Characterisation of paediatric brain tumours by their MRS metabolite profiles.

1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.

Optimization of quasi-diffusion magnetic resonance imaging for quantitative accuracy and time-efficient acquisition.

PURPOSE: Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, D 1 , 2 $$ {D}_{1,2} $$ in mm2  s-1 and a fractional exponent, α $$ \upalpha $$ , defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. METHODS: Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between b = 0 $$ b=0 $$ and 5000 s mm-2 . The effects of varying maximum b-value ( b max $$ {b}_{\mathrm{max}} $$ ), number of b-value shells, and the effects of Rician noise were investigated. RESULTS: QDTI measures showed b max $$ {b}_{\mathrm{max}} $$ dependence, most significantly for α $$ \upalpha $$ in white matter, which monotonically decreased with higher b max $$ {b}_{\mathrm{max}} $$ leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of D 1 , 2 $$ \kern0.50em {D}_{1,2} $$ and underestimation of α $$ \upalpha $$ in white matter, and overestimation of D 1 , 2 $$ {D}_{1,2} $$ and α $$ \upalpha $$ anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at b max = 5000 $$ {b}_{\mathrm{max}}=5000 $$  s mm-2 , and 4 b-value shells at b max = 3960 $$ {b}_{\mathrm{max}}=3960 $$  s mm-2 , providing minimal bias in D 1 , 2 $$ {D}_{1,2} $$ and α $$ \upalpha $$ compared to the MbR. CONCLUSION: A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.

The PASTIS trial: Testing tadalafil for possible use in vascular cognitive impairment.

INTRODUCTION: There are few randomized clinical trials in vascular cognitive impairment (VCI). This trial tested the hypothesis that the PDE5 inhibitor tadalafil, a widely used vasodilator, increases cerebral blood flow (CBF) in older people with symptomatic small vessel disease, the main cause of VCI. METHODS: In a double-blind, placebo-controlled, cross-over trial, participants received tadalafil (20 mg) and placebo on two visits ≥7 days apart (randomized to order of treatment). The primary endpoint, change in subcortical CBF, was measured by arterial spin labelling. RESULTS: Tadalafil increased CBF non-significantly in all subcortical areas (N = 55, age: 66.8 (8.6) years) with greatest treatment effect within white matter hyperintensities (+9.8%, P = .0960). There were incidental treatment effects on systolic and diastolic blood pressure (-7.8, -4.9 mmHg; P < .001). No serious adverse events were observed. DISCUSSION: This trial did not identify a significant treatment effect of single-administration tadalafil on subcortical CBF. To detect treatment effects may require different dosing regimens.

Systematic Review of Magnetic Resonance Lymphangiography From a Technical Perspective.

BACKGROUND: Clinical examination and lymphoscintigraphy are the current standard for investigating lymphatic function. Magnetic resonance imaging (MRI) facilitates three-dimensional (3D), nonionizing imaging of the lymphatic vasculature, including functional assessments of lymphatic flow, and may improve diagnosis and treatment planning in disease states such as lymphedema. PURPOSE: To summarize the role of MRI as a noninvasive technique to assess lymphatic drainage and highlight areas in need of further study. STUDY TYPE: Systematic review. POPULATION: In October 2019, a systematic literature search (PubMed) was performed to identify articles on magnetic resonance lymphangiography (MRL). FIELD STRENGTH/SEQUENCE: No field strength or sequence restrictions. ASSESSMENT: Article quality assessment was conducted using a bespoke protocol, designed with heavy reliance on the National Institutes of Health quality assessment tool for case series studies and Downs and Blacks quality checklist for health care intervention studies. STATISTICAL TESTS: The results of the original research articles are summarized. RESULTS: From 612 identified articles, 43 articles were included and their protocols and results summarized. Field strength was 1.5 or 3.0 T in all studies, with 25/43 (58%) employing 3.0 T imaging. Most commonly, imaging of the peripheries, upper and lower limbs including the pelvis (32/43, 74%), and the trunk (10/43, 23%) is performed, including two studies covering both regions. Imaging protocols were heterogenous; however, T2 -weighted and contrast-enhanced T1 -weighted images are routinely acquired and demonstrate the lymphatic vasculature. Edema, vessel, quantity and morphology, and contrast uptake characteristics are commonly reported indicators of lymphatic dysfunction. DATA CONCLUSION: MRL is uniquely placed to yield large field of view, qualitative and quantitative, 3D imaging of the lymphatic vasculature. Despite study heterogeneity, consensus is emerging regarding MRL protocol design. MRL has the potential to dramatically improve understanding of the lymphatics and detect disease, but further optimization, and research into the influence of study protocol differences, is required before this is fully realized. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Analyses of peripheral blood dendritic cells and magnetic resonance spectroscopy support dysfunctional neuro-immune crosstalk in Tourette syndrome.

BACKGROUND: Evidence supports that neurodevelopmental diseases, such as Tourette syndrome (TS), may involve dysfunctional neural-immune crosstalk. This could lead to altered brain maturation and differences in immune and stress responses. Dendritic cells (DCs) play a major role in immunity as professional antigen-presenting cells; changes in their frequency have been observed in several autoimmune conditions. METHODS: In 18 TS patients (15 on stable pharmacological treatment, three unmedicated) and 18 age-matched healthy volunteers (HVs), we explored circulating blood-derived DCs and their relationship with clinical variables and brain metabolites, measured via proton magnetic resonance spectroscopy (1H-MRS). DC subsets, including plasmacytoid and myeloid type 1 and 2 dendritic cells (MDC1, MDC2), were studied with flow cytometry. 1H-MRS was used to measure total choline, glutamate plus glutamine, total creatine (tCr), and total N-acetylaspartate and N-acetylaspartyl-glutamate levels in frontal white matter (FWM) and the putamen. RESULTS: We did not observe differences in absolute concentrations of DC subsets or brain inflammatory metabolites between patients and HVs. However, TS patients manifesting anxiety showed a significant increase in MDC1s compared to TS patients without anxiety (p = 0.01). We also found a strong negative correlation between MDC1 frequency and tCr in the FWM of patients with TS (p = 0.0015), but not of HVs. CONCLUSION: Elevated frequencies of the MDC1 subset in TS patients manifesting anxiety may reflect a proinflammatory status, potentially facilitating altered neuro-immune crosstalk. Furthermore, the strong inverse correlation between brain tCr levels and MDC1 subset frequency in TS patients suggests a potential association between proinflammatory status and metabolic changes in sensitive brain regions.

Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique.

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1-4 â€‹min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s-1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 â€‹s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena.

Methodological consensus on clinical proton MRS of the brain: Review and recommendations.

Proton MRS (1 H 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.

Assessment of the direct effects of DDAH I on tumour angiogenesis in vivo.

Nitric oxide (NO) has been strongly implicated in glioma progression and angiogenesis. The endogenous inhibitors of NO synthesis, asymmetric dimethylarginine (ADMA) and N-monomethyl-L-arginine (L-NMMA), are metabolized by dimethylarginine dimethylaminohydrolase (DDAH), and hence, DDAH is an intracellular factor that regulates NO. However, DDAH may also have an NO-independent action. We aimed to investigate whether DDAH I has any direct role in tumour vascular development and growth independent of its NO-mediated effects, in order to establish the future potential of DDAH inhibition as an anti-angiogenic treatment strategy. A clone of rat C6 glioma cells deficient in NO production expressing a pTet Off regulatable element was identified and engineered to overexpress DDAH I in the absence of doxycycline. Xenografts derived from these cells were propagated in the presence or absence of doxycycline and susceptibility magnetic resonance imaging used to assess functional vasculature in vivo. Pathological correlates of tumour vascular density, maturation and function were also sought. In the absence of doxycycline, tumours exhibited high DDAH I expression and activity, which was suppressed in its presence. However, overexpression of DDAH I had no measurable effect on tumour growth, vessel density, function or maturation. These data suggest that in C6 gliomas DDAH has no NO-independent effects on tumour growth and angiogenesis, and that the therapeutic potential of targeting DDAH in gliomas should only be considered in the context of NO regulation.

Reduced anterior cingulate grey matter volume in painful hand osteoarthritis.

OBJECTIVE: Increasing evidence supports the role of central sensitisation in osteoarthritis (OA) pain. In this study, we used neuroimaging to compare pain-processing regions of the brain in participants with and without hand OA. We then assessed for volumetric changes in these brain regions following treatment with centrally acting analgesics. METHODS: Participants with hand OA (n = 28) underwent T1-weighted MRI of the brain before and after 12 weeks of treatment with pregabalin, duloxetine or placebo. Grey matter volume in the anterior cingulate cortex (ACC), insular cortex and thalamus was compared to non-OA control subjects (n = 11) using FreeSurfer regional volumetric analysis and voxel-based morphometry, and evaluated for differences pre- and post-treatment. RESULTS: Relative to non-OA controls, hand OA participants had areas of reduced grey matter volume in the ACC at baseline (p = 0.007). Regional volumetric differences in the ACC persisted after 13 weeks' treatment with pregabalin or duloxetine (p = 0.004) with no significant differences between treatment cohorts, despite improvements in NRS pain scores for pregabalin (p = 0.005) and duloxetine (p = 0.050). The ACC grey matter changes persisted despite a significant improvement in pain in the pregabalin and duloxetine groups vs. placebo. No structural differences were observed in the insular cortex or thalamus at baseline or following treatment. CONCLUSION: Our study found evidence of reduced ACC grey matter volume in participants with hand arthritis that persisted after treatment with centrally acting analgesics pregabalin and duloxetine, respectively. The sustained changes observed in the ACC in our study could reflect the relatively short duration of treatment, or that the differences observed are irreversible volume changes due to chronic pain that are established over time.

Microarray analysis of bone marrow lesions in osteoarthritis demonstrates upregulation of genes implicated in osteochondral turnover, neurogenesis and inflammation.

OBJECTIVE: Bone marrow lesions (BMLs) are well described in osteoarthritis (OA) using MRI and are associated with pain, but little is known about their pathological characteristics and gene expression. We evaluated BMLs using novel tissue analysis tools to gain a deeper understanding of their cellular and molecular expression. METHODS: We recruited 98 participants, 72 with advanced OA requiring total knee replacement (TKR), 12 with mild OA and 14 non-OA controls. Participants were assessed for pain (using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)) and with a knee MRI (using MOAKS). Tissue was then harvested at TKR for BML analysis using histology and tissue microarray. RESULTS: The mean (SD) WOMAC pain scores were significantly increased in advanced OA 59.4 (21.3) and mild OA 30.9 (20.3) compared with controls 0.5 (1.28) (p<0.0001). MOAKS showed all TKR tissue analysed had BMLs, and within these lesions, bone marrow volume was starkly reduced being replaced by dense fibrous connective tissue, new blood vessels, hyaline cartilage and fibrocartilage. Microarray comparing OA BML and normal bone found a significant difference in expression of 218 genes (p<0.05). The most upregulated genes included stathmin 2, thrombospondin 4, matrix metalloproteinase 13 and Wnt/Notch/catenin/chemokine signalling molecules that are known to constitute neuronal, osteogenic and chondrogenic pathways. CONCLUSION: Our study is the first to employ detailed histological analysis and microarray techniques to investigate knee OA BMLs. BMLs demonstrated areas of high metabolic activity expressing pain sensitisation, neuronal, extracellular matrix and proinflammatory signalling genes that may explain their strong association with pain.

Can MRI T1 be used to detect early changes in 5xFAD Alzheimer's mouse brain?

OBJECTIVES: In the present study, we have tested whether MRI T1 relaxation time is a sensitive marker to detect early stages of amyloidosis and gliosis in the young 5xFAD transgenic mouse, a well-established animal model for Alzheimer's disease. MATERIALS AND METHODS: 5xFAD and wild-type mice were imaged in a 4.7 T Varian horizontal bore MRI system to generate T1 quantitative maps using the spin-echo multi-slice sequence. Following immunostaining for glial fibrillary acidic protein, Iba-1, and amyloid-ß, T1 and area fraction of staining were quantified in the posterior parietal and primary somatosensory cortex and corpus callosum. RESULTS: In comparison with age-matched wild-type mice, we observed first signs of amyloidosis in 2.5-month-old 5xFAD mice, and development of gliosis in 5-month-old 5xFAD mice. In contrast, MRI T1 relaxation times of young, i.e., 2.5- and 5-month-old, 5xFAD mice were not significantly different to those of age-matched wild-type controls. Furthermore, although disease progression was detectable by increased amyloid-ß load in the brain of 5-month-old 5xFAD mice compared with 2.5-month-old 5xFAD mice, MRI T1 relaxation time did not change. CONCLUSIONS: In summary, our data suggest that MRI T1 relaxation time is neither a sensitive measure of disease onset nor progression at early stages in the 5xFAD mouse transgenic mouse model.

Investigating the role of tumour cell derived iNOS on tumour growth and vasculature in vivo using a tetracycline regulated expression system.

Nitric oxide (NO) is a free radical signalling molecule involved in various physiological and pathological processes, including cancer. Both tumouricidal and tumour promoting effects have been attributed to NO, making its role in cancer biology controversial and unclear. To investigate the specific role of tumour-derived NO in vascular development, C6 glioma cells were genetically modified to include a doxycycline regulated gene expression system that controls the expression of an antisense RNA to inducible nitric oxide synthase (iNOS) to manipulate endogenous iNOS expression. Xenografts of these cells were propagated in the presence or absence of doxycycline. Susceptibility magnetic resonance imaging (MRI), initially with a carbogen (95% O2/5% CO2) breathing challenge and subsequently an intravascular blood pool contrast agent, was used to assess haemodynamic vasculature (ΔR2*) and fractional blood volume (fBV), and correlated with histopathological assessment of tumour vascular density, maturation and function. Inhibition of NO production in C6 gliomas led to significant growth delay and inhibition of vessel maturation. Parametric fBV maps were used to identify vascularised regions from which the carbogen-induced ΔR2* was measured and found to be positively correlated with vessel maturation, quantified ex vivo using fluorescence microscopy for endothelial and perivascular cell staining. These data suggest that tumour-derived iNOS is an important mediator of tumour growth and vessel maturation, hence a promising target for anti-vascular cancer therapies. The combination of ΔR2* response to carbogen and fBV MRI can provide a marker of tumour vessel maturation that could be applied to non-invasively monitor treatment response to iNOS inhibitors.

Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three-dimensional shape analysis.

PURPOSE: Glioblastoma multiforme (GBM) and brain metastasis (MET) are the most common intra-axial brain neoplasms in adults and often pose a diagnostic dilemma using standard clinical MRI. These tumor types require different oncological and surgical management, which subsequently influence prognosis and clinical outcome. METHODS: Here, we hypothesize that GBM and MET possess different three-dimensional (3D) morphological attributes based on their physical characteristics. A 3D morphological analysis was applied on the tumor surface defined by our diffusion tensor imaging (DTI) segmentation technique. It segments the DTI data into clusters representing different isotropic and anisotropic water diffusion characteristics, from which a distinct surface boundary between healthy and pathological tissue was identified. Morphometric features of shape index and curvedness were then computed for each tumor surface and used to build a morphometric model of GBM and MET pathology with the goal of developing a tumor classification method based on shape characteristics. RESULTS: Our 3D morphometric method was applied on 48 untreated brain tumor patients. Cross-validation resulted in a 95.8% accuracy classification with only two shape features needed and that can be objectively derived from quantitative imaging methods. CONCLUSION: The proposed 3D morphometric analysis framework can be applied to distinguish GBMs from solitary METs. Magn Reson Med 75:2505-2516, 2016. © 2015 Wiley Periodicals, Inc.

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering.

PURPOSE: To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method. METHODS: In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. RESULTS: An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. CONCLUSION: The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis.

(1) H 2D MRSI tissue type analysis of gliomas.

PURPOSE: To decompose 1H MR spectra of glioma patients into normal and abnormal tissue proportions for tumor classification and delineation. METHODS: Anatomical imaging and 1H magnetic resonance spectroscopic imaging data have been acquired from 11 grade II and 13 grade IV glioma patients. LCModel was used to decompose the magnetic resonance spectroscopic imaging data into normal brain, grade II, and grade IV tissue proportions using a tissue type basis set. Simulations were conducted to evaluate the accuracy of the methodology. Results were visualized using colormaps and abnormality contours showing tumor grade and extent. RESULTS: Simulations suggest that infiltrative tumor proportions as low as 20% can be identified at the typical 1H magnetic resonance spectroscopy signal-to-noise found in vivo. Tumor grading according to the highest estimated tumor grade within a lesion gave a classification accuracy of 86% discriminating between grade II and grade IV glioma. Voxels with significant proportions of tumor type spectra were found beyond the margins of contrast enhancement for most grade IV cases consistent with infiltration whereas the abnormality contours show that some tumors are confined within the hyperintensities shown by both post contrast T1 weighted and T2 weighted imaging. CONCLUSION: LCModel can be used to decompose 1H MR spectra into proportions of normal and abnormal tissue to identify tumor extent, infiltration, and overall grade.

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.

Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain.

The purpose of this work was to assess the reproducibility of diffusion imaging, and in particular the apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM) parameters and diffusion tensor imaging (DTI) parameters, across multiple centres using clinically available protocols with limited harmonization between sequences. An ice-water phantom and nine healthy volunteers were scanned across fives centres on eight scanners (four Siemens 1.5T, four Philips 3T). The mean ADC, IVIM parameters (diffusion coefficient D and perfusion fraction f) and DTI parameters (mean diffusivity MD and fractional anisotropy FA), were measured in grey matter, white matter and specific brain sub-regions. A mixed effect model was used to measure the intra- and inter-scanner coefficient of variation (CV) for each of the five parameters. ADC, D, MD and FA had a good intra- and inter-scanner reproducibility in both grey and white matter, with a CV ranging between 1% and 7.4%; mean 2.6%. Other brain regions also showed high levels of reproducibility except for small structures such as the choroid plexus. The IVIM parameter f had a higher intra-scanner CV of 8.4% and inter-scanner CV of 24.8%. No major difference in the inter-scanner CV for ADC, D, MD and FA was observed when analysing the 1.5T and 3T scanners separately. ADC, D, MD and FA all showed good intra-scanner reproducibility, with the inter-scanner reproducibility being comparable or faring slightly worse, suggesting that using data from multiple scanners does not have an adverse effect compared with using data from the same scanner. The IVIM parameter f had a poorer inter-scanner CV when scanners of different field strengths were combined, and the parameter was also affected by the scan acquisition resolution. This study shows that the majority of diffusion MRI derived parameters are robust across 1.5T and 3T scanners and suitable for use in multi-centre clinical studies and trials.

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.

Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging.

The management and treatment of high-grade glioblastoma multiforme (GBM) and solitary metastasis (MET) are very different and influence the prognosis and subsequent clinical outcomes. In the case of a solitary MET, diagnosis using conventional radiology can be equivocal. Currently, a definitive diagnosis is based on histopathological analysis on a biopsy sample. Here, we present a computerised decision support framework for discrimination between GBM and solitary MET using MRI, which includes: (i) a semi-automatic segmentation method based on diffusion tensor imaging; (ii) two-dimensional morphological feature extraction and selection; and (iii) a pattern recognition module for automated tumour classification. Ground truth was provided by histopathological analysis from pre-treatment stereotactic biopsy or at surgical resection. Our two-dimensional morphological analysis outperforms previous methods with high cross-validation accuracy of 97.9% and area under the receiver operating characteristic curve of 0.975 using a neural networks-based classifier.

Multicentre evaluation of the INTERPRET decision support system 2.0 for brain tumour classification.

In a previous study, we have shown the added value of (1) H MRS for the neuroradiological characterisation of adult human brain tumours. In that study, several methods of MRS analysis were used, and a software program, the International Network for Pattern Recognition of Tumours Using Magnetic Resonance Decision Support System 1.0 (INTERPRET DSS 1.0), with a short-TE classifier, provided the best results. Since then, the DSS evolved into a version 2.0 that contains an additional long-TE classifier. This study has two objectives. First, to determine whether clinicians with no experience of spectroscopy are comparable with spectroscopists in the use of the system, when only minimum training in the use of the system was given. Second, to assess whether or not a version with another TE is better than the initial version. We undertook a second study with the same cases and nine evaluators to assess whether the diagnostic accuracy of DSS 2.0 was comparable with the values obtained with DSS 1.0. In the second study, the analysis protocol was flexible in comparison with the first one to mimic a clinical environment. In the present study, on average, each case required 5.4 min by neuroradiologists and 9 min by spectroscopists for evaluation. Most classes and superclasses of tumours gave the same results as with DSS 1.0, except for astrocytomas of World Health Organization (WHO) grade III, in which performance measured as the area under the curve (AUC) decreased: AUC = 0.87 (0.72-1.02) with DSS 1.0 and AUC = 0.62 (0.55-0.70) with DSS 2.0. When analysing the performance of radiologists and spectroscopists with respect to DSS 1.0, the results were the same for most classes. Having data with two TEs instead of one did not affect the results of the evaluation.