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Journal articleFerreira PF, Kilner PJ, McGill L-A, et al., 2014,
In vivo cardiovascular magnetic resonance diffusion tensor imaging shows evidence of abnormal myocardial laminar orientations and mobility in hypertrophic cardiomyopathy
, Journal of Cardiovascular Magnetic Resonance, Vol: 16, Pages: 1-16, ISSN: 1097-6647BackgroundCardiac diffusion tensor imaging (cDTI) measures the magnitudes and directions of intramyocardial water diffusion. Assuming the cross-myocyte components to be constrained by the laminar microstructures of myocardium, we hypothesized that cDTI at two cardiac phases might identify any abnormalities of laminar orientation and mobility in hypertrophic cardiomyopathy (HCM).MethodsWe performed cDTI in vivo at 3 Tesla at end-systole and late diastole in 11 healthy controls and 11 patients with HCM, as well as late gadolinium enhancement (LGE) for detection of regional fibrosis.ResultsVoxel-wise analysis of diffusion tensors relative to left ventricular coordinates showed expected transmural changes of myocardial helix-angle, with no significant differences between phases or between HCM and control groups. In controls, the angle of the second eigenvector of diffusion (E2A) relative to the local wall tangent plane was larger in systole than diastole, in accord with previously reported changes of laminar orientation. HCM hearts showed higher than normal global E2A in systole (63.9° vs 56.4° controls, p =0.026) and markedly raised E2A in diastole (46.8° vs 24.0° controls, p < 0.001). In hypertrophic regions, E2A retained a high, systole-like angulation even in diastole, independent of LGE, while regions of normal wall thickness did not (LGE present 57.8°, p =0.0028, LGE absent 54.8°, p =0.0022 vs normal thickness 38.1°).ConclusionsIn healthy controls, the angles of cross-myocyte components of diffusion were consistent with previously reported transmural orientations of laminar microstructures and their changes with contraction. In HCM, especially in hypertrophic regions, they were consistent with hypercontraction in systole and failure of relaxation in diastole. Further investigation of this finding is required as previously postulated effects of strain might be a confounding factor.
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Conference paperSoltaninejad M, Ye X, Yang G, et al., 2014,
Brain tumour grading in different MRI protocols using SVM on statistical features
, Medical Image Understanding and Analysis, Publisher: British Machine Vision AssociationIn this paper a feasibility study of brain MRI dataset classification, using ROIs which have been segmented either manually or through a superpixel based method in conjunction with statistical pattern recognition methods is presented. In our study, 471 extracted ROIs from 21 Brain MRI datasets are used, in order to establish which features distinguish better between three grading classes. Thirty-eight statistical measurements were collected from the ROIs. We found by using the Leave-One-Out method that the combination of the features from the 1st and 2nd order statistics, achieved high classification accuracy in pair-wise grading comparisons.
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Journal articleJordan CD, McWalter EJ, Monu UD, et al., 2014,
Variability of CubeQuant T1ρ, quantitative DESS T2, and cones sodium MRI in knee cartilage
, Osteoarthritis and Cartilage, Vol: 22, Pages: 1559-1567, ISSN: 1063-4584 -
Journal articleYang G, Raschke F, Barrick TR, et al., 2014,
Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering
, Magnetic Resonance in Medicine, Vol: 74, Pages: 868-878, ISSN: 0740-3194PurposeTo investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method.MethodsIn vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H 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.ResultsAn 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 1H 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.ConclusionPurposeTo investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method.MethodsIn vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H 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.ResultsAn accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishi
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Journal articleYang G, Jones TL, Barrick TR, et al., 2014,
Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the <i>p</i>:<i>q</i> tensor decomposition of diffusion tensor imaging
, NMR IN BIOMEDICINE, Vol: 27, Pages: 1103-1111, ISSN: 0952-3480- Cite
- Citations: 42
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Journal articleJones TL, Byrnes TJ, Yang G, et al., 2014,
Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique
, Neuro-Oncology, Vol: 17, Pages: 466-476, ISSN: 1522-8517Background 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.
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Journal articleNielles-Vallespin S, Mekkaoui C, Gatehouse P, et al., 2014,
In Vivo Diffusion Tensor MRI of the Human Heart: Reproducibility of Breath-Hold and Navigator Based Approaches (vol 70, pg 454, 2013)
, MAGNETIC RESONANCE IN MEDICINE, Vol: 72, Pages: 599-599, ISSN: 0740-3194- Author Web Link
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- Citations: 5
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Journal articleLally P, Pauliah S, Price D, et al., 2014,
PC.45 Quantification of N-Acetylaspartate Concentration in the Neonatal Brain: Initial Results from the Multi-Centre Marble Study.
, Arch Dis Child Fetal Neonatal Ed, Vol: 99 Suppl 1Early cerebral proton magnetic resonance spectroscopy (MRS) predicts medium-term outcomes in neonatal encephalopathy (NE). Metabolite peak-area ratios are most commonly used for prognosis, but conflate pathological information from different metabolites. N-acetylaspartate (NAA) is predominantly neuronal and neuronal loss should result in reduced NAA absolute-concentration ([NAA]). Thus, thalamic [NAA] should offer significant prognostic value but is difficult to measure in a clinical setting. We have established a protocol for multi-centre [NAA] measurement with the aim to use it as a surrogate biomarker in phase II clinical trials.
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Journal articleLally P, Price D, Bainbridge A, et al., 2014,
PC.26 Feasibility of Magnetic Resonance Spectroscopy in Examining Thalamic Metabolite Concentrations in a Multi-Centre Study of Neonatal Encephalopathy.
, Arch Dis Child Fetal Neonatal Ed, Vol: 99 Suppl 1, Pages: A44-A45Proton magnetic resonance spectroscopy (MRS) has high prognostic value in hypoxic ischaemic encephalopathy (HIE), however its multi-centre application is limited by inconsistencies between scanners and protocols. N-acetylaspartate (NAA) is predominantly neuronal: cerebral NAA concentration may be a more reliable HIE-severity biomarker than lactate/NAA.
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Journal articleLally P, Arthurs O, Addison S, et al., 2014,
PFM.33 Estimating Maceration Severity Using Whole Body Magnetic Resonance T2 Relaxometry.
, Arch Dis Child Fetal Neonatal Ed, Vol: 99 Suppl 1, Pages: A92-A93Magnetic resonance (MR) imaging is an ideal modality to observe gross global changes in tissue structure, as is present with maceration. As tissue degrades, its MR transverse relaxation time (T2) should increase, with relaxometry methods enabling quantitative measurement of this.
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Contact
For enquiries about the MRI Physics Collective, please contact:
Mary Finnegan
Senior MR Physicist at the Imperial College Healthcare NHS Trust
Pete Lally
Assistant Professor in Magnetic Resonance (MR) Physics at Imperial College
Jan Sedlacik
MR Physicist at the Robert Steiner MR Unit, Hammersmith Hospital Campus