No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
Three-dimensional liver motion tracking using real-time two-dimensional MRI
1. P. J. Keall, G. S. Mageras, J. M. Balter, R. S. Emery, K. M. Forster, S. B. Jiang, J. M. Kapatoes, D. A. Low, M. J. Murphy, B. R. Murray, C. R. Ramsey, M. B. Van Herk, S. S. Vedam, J. W. Wong, and E. Yorke, “The management of respiratory motion in radiation oncology report of AAPM Task Group 76,” Med. Phys. 33, 3874–3900 (2006).
4. B. W. Raaymakers, J. J. Lagendijk, J. Overweg, J. G. Kok, A. J. Raaijmakers, E. M. Kerkhof, R. W. van der Put, I. Meijsing, S. P. Crijns, F. Benedosso, V. M. van, C. H. de Graaff, J. Allen, and K. J. Brown, “Integrating a 1.5 T MRI scanner with a 6 MV accelerator: Proof of concept,” Phys. Med. Biol. 54, N229–N237 (2009).
5. B. G. Fallone, B. Murray, S. Rathee, T. Stanescu, S. Steciw, S. Vidakovic, E. Blosser, and D. Tymofichuk, “First MR images obtained during megavoltage photon irradiation from a prototype integrated linac-MR system,” Med. Phys. 36, 2084–2088 (2009).
6. J. F. Dempsey, D. Benoit, J. R. Fitzsimmons, A. Haghighat, J. G. Li, D. A. Low, S. Mutic, J. R. Palta, H. E. Romeijn, and G. E. Sjoden, “A device for realtime 3D image-guided IMRT,” Int. J. Radiat. Oncol., Biol., Phys. 63, S202 (2005).
7. S. P. Crijns, B. W. Raaymakers, and J. J. Lagendijk, “Proof of concept of MRI-guided tracked radiation delivery: Tracking one-dimensional motion,” Phys. Med. Biol. 57, 7863–7872 (2012).
8. J. Yun, K. Wachowicz, M. Mackenzie, S. Rathee, D. Robinson, and B. G. Fallone, “First demonstration of intrafractional tumor-tracked irradiation using 2D phantom MR images on a prototype linac-MR,” Med. Phys. 40, 051718 (12pp.) (2013).
9. M. K. Stam, S. P. Crijns, B. A. Zonnenberg, M. M. Barendrecht, V. M. van, J. J. Lagendijk, and B. W. Raaymakers, “Navigators for motion detection during real-time MRI-guided radiotherapy,” Phys. Med. Biol. 57, 6797–6805 (2012).
10. R. Song, A. Tipirneni, P. Johnson, R. B. Loeffler, and C. M. Hillenbrand, “Evaluation of respiratory liver and kidney movements for MRI navigator gating,” J. Magn. Reson. Imaging 33, 143–148 (2011).
12. T. Bjerre, S. Crijns, P. M. Rosenschold, M. Aznar, L. Specht, R. Larsen, and P. Keall, “Three-dimensional MRI-linac intra-fraction guidance using multiple orthogonal cine-MRI planes,” Phys. Med. Biol. 58, 4943–4950 (2013).
13. E. Tryggestad, A. Flammang, R. Hales, J. Herman, J. Lee, T. McNutt, T. Roland, S. M. Shea, and J. Wong, “4D tumor centroid tracking using orthogonal 2D dynamic MRI: Implications for radiotherapy planning,” Med. Phys. 40, 091712 (12pp.) (2013).
14. M. S. Hansen, D. Atkinson, and T. S. Sorensen, “Cartesian SENSE and k-t SENSE reconstruction using commodity graphics hardware,” Magn. Reson. Med. 59, 463–468 (2008).
15. T. S. Sorensen, D. Atkinson, T. Schaeffter, and M. S. Hansen, “Real-time reconstruction of sensitivity encoded radial magnetic resonance imaging using a graphics processing unit,” IEEE Trans. Med. Imaging 28, 1974–1985 (2009).
16. L. Brix, T. S. Sorensen, Y. Berber, M. Ries, B. Stausbol-Gron, and S. Ringgaard, “Feasibility of interactive magnetic resonance imaging of moving anatomy for clinical practice,” Clin. Physiol. Funct. Imaging 34, 32–38 (2014).
17. B. Bussels, L. Goethals, M. Feron, D. Bielen, S. Dymarkowski, P. Suetens, and K. Haustermans, “Respiration-induced movement of the upper abdominal organs: A pitfall for the three-dimensional conformal radiation treatment of pancreatic cancer,” Radiother. Oncol. 68, 69–74 (2003).
18. S. Shimizu, H. Shirato, B. Xo, K. Kagei, T. Nishioka, S. Hashimoto, K. Tsuchiya, H. Aoyama, and K. Miyasaka, “Three-dimensional movement of a liver tumor detected by high-speed magnetic resonance imaging,” Radiother. Oncol. 50, 367–370 (1999).
19. N. Koch, H. H. Liu, G. Starkschall, M. Jacobson, K. Forster, Z. Liao, R. Komaki, and C. W. Stevens, “Evaluation of internal lung motion for respiratory-gated radiotherapy using MRI: Part I–correlating internal lung motion with skin fiducial motion,” Int. J. Radiat. Oncol., Biol., Phys. 60, 1459–1472 (2004).
20. A. Kirilova, G. Lockwood, P. Choi, N. Bana, M. A. Haider, K. K. Brock, C. Eccles, and L. A. Dawson, “Three-dimensional motion of liver tumors using cine-magnetic resonance imaging,” Int. J. Radiat. Oncol., Biol., Phys. 71, 1189–1195 (2008).
21. J. Cai, P. W. Read, T. A. Altes, J. A. Molloy, J. R. Brookeman, and K. Sheng, “Evaluation of the reproducibility of lung motion probability distribution function (PDF) using dynamic MRI,” Phys. Med. Biol. 52, 365–373 (2007).
22. J. Cai, P. W. Read, J. M. Larner, D. R. Jones, S. H. Benedict, and K. Sheng, “Reproducibility of interfraction lung motion probability distribution function using dynamic MRI: Statistical analysis,” Int. J. Radiat. Oncol., Biol., Phys. 72, 1228–1235 (2008).
23. M. Feng, J. M. Balter, D. Normolle, S. Adusumilli, Y. Cao, T. L. Chenevert, and E. Ben-Josef, “Characterization of pancreatic tumor motion using cine MRI: Surrogates for tumor position should be used with caution,” Int. J. Radiat. Oncol., Biol., Phys. 74, 884–891 (2009).
24. M. Ries, B. D. de Senneville, S. Roujol, Y. Berber, B. Quesson, and C. Moonen, “Real-time 3D target tracking in MRI guided focused ultrasound ablations in moving tissues,” Magn. Reson. Med. 64, 1704–1712 (2010).
25. M. A. Griswold, P. M. Jakob, R. M. Heidemann, M. Nittka, V. Jellus, J. Wang, B. Kiefer, and A. Haase, “Generalized autocalibrating partially parallel acquisitions (GRAPPA),” Magn. Reson. Med. 47, 1202–1210 (2002).
26. M. S. Hansen and T. S. Sorensen, “Gadgetron: An open source framework for medical image reconstruction,” Magn. Reson. Med. 69, 1768–1776 (2013).
27. J. C. Park, S. H. Park, J. H. Kim, S. M. Yoon, S. Y. Song, Z. Liu, B. Song, K. Kauweloa, M. J. Webster, A. Sandhu, L. K. Mell, S. B. Jiang, A. J. Mundt, and W. Y. Song, “Liver motion during cone beam computed tomography guided stereotactic body radiation therapy,” Med. Phys. 39, 6431–6442 (2012).
28. M. von Siebenthal, G. Szekely, U. Gamper, P. Boesiger, A. Lomax, and P. Cattin, “4D MR imaging of respiratory organ motion and its variability,” Phys. Med. Biol. 52, 1547–1564 (2007).
29. E. S. Worm, M. Hoyer, W. Fledelius, A. T. Hansen, and P. R. Poulsen, “Variations in magnitude and directionality of respiratory target motion throughout full treatment courses of stereotactic body radiotherapy for tumors in the liver,” Acta Oncol. 52, 1437–1444 (2013).
30. P. R. Poulsen, W. Fledelius, B. Cho, and P. Keall, “Image-based dynamic multileaf collimator tracking of moving targets during intensity-modulated arc therapy,” Int. J. Radiat. Oncol., Biol., Phys. 83, e265–e271 (2012).
31. T. Rohlfing, C. R. Maurer Jr., W. G. O’Dell, and J. Zhong, “Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images,” Med. Phys. 31, 427–432 (2004).
32. E. S. Worm, M. Hoyer, W. Fledelius, and P. R. Poulsen, “Three-dimensional, time-resolved, intrafraction motion monitoring throughout stereotactic liver radiation therapy on a conventional linear accelerator,” Int. J. Radiat. Oncol., Biol., Phys. 86, 190–197 (2013).
33. J. L. Hallman, S. Mori, G. C. Sharp, H. M. Lu, T. S. Hong, and G. T. Chen, “A four-dimensional computed tomography analysis of multiorgan abdominal motion,” Int. J. Radiat. Oncol., Biol., Phys. 83, 435–441 (2012).
34. A. S. Beddar, K. Kainz, T. M. Briere, Y. Tsunashima, T. Pan, K. Prado, R. Mohan, M. Gillin, and S. Krishnan, “Correlation between internal fiducial tumor motion and external marker motion for liver tumors imaged with 4D-CT,” Int. J. Radiat. Oncol., Biol., Phys. 67, 630–638 (2007).
35. Y. Seppenwoolde, W. Wunderink, S. R. Wunderink-van Veen, P. Storchi, R. A. Mendez, and B. J. Heijmen, “Treatment precision of image-guided liver SBRT using implanted fiducial markers depends on marker-tumour distance,” Phys. Med. Biol. 56, 5445–5468 (2011).
Article metrics loading...
Combined magnetic resonance imaging (MRI) systems and linear accelerators for radiotherapy (MR-Linacs) are currently under development. MRI is noninvasive and nonionizing and can produce images with high soft tissue contrast. However, new tracking methods are required to obtain fast real-time spatial target localization. This study develops and evaluates a method for tracking three-dimensional (3D) respiratory liver motion in two-dimensional (2D) real-time MRI image series with high temporal and spatial resolution.
The proposed method for 3D tracking in 2D real-time MRI series has three steps: (1) Recording of a 3D MRI scan and selection of a blood vessel (or tumor) structure to be tracked in subsequent 2D MRI series. (2) Generation of a library of 2D image templates oriented parallel to the 2D MRI image series by reslicing and resampling the 3D MRI scan. (3) 3D tracking of the selected structure in each real-time 2D image by finding the template and template position that yield the highest normalized cross correlation coefficient with the image. Since the tracked structure has a known 3D position relative to each template, the selection and 2D localization of a specific template translates into quantification of both the through-plane and in-plane position of the structure. As a proof of principle, 3D tracking of liver blood vessel structures was performed in five healthy volunteers in two 5.4 Hz axial, sagittal, and coronal real-time 2D MRI series of 30 s duration. In each 2D MRI series, the 3D localization was carried out twice, using nonoverlapping template libraries, which resulted in a total of 12 estimated 3D trajectories per volunteer. Validation tests carried out to support the tracking algorithm included quantification of the breathing induced 3D liver motion and liver motion directionality for the volunteers, and comparison of 2D MRI estimated positions of a structure in a watermelon with the actual positions.
Axial, sagittal, and coronal 2D MRI series yielded 3D respiratory motion curves for all volunteers. The motion directionality and amplitude were very similar when measured directly as in-plane motion or estimated indirectly as through-plane motion. The mean peak-to-peak breathing amplitude was 1.6 mm (left-right), 11.0 mm (craniocaudal), and 2.5 mm (anterior-posterior). The position of the watermelon structure was estimated in 2D MRI images with a root-mean-square error of 0.52 mm (in-plane) and 0.87 mm (through-plane).
A method for 3D tracking in 2D MRI series was developed and demonstrated for liver tracking in volunteers. The method would allow real-time 3D localization with integrated MR-Linac systems.
Full text loading...
Most read this month