Literature DB >> 23228309

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

Julio Cárdenas-Rodríguez1, Christine M Howison, Mark D Pagel.   

Abstract

Dynamic contrast enhanced MRI (DCE-MRI) has utility for improving clinical diagnoses of solid tumors, and for evaluating the early responses of anti-angiogenic chemotherapies. The Reference Region Model (RRM) can improve the clinical implementation of DCE-MRI by substituting the contrast enhancement of muscle for the Arterial Input Function that is used in traditional DCE-MRI methodologies. The RRM is typically fitted to experimental results with a non-linear least squares algorithm. This report demonstrates that this algorithm produces inaccurate and imprecise results when DCE-MRI results have low SNR or slow temporal resolution. To overcome this limitation, a linear least-squares algorithm has been derived for the Reference Region Model. This new algorithm improves accuracy and precision of fitting the Reference Region Model to DCE-MRI results, especially for voxel-wise analyses. This linear algorithm is insensitive to injection speeds, and has 300- to 8000-fold faster calculation speed relative to the non-linear algorithm. The linear algorithm produces more accurate results for over a wider range of permeabilities and blood volumes of tumor vasculature. This new algorithm, termed the Linear Reference Region Model, has strong potential to improve clinical DCE-MRI evaluations.
Copyright © 2013 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 23228309      PMCID: PMC3639320          DOI: 10.1016/j.mri.2012.10.008

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  28 in total

1.  Efficient method for calculating kinetic parameters using T1-weighted dynamic contrast-enhanced magnetic resonance imaging.

Authors:  Kenya Murase
Journal:  Magn Reson Med       Date:  2004-04       Impact factor: 4.668

2.  An analysis of the pharmacokinetic parameter ratios in DCE-MRI using the reference region model.

Authors:  Joonsang Lee; Simon Platt; Marc Kent; Qun Zhao
Journal:  Magn Reson Imaging       Date:  2011-11-08       Impact factor: 2.546

3.  The use of a reference tissue arterial input function with low-temporal-resolution DCE-MRI data.

Authors:  M Heisen; X Fan; J Buurman; N A W van Riel; G S Karczmar; B M ter Haar Romeny
Journal:  Phys Med Biol       Date:  2010-08-03       Impact factor: 3.609

4.  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

5.  Study of onset time-shift and injection duration in DCE-MRI: a comparison of a reference region model with the general kinetic model.

Authors:  Ing-Tsung Hsiao; Yen-Peng Liao; Ho-Ling Liu
Journal:  NMR Biomed       Date:  2009-12-11       Impact factor: 4.044

6.  The use of the Levenberg-Marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data.

Authors:  T S Ahearn; R T Staff; T W Redpath; S I K Semple
Journal:  Phys Med Biol       Date:  2005-04-13       Impact factor: 3.609

7.  Temporal sampling requirements for the tracer kinetics modeling of breast disease.

Authors:  E Henderson; B K Rutt; T Y Lee
Journal:  Magn Reson Imaging       Date:  1998-11       Impact factor: 2.546

8.  Semiquantitative analysis of dynamic contrast enhanced MRI in cancer patients: Variability and changes in tumor tissue over time.

Authors:  Milica Medved; Greg Karczmar; Cheng Yang; James Dignam; Thomas F Gajewski; Hedy Kindler; Everett Vokes; Peter MacEneany; Myrosia T Mitchell; Walter M Stadler
Journal:  J Magn Reson Imaging       Date:  2004-07       Impact factor: 4.813

9.  Temporal sampling requirements for reference region modeling of DCE-MRI data in human breast cancer.

Authors:  Catherine R Planey; E Brian Welch; Lei Xu; A Bapsi Chakravarthy; J Christopher Gatenby; Darla Freehardt; Ingrid Mayer; Ingrid Meszeoly; Mark Kelley; Julie Means-Powell; John C Gore; Thomas E Yankeelov
Journal:  J Magn Reson Imaging       Date:  2009-07       Impact factor: 4.813

Review 10.  DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents.

Authors:  J P B O'Connor; A Jackson; G J M Parker; G C Jayson
Journal:  Br J Cancer       Date:  2007-01-09       Impact factor: 7.640

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  14 in total

1.  Dynamic contrast enhanced magnetic resonance imaging of an orthotopic pancreatic cancer mouse model.

Authors:  Hyunki Kim; Sharon Samuel; John W Totenhagen; Marie Warren; Jeffrey C Sellers; Donald J Buchsbaum
Journal:  J Vis Exp       Date:  2015-04-18       Impact factor: 1.355

2.  Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping.

Authors:  Ramin Jafari; Shalini Chhabra; Martin R Prince; Yi Wang; Pascal Spincemaille
Journal:  Magn Reson Med       Date:  2017-08-22       Impact factor: 4.668

3.  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

4.  Linearization improves the repeatability of quantitative dynamic contrast-enhanced MRI.

Authors:  Kyle M Jones; Mark D Pagel; Julio Cárdenas-Rodríguez
Journal:  Magn Reson Imaging       Date:  2017-11-15       Impact factor: 2.546

5.  Characterization of tumor vascular permeability using natural dextrans and CEST MRI.

Authors:  Yuguo Li; Yuan Qiao; Hanwei Chen; Renyuan Bai; Verena Staedtke; Zheng Han; Jiadi Xu; Kannie W Y Chan; Nirbhay Yadav; Jeff W M Bulte; Shibin Zhou; Peter C M van Zijl; Guanshu Liu
Journal:  Magn Reson Med       Date:  2017-11-28       Impact factor: 4.668

6.  Aberrant glioblastoma neovascularization patterns and their correlation with DCE-MRI-derived parameters following temozolomide and bevacizumab treatment.

Authors:  Wei Xue; Xuesong Du; Hao Wu; Heng Liu; Tian Xie; Haipeng Tong; Xiao Chen; Yu Guo; Weiguo Zhang
Journal:  Sci Rep       Date:  2017-10-24       Impact factor: 4.379

7.  A light-fluence-independent method for the quantitative analysis of dynamic contrast-enhanced multispectral optoacoustic tomography (DCE MSOT).

Authors:  Clinton W Hupple; Stefan Morscher; Neal C Burton; Mark D Pagel; Lacey R McNally; Julio Cárdenas-Rodríguez
Journal:  Photoacoustics       Date:  2018-05-03

8.  Differentiation of Myositis-Induced Models of Bacterial Infection and Inflammation with T2-Weighted, CEST, and DCE-MRI.

Authors:  Joshua M Goldenberg; Alexander J Berthusen; Julio Cárdenas-Rodríguez; Mark D Pagel
Journal:  Tomography       Date:  2019-09

9.  Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function.

Authors:  Yi-Jui Liu; Hou-Ting Yang; Melissa Min-Szu Yao; Shao-Chieh Lin; Der-Yang Cho; Wu-Chung Shen; Chun-Jung Juan; Wing P Chan
Journal:  Sci Rep       Date:  2021-02-03       Impact factor: 4.379

10.  Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method.

Authors:  Shoshana B Ginsburg; Pekka Taimen; Harri Merisaari; Paula Vainio; Peter J Boström; Hannu J Aronen; Ivan Jambor; Anant Madabhushi
Journal:  J Magn Reson Imaging       Date:  2016-06-10       Impact factor: 5.119

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