Publications

Search or filter publications

Search publications Search

Filter by type:

Filter by publication type

• Book
• Book chapter
• Conference paper
• Journal article
• Other
• Patent
• Poster
• Report
• Scholarly edition
• Software
• Thesis dissertation
• Working paper

Filter by year:

Filter from 2026202520242023202220212020201920182017201620152014201320122011201020092008200720062005200420032002 to Filter to 2026202520242023202220212020201920182017201620152014201320122011201020092008200720062005200420032002

---

Search results

• Journal article
  Jordan CD, McWalter EJ, Monu UD, Watkins RD, Chen W, Bangerter NK, Hargreaves BA, Gold GEet 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
  • Publisher Web Link
  • Cite
• Journal article
  Yang G, Raschke F, Barrick TR, Howe FAet al., 2014,

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

  , Magnetic Resonance in Medicine, Vol: 74, Pages: 868-878, ISSN: 0740-3194

  PurposeTo 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

  • Abstract
  • Cite
• Journal article
  Yang G, Jones TL, Barrick TR, Howe FAet 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
• Journal article
  Jones TL, Byrnes TJ, Yang G, Howe FA, Bell BA, Barrick TRet al., 2014,

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

  , Neuro-Oncology, Vol: 17, Pages: 466-476, ISSN: 1522-8517

  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.

  • Abstract
  • Cite
• Journal article
  Nielles-Vallespin S, Mekkaoui C, Gatehouse P, Reese TG, Keegan J, Ferreira PF, Collins S, Speier P, Feiweier T, de Silva R, Jackowski MP, Pennell DJ, Sosnovik DE, Firmin Det 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
  • Cite
  • Citations: 5
• Journal article
  Lally P, Pauliah S, Price D, Bainbridge A, Addison S, Soe A, Pattnayak S, Satodia P, Wayte S, Ng'andwe E, Clarke P, Johnson G, Harigopal S, English P, Abernethy LJ, Turner M, Cheong J, Shankaran S, Cady E, Thayyil Set 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 1

  Early 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.

  • Abstract
  • Author Web Link
  • Cite
• Journal article
  Lally P, Price D, Bainbridge A, Pauliah S, Satodia P, Wayte S, Abernethy L, Turner M, Basheer A, Alavi A, Kirmi O, Jones B, Shankaran S, Cady E, Thayyil Set 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-A45

  Proton 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.

  • Abstract
  • Author Web Link
  • Cite
• Journal article
  Lally P, Arthurs O, Addison S, Alavi A, Sebire NJ, Taylor A, Thayyil Set 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-A93

  Magnetic 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.

  • Abstract
  • Author Web Link
  • Cite
• Journal article
  Addison S, Arthurs O, Lally P, Alavi A, Taylor A, Sebire NJ, Thayyil Set al., 2014,

  PFM.25 Assessment of visceral maceration using post-mortem magnetic resonance imaging in fetuses.

  , Arch Dis Child Fetal Neonatal Ed, Vol: 99 Suppl 1

  Post-mortem magnetic resonance imaging (PM MRI) is increasingly used as an alternative for perinatal autopsy, however the artefacts related to maceration has not been described.

  • Abstract
  • Author Web Link
  • Cite
• Journal article
  Pauliah S, Lally P, Bainbridge A, Price D, Addison S, Cowan F, Satodia P, Wayte S, Soe A, Pattnayak S, Harigopal S, Abernethy LJ, Turner M, Clarke P, Cheong J, Basheer S, Alavi A, Shankaran S, Cady E, Thayyil Set al., 2014,

  8.8 Neonatal Encephalopathy in the Cooling Therapy era - Preliminary Cerebral Magnetic Resonance results from the Marble Consortium.

  , Arch Dis Child Fetal Neonatal Ed, Vol: 99 Suppl 1, Pages: A13-A14

  Although cerebral metabolic changes during neonatal encephalopathy (NE) have been well characterised using magnetic resonance spectroscopy (MRS) in single-centre studies, the widespread effect of therapeutic hypothermia is less clear.

  • Abstract
  • Author Web Link
  • Cite

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.