AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Precision Radiation Oncology Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Original Article | Open Access

A pilot study of respiratory motion characterization in the abdomen using a fast volumetric 4D-MRI for MR-guided radiotherapy

Oi Lei Wong1, Max Wai Kong Law2, Darren Ming Chun Poon3, Raymond Wai Hung Yung1, Siu ki Yu2, Kin yin Cheung2, Jing Yuan1 ( )
Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
Comprehensive Oncology Center, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
Show Author Information

Abstract

Purpose

This pilot study explored the use of fast volumetric four-dimensional magnetic resonance imaging (4D-MRI) for motion characterization in the abdomen, ultimately aiming for the translation to future MR-guided radiotherapy (MRgRT) applications.

Methods

Nine healthy subjects and four patients underwent shallow free-breathing abdominal scans on a 1.5 T MR simulator using a fast volumetric 4D-MRI sequence with a temporal resolution up to three frames per second. The reference left/right liver and gross target volume (GTV) were segmented on the first frame, and the following consecutive dynamic images were nonlinearly registered to the reference to create the dynamic binary masks. Respiratory probabilistic volumes (rPPVj,i%), which correspond to the probability of being occupied by a volume of interest (VOI), were generated by summating the dynamic binary masks. Subject-specific rPPV144,0% and rPPV144,5% of the GTV were quantitatively compared to the 4D computed tomography (CT)-determined internal target volume (ITV) using the dice similarity coefficient (DSC), volumetric difference, and Hausdorff distance (HD).

Results

The results showed slightly smaller rPPV144,0% and rPPV144,5% but larger DSC and small HD compared to the 4D-CT-determined ITV, which preliminarily demonstrates the feasibility and capability of characterizing abdominal respiratory motion using the volumetric 4D-MRI-derived rPPV approach.

Conclusion

The potential usefulness of fast volumetric 4D-MRI for personalized motion management in future MRgRT is proposed.

Keywords

4D-MRIinternal target volume (ITV)MR-guided radiotherapy (MRgRT)respiratory motionrespiratory positional probability volumes (rPPV)

References

1

Pollard JM, Wen Z, Sadagopan R, Wang J, Ibbott GS. The future of image-guided radiotherapy will be MR guided. Br J Radiol. 2017; 90(1073):20160667. https://doi.org/10.1259/bjr.20160667
Crossref Google Scholar
2

Mutic S, Dempsey JF. The ViewRay system: magnetic resonance-guided and controlled radiotherapy. Semin Radiat Oncol. 2014; 24(3): 196-199.
Crossref Google Scholar
3

Lagendijk JJW, Raaymakers BW, Van Den Berg CAT, et al. MR guidance in radiotherapy. Phys Med Biol. 2014; 59(21): R349-R369.
Crossref Google Scholar
4

van Herk M, McWilliam A, Dubec M, Faivre-Finn C, Choudhury A. Magnetic resonance imaging–guided radiation therapy: a short strengths, weaknesses, opportunities, and threats analysis. Int J Radiat Oncol Biol Phys. 2018; 101(5): 1057-1060.
Crossref Google Scholar
5

Kurz C, Buizza G, Landry G, et al. Medical physics challenges in clinical MR-guided radiotherapy. Radiat Oncol. 2020; 15(1): 93.
Crossref Google Scholar
6

Zhang J, Srivastava S, Wang C, et al. Clinical evaluation of 4D MRI in the delineation of gross and internal tumor volumes in comparison with 4DCT. J Appl Clin Med Phys. 2019; 20(9): 51-60.
Crossref Google Scholar
7

Han S, Liang X, Li T, Yin F-FF, Cai J. Slice-stacking T2-weighted MRI for fast determination of internal target volume for liver tumor. Quant Imaging Med Surg. 2021; 11(1): 32-42.
Crossref Google Scholar
8

Placidi L, Cusumano D, Boldrini L, et al. Quantitative analysis of MRI-guided radiotherapy treatment process time for tumor real-time gating efficiency. J Appl Clin Med Phys. 2020; 21(11): 70-79.
Crossref Google Scholar
9

Seregni M, Paganelli C, Lee D, et al. Motion prediction in MRI-guided radiotherapy based on interleaved orthogonal cine-MRI. Phys Med Biol. 2016; 61(2): 872-887.
Crossref Google Scholar
10

Paganelli C, Lee D, Kipritidis J, et al. Feasibility study on 3D image reconstruction from 2D orthogonal cine-MRI for MRI-guided radiotherapy. J Med Imaging Radiat Oncol. 2018; 62(3): 389-400.
Crossref Google Scholar
11

Keijnemans K, Borman PTS, van Lier ALHMW, Verhoeff JJC, Raaymakers BW, Fast MF. Simultaneous multi-slice accelerated 4D-MRI for radiotherapy guidance. Phys Med Biol. 2021; 66(9):095014. https://doi.org/10.1088/1361-6560/ABF591
Crossref Google Scholar
12

Fernandes AT, Apisarnthanarax S, Yin L, et al. Comparative assessment of liver tumor motion using cine-magnetic resonance imaging versus 4-dimensional computed tomography. Int J Radiat Oncol Biol Phys. 2015; 91(5): 1034-1040.
Crossref Google Scholar
13

Noel CE, Parikh PJ. Effect of mid-scan breathing changes on quality of 4DCT using a commercial phase-based sorting algorithm. Med Phys. 2011; 38(5): 2430-2438.
Crossref Google Scholar
14

Li T, Xing L. Optimizing 4D cone-beam CT acquisition protocol for external beam radiotherapy. Int J Radiat Oncol Biol Phys. 2007; 67(4): 1211-1219.
Crossref Google Scholar
15

Ge J, Santanam L, Noel C, Parikh PJ. Planning 4-dimensional computed tomography (4DCT) cannot adequately represent daily intrafractional motion of abdominal tumors. Int J Radiat Oncol Biol Phys. 2013; 85(4): 999-1005.
Crossref Google Scholar
16

Wang Y, Liu T, Chen H, Bai P, Zhan Q, Liang X. Comparison of internal target volumes defined by three-dimensional, four-dimensional, and cone-beam computed tomography images of a motion phantom. Ann Transl Med. 2020; 8(22): 1488-1488.
Crossref Google Scholar
17

Cusumano D, Dhont J, Boldrini L, et al. Reliability of ITV approach to varying treatment fraction time: a retrospective analysis based on 2D cine MR images. Radiat Oncol. 2020; 15(1): 152. https://doi.org/10.1186/s13014-020-01530-6
Crossref Google Scholar
18

van Kesteren Z, van der Horst A, Gurney-Champion OJ, et al. A novel amplitude binning strategy to handle irregular breathing during 4DMRI acquisition: improved imaging for radiotherapy purposes. Radiat Oncol. 2019; 14(1): 80.
Crossref Google Scholar
19

Mickevicius NJ, Paulson ES. Simultaneous acquisition of orthogonal plane cine imaging and isotropic 4D-MRI using super-resolution. Radiother Oncol. 2019; 136: 121-129.
Crossref Google Scholar
20

Sun D, Liang X, Yin F, Cai J. Probability-based 3D κ-space sorting for motion robust 4D-MRI. Quant Imaging Med Surg. 2019; 9(7): 1326-1336.
Crossref Google Scholar
21

Thomas DH, Santhanam A, Kishan AU, et al. Initial clinical observations of intra- and interfractional motion variation in MR-guided lung SBRT. Br J Radiol. 2018; 91(1083). 20170522. https://doi.org/10.1259/bjr.20170522
Crossref Google Scholar
22

Lamb JM, Ginn JS, O'Connell DP, et al. Dosimetric validation of a magnetic resonance image gated radiotherapy system using a motion phantom and radiochromic film. J Appl Clin Med Phys. 2017; 18(3): 163-169.
Crossref Google Scholar
23

Paulson ES, Ahunbay E, Chen X, et al. 4D-MRI driven MR-guided online adaptive radiotherapy for abdominal stereotactic body radiation therapy on a high field MR-Linac: implementation and initial clinical experience. Clin Transl Radiat Oncol. 2020; 23: 72-79.
Crossref Google Scholar
24

Han F, Zhou Z, Cao M, Yang Y, Sheng K, Hu P. Respiratory motion-resolved, self-gated 4D-MRI using rotating cartesian k-space (ROCK). Med Phys. 2017; 44(4): 1359-1368.
Crossref Google Scholar
25

Xiao H, Ni R, Zhi S, et al. A dual-supervised deformation estimation model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy. Med Phys. 2022. https://doi.org/10.1002/mp.15542. Online ahead of print.
Crossref Google Scholar
26

Rabe M, Thieke C, D€ M, et al. Real-time 4DMRI-based internal target volume definition for moving lung tumors. Med Phys. 2020; 47(4): 1431-1442.
Crossref Google Scholar
27

Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011; 54(3): 2033-2044.
Crossref Google Scholar
28

Keiper TD, Tai A, Chen X, et al. Feasibility of real-time motion tracking using cine MRI during MR-guided radiation therapy for abdominal targets. Med Phys. 2020; 47(8): 3554-3566.
Crossref Google Scholar
29

Finazzi T, van Sörnsen de Koste JR, Palacios MA, et al. Delivery of magnetic resonance-guided single-fraction stereotactic lung radiotherapy. Phys Imaging Radiat Oncol. 2020; 14: 17-23.
Crossref Google Scholar
30

Harris W, Yin FF, Cai J, Ren L. Volumetric cine magnetic resonance imaging (VC-MRI) using motion modeling, free-form deformation and multi-slice undersampled 2D cine MRI reconstructed with spatio-temporal low-rank decomposition. Quant Imaging Med Surg. 2020; 10(2): 432-450.
Crossref Google Scholar
31

Nie X, Huang K, Deasy J, Rimner A, Li G. Enhanced super-resolution reconstruction of T1w time-resolved 4DMRI in low-contrast tissue using 2-step hybrid deformable image registration. J Appl Clin Med Phys. 2020; 21(10): 25-39.
Crossref Google Scholar
32

Akino Y, Oh RJ, Masai N, Shiomi H, Inoue T. Evaluation of potential internal target volume of liver tumors using cine-MRI. Med Phys. 2014; 41(11).111704. https://doi.org/10.1118/1.4896821
Crossref Google Scholar
33

Rohlfing T, Maurer CR, O'Dell WG, Zhong J. Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images. Med Phys. 2004; 31(3): 427-432.
Crossref Google Scholar
34

Landberg T, Chavaudra J, Dobbs J, et al. Report 62 J Int Comm Radiat Units Meas. 1999; 32(1). https://doi.org/10.1093/jicru/os32.1.report62
Crossref Google Scholar
35

den Boer D, Veldman JK, van Tienhoven G, Bel A, van Kesteren Z. Evaluating differences in respiratory motion estimates during radiotherapy: a single planning 4DMRI versus daily 4DMRI. Radiat Oncol. 2021; 16(1): 188. https://doi.org/10.1186/S13014-021-01915-1
Crossref Google Scholar
36

Rietzel E, Chen GTY. Improving retrospective sorting of 4D computed tomography data. Med Phys. 2006; 33(2): 377-379.
Crossref Google Scholar
37

Shimizu S, Shirato H, Aoyama H, et al. High-speed magnetic resonance imaging for four-dimensional treatment planning of conformal radiotherapy of moving body tumors. Int J Radiat Oncol. 2000; 48(2): 471-474.
Crossref Google Scholar
38

Stemkens B, Tijssen RHN, De Senneville BD, et al. Optimizing 4-dimensional magnetic resonance imaging data sampling for respiratory motion analysis of pancreatic tumors. Int J Radiat Oncol Biol Phys. 2015; 91(3): 571-578.
Crossref Google Scholar
39

Yuan J, Wong OL, Zhou Y, Chueng KY, Yu SK. A fast volumetric 4D-MRI with sub-second frame rate for abdominal motion monitoring and characterization in MRI-guided radiotherapy. Quant Imaging Med Surg. 2019; 9(7): 1303-1314.
Crossref Google Scholar
Precision Radiation Oncology
Volume 6 Issue 2,
June 2022
Pages 100-109
DOI: 10.1002/pro6.1153
Cite this article:
Wong OL, Law MWK, Poon DMC, et al. A pilot study of respiratory motion characterization in the abdomen using a fast volumetric 4D-MRI for MR-guided radiotherapy. Precision Radiation Oncology, 2022, 6(2): 100-109. https://doi.org/10.1002/pro6.1153

379

Views

0

Crossref

1

Scopus

Altmetrics
Received: 02 March 2022
Revised: 07 April 2022
Accepted: 13 April 2022
Published: 08 May 2022
© 2022 The Authors. Precision Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Shandong Cancer Hospital & Institute.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Return