Magnetic resonance lymphangiography: Establishing normal.

BACKGROUND: Despite an increased interest in visualizing the lymphatic vessels with magnetic resonance lymphangiography (MRL), little literature is available describing their appearance in nonlymphedematous individuals. To determine lymphatic abnormalities, an understanding of how healthy lymphatic vessels appear and behave needs to be established. Therefore, in this study, MRL of individuals without a history of lymphatic disease was performed. METHODS: A total of 25 individuals (15 women) underwent MRL of their lower limbs using a 3.0 T Philips magnetic resonance imaging scanner (Philips Medical Systems). The first nine participants were recruited to establish the concentration of gadolinium-based contrast agent (GBCA) to administer, with the remainder imaged before and after interdigital forefoot GBCA injections at the optimized dose. Outcomes, including lymphatic vessel diameter, tortuosity, and frequency of drainage via particular drainage routes, were recorded. RESULTS: Healthy lymphatic vessels following the anteromedial pathway were routinely observed in post-contrast T1-weighted images (average tortuosity, 1.09 ± 0.03), with an average of 2.16 ± 0.93 lymphatic vessels with a diameter of 2.47 ± 0.50 mm crossing the anterior ankle. In six limbs, vessels following the anterolateral pathways were observed. No vessels traversing the posterior of the legs were seen. In a subset of 10 vessels, the lymphatic signal, measured at the ankle, peaked 29 minutes, 50 seconds ± 9 minutes, 29 seconds after GBCA administration. No lymphatic vessels were observed in T2-weighted images. CONCLUSIONS: Contrast-enhanced MRL reliably depicts the lymphatic vessels in the legs of healthy controls. Following interdigital contrast injection, anteromedial drainage appears dominant. Quantitative measures related to lymphatic vessel size, tortuosity, and drainage rate are readily obtainable and could be beneficial for detecting even subtle lymphatic impairment.

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.

Radiofrequency thalamotomy for tremor produces focused and predictable lesions shown on magnetic resonance images.

Radiofrequency thalamotomy is a neurosurgical management option for medically-refractory tremor. In this observational study, we evaluate the MRI features of the resultant lesion, their temporal dynamics, and how they vary depending on surgical factors. We report on lesion characteristics including size and location, as well as how these vary over time and across different MRI sequences. Data from 12 patients (2 essential tremor, 10 Parkinson's disease) who underwent unilateral radiofrequency thalamotomy for tremor were analysed. Lesion characteristics were compared across five structural sequences. Volumetric analysis of lesion features was performed at early (<5 weeks) and late (>5 months) timepoints by manual segmentation. Lesion location was determined after registration of lesions to standard space. All patients showed tremor improvement (clinical global impressions scale) postoperatively. Chronic side-effects included balance disturbances (n = 4) and worsening mobility due to parkinsonism progression (n = 1). Early lesion features including a necrotic core, cytotoxic oedema and perilesional oedema were best demarcated on T2-weighted sequences. Multiple lesions were associated with greater cytotoxic oedema compared with single lesions (T2-weighted mean volume: 537 ± 112 mm³ versus 302 ± 146 mm³, P = 0.028). Total lesion volume reduced on average by 90% between the early and late scans (T2-weighted mean volume: 918 ± 517 versus 75 ± 50 mm³, t = 3.592, P = 0.023, n = 5), with comparable volumes demonstrated at ∼6 months after surgery. Lesion volumes on susceptibility-weighted images were larger than those of T2-weighted images at later timepoints. Radiofrequency thalamotomy produces focused and predictable lesion imaging characteristics over time. T2-weighted scans distinguish between the early lesion core and oedema characteristics, while lesions may remain more visible on susceptibility-weighted images in the months following surgery. Scanning patients in the immediate postoperative period and then at 6 months is clinically meaningful for understanding the anatomical basis of the transient and permanent effects of thalamotomy.

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.

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.

Tissue-type mapping of gliomas.

PURPOSE: To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. MATERIALS AND METHODS: We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of "pure" low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T2-weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. RESULTS: Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p < 0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n = 16) tumours, in grade II gemistocytic rich astrocytomas (n = 3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n = 20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p < 0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival >2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1H MRS characteristics. CONCLUSIONS: 1H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours.

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.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

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.

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

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

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.

Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy.

Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.

Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.

BACKGROUND: There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. METHODS: DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. RESULTS: Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. CONCLUSIONS: D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning.

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.

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.

(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.

New insights into the impact of neuro-inflammation in rheumatoid arthritis.

Rheumatoid arthritis (RA) is considered to be, in many respects, an archetypal autoimmune disease that causes activation of pro-inflammatory pathways resulting in joint and systemic inflammation. RA remains a major clinical problem with the development of several new therapies targeted at cytokine inhibition in recent years. In RA, biologic therapies targeted at inhibition of tumor necrosis factor alpha (TNFα) have been shown to reduce joint inflammation, limit erosive change, reduce disability and improve quality of life. The cytokine TNFα has a central role in systemic RA inflammation and has also been shown to have pro-inflammatory effects in the brain. Emerging data suggests there is an important bidirectional communication between the brain and immune system in inflammatory conditions like RA. Recent work has shown how TNF inhibitor therapy in people with RA is protective for Alzheimer's disease. Functional MRI studies to measure brain activation in people with RA to stimulus by finger joint compression, have also shown that those who responded to TNF inhibition showed a significantly greater activation volume in thalamic, limbic, and associative areas of the brain than non-responders. Infections are the main risk of therapies with biologic drugs and infections have been shown to be related to disease flares in RA. Recent basic science data has also emerged suggesting that bacterial components including lipopolysaccharide induce pain by directly activating sensory neurons that modulate inflammation, a previously unsuspected role for the nervous system in host-pathogen interactions. In this review, we discuss the current evidence for neuro-inflammation as an important factor that impacts on disease persistence and pain in RA.

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.