Literature DB >> 17354761

A fast method of generating pharmacokinetic maps from dynamic contrast-enhanced images of the breast.

Anne L Martel1.   

Abstract

A new approach to fitting pharmacokinetic models to DCE-MRI data is described. The method relies on fitting individual concentration curves to a small set of basis functions and then making use of a look up table to relate the fitting coefficients to pre-calculated pharmacokinetic parameters. This is significantly faster than traditional non-linear fitting methods. Using simulated data and assuming a Tofts model, the accuracy of this direct approach is compared to the Levenberg-Marquardt algorithm. The effect of signal to noise ratio and the number of basis functions used on the accuracy is investigated. The basis fitting approach is slightly less accurate than the traditional non-linear least squares approach but the ten-fold improvement in speed makes the new technique useful as it can be used to generate pharmacokinetic maps in a clinically acceptable timeframe.

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Year:  2006        PMID: 17354761     DOI: 10.1007/11866763_13

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  3 in total

1.  Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

Authors:  Shannon C Agner; Salil Soman; Edward Libfeld; Margie McDonald; Kathleen Thomas; Sarah Englander; Mark A Rosen; Deanna Chin; John Nosher; Anant Madabhushi
Journal:  J Digit Imaging       Date:  2011-06       Impact factor: 4.056

2.  Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response.

Authors:  R Venkatasubramanian; R B Arenas; M A Henson; N S Forbes
Journal:  Br J Cancer       Date:  2010-07-13       Impact factor: 7.640

3.  Principal component analysis of breast DCE-MRI adjusted with a model-based method.

Authors:  Erez Eyal; Daria Badikhi; Edna Furman-Haran; Fredrick Kelcz; Kevin J Kirshenbaum; Hadassa Degani
Journal:  J Magn Reson Imaging       Date:  2009-11       Impact factor: 4.813

  3 in total

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