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• Journal article
  Yang G, Jones TL, Howe FA, Barrick TRet al., 2015,

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

  , Magnetic Resonance in Medicine, Vol: 75, Pages: 2505-2516, ISSN: 0740-3194

  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.

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• Journal article
  Yang G, Nawaz T, Barrick T, Howe F, Slabaugh Get al., 2015,

  Discrete Wavelet Transform Based Whole-Spectral and Sub-Spectral Analysis for Improved Brain Tumour Clustering using Single Voxel MR Spectroscopy

  , IEEE Transactions on Biomedical Engineering, Vol: 62, Pages: 2860-2866, ISSN: 0018-9294

  Many approaches have been considered for automatic grading of brain tumours by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumour grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or sub-spectral 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 tumours. The combination of DWT-based whole-spectral or sub-spectral 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.

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• Journal article
  Bosaily AE-S, Parker C, Brown LC, Gabe R, Hindley RG, Kaplan R, Emberton M, Ahmed HUet al., 2015,

  PROMIS - Prostate MR imaging study: A paired validating cohort study evaluating the role of multi-parametric MRI in men with clinical suspicion of prostate cancer

  , CONTEMPORARY CLINICAL TRIALS, Vol: 42, Pages: 26-40, ISSN: 1551-7144
  • Author Web Link
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  • Citations: 84
• Patent
  Yang G, Barrick T, Howe F, 2015,

  Analysing MRI Data to Determine Tumour Type

  , WO/2015/079235
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• Journal article
  Montaldo P, Pauliah SS, Lally PJ, Olson L, Thayyil Set al., 2015,

  Cooling in a low-resource environment: Lost in translation

  , SEMINARS IN FETAL & NEONATAL MEDICINE, Vol: 20, Pages: 72-79, ISSN: 1744-165X
  • Author Web Link
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  • Citations: 54
• Patent
  Yang G, Barrick T, Jones T, Howe Fet al., 2015,

  Analysing MRI Data to Determine Tumour Type

  , WO/2015/040434
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• Journal article
  Grech-Sollars M, Hales PW, Miyazaki K, Raschke F, Rodriguez D, Wilson M, Gill SK, Banks T, Saunders DE, Clayden JD, Gwilliam MN, Barrick TR, Morgan PS, Davies NP, Rossiter J, Auer DP, Grundy R, Leach MO, Howe FA, Peet AC, Clark CAet al., 2015,

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

  , NMR in Biomedicine, Vol: 28, Pages: 468-485, ISSN: 0952-3480

  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.

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• Conference paper
  Wang H, Bangerter NK, Chen L, Adluru G, DiBella EVRet al., 2015,

  Radial CAIPIRINHA for Rapid 6 Slice Myocardial Perfusion Without Magnetization Preparation

  , ISMRM 23rd Annual Meeting & Exhibition
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• Journal article
  Scott AD, Nielles-Vallespin S, Ferreira P, McGill LA, Pennell DJ, Firmin Det al., 2015,

  Improving the accuracy of cardiac DTI by averaging the complex data

  , Journal of Cardiovascular Magnetic Resonance, Pages: 1-3, ISSN: 1097-6647
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• Journal article
  McGill LA, Scott AD, Ferreira P, Nielles-Vallespin S, Ismail TF, Kilner PJ, Gatehouse P, Prasad SK, Giannakidis A, Firmin D, Pennell DJet al., 2015,

  Heterogeneity of diffusion tensor imaging measurements of fractional anisotropy and mean diffusivity in normal human hearts in vivo

  , Journal of Cardiovascular Magnetic Resonance, Vol: 17, ISSN: 1097-6647
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