with high Cho and low NAA relative to values estimated from normal-appearing tissue. RESULTS: The MRSI coverage of the brain was between 70% and 76%. The MTVNAA were almost entirely contained within the edema, and the correlation between the 2 volumes was significant (r=0.68, P=.001). In contrast, a considerable fraction of MTVCho was outside of the edema (median, 33%) and for some patients it was also outside of the CTV46 and CTV60. These untreated volumes were greater than 10% for 7 patients (37%) in the study, and on average more than one-third (34.3%) of the MTVCho for these patients were outside of CTV60. CONCLUSIONS: This study demonstrates the potential usefulness of whole-brain MRSI for radiation therapy planning of GBM and revealed that areas of metabolically active tumor are not covered by standard RT volumes. The described integration of MTV into the RT system will pave the way to future clinical trials investigating outcomes in patients treated based on metabolic information.

Clinical proton MR spectroscopy in central nervous system disorders.

A large body of published work shows that proton (hydrogen 1 [(1)H]) magnetic resonance (MR) spectroscopy has evolved from a research tool into a clinical neuroimaging modality. Herein, the authors present a summary of brain disorders in which MR spectroscopy has an impact on patient management, together with a critical consideration of common data acquisition and processing procedures. The article documents the impact of (1)H MR spectroscopy in the clinical evaluation of disorders of the central nervous system. The clinical usefulness of (1)H MR spectroscopy has been established for brain neoplasms, neonatal and pediatric disorders (hypoxia-ischemia, inherited metabolic diseases, and traumatic brain injury), demyelinating disorders, and infectious brain lesions. The growing list of disorders for which (1)H MR spectroscopy may contribute to patient management extends to neurodegenerative diseases, epilepsy, and stroke. To facilitate expanded clinical acceptance and standardization of MR spectroscopy methodology, guidelines are provided for data acquisition and analysis, quality assessment, and interpretation. Finally, the authors offer recommendations to expedite the use of robust MR spectroscopy methodology in the clinical setting, including incorporation of technical advances on clinical units.

Association of metabolite concentrations and water diffusivity in normal appearing brain tissue with glioma grade.

BACKGROUND AND PURPOSE: Studies of brain tumors have identified altered tissue metabolism and water diffusion in MRI normal appearing tissue regions. In this retrospective study the relationship of these imaging measures with tumor grade in gliomas was investigated. METHODS: MR spectroscopic imaging of whole brain and mean diffusivity (MD) measurements were obtained in subjects with untreated glioma and from normal control subjects. Mean metabolite values for N-acetylaspartate (NAA), total creatine (Cre), and total choline (Cho) were obtained in gray- and white-matter regions for the hemisphere contralateral to the tumor location, and MD values were obtained from contralateral normal-appearing white matter. Analyses tested for differences in mean values between subject groups while accounting for age. RESULTS: Analysis demonstrated increased NAA/Cre and MD, and decreased Cho/NAA for all tumor grades relative to control values. Differences between tumor grades were also observed for NAA, NAA/Cre, and Cho/NAA. Abnormal values of water diffusion were also observed, but with only a weak association between alterations in diffusion and tissue metabolites. CONCLUSIONS: This study supports previous observations of altered tissue metabolism and water diffusion in normal-appearing white matter while additionally finding differences of metabolite values in gray matter and an association with tumor grade.