Literature DB >> 19829742

Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and Examples.

Thomas E Yankeelov1, John C Gore.   

Abstract

Dynamic contrast enhanced MRI (DCE-MRI) enables the quantitative assessment of tumor status and has found application in both pre-clinical tumor models as well as clinical oncology. DCE-MRI requires the serial acquisition of images before and after the injection of a paramagnetic contrast agent so that the variation of MR signal intensity with time can be recorded for each image voxel. As the agent enters into a tissue, it changes the MR signal intensity from the tissue to a degree that depends on the local concentration. After the agent is transported out of the tissue, the MR signal intensity returns to its' baseline value. By analyzing the associated signal intensity time course using an appropriate mathematical model, physiological parameters related to blood flow, vessel permeability, and tissue volume fractions can be extracted for each voxel or region of interest.In this review we first discuss the basic physics of this methodology, and then present technical aspects of how DCE-MRI data are acquired and analyzed. We also discuss appropriate models of contrast agent kinetics and how these can be used to elucidate tissue characteristics of importance in cancer biology. We conclude by briefly summarizing some future goals and demands of DCE-MRI.

Entities:  

Year:  2009        PMID: 19829742      PMCID: PMC2760951          DOI: 10.2174/157340507780619179

Source DB:  PubMed          Journal:  Curr Med Imaging Rev        ISSN: 1573-4056


  117 in total

1.  Deuterium NMR tissue perfusion measurements using the tracer uptake approach: I. Optimization of methods.

Authors:  N E Simpson; Z He; J L Evelhoch
Journal:  Magn Reson Med       Date:  1999-07       Impact factor: 4.668

2.  Accurate and rapid quantitative dynamic contrast-enhanced breast MR imaging using spoiled gradient-recalled echoes and bookend T(1) measurements.

Authors:  G O Cron; G Santyr; F Kelcz
Journal:  Magn Reson Med       Date:  1999-10       Impact factor: 4.668

3.  Modeling tissue contrast agent concentration: a solution to the tissue homogeneity model using a simulated arterial input function.

Authors:  G R Moran; F S Prato
Journal:  Magn Reson Med       Date:  2001-01       Impact factor: 4.668

4.  Correlation of high-resolution breast MR imaging with histopathology; validation of a technique.

Authors:  A E Holland; R E Hendrick; H Jin; P D Russ; J O Barentsz; R Holland
Journal:  J Magn Reson Imaging       Date:  2000-06       Impact factor: 4.813

5.  Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model.

Authors:  Thomas E Yankeelov; Jeffrey J Luci; Martin Lepage; Rui Li; Laura Debusk; P Charles Lin; Ronald R Price; John C Gore
Journal:  Magn Reson Imaging       Date:  2005-05       Impact factor: 2.546

6.  MR histological correlation: a method for cutting specimens along the imaging plane in animal or ex vivo experiments.

Authors:  Olivier Rouvière; Carol Reynolds; Thomas Hulshizer; Phillip Rossman; Yuan Le; Joel P Felmlee; Richard L Ehman
Journal:  J Magn Reson Imaging       Date:  2006-01       Impact factor: 4.813

7.  Comparative study into the robustness of compartmental modeling and model-free analysis in DCE-MRI studies.

Authors:  Caleb Roberts; Basma Issa; Andrew Stone; Alan Jackson; John C Waterton; Geoffrey J M Parker
Journal:  J Magn Reson Imaging       Date:  2006-04       Impact factor: 4.813

8.  Correlation of dynamic contrast-enhanced magnetic resonance imaging with histologic tumor grade: comparison of macromolecular and small-molecular contrast media.

Authors:  H Daldrup; D M Shames; M Wendland; Y Okuhata; T M Link; W Rosenau; Y Lu; R C Brasch
Journal:  Pediatr Radiol       Date:  1998-02

9.  Dynamic contrast-enhanced MRI using macromolecular contrast media for monitoring the response to isolated limb perfusion in experimental soft-tissue sarcomas.

Authors:  A Preda; P A Wielopolski; T L M Ten Hagen; M van Vliet; J F Veenland; G Ambagtsheer; S T van Tiel; M W Vogel; A M M Eggermont; G P Krestin; C F van Dijke
Journal:  MAGMA       Date:  2004-10-10       Impact factor: 2.310

10.  Reading protocol for dynamic contrast-enhanced MR images of the breast: sensitivity and specificity analysis.

Authors:  Ruth M L Warren; Linda Pointon; Deborah Thompson; Rebecca Hoff; Fiona J Gilbert; Anwar Padhani; Doug Easton; Sunil R Lakhani; Martin O Leach
Journal:  Radiology       Date:  2005-09       Impact factor: 11.105

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  159 in total

1.  Fast cardiac T1 mapping in mice using a model-based compressed sensing method.

Authors:  Wen Li; Mark Griswold; Xin Yu
Journal:  Magn Reson Med       Date:  2011-12-09       Impact factor: 4.668

2.  Differential microstructure and physiology of brain and bone metastases in a rat breast cancer model by diffusion and dynamic contrast enhanced MRI.

Authors:  Matthew D Budde; Eric Gold; E Kay Jordan; Joseph A Frank
Journal:  Clin Exp Metastasis       Date:  2011-11-01       Impact factor: 5.150

3.  Feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory.

Authors:  Haoyu Wang; Yanwei Miao; Kun Zhou; Yanming Yu; Shanglian Bao; Qiang He; Yongming Dai; Stephanie Y Xuan; Bisher Tarabishy; Yongquan Ye; Jiani Hu
Journal:  Med Phys       Date:  2010-09       Impact factor: 4.071

4.  Calibrating a Predictive Model of Tumor Growth and Angiogenesis with Quantitative MRI.

Authors:  David A Hormuth; Angela M Jarrett; Xinzeng Feng; Thomas E Yankeelov
Journal:  Ann Biomed Eng       Date:  2019-04-08       Impact factor: 3.934

5.  The clinical value of dynamic contrast-enhanced MRI in differential diagnosis of malignant and benign ovarian lesions.

Authors:  Xian Li; Jun-Li Hu; Lai-Min Zhu; Xin-Hai Sun; Hua-Qiang Sheng; Ning Zhai; Xi-Bin Hu; Chu-Ran Sun; Bin Zhao
Journal:  Tumour Biol       Date:  2015-02-28

6.  Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

Authors:  Joshua M Goldenberg; Julio Cárdenas-Rodríguez; Mark D Pagel
Journal:  Magn Reson Med       Date:  2018-09-17       Impact factor: 4.668

7.  A comparison of individual and population-derived vascular input functions for quantitative DCE-MRI in rats.

Authors:  David A Hormuth; Jack T Skinner; Mark D Does; Thomas E Yankeelov
Journal:  Magn Reson Imaging       Date:  2014-01-07       Impact factor: 2.546

8.  Is there any correlation between model-based perfusion parameters and model-free parameters of time-signal intensity curve on dynamic contrast enhanced MRI in breast cancer patients?

Authors:  Boram Yi; Doo Kyoung Kang; Dukyong Yoon; Yong Sik Jung; Ku Sang Kim; Hyunee Yim; Tae Hee Kim
Journal:  Eur Radiol       Date:  2014-02-21       Impact factor: 5.315

9.  Dynamic Contrast-Enhanced MRI in Patients with Brain Metastases Undergoing Laser Interstitial Thermal Therapy: A Pilot Study.

Authors:  J I Traylor; D C A Bastos; D Fuentes; M Muir; R Patel; V A Kumar; R J Stafford; G Rao; S S Prabhu
Journal:  AJNR Am J Neuroradiol       Date:  2019-08-01       Impact factor: 3.825

10.  A linear algorithm of the reference region model for DCE-MRI is robust and relaxes requirements for temporal resolution.

Authors:  Julio Cárdenas-Rodríguez; Christine M Howison; Mark D Pagel
Journal:  Magn Reson Imaging       Date:  2012-12-08       Impact factor: 2.546

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