Literature DB >> 28685419

Joint deformable liver registration and bias field correction for MR-guided HDR brachytherapy.

Marko Rak1, Tim König2, Klaus D Tönnies3, Mathias Walke4, Jens Ricke5, Christian Wybranski6.   

Abstract

PURPOSE: In interstitial high-dose rate brachytherapy, liver cancer is treated by internal radiation, requiring percutaneous placement of applicators within or close to the tumor. To maximize utility, the optimal applicator configuration is pre-planned on magnetic resonance images. The pre-planned configuration is then implemented via a magnetic resonance-guided intervention. Mapping the pre-planning information onto interventional data would reduce the radiologist's cognitive load during the intervention and could possibly minimize discrepancies between optimally pre-planned and actually placed applicators.
METHODS: We propose a fast and robust two-step registration framework suitable for interventional settings: first, we utilize a multi-resolution rigid registration to correct for differences in patient positioning (rotation and translation). Second, we employ a novel iterative approach alternating between bias field correction and Markov random field deformable registration in a multi-resolution framework to compensate for non-rigid movements of the liver, the tumors and the organs at risk. In contrast to existing pre-correction methods, our multi-resolution scheme can recover bias field artifacts of different extents at marginal computational costs.
RESULTS: We compared our approach to deformable registration via B-splines, demons and the SyN method on 22 registration tasks from eleven patients. Results showed that our approach is more accurate than the contenders for liver as well as for tumor tissues. We yield average liver volume overlaps of 94.0 ± 2.7% and average surface-to-surface distances of 2.02 ± 0.87 mm and 3.55 ± 2.19 mm for liver and tumor tissue, respectively. The reported distances are close to (or even below) the slice spacing (2.5 - 3.0 mm) of our data. Our approach is also the fastest, taking 35.8 ± 12.8 s per task.
CONCLUSION: The presented approach is sufficiently accurate to map information available from brachytherapy pre-planning onto interventional data. It is also reasonably fast, providing a starting point for computer-aidance during intervention.

Entities:  

Keywords:  Bias field correction; Deformable registration; High-dose rate brachytherapy; Liver intervention; Magnetic resonance imaging

Mesh:

Year:  2017        PMID: 28685419     DOI: 10.1007/s11548-017-1633-2

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  28 in total

1.  Parametric estimate of intensity inhomogeneities applied to MRI.

Authors:  M Styner; C Brechbühler; G Székely; G Gerig
Journal:  IEEE Trans Med Imaging       Date:  2000-03       Impact factor: 10.048

2.  Semiautomatic 3-D image registration as applied to interventional MRI liver cancer treatment.

Authors:  A Carrillo; J L Duerk; J S Lewin; D L Wilson
Journal:  IEEE Trans Med Imaging       Date:  2000-03       Impact factor: 10.048

3.  PET-CT image registration in the chest using free-form deformations.

Authors:  David Mattes; David R Haynor; Hubert Vesselle; Thomas K Lewellen; William Eubank
Journal:  IEEE Trans Med Imaging       Date:  2003-01       Impact factor: 10.048

4.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit.

Authors:  Terry S Yoo; Michael J Ackerman; William E Lorensen; Will Schroeder; Vikram Chalana; Stephen Aylward; Dimitris Metaxas; Ross Whitaker
Journal:  Stud Health Technol Inform       Date:  2002

5.  Dose escalation to dominant intraprostatic lesions with MRI-transrectal ultrasound fusion High-Dose-Rate prostate brachytherapy. Prospective phase II trial.

Authors:  Alfonso Gomez-Iturriaga; Francisco Casquero; Arantza Urresola; Ana Ezquerro; Jose I Lopez; Jose M Espinosa; Pablo Minguez; Roberto Llarena; Ana Irasarri; Pedro Bilbao; Juanita Crook
Journal:  Radiother Oncol       Date:  2016-02-15       Impact factor: 6.280

6.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data.

Authors:  J G Sled; A P Zijdenbos; A C Evans
Journal:  IEEE Trans Med Imaging       Date:  1998-02       Impact factor: 10.048

7.  Integrating segmentation information for improved MRF-based elastic image registration.

Authors:  Dwarikanath Mahapatra; Ying Sun
Journal:  IEEE Trans Image Process       Date:  2011-07-25       Impact factor: 10.856

8.  Multimodality non-rigid image registration for planning, targeting and monitoring during CT-guided percutaneous liver tumor cryoablation.

Authors:  Haytham Elhawary; Sota Oguro; Kemal Tuncali; Paul R Morrison; Servet Tatli; Paul B Shyn; Stuart G Silverman; Nobuhiko Hata
Journal:  Acad Radiol       Date:  2010-11       Impact factor: 3.173

9.  N4ITK: improved N3 bias correction.

Authors:  Nicholas J Tustison; Brian B Avants; Philip A Cook; Yuanjie Zheng; Alexander Egan; Paul A Yushkevich; James C Gee
Journal:  IEEE Trans Med Imaging       Date:  2010-04-08       Impact factor: 10.048

10.  Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd-EOB-DTPA-enhanced MRI.

Authors:  Laura Fernandez-de-Manuel; Gert Wollny; Jan Kybic; Daniel Jimenez-Carretero; Jose M Tellado; Enrique Ramon; Manuel Desco; Andres Santos; Javier Pascau; Maria J Ledesma-Carbayo
Journal:  Med Image Anal       Date:  2013-09-13       Impact factor: 8.545

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