Literature DB >> 23674304

Classic models for dynamic contrast-enhanced MRI.

Steven P Sourbron1, David L Buckley.   

Abstract

Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.
Copyright © 2013 John Wiley & Sons, Ltd.

Entities:  

Keywords:  DCE-MRI; DSC-MRI; deconvolution; perfusion; permeability; tracer-kinetic models

Mesh:

Substances:

Year:  2013        PMID: 23674304     DOI: 10.1002/nbm.2940

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


  115 in total

1.  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

Review 2.  Defining the endpoints: how to measure the efficacy of drugs that are active against central nervous system metastases.

Authors:  Alessandra Fabi; Antonello Vidiri
Journal:  Transl Lung Cancer Res       Date:  2016-12

3.  Performance of an efficient image-registration algorithm in processing MR renography data.

Authors:  Christopher C Conlin; Jeff L Zhang; Florian Rousset; Clement Vachet; Yangyang Zhao; Kathryn A Morton; Kristi Carlston; Guido Gerig; Vivian S Lee
Journal:  J Magn Reson Imaging       Date:  2015-07-14       Impact factor: 4.813

4.  Human cerebral blood volume measurements using dynamic contrast enhancement in comparison to dynamic susceptibility contrast MRI.

Authors:  Moran Artzi; Gilad Liberman; Guy Nadav; Faina Vitinshtein; Deborah T Blumenthal; Felix Bokstein; Orna Aizenstein; Dafna Ben Bashat
Journal:  Neuroradiology       Date:  2015-04-07       Impact factor: 2.804

Review 5.  Blood-brain barrier imaging in human neuropathologies.

Authors:  Ronel Veksler; Ilan Shelef; Alon Friedman
Journal:  Arch Med Res       Date:  2014-11-29       Impact factor: 2.235

Review 6.  State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications.

Authors:  Magalie Viallon; Victor Cuvinciuc; Benedicte Delattre; Laura Merlini; Isabelle Barnaure-Nachbar; Seema Toso-Patel; Minerva Becker; Karl-Olof Lovblad; Sven Haller
Journal:  Neuroradiology       Date:  2015-04-10       Impact factor: 2.804

Review 7.  Functional MRI and CT biomarkers in oncology.

Authors:  J M Winfield; G S Payne; N M deSouza
Journal:  Eur J Nucl Med Mol Imaging       Date:  2015-01-13       Impact factor: 9.236

8.  Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity.

Authors:  SoHyun Han; Radka Stoyanova; Hansol Lee; Sean D Carlin; Jason A Koutcher; HyungJoon Cho; Ellen Ackerstaff
Journal:  Magn Reson Med       Date:  2017-07-20       Impact factor: 4.668

9.  Image registration for quantitative parametric response mapping of cancer treatment response.

Authors:  Jennifer L Boes; Benjamin A Hoff; Nola Hylton; Martin D Pickles; Lindsay W Turnbull; Anne F Schott; Alnawaz Rehemtulla; Ryan Chamberlain; Benjamin Lemasson; Thomas L Chenevert; Craig J Galbán; Charles R Meyer; Brian D Ross
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

10.  Perfusion of the placenta assessed using arterial spin labeling and ferumoxytol dynamic contrast enhanced magnetic resonance imaging in the rhesus macaque.

Authors:  Kai D Ludwig; Sean B Fain; Sydney M Nguyen; Thaddeus G Golos; Scott B Reeder; Ian M Bird; Dinesh M Shah; Oliver E Wieben; Kevin M Johnson
Journal:  Magn Reson Med       Date:  2018-10-25       Impact factor: 4.668

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.