リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Comparative study of sub-second temporal resolution 4D-MRI and 4D-CT for target motion assessment in a phantom model」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Comparative study of sub-second temporal resolution 4D-MRI and 4D-CT for target motion assessment in a phantom model

Wang, Tianyuan Sofue, Keitaro Shimada, Ryuji Ishihara, Takeaki Yada, Ryuichi Miyamoto, Masanori Sasaki, Ryohei Murakami, Takamichi 神戸大学

2023.09.21

概要

To develop and investigate the feasibility of sub-second temporal resolution volumetric T1-weighted four-dimensional (4D-) MRI in comparison with 4D-CT for respiratory-correlated motion assessment using an MRI/CT-compatible phantom. Sub-second high temporal resolution (0.5 s) gradient-echo T1-weighted 4D-MRI was developed using a volumetric acquisition scheme with compressed sensing. An MRI/CT-compatible motion phantom (simulated liver tumor) with three sinusoidal movements of amplitudes and two respiratory patterns was introduced and imaged with 4D-MRI and 4D-CT to investigate the geometric accuracy of the target movement. The geometric accuracy, including centroid position, volume, similarity index of dice similarity coefficient (DSC), and Hausdorff distance (HD), was systematically evaluated. Proposed 4D-MRI achieved a similar geometric accuracy compared with 4D-CT regarding the centroid position, volume, and similarity index. The observed position differences of the absolute average centroid were within 0.08 cm in 4D-MRI and 0.03 cm in 4D-CT, less than the 1-pixel resolution for each modality. The observed volume difference in 4D-MRI/4D-CT was within 0.73 cm³ (4.5%)/0.29 cm³ (2.1%) for a large target and 0.06 cm³ (11.3%)/0.04 cm³ (11.6%) for a small target. The observed DSC values for 4D-MRI/4D-CT were at least 0.93/0.95 for the large target and 0.83/0.84 for the small target. The maximum HD values were 0.25 cm/0.31 cm for the large target and 0.21 cm/0.15 cm for the small target. Although 4D-CT potentially exhibit superior numerical accuracy in phantom studies, the proposed high temporal resolution 4D-MRI demonstrates sub-millimetre geometric accuracy comparable to that of 4D-CT. These findings suggest that the 4D-MRI technique is a viable option for characterizing motion and generating phase-dependent internal target volumes within the realm of radiotherapy.

この論文で使われている画像

参考文献

1. Low, D. A. et al. A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing.

Med. Phys. 30, 1254–1263 (2003).

2. Xi, M. et al. Defining internal target volume (ITV) for hepatocellular carcinoma using four-dimensional CT. Radiother. Oncol. 84,

272–278 (2007).

3. Francis, I. R. et al. Multidetector CT of the liver and hepatic neoplasms: Effect of multiphasic imaging on tumor conspicuity and

vascular enhancement. AJR Am. J. Roentgenol. 180, 1217–1224 (2003).

4. Keall, P. J. et al. The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med. Phys. 33,

3874–3900 (2006).

5. Han, F. et al. Respiratory motion-resolved, self-gated 4D-MRI using rotating Cartesian k-space (ROCK). Med. Phys. 44, 1359–1368

(2017).

6. Paganelli, C., Summers, P., Bellomi, M., Baroni, G. & Riboldi, M. Liver 4DMRI: A retrospective image-based sorting method. Med.

Phys. 42, 4814–4821 (2015).

7. Stemkens, B., Paulson, E. S. & Tijssen, R. H. N. Nuts and bolts of 4D-MRI for radiotherapy. Phys. Med. Biol. 63, 21TR01 (2018).

8. Mickevicius, N. J. & Paulson, E. S. Investigation of undersampling and reconstruction algorithm dependence on respiratory correlated 4D-MRI for online MR-guided radiation therapy. Phys. Med. Biol. 62, 2910–2921 (2017).

9. Freedman, J. N. et al. T2-Weighted 4D magnetic resonance imaging for application in magnetic resonance–guided radiotherapy

treatment planning. Investig. Radiol. 52, 563–573 (2017).

10. Blackall, J. M. et al. MRI-based measurements of respiratory motion variability and assessment of imaging strategies for radiotherapy planning. Phys. Med. Biol. 51, 4147–4169 (2006).

11. Tokuda, J. et al. Adaptive 4D MR imaging using navigator-based respiratory signal for MRI-guided therapy. Magn. Reson. Med.

59, 1051–1061 (2008).

12. Van den Bos, I. C. et al. MR imaging of hepatocellular carcinoma: Relationship between lesion size and imaging findings, including

signal intensity and dynamic enhancement patterns. J. Magn. Reson. Imaging 26, 1548–1555 (2007).

13. Murakami, T., Sofue, K. & Hori, M. Diagnosis of hepatocellular carcinoma using Gd-EOB-DTPA MR imaging. Magn. Reson. Med.

Sci. 21, 168–181 (2022).

14. Vogl, T. J. et al. Liver tumors: Comparison of MR imaging with Gd-EOB-DTPA and Gd-DTPA. Radiology 200, 59–67 (1996).

15. Jaspan, O. N., Fleysher, R. & Lipton, M. L. Compressed sensing MRI: A review of the clinical literature. Br. J. Radiol. 88, 20150487

(2015).

16. Usman, M. et al. Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. Magn. Reson. Med. 70, 504–516

(2013).

17. Oar, A. et al. Comparison of four dimensional computed tomography and magnetic resonance imaging in abdominal radiotherapy

planning. Phys. Imaging Radiat. Oncol. 7, 70–75 (2018).

18. Yuan, J. et al. A fast volumetric 4D-MRI with sub-second frame rate for abdominal motion monitoring and characterization in

MRI-guided radiotherapy. Quant. Imaging Med. Surg. 9, 1303 (2019).

19. Yue, Y. et al. Geometric validation of self-gating k-space-sorted 4D-MRI vs 4D-CT using a respiratory motion phantom. Med.

Phys. 42, 5787–5797 (2015).

20. Akino, Y. et al. Evaluation of the accuracy of the CyberKnife synchrony™ respiratory tracking system using a plastic scintillator.

Med. Phys. 45, 3506–3515 (2018).

Scientific Reports |

Vol:.(1234567890)

(2023) 13:15685 |

https://doi.org/10.1038/s41598-023-42773-z

12

www.nature.com/scientificreports/

21. Al-Mayah, A. et al. Biomechanical-based image registration for head and neck radiation treatment. Phys. Med. Biol. 55, 6491–6500

(2010).

22. Huttenlocher, D. P., Klanderman, G. A. & Rucklidge, W. J. Comparing images using the Hausdorff distance. IEEE Trans. Pattern

Anal. Mach. Intell. 15, 850–863 (1993).

23. Feng, L. et al. XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed

sensing. Magn. Reson. Med. 75, 775–788 (2016).

24. Winkelmann, S., Schaeffter, T., Koehler, T., Eggers, H. & Doessel, O. An optimal radial profile order based on the Golden Ratio

for time-resolved MRI. IEEE Trans. Med. Imaging 26, 68–76 (2007).

25. Biederer, J. et al. 4D-Imaging of the lung: Reproducibility of lesion size and displacement on helical CT, MRI, and cone beam CT

in a ventilated ex vivo system. Int. J. Radiat. Oncol. Biol. Phys. 73, 919–926 (2009).

26. Mahadevan, A. et al. Stereotactic Body Radiotherapy (SBRT) for liver metastasis–clinical outcomes from the international multiinstitutional RSSearch® Patient Regstry. Radiat. Oncol. 13(1), 1–11 (2018).

27. Apisarnthanarax, S. et al. External beam radiation therapy for primary liver cancers: An ASTRO clinical practice guideline. Pract.

Radiat. Oncol. 12(1), 28–51 (2022).

28. Kuo, H.-T. et al. Impact of tumor size on outcome after stereotactic body radiation therapy for inoperable hepatocellular carcinoma.

Medicine 96(50), e9249 (2017).

29. Hall, W. A. et al. Initial clinical experience of stereotactic body radiation therapy (SBRT) for liver metastases, primary liver malignancy, and pancreatic abdominal nodal recurrence with 4D-MRI based online adaptation and real-time MRI monitoring using a

1.5 Tesla MR-Linac. Int. J. Radiat. Oncol. Biol. Phys. 105(1), S90 (2019).

30. Shiani, A., Narayanan, S., Pena, L. & Friedman, M. The role of diagnosis and treatment of underlying liver disease for the prognosis

of primary liver cancer. Cancer Control 24, 1073274817729240 (2017).

31. Feng, M. et al. Individualized adaptive stereotactic body radiotherapy for liver tumors in patients at high risk for liver damage: A

phase 2 clinical trial. JAMA Oncol. 4, 40–47 (2018).

32. Tsujita, Y. et al. Prediction of post-hepatectomy liver failure using gadoxetic acid-enhanced magnetic resonance imaging for

hepatocellular carcinoma with portal vein invasion. Eur. J. Radiol. 130, 109189 (2020).

33. Li, G. et al. Direct comparison of respiration-correlated four-dimensional magnetic resonance imaging reconstructed using concurrent internal navigator and external bellows. Int. J. Radiat. Oncol. Biol. Phys. 97, 596–605 (2017).

34. van de Lindt, T. et al. A self-sorting coronal 4D-MRI method for daily image guidance of liver lesions on an MR-LINAC. Int. J.

Radiat. Oncol. Biol. Phys. 102, 875–884 (2018).

35. Liu, Y. et al. Accuracy of respiratory motion measurement of 4D-MRI: A comparison between cine and sequential acquisition.

Med. Phys. 43, 179–187 (2016).

36. Park, S. et al. Autoencoder-inspired convolutional network-based super-resolution method in MRI. IEEE J. Transl. Eng. Health

Med. 9, 1–13 (2021).

37. Tamada, D. Review: Noise and Artifact Reduction for MRI Using Deep Learning. arXiv preprint arXiv:​2002.​12889 (2020).

38. Liu, Y. et al. MRI-based treatment planning for liver stereotactic body radiotherapy: Validation of a deep learning-based synthetic

CT generation method. Br. J. Radiol. 92, 20190067 (2019).

Author contributions

T.W. wrote the main manuscript text and prepared figures and tables. K.S. is the corresponding author, gave

advices through the whole study and revised the main manuscript, figures and tables. R.Shimada. participated

in the imaging acquisition with MRI scanner, prepared Fig. 2a and revised the manuscript. T.I. gave the advices

for the imaging evaluation. R.Y. participated in the preparation of motion phantom. M.M. participated in the

imaging acquisition with CT scanner. R.Sasaki., T.M. reviewed the manuscript and gave advices.

Funding

This work was partly supported by the Grant-in-Aid for Scientific Research of the Japan Society for the Promotion of Science (grant numbers 21K07620, 22H03026 and 19K17235).

Competing interests The authors declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​

10.​1038/​s41598-​023-​42773-z.

Correspondence and requests for materials should be addressed to K.S.

Reprints and permissions information is available at www.nature.com/reprints.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and

institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International

License, which permits use, sharing, adaptation, distribution and reproduction in any medium or

format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the

Creative Commons licence, and indicate if changes were made. The images or other third party material in this

article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the

material. If material is not included in the article’s Creative Commons licence and your intended use is not

permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from

the copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

© The Author(s) 2023

Scientific Reports |

(2023) 13:15685 |

https://doi.org/10.1038/s41598-023-42773-z

13

Vol.:(0123456789)

...

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文

一発検索!

この論文の関連論文を見る