Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

被引:26
|
作者
Ou, Yangming [1 ]
Weinstein, Susan P. [1 ]
Conant, Emily F. [1 ]
Englander, Sarah [1 ]
Da, Xiao [1 ]
Gaonkar, Bilwaj [1 ]
Hsieh, Meng-Kang [1 ]
Rosen, Mark [1 ]
DeMichele, Angela [1 ]
Davatzikos, Christos [1 ]
Kontos, Despina [1 ]
机构
[1] Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA
关键词
deformable image registration; breast cancer; longitudinal breast MRI; tumor changes; evaluation; treatment; FREE-FORM DEFORMATION; PRESERVING NONRIGID REGISTRATION; BREAST-CANCER HETEROGENEITY; IMAGE REGISTRATION; PATHOLOGICAL RESPONSE; MAMMOGRAM REGISTRATION; MR-IMAGES; VOLUME; PREDICTION; MODEL;
D O I
10.1002/mrm.25368
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PurposeTo evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. MethodsBreast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation. ResultsDRAMMS had the smallest landmark errors (6.054.86 mm), followed by the intensity-based methods CC-FFD (8.07 +/- 3.86 mm), NMI-FFD (8.21 +/- 3.81 mm), SSD-FFD (9.46 +/- 4.55 mm), Demons (10.76 +/- 6.01 mm), and Diffeomorphic Demons (10.82 +/- 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups. ConclusionsThe DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment. Magn Reson Med 73:2343-2356, 2015. (c) 2014 Wiley Periodicals, Inc.
引用
收藏
页码:2343 / 2356
页数:14
相关论文
共 50 条
  • [1] Quantification of Tumor Changes during Neoadjuvant Chemotherapy with Longitudinal Breast DCE-MRI Registration
    Wu, Jia
    Ou, Yangming
    Weinstein, Susan P.
    Conant, Emily F.
    Yu, Ning
    Hoshmand, Vahid
    Keller, Brad
    Ashraf, Ahmed B.
    Rosen, Mark
    DeMichele, Angela
    Davatzikos, Christos
    Kontos, Despina
    MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS, 2015, 9414
  • [2] Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: Preliminary results
    Atuegwu, Nkiruka C.
    Li, Xia
    Arlinghaus, Lori R.
    Abramson, Richard G.
    Williams, Jason M.
    Chakravarthy, A. Bapsi
    Abramson, Vandana G.
    Yankeelov, Thomas E.
    MEDICAL PHYSICS, 2014, 41 (05)
  • [3] Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial
    Thakran, Snekha
    Cohen, Eric
    Jahani, Nariman
    Weinstein, Susan P.
    Pantalone, Lauren
    Hylton, Nola
    Newitt, David
    DeMichele, Angela
    Davatzikos, Christos
    Kontos, Despina
    TRANSLATIONAL ONCOLOGY, 2022, 20
  • [4] Neoadjuvant chemotherapy response evaluation in breast cancer based on mammogram registration and tumor segmentation
    Salhi A.
    Melouah N.
    Hayet F.M.
    Layachi S.
    Bouguettaya A.
    Pattern Recognition and Image Analysis, 2017, 27 (1) : 122 - 130
  • [5] An algorithm for longitudinal registration of PET/CT images acquired during neoadjuvant chemotherapy in breast cancer: preliminary results
    Li, Xia
    Abramson, Richard G.
    Arlinghaus, Lori R.
    Chakravarthy, Anuradha Bapsi
    Abramson, Vandana
    Mayer, Ingrid
    Farley, Jaime
    Delbeke, Dominique
    Yankeelov, Thomas E.
    EJNMMI RESEARCH, 2012, 2 : 1 - 11
  • [6] An algorithm for longitudinal registration of PET/CT images acquired during neoadjuvant chemotherapy in breast cancer: preliminary results
    Xia Li
    Richard G Abramson
    Lori R Arlinghaus
    Anuradha Bapsi Chakravarthy
    Vandana Abramson
    Ingrid Mayer
    Jaime Farley
    Dominique Delbeke
    Thomas E Yankeelov
    EJNMMI Research, 2
  • [7] Deformable Registration for Longitudinal Breast MRI Screening
    Mehrabian, Hatef
    Richmond, Lara
    Lu, Yingli
    Martel, Anne L.
    JOURNAL OF DIGITAL IMAGING, 2018, 31 (05) : 718 - 726
  • [8] Deformable image registration with geometric changes
    Yu Liu
    Bo Zhu
    Frontiers of Information Technology & Electronic Engineering, 2015, 16 : 829 - 837
  • [9] Deformable image registration with geometric changes
    Liu, Yu
    Zhu, Bo
    FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, 2015, 16 (10) : 829 - 837
  • [10] Deformable image registration with deep network priors: a study on longitudinal PET images
    Fourcade, Constance
    Ferrer, Ludovic
    Moreau, Noemie
    Santini, Gianmarco
    Brennan, Aislinn
    Rousseau, Caroline
    Lacombe, Marie
    Fleury, Vincent
    Colombie, Mathilde
    Jezequel, Pascal
    Rubeaux, Mathieu
    Mateus, Diana
    PHYSICS IN MEDICINE AND BIOLOGY, 2022, 67 (15)