Literature DB >> 19904036

Contour propagation in MRI-guided radiotherapy treatment of cervical cancer: the accuracy of rigid, non-rigid and semi-automatic registrations.

R W van der Put1, E M Kerkhof, B W Raaymakers, I M Jürgenliemk-Schulz, J J W Lagendijk.   

Abstract

External beam radiation treatment for patients with cervical cancer is hindered by the relatively large motion of the target volume. A hybrid MRI-accelerator system makes it possible to acquire online MR images during treatment in order to correct for motion and deformation. To fully benefit from such a system, online delineation of the target volumes is necessary. The aim of this study is to investigate the accuracy of rigid, non-rigid and semi-automatic registrations of MR images for interfractional contour propagation in patients with cervical cancer. Registration using mutual information was performed on both bony anatomy and soft tissue. A B-spline transform was used for the non-rigid method. Semi-automatic registration was implemented with a point set registration algorithm on a small set of manual landmarks. Online registration was simulated by application of each method to four weekly MRI scans for each of 33 cervical cancer patients. Evaluation was performed by distance analysis with respect to manual delineations. The results show that soft-tissue registration significantly (P < 0.001) improves the accuracy of contour propagation compared to registration based on bony anatomy. A combination of user-assisted and non-rigid registration provides the best results with a median error of 3.2 mm (1.4-9.9 mm) compared to 5.9 mm (1.7-19.7 mm) with bone registration (P < 0.001) and 3.4 mm (1.3-19.1 mm) with non-rigid registration (P = 0.01). In a clinical setting, the benefit may be further increased when outliers can be removed by visual inspection of the online images. We conclude that for external beam radiation treatment of cervical cancer, online MRI imaging will allow target localization based on soft tissue visualization, which provides a significantly higher accuracy than localization based on bony anatomy. The use of limited user input to guide the registration increases overall accuracy. Additional non-rigid registration further reduces the propagation error and negates errors caused by small observer variations.

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Year:  2009        PMID: 19904036     DOI: 10.1088/0031-9155/54/23/007

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  7 in total

1.  Registration accuracy for MR images of the prostate using a subvolume based registration protocol.

Authors:  Joakim H Jonsson; Patrik Brynolfsson; Anders Garpebring; Mikael Karlsson; Karin Söderström; Tufve Nyholm
Journal:  Radiat Oncol       Date:  2011-06-16       Impact factor: 3.481

2.  Simultaneous nonrigid registration, segmentation, and tumor detection in MRI guided cervical cancer radiation therapy.

Authors:  Chao Lu; Sudhakar Chelikani; David A Jaffray; Michael F Milosevic; Lawrence H Staib; James S Duncan
Journal:  IEEE Trans Med Imaging       Date:  2012-02-06       Impact factor: 10.048

3.  Evaluation of automatic contour propagation in T2-weighted 4DMRI for normal-tissue motion assessment using internal organ-at-risk volume (IRV).

Authors:  Jingjing Zhang; Svetlana Markova; Alejandro Garcia; Kirk Huang; Xingyu Nie; Wookjin Choi; Wei Lu; Abraham Wu; Andreas Rimner; Guang Li
Journal:  J Appl Clin Med Phys       Date:  2018-08-15       Impact factor: 2.102

Review 4.  Realizing the potential of magnetic resonance image guided radiotherapy in gynaecological and rectal cancer.

Authors:  Ingrid M White; Erica Scurr; Andreas Wetscherek; Gina Brown; Aslam Sohaib; Simeon Nill; Uwe Oelfke; David Dearnaley; Susan Lalondrelle; Shreerang Bhide
Journal:  Br J Radiol       Date:  2019-05-14       Impact factor: 3.039

5.  Fully automated segmentation of clinical target volume in cervical cancer from magnetic resonance imaging with convolutional neural network.

Authors:  Fatemeh Zabihollahy; Akila N Viswanathan; Ehud J Schmidt; Junghoon Lee
Journal:  J Appl Clin Med Phys       Date:  2022-07-27       Impact factor: 2.243

6.  A quantitative comparison of the performance of three deformable registration algorithms in radiotherapy.

Authors:  Daniella Fabri; Valentina Zambrano; Amon Bhatia; Hugo Furtado; Helmar Bergmann; Markus Stock; Christoph Bloch; Carola Lütgendorf-Caucig; Supriyanto Pawiro; Dietmar Georg; Wolfgang Birkfellner; Michael Figl
Journal:  Z Med Phys       Date:  2013-08-19       Impact factor: 4.820

7.  Deforming to Best Practice: Key considerations for deformable image registration in radiotherapy.

Authors:  Jeffrey Barber; Johnson Yuen; Michael Jameson; Laurel Schmidt; Jonathan Sykes; Alison Gray; Nicholas Hardcastle; Callie Choong; Joel Poder; Amy Walker; Adam Yeo; Ben Archibald-Heeren; Kristie Harrison; Annette Haworth; David Thwaites
Journal:  J Med Radiat Sci       Date:  2020-08-02
  7 in total

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