Literature DB >> 19525078

A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response.

Xia Li1, Benoit M Dawant, E Brian Welch, A Bapsi Chakravarthy, Darla Freehardt, Ingrid Mayer, Mark Kelley, Ingrid Meszoely, John C Gore, Thomas E Yankeelov.   

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can estimate parameters relating to blood flow and tissue volume fractions and therefore may be used to characterize the response of breast tumors to treatment. To assess treatment response, values of these DCE-MRI parameters are observed at different time points during the course of treatment. We propose a method whereby DCE-MRI data sets obtained in separate imaging sessions can be co-registered to a common image space, thereby retaining spatial information so that serial DCE-MRI parameter maps can be compared on a voxel-by-voxel basis. In performing inter-session breast registration, one must account for patient repositioning and breast deformation, as well as changes in tumor shape and volume relative to other imaging sessions. One challenge is to optimally register the normal tissues while simultaneously preventing tumor distortion. We accomplish this by extending the adaptive bases algorithm through adding a tumor-volume preserving constraint in the cost function. We also propose a novel method to generate the simulated breast magnetic resonance (MR) images, which can be used to evaluate the proposed registration algorithm quantitatively. The proposed nonrigid registration algorithm is applied to both simulated and real longitudinal 3D high resolution MR images and the obtained transformations are then applied to lower resolution physiological parameter maps obtained via DCE-MRI. The registration results demonstrate the proposed algorithm can successfully register breast MR images acquired at different time points and allow for analysis of the registered parameter maps.

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Year:  2009        PMID: 19525078      PMCID: PMC2763059          DOI: 10.1016/j.mri.2009.05.007

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


  35 in total

1.  Nonrigid registration using free-form deformations: application to breast MR images.

Authors:  D Rueckert; L I Sonoda; C Hayes; D L Hill; M O Leach; D J Hawkes
Journal:  IEEE Trans Med Imaging       Date:  1999-08       Impact factor: 10.048

2.  New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada.

Authors:  P Therasse; S G Arbuck; E A Eisenhauer; J Wanders; R S Kaplan; L Rubinstein; J Verweij; M Van Glabbeke; A T van Oosterom; M C Christian; S G Gwyther
Journal:  J Natl Cancer Inst       Date:  2000-02-02       Impact factor: 13.506

3.  Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint.

Authors:  Torsten Rohlfing; Calvin R Maurer; David A Bluemke; Michael A Jacobs
Journal:  IEEE Trans Med Imaging       Date:  2003-06       Impact factor: 10.048

4.  Evidence for shutter-speed variation in CR bolus-tracking studies of human pathology.

Authors:  Thomas E Yankeelov; William D Rooney; Wei Huang; Jonathan P Dyke; Xin Li; Alina Tudorica; Jing-Huei Lee; Jason A Koutcher; Charles S Springer
Journal:  NMR Biomed       Date:  2005-05       Impact factor: 4.044

5.  Evaluation of 3D modality-independent elastography for breast imaging: a simulation study.

Authors:  J J Ou; R E Ong; T E Yankeelov; M I Miga
Journal:  Phys Med Biol       Date:  2007-12-19       Impact factor: 3.609

Review 6.  Modeling tracer kinetics in dynamic Gd-DTPA MR imaging.

Authors:  P S Tofts
Journal:  J Magn Reson Imaging       Date:  1997 Jan-Feb       Impact factor: 4.813

7.  Multimodality image registration by maximization of mutual information.

Authors:  F Maes; A Collignon; D Vandermeulen; G Marchal; P Suetens
Journal:  IEEE Trans Med Imaging       Date:  1997-04       Impact factor: 10.048

8.  Monitoring breast cancer response to neoadjuvant systemic chemotherapy using parametric contrast-enhanced MRI: a pilot study.

Authors:  Chen-Pin Chou; Ming-Ting Wu; Hong-Tai Chang; Yu-Shin Lo; Huay-Ben Pan; Hadassa Degani; Edna Furman-Haran
Journal:  Acad Radiol       Date:  2007-05       Impact factor: 3.173

Review 9.  Functional tumor imaging with dynamic contrast-enhanced magnetic resonance imaging.

Authors:  Peter L Choyke; Andrew J Dwyer; Michael V Knopp
Journal:  J Magn Reson Imaging       Date:  2003-05       Impact factor: 4.813

10.  Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: initial clinical results.

Authors:  Anwar R Padhani; Carmel Hayes; Laura Assersohn; Trevor Powles; Andreas Makris; John Suckling; Martin O Leach; Janet E Husband
Journal:  Radiology       Date:  2006-03-16       Impact factor: 11.105

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  30 in total

1.  Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching.

Authors:  Minjeong Kim; Guorong Wu; Dinggang Shen
Journal:  Med Phys       Date:  2012-01       Impact factor: 4.071

2.  Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy.

Authors:  Nkiruka C Atuegwu; Lori R Arlinghaus; Xia Li; E Brian Welch; Bapsi A Chakravarthy; John C Gore; Thomas E Yankeelov
Journal:  Magn Reson Med       Date:  2011-09-28       Impact factor: 4.668

3.  Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.

Authors:  Jared A Weis; Michael I Miga; Thomas E Yankeelov
Journal:  Comput Methods Appl Mech Eng       Date:  2016-09-01       Impact factor: 6.756

4.  Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: preliminary results.

Authors:  Nkiruka C Atuegwu; Xia Li; Lori R Arlinghaus; Richard G Abramson; Jason M Williams; A Bapsi Chakravarthy; Vandana G Abramson; Thomas E Yankeelov
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

5.  Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results.

Authors:  Hakmook Kang; Allison Hainline; Lori R Arlinghaus; Stephanie Elderidge; Xia Li; Vandana G Abramson; Anuradha Bapsi Chakravarthy; Richard G Abramson; Brian Bingham; Kareem Fakhoury; Thomas E Yankeelov
Journal:  J Med Imaging (Bellingham)       Date:  2017-12-29

6.  Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.

Authors:  Xia Li; Hakmook Kang; Lori R Arlinghaus; Richard G Abramson; A Bapsi Chakravarthy; Vandana G Abramson; Jaime Farley; Melinda Sanders; Thomas E Yankeelov
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

7.  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

8.  Current and future trends in magnetic resonance imaging assessments of the response of breast tumors to neoadjuvant chemotherapy.

Authors:  Lori R Arlinghaus; Xia Li; Mia Levy; David Smith; E Brian Welch; John C Gore; Thomas E Yankeelov
Journal:  J Oncol       Date:  2010-09-29       Impact factor: 4.375

9.  Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model.

Authors:  Jared A Weis; Michael I Miga; Lori R Arlinghaus; Xia Li; Vandana Abramson; A Bapsi Chakravarthy; Praveen Pendyala; Thomas E Yankeelov
Journal:  Cancer Res       Date:  2015-09-02       Impact factor: 12.701

Review 10.  Diffusion MRI in early cancer therapeutic response assessment.

Authors:  C J Galbán; B A Hoff; T L Chenevert; B D Ross
Journal:  NMR Biomed       Date:  2016-01-15       Impact factor: 4.044

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