Literature DB >> 20886568

Diffusion imaging of brain tumors.

Stephan E Maier1, Yanping Sun, Robert V Mulkern.   

Abstract

MRI offers a tremendous armamentarium of different methods that can be employed in brain tumor characterization. MR diffusion imaging has become a widely accepted method to probe for the presence of fluid pools and molecular tissue water mobility. For most clinical applications of diffusion imaging, it is assumed that the diffusion signal vs diffusion weighting factor b decays monoexponentially. Within this framework, the measurement of a single diffusion coefficient in brain tumors permits an approximate categorization of tumor type and, for some tumors, definitive diagnosis. In most brain tumors, when compared with normal brain tissue, the diffusion coefficient is elevated. The presence of peritumoral edema, which also exhibits an elevated diffusion coefficient, often precludes the delineation of the tumor on the basis of diffusion information alone. Serially obtained diffusion data are useful to document and even predict the cellular response to drug or radiation therapy. Diffusion measurements in tissues over an extended range of b factors have clearly shown that the monoparametric description of the MR diffusion signal decay is incomplete. Very high diffusion weighting on clinical systems requires substantial compromise in spatial resolution. However, after suitable analysis, superior separation of malignant brain tumors, peritumoral edema and normal brain tissue can be achieved. These findings are also discussed in the light of tissue-specific differences in membrane structure and the restrictions exerted by membranes on diffusion. Finally, measurement of the directional dependence of diffusion permits the assessment of white matter integrity and dislocation. Such information, particularly in conjunction with advanced post-processing, is considered to be immensely useful for therapy planning. Diffusion imaging, which permits monoexponential analysis and provides directional diffusion information, is performed routinely in brain tumor patients. More advanced methods require improvement in acquisition speed and spatial resolution to gain clinical acceptance.
Copyright © 2010 John Wiley & Sons, Ltd.

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Year:  2010        PMID: 20886568      PMCID: PMC3000221          DOI: 10.1002/nbm.1544

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  109 in total

1.  Highly diffusion-sensitized MRI of brain: dissociation of gray and white matter.

Authors:  T Yoshiura; O Wu; A Zaheer; T G Reese; A G Sorensen
Journal:  Magn Reson Med       Date:  2001-05       Impact factor: 4.668

2.  Biexponential apparent diffusion coefficient parametrization in adult vs newborn brain.

Authors:  R V Mulkern; S Vajapeyam; R L Robertson; P A Caruso; M J Rivkin; S E Maier
Journal:  Magn Reson Imaging       Date:  2001-06       Impact factor: 2.546

3.  Multishot diffusion-weighted FSE using PROPELLER MRI.

Authors:  James G Pipe; Victoria G Farthing; Kirsten P Forbes
Journal:  Magn Reson Med       Date:  2002-01       Impact factor: 4.668

4.  Tumor involvement of the corticospinal tract: diffusion magnetic resonance tractography with intraoperative correlation.

Authors:  A I Holodny; T H Schwartz; M Ollenschleger; W C Liu; M Schulder
Journal:  J Neurosurg       Date:  2001-12       Impact factor: 5.115

5.  Normal brain and brain tumor: multicomponent apparent diffusion coefficient line scan imaging.

Authors:  S E Maier; P Bogner; G Bajzik; H Mamata; Y Mamata; I Repa; F A Jolesz; R V Mulkern
Journal:  Radiology       Date:  2001-06       Impact factor: 11.105

6.  Monitoring response to convection-enhanced taxol delivery in brain tumor patients using diffusion-weighted magnetic resonance imaging.

Authors:  Y Mardor; Y Roth; Z Lidar; T Jonas; R Pfeffer; S E Maier; M Faibel; D Nass; M Hadani; A Orenstein; J S Cohen; Z Ram
Journal:  Cancer Res       Date:  2001-07-01       Impact factor: 12.701

7.  Quantitative MR evaluation of intracranial epidermoid tumors by fast fluid-attenuated inversion recovery imaging and echo-planar diffusion-weighted imaging.

Authors:  S Chen; F Ikawa; K Kurisu; K Arita; J Takaba; Y Kanou
Journal:  AJNR Am J Neuroradiol       Date:  2001 Jun-Jul       Impact factor: 3.825

8.  The role of diffusion-weighted imaging in patients with brain tumors.

Authors:  K Kono; Y Inoue; K Nakayama; M Shakudo; M Morino; K Ohata; K Wakasa; R Yamada
Journal:  AJNR Am J Neuroradiol       Date:  2001 Jun-Jul       Impact factor: 3.825

9.  Evaluating pediatric brain tumor cellularity with diffusion-tensor imaging.

Authors:  K M Gauvain; R C McKinstry; P Mukherjee; A Perry; J J Neil; B A Kaufman; R J Hayashi
Journal:  AJR Am J Roentgenol       Date:  2001-08       Impact factor: 3.959

10.  Validation of diffusion tensor magnetic resonance axonal fiber imaging with registered manganese-enhanced optic tracts.

Authors:  C P Lin; W Y Tseng; H C Cheng; J H Chen
Journal:  Neuroimage       Date:  2001-11       Impact factor: 6.556

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  53 in total

1.  In vivo modeling of interstitial pressure in a porcine model: approximation of poroelastic properties and effects of enhanced anatomical structure modeling.

Authors:  Saramati Narasimhan; Jared A Weis; Hernán F J González; Reid C Thompson; Michael I Miga
Journal:  J Med Imaging (Bellingham)       Date:  2018-12-06

Review 2.  The use of diffusion weighted imaging to evaluate pathology outside the brain parenchyma in neuroimaging studies.

Authors:  Philip Benjamin; Faraan Khan; Andrew D MacKinnon
Journal:  Br J Radiol       Date:  2017-02-14       Impact factor: 3.039

3.  Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model.

Authors:  Yi Sui; He Wang; Guanzhong Liu; Frederick W Damen; Christian Wanamaker; Yuhua Li; Xiaohong Joe Zhou
Journal:  Radiology       Date:  2015-06-02       Impact factor: 11.105

4.  Mean Diffusional Kurtosis in Patients with Glioma: Initial Results with a Fast Imaging Method in a Clinical Setting.

Authors:  A Tietze; M B Hansen; L Østergaard; S N Jespersen; R Sangill; T E Lund; M Geneser; M Hjelm; B Hansen
Journal:  AJNR Am J Neuroradiol       Date:  2015-05-14       Impact factor: 3.825

5.  Differentiating low- and high-grade pediatric brain tumors using a continuous-time random-walk diffusion model at high b-values.

Authors:  M Muge Karaman; Yi Sui; He Wang; Richard L Magin; Yuhua Li; Xiaohong Joe Zhou
Journal:  Magn Reson Med       Date:  2015-10-31       Impact factor: 4.668

6.  Distinct effects of nuclear volume fraction and cell diameter on high b-value diffusion MRI contrast in tumors.

Authors:  Nathan S White; Anders M Dale
Journal:  Magn Reson Med       Date:  2013-12-19       Impact factor: 4.668

7.  Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer.

Authors:  David A Hormuth; Jared A Weis; Stephanie L Barnes; Michael I Miga; Vito Quaranta; Thomas E Yankeelov
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-12-13       Impact factor: 7.038

8.  The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter.

Authors:  Susie Y Huang; Aapo Nummenmaa; Thomas Witzel; Tanguy Duval; Julien Cohen-Adad; Lawrence L Wald; Jennifer A McNab
Journal:  Neuroimage       Date:  2014-12-09       Impact factor: 6.556

9.  The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE).

Authors:  Filip Szczepankiewicz; Danielle van Westen; Elisabet Englund; Carl-Fredrik Westin; Freddy Ståhlberg; Jimmy Lätt; Pia C Sundgren; Markus Nilsson
Journal:  Neuroimage       Date:  2016-07-20       Impact factor: 6.556

10.  Quantitative multiparametric MRI assessment of glioma response to radiotherapy in a rat model.

Authors:  Xiaohua Hong; Li Liu; Meiyun Wang; Kai Ding; Ying Fan; Bo Ma; Bachchu Lal; Betty Tyler; Antonella Mangraviti; Silun Wang; John Wong; John Laterra; Jinyuan Zhou
Journal:  Neuro Oncol       Date:  2013-12-22       Impact factor: 12.300

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