Literature DB >> 16489190

Enhanced visualization and quantification of magnetic resonance diffusion tensor imaging using the p:q tensor decomposition.

A Peña1, H A L Green, T A Carpenter, S J Price, J D Pickard, J H Gillard.   

Abstract

Many scalar measures have been proposed to quantify magnetic resonance diffusion tensor imaging (MR DTI) data in the brain. However, only two parameters are commonly used in the literature: mean diffusion (D) and fractional anisotropy (FA). We introduce a visualization technique which permits the simultaneous analysis of an additional five scalar measures. This enhanced diversity is important, as it is not known a priori which of these measures best describes pathological changes for brain tissue. The proposed technique is based on a tensor transformation, which decomposes the diffusion tensor into its isotropic (p) and anisotropic (q) components. To illustrate the use of this technique, diffusion tensor imaging was performed on a healthy volunteer, a sequential study in a patient with recent stroke, a patient with hydrocephalus and a patient with an intracranial tumour. Our results demonstrate a clear distinction between different anatomical regions in the normal volunteer and the evolution of the pathology in the patients. In the normal volunteer, the brain parenchyma values for p and q fell into a narrow band with 0.976<p<1.063 x 10(-3) mm2 s(-1) and 0.15<q<1.08 x 10(-3) mm2 s(-1). The noise appeared as a compact cluster with (p,q) components (0.011, 0.141) x 10(-3) mm2 s(-1), while the cerebrospinal fluid was (3.320, 0.330) x 10(-3) mm2 s(-1). In the stroke patient, the ischaemic area demonstrated a trajectory composed of acute, sub-acute and chronic phases. The components of the lesion were (0.824, 0.420), (0.884, 0.254), (2.624, 0.325) at 37 h, 1 week and 1 month, respectively. The internal capsule of the hydrocephalus patient demonstrated a larger dispersion in the p:q plane suggesting disruption. Finally, there was clear white matter tissue destruction in the tumour patient. In summary, the p:q decomposition enhances the visualization and quantification of MR DTI data in both normal and pathological conditions.

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Year:  2006        PMID: 16489190     DOI: 10.1259/bjr/24908512

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  22 in total

1.  Diffusion tensor imaging findings in young children with benign external hydrocephalus differ from the normal population.

Authors:  M Sun; W Yuan; D A Hertzler; A Cancelliere; M Altaye; F T Mangano
Journal:  Childs Nerv Syst       Date:  2011-12-14       Impact factor: 1.475

Review 2.  Methodology of diffusion-weighted, diffusion tensor and magnetisation transfer imaging.

Authors:  S J Price; D J Tozer; J H Gillard
Journal:  Br J Radiol       Date:  2011-12       Impact factor: 3.039

3.  Evaluation of low-grade glioma structural changes after chemotherapy using DTI-based histogram analysis and functional diffusion maps.

Authors:  Antonella Castellano; Marina Donativi; Roberta Rudà; Giorgio De Nunzio; Marco Riva; Antonella Iadanza; Luca Bertero; Matteo Rucco; Lorenzo Bello; Riccardo Soffietti; Andrea Falini
Journal:  Eur Radiol       Date:  2015-08-30       Impact factor: 5.315

4.  Diagnostic performance of regional DTI-derived tensor metrics in glioblastoma multiforme: simultaneous evaluation of p, q, L, Cl, Cp, Cs, RA, RD, AD, mean diffusivity and fractional anisotropy.

Authors:  David Cortez-Conradis; Rafael Favila; Keila Isaac-Olive; Manuel Martinez-Lopez; Camilo Rios; Ernesto Roldan-Valadez
Journal:  Eur Radiol       Date:  2012-10-21       Impact factor: 5.315

5.  Increased intratumoral infiltration in IDH wild-type lower-grade gliomas observed with diffusion tensor imaging.

Authors:  Eric Aliotta; Prem P Batchala; David Schiff; Beatriz M Lopes; Jason T Druzgal; Sugoto Mukherjee; Sohil H Patel
Journal:  J Neurooncol       Date:  2019-09-17       Impact factor: 4.130

6.  Combining Diffusion Tensor Metrics and DSC Perfusion Imaging: Can It Improve the Diagnostic Accuracy in Differentiating Tumefactive Demyelination from High-Grade Glioma?

Authors:  S B Hiremath; A Muraleedharan; S Kumar; C Nagesh; C Kesavadas; M Abraham; T R Kapilamoorthy; B Thomas
Journal:  AJNR Am J Neuroradiol       Date:  2017-02-16       Impact factor: 3.825

7.  Partial correlation analyses of global diffusion tensor imaging-derived metrics in glioblastoma multiforme: Pilot study.

Authors:  David Cortez-Conradis; Camilo Rios; Sergio Moreno-Jimenez; Ernesto Roldan-Valadez
Journal:  World J Radiol       Date:  2015-11-28

Review 8.  Imaging biomarkers of brain tumour margin and tumour invasion.

Authors:  S J Price; J H Gillard
Journal:  Br J Radiol       Date:  2011-12       Impact factor: 3.039

Review 9.  Assessing and monitoring intratumor heterogeneity in glioblastoma: how far has multimodal imaging come?

Authors:  Natalie R Boonzaier; Sara G M Piccirillo; Colin Watts; Stephen J Price
Journal:  CNS Oncol       Date:  2015-10-26

Review 10.  Artificial intelligence in tumor subregion analysis based on medical imaging: A review.

Authors:  Mingquan Lin; Jacob F Wynne; Boran Zhou; Tonghe Wang; Yang Lei; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  J Appl Clin Med Phys       Date:  2021-06-24       Impact factor: 2.102

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