Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status.

OBJECTIVES: To investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer. METHODS: Pre-menopausal women aged 40-50 years old were scanned at 3 T, employing a standard breast protocol including a DCE-MRI (35 and 30 participants in high- and low-risk groups, respectively). The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts. RESULTS: Intra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk. CONCLUSIONS: This study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement. KEY POINTS: • A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention. • Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection. • No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.

Improving fiducial and prostate capsule visualization for radiotherapy planning using MRI.

BACKGROUND AND PURPOSE: Intraprostatic fiducial markers (FM) improve the accuracy of radiotherapy (RT) delivery. Here we assess geometric integrity and contouring consistency using a T2*-weighted (T2*W) sequence alone, which allows visualization of the FM. MATERIAL AND METHODS: Ten patients scanned within the Prostate Advances in Comparative Evidence (PACE) trial (NCT01584258) had prostate images acquired with computed tomography (CT) and Magnetic Resonance (MR) Imaging: T2-weighted (T2W) and T2*W sequences. The prostate was contoured independently on each imaging dataset by three clinicians. Interobserver variability was assessed using comparison indices with Monaco ADMIRE (research version 2.0, Elekta AB) and examined for statistical differences between imaging sets. CT and MR images of two test objects were acquired to assess geometric distortion and accuracy of marker positioning. The first was a linear test object comprising straight tubes in three orthogonal directions, the second was a smaller test object with markers suspended in gel. RESULTS: Interobserver variability for prostate contouring was lower for both T2W and T2*W compared to CT, this was statistically significant when comparing CT and T2*W images. All markers are visible in T2*W images with 29/30 correctly identified, only 3/30 are visible in T2W images. Assessment of geometric distortion revealed in-plane displacements were under 0.375 mm in MRI, and through plane displacements could not be detected. The signal loss in the MR images is symmetric in relation to the true marker position shown in CT images. CONCLUSION: Prostate T2*W images are geometrically accurate, and yield consistent prostate contours. This single sequence can be used to identify FM and for prostate delineation in a mixed MR-CT workflow.

The contribution of diffusion tensor imaging and magnetic resonance spectroscopy for the differentiation of breast lesions at 3T.

BACKGROUND: Conventional breast magnetic resonance imaging (MRI), including dynamic contrast-enhanced MR mammography (DCE-MRM), may lead to ambiguous diagnosis and unnecessary biopsies. PURPOSE: To investigate the contribution of proton MR spectroscopy (1H-MRS) combined with diffusion tensor imaging (DTI) metrics in the discrimination between benign and malignant breast lesions. MATERIAL AND METHODS: Fifty-one women with known breast abnormalities from conventional imaging were examined on a 3T MR scanner. DTI was performed during breast MRI, and fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were measured in the breast lesions and the contralateral normal breast. FA and ADC were compared between malignant lesions, benign lesions, and normal tissue. 1H-MRS was performed after gadolinium administration and choline peak was qualitatively evaluated. RESULTS: In our study 1H-MRS showed a sensitivity of 93.5%, specificity 80%, and accuracy 88.2%. FA was significantly higher in breast carcinomas compared to benign lesions. However, no significant difference was observed in ADC between benign and malignant lesions. The combination of Cho presence and FA achieved higher levels of accuracy and specificity in discriminating malignant from benign lesions over Cho presence or FA alone. CONCLUSION: In conclusion, applying DTI and 1H-MRS together, adds incremental diagnostic value in the characterization of breast lesions and may sufficiently improve the low specificity of conventional breast MRI.

Application value of 3T ¹H-magnetic resonance spectroscopy in diagnosing breast tumors.

BACKGROUND: Assessment of breast lesions with magnetic resonance imaging (MRI) provides a means for lesion detection and diagnosis. Proton (hydrogen-1) magnetic resonance spectroscopy ((1)H-MRS) has been proposed as a useful diagnostic technique in providing metabolic information of suspicious breast lesions. PURPOSE: To determine the clinical significance of in-vivo single voxel (1)H-MRS at 3T in the assessment of benign and malignant breast lesions in combination with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIAL AND METHODS: Twenty-four women with known breast abnormalities from conventional imaging (mammography, ultrasonography) underwent DCE-MRI at a 3T MR scanner and 26 breast lesions were detected. Breast lesions were assessed according BI-RADS classification. Single voxel (1)H-MRS was performed after gadolinium administration and choline peak was qualitatively evaluated. All lesions were confirmed histologically from the surgically excised specimens. Sensitivity, specificity, and accuracy of the (1)H-MRS, of the BI-RADS classification and of their combination (DCE-MRI + (1)H-MRS) were calculated. RESULTS: Fifteen out of 26 lesions proved to be malignant and 11 proved to be benign. In our study (1)H-MRS showed sensitivity 80%, specificity 81.8%, and accuracy 80.7%. DCE-MRI showed sensitivity 100%, specificity 63.6%, and accuracy 84.6%. The combination of DCE-MRI and (1)H-MRS provided higher accuracy (96.4%), as well as higher specificity 81.8% compared to BI-RADS classification. CONCLUSION: The combined use of (1)H-MRS and DCE-MRI found to have improved diagnostic performance in the assessment of equivocal breast lesions. (1)H-MRS can be used as a useful adjunct during lesion characterization in clinical routine in cases classified as BI-RADS 3 and 4.

Descriptive analysis of MRI functional changes occurring during reduced dose radiotherapy for myxoid liposarcomas.

OBJECTIVES: Myxoid liposarcomas (MLS) show enhanced response to radiotherapy due to their distinctive vascular pattern and therefore could be effectively treated with lower radiation doses. This is a descriptive study to explore the use of functional MRI to identify response in a uniform cohort of MLS patients treated with reduced dose radiotherapy. METHODS: 10 patients with MLS were imaged pre-, during, and post-radiotherapy receiving reduced dose radiotherapy and the response to treatment was histopathologically assessed post-radiotherapy. Apparent diffusion coefficient (ADC), T2* relaxation time, volume transfer constant (Ktrans), initial area under the gadolinium curve over 60 s (IAUGC60) and (Gd) were estimated for a central tumour volume. RESULTS: All parameters showed large inter- and intrasubject variabilities. Pre-treatment (Gd), IAUGC60 and Ktrans were significantly different between responders and non-responders. Post-radiotherapy reductions from baseline were demonstrated for T2*, (Gd), IAUGC60 and Ktrans for responders. No statistically significant ADC differences were demonstrated between the two response groups. Significantly greater early tumour volume reductions were observed for responders. CONCLUSIONS: MLS are heterogenous lesions, characterised by a slow gradual contrast-agent uptake. Pre-treatment vascular parameters, early changes to tumour volume, vascular parameters and T2* have potential in identifying response to treatment. The delayed (Gd) is a suitable descriptive parameter, relying simply on T1 measurements. Volume changes precede changes in MLS functionality and could be used to identify early response. ADVANCES IN KNOWLEDGE: MLS are are characterised by slow gradual contrast-agent uptake. Measurement of the delayed contrast-agent uptake (Gd) is simple to implement and able to discriminate response.

An automated approach for the optimised estimation of breast density with Dixon methods.

OBJECTIVE: To present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments. METHODS: Dixon images were acquired in 14 subjects with different T1 weightings (flip angles, FA, 4°/16°). Our method corrects intensity differences between water (W) and fat (F) images via the application of a uniform scaling factor (SF), determined subject-by-subject. Based on the postulation that optimal SFs yield relatively featureless summed fat/scaled-water (F+WSF) images, each SF was chosen as that which generated the lowest 95th-percentile in the absolute spatial-gradient image-volume of F+WSF . Water-fraction maps were calculated for data acquired with low/high FAs, and BD (%) was the total percentage water within each breast volume. RESULTS: Corrected/uncorrected BD ranged from, respectively, 10.9-71.8%/8.9-66.7% for low-FA data to 8.1-74.3%/5.6-54.3% for high-FA data. Corrected metrics had an average absolute increase in BD of 6.4% for low-FA data and 18.4% for high-FA data. BD values estimated from low- and high-FA data were closer following SF-correction. CONCLUSION: Our results demonstrate need for scaling in such BD assessments, where our method brought high-FA and low-FA data into closer agreement. ADVANCES IN KNOWLEDGE: We demonstrated a feasible method to address a main source of inaccuracy in Dixon-based BD measurements.

Neuroimaging methods in Epilepsy of Temporal Origin.

BACKGROUND: Temporal Lobe Epilepsy (TLE) comprises the most common form of symptomatic refractory focal epilepsy in adults. Accurate lateralization and localization of the epileptogenic focus are a significant prerequisite for determining surgical candidacy once the patient has been deemed medically intractable. Structural MR imaging, clinical, electrophysiological, and neurophysiological data have an established role in the localization of the epileptogenic foci. Nevertheless, hippocampal sclerosis cannot be detected on MR images in more than 30% of patients with TLE, and the presurgical assessment remains controversial. DISCUSSION: In the last years, advanced MR imaging techniques, such as 1H-MRS, DWI, DTI, DSCI, and fMRI, may provide valuable additional information regarding the physiological and metabolic characterization of brain tissue. MR imaging has shifted towards functional and molecular imaging, thus, promising to improve the accuracy regarding the lateralization and the localization of the epileptogenic focus. Additionally, nuclear medicine studies, such as SPECT and PET imaging modalities, have become an asset for the decoding of brain function and activity, and can be diagnostically helpful as well, since they provide valuable data regarding the altered metabolic activity of the seizure foci. CONCLUSION: Overall, advanced MRI, SPECT, and PET imaging techniques are increasingly becoming an essential part of TLE diagnostics, when the epileptogenic area is not identified on structural MRI or when structural MRI, clinical, and electrophysiological findings are not in concordance.

An evaluation of systematic errors on marker-based registration of computed tomography and magnetic resonance images of the liver.

We demonstrated a general method to evaluate systematic errors related to Magnetic Resonance (MR) imaging sequences in marker-based co-registration of MR and Computed Tomography (CT) images, and investigated the effect of MR image quality in the co-registration process using clinical MR and CT protocols for stereotactic ablative body radiotherapy (SABR) planning of the liver. Small systematic errors (under 1.6 mm) were detected, unlikely to be a clinical risk to liver SABR. The least favourable marker configuration was found to be a co-planar arrangement parallel to the transaxial image plane.

Pre-treatment functional MRI of breast cancer: T2* evaluation at 3 T and relationship to dynamic contrast-enhanced and diffusion-weighted imaging.

PURPOSE: Baseline T2* relaxation time has been proposed as an imaging biomarker in cancer, in addition to Dynamic Contrast-Enhanced (DCE) MRI and diffusion-weighted imaging (DWI) parameters. The purpose of the current work is to investigate sources of error in T2* measurements and the relationship between T2* and DCE and DWI functional parameters in breast cancer. METHODS: Five female volunteers and thirty-two women with biopsy proven breast cancer were scanned at 3 T, with Research Ethics Committee approval. T2* values of the normal breast were acquired from high-resolution, low-resolution and fat-suppressed gradient-echo sequences in volunteers, and compared. In breast cancer patients, pre-treatment T2*, DCE MRI and DWI were performed at baseline. Pathologically complete responders at surgery and non-responders were identified and compared. Principal component analysis (PCA) and cluster analysis (CA) were performed. RESULTS: There were no significant differences between T2* values from high-resolution, low-resolution and fat-suppressed datasets (p > 0.05). There were not significant differences between baseline functional parameters in responders and non-responders (p > 0.05). However, there were differences in the relationship between T2* and contrast-agent uptake in responders and non-responders. Voxels of similar characteristics were grouped in 5 clusters, and large intra-tumoural variations of all parameters were demonstrated. CONCLUSION: Breast T2* measurements at 3 T are robust, but spatial resolution should be carefully considered. T2* of breast tumours at baseline is unrelated to DCE and DWI parameters and contribute towards describing functional heterogeneity of breast tumours.

Quantitative evaluation of contrast agent uptake in standard fat-suppressed dynamic contrast-enhanced MRI examinations of the breast.

PURPOSE: To propose a method to quantify T1 and contrast agent uptake in breast dynamic contrast-enhanced (DCE) examinations undertaken with standard clinical fat-suppressed MRI sequences and to demonstrate the proposed approach by comparing the enhancement characteristics of lobular and ductal carcinomas. METHODS: A standard fat-suppressed DCE of the breast was performed at 1.5 T (Siemens Aera), followed by the acquisition of a proton density (PD)-weighted sequence, also fat suppressed. Both sequences were characterized with test objects (T1 ranging from 30 ms to 2,400 ms) and calibration curves were obtained to enable T1 calculation. The reproducibility and accuracy of the calibration curves were also investigated. Healthy volunteers and patients were scanned with Ethics Committee approval. The effect of B0 field inhomogeneity was assessed in test objects and healthy volunteers. The T1 of breast tumors was calculated at different time points (pre-, peak-, and post-contrast agent administration) for 20 patients, pre-treatment (10 lobular and 10 ductal carcinomas) and the two cancer types were compared (Wilcoxon rank-sum test). RESULTS: The calibration curves proved to be highly reproducible (coefficient of variation under 10%). T1 measurements were affected by B0 field inhomogeneity, but frequency shifts below 50 Hz introduced only 3% change to fat-suppressed T1 measurements of breast parenchyma in volunteers. The values of T1 measured pre-, peak-, and post-contrast agent administration demonstrated that the dynamic range of the DCE sequence was correct, that is, image intensity is approximately directly proportional to 1/T1 for that range. Significant differences were identified in the width of the distributions of the post-contrast T1 values between lobular and ductal carcinomas (P < 0.05); lobular carcinomas demonstrated a wider range of post-contrast T1 values, potentially related to their infiltrative growth pattern. CONCLUSIONS: This work has demonstrated the feasibility of fat-suppressed T1 measurements as a tool for clinical studies. The proposed quantitative approach is practical, enabled the detection of differences between lobular and invasive ductal carcinomas, and further enables the optimization of DCE protocols by tailoring the dynamic range of the sequence to the values of T1 measured.

Quality assurance in MRI breast screening: comparing signal-to-noise ratio in dynamic contrast-enhanced imaging protocols.

MRI has been extensively used in breast cancer staging, management and high risk screening. Detection sensitivity is paramount in breast screening, but variations of signal-to-noise ratio (SNR) as a function of position are often overlooked. We propose and demonstrate practical methods to assess spatial SNR variations in dynamic contrast-enhanced (DCE) breast examinations and apply those methods to different protocols and systems. Four different protocols in three different MRI systems (1.5 and 3.0 T) with receiver coils of different design were employed on oil-filled test objects with and without uniformity filters. Twenty 3D datasets were acquired with each protocol; each dataset was acquired in under 60 s, thus complying with current breast DCE guidelines. In addition to the standard SNR calculated on a pixel-by-pixel basis, we propose other regional indices considering the mean and standard deviation of the signal over a small sub-region centred on each pixel. These regional indices include effects of the spatial variation of coil sensitivity and other structured artefacts. The proposed regional SNR indices demonstrate spatial variations in SNR as well as the presence of artefacts and sensitivity variations, which are otherwise difficult to quantify and might be overlooked in a clinical setting. Spatial variations in SNR depend on protocol choice and hardware characteristics. The use of uniformity filters was shown to lead to a rise of SNR values, altering the noise distribution. Correlation between noise in adjacent pixels was associated with data truncation along the phase encoding direction. Methods to characterise spatial SNR variations using regional information were demonstrated, with implications for quality assurance in breast screening and multi-centre trials.

Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data.

INTRODUCTION: A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification. METHODS: MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data. RESULTS: The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79% in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible. CONCLUSIONS: FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.

A quantitative evaluation of damage in normal appearing white matter in patients with multiple sclerosis using diffusion tensor MR imaging at 3 T.

The white matter (WM) of the brain is damaged in multiple sclerosis (MS), even in areas that appear normal on standard MR imaging. The purpose of our study is to evaluate the damage of normal appearing white matter (NAWM) in patients with MS. In our study, 84 MS patients and 42 healthy adults underwent a routine brain MRI, including also diffusion tensor imaging (DTI). All studies were performed on a 3 T MRI scanner. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were obtained. The DTI parameters of NAWM were correlated with expanded disability status scales (EDSS) scores. Our results showed statistically significant differences in FA and ADC values between MS plaques and the symmetrical NAWM, as also between NAWM and the respective white matter in controls. The ADC values of the NAWM correlated with the EDSS scores. The present study demonstrated damage of the NAWM in MS patients, using DTI in 3.0 T. DTI may be used in the detection of subtle damage of the white matter.

The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives.

The role of conventional Magnetic Resonance Imaging (MRI) in the detection of cerebral tumors has been well established. However its excellent soft tissue visualization and variety of imaging sequences are in many cases non-specific for the assessment of brain tumor grading. Hence, advanced MRI techniques, like Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and Dynamic-Susceptibility Contrast Imaging (DSCI), which are based on different contrast principles, have been used in the clinical routine to improve diagnostic accuracy. The variety of quantitative information derived from these techniques provides significant structural and functional information in a cellular level, highlighting aspects of the underlying brain pathophysiology. The present work, reviews physical principles and recent results obtained using DWI/DTI and DSCI, in tumor characterization and grading of the most common cerebral neoplasms, and discusses how the available MR quantitative data can be utilized through advanced methods of analysis, in order to optimize clinical decision making.

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques.

In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and non-invasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data.

PURPOSE: Differentiation of glioblastomas from metastases is clinical important, but may be difficult even for expert observers. To investigate the contribution of machine learning algorithms in the differentiation of glioblastomas multiforme (GB) from metastases, we developed and tested a pattern recognition system based on 3T magnetic resonance (MR) data. MATERIALS AND METHODS: Single and multi-voxel proton magnetic resonance spectroscopy (1H-MRS) and dynamic susceptibility contrast (DSC) MRI scans were performed on 49 patients with solitary brain tumors (35 glioblastoma multiforme and 14 metastases). Metabolic (NAA/Cr, Cho/Cr, (Lip [Formula: see text] Lac)/Cr) and perfusion (rCBV) parameters were measured in both intratumoral and peritumoral regions. The statistical significance of these parameters was evaluated. For the classification procedure, three datasets were created to find the optimum combination of parameters that provides maximum differentiation. Three machine learning methods were utilized: Naïve-Bayes, Support Vector Machine (SVM) and [Formula: see text]-nearest neighbor (KNN). The discrimination ability of each classifier was evaluated with quantitative performance metrics. RESULTS: Glioblastoma and metastases were differentiable only in the peritumoral region of these lesions ([Formula: see text]). SVM achieved the highest overall performance (accuracy 98%) for both the intratumoral and peritumoral areas. Naïve-Bayes and KNN presented greater variations in performance. The proper selection of datasets plays a very significant role as they are closely correlated to the underlying pathophysiology. CONCLUSION: The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.

Distinct peak at 3.8 ppm observed by 3T MR spectroscopy in meningiomas, while nearly absent in high-grade gliomas and cerebral metastases.

The purpose of the present study was to evaluate distinct metabolic features of meningiomas to distinguish them from other brain lesions using proton magnetic resonance spectroscopy. The study was performed on 17 meningiomas, 24 high-grade gliomas and 9 metastases. Elevated signal intensity at 3.8 ppm observed in low TE spectra adequately differentiated meningioma from other brain tumors while alanine was not indicative of meningioma occurrence; the presence of lipids and lactate did not provide a strong index for meningioma malignancy.

Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T.

PURPOSE: To assess the contribution of (1)H-magnetic resonance spectroscopy (1H-MRS), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic susceptibility contrast-enhanced (DSCE) imaging metrics in the differentiation of glioblastomas from solitary metastasis, and particularly to clarify the controversial reports regarding the hypothesis that there should be a significant differentiation between the intratumoral and peritumoral areas. METHODS: Conventional MR imaging, (1)H-MRS, DWI, DTI and DSCE MRI was performed on 49 patients (35 glioblastomas multiforme, 14 metastases) using a 3.0-T MR unit. Metabolite ratios, apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV) were measured in the intratumoral and peritumoral regions of the lesions. Receiver-operating characteristic analysis was used to obtain the cut-off values for the parameters presenting a statistical difference between the two tumor groups. Furthermore, we investigated the potential effect of the region of interest (ROI) size on the quantification of diffusion properties in the intratumoral region of the lesions, by applying two different ROI methods. RESULTS: Peritumoral N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/Cr, Cho/NAA and rCBV significantly differentiated glioblastomas from intracranial metastases. ADC and FA presented no significant difference between the two tumor groups. CONCLUSIONS: 1H-MRS and dynamic susceptibility measurements in the peritumoral regions may definitely aid in the differentiation of glioblastomas and solitary metastases. The quantification of the diffusion properties in the intratumoral region is independent of the ROI size placed.