Assessing image registration method for synthetic CT evaluation in MRI-only radiotherapy treatment planning

Authors

  • Pareena Earwong Master of Science Program in Medical Physics, Faculty of Medicine Ramathibodi Hospital, Mahidol University
  • Sithiphong Suphaphong Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University
  • Ladawan Worapruekjaru Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University
  • Napatsorn Thumyongkit Master of Science Program in Medical Physics, Faculty of Medicine Ramathibodi Hospital, Mahidol University
  • Chuleeporn Jiarpinitnun Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University
  • Chanon Puttanawarut Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University
  • Sawwanee Asavaphatiboon Division of Diagnostic Radiology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University
  • Suphalak Khachonkham Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University

Keywords:

synthetic CT, MRI-only, Rigid image registration, Deformable image registration, Treatment planning

Abstract

Background: Image registration is a key process of synthetic CT (sCT) evaluation for clinical MRI-only radiotherapy implementation, which lacks a comparative study investigating appropriate image registration methods.

Objective: To investigate the appropriate image registration methods for the sCT generated by a commercial convolutional neural network-based algorithm by comparing image intensity and dosimetry between sCT and planning CT (pCT) in the head-neck (H&N) and prostate.

Materials and Methods: This retrospective study included 10 patients with H&N (5) and prostate (5) cancer who underwent CT and MRI simulations for volumetric modulated arc therapy. The sCT was generated by the software MRI Planner™. The corresponding pCT was registered to the sCT using rigid image registration (RIR) and deformable image registration (DIR) based on the B-spline, creating rigid pCT (rpCT) and deformed pCT (dpCT), respectively. The pCT plan parameters were transferred and re-calculated with the fixed monitor unit on the registered sCT. The rpCT-sCT was compared to the dpCT-sCT in terms of HU accuracy evaluation using the mean absolute error (MAE), geometric evaluation using the dice similarity coefficient (DSC), and dosimetric evaluation using the dose-volume-histogram and gamma analysis.

Results: The DIR method improved the MAE by an average of 25.56% for the H&N and 61.85% for the prostate compared to the RIR method. The mean DSCs of the H&N of 0.73-0.97 in the RIR method were increased to 0.83-1.00 in the DIR method. The mean DSCs of the prostate of 0.69-0.95 in the RIR method were increased to 0.77-1.00 in the DIR method. The RIR method provided the maximum dose difference of 4.22%, which was decreased to within 2.00% for PTV and OARs using the DIR method for both H&N and prostate, except for high dose differences in the bladder and rectum caused by excessive volume and shape differences uncorrected by the DIR method.

Conclusion: The DIR method achieved better results in image intensity and dosimetry in sCT evaluation compared to the RIR method by minimizing the uncertainty from the anatomical misalignment between pCT and MRI acquisitions for the H&N and prostate.

References

Pereira GC, Traughber M, Muzic Jr. RF. The role of imaging in radiation therapy planning: past, present, and future. BioMed Res Int. 2014;2014:1-9.

Palmér E, Karlsson A, Nordström F, Petruson K, Siversson C, Ljungberg M, et al. Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy. Phys Imaging in Radiat Oncol. 2021;17:36-42.

Edmund JM, Nyholm T. A review of substitute CT generation for MRI-only radiation therapy. Radiat Oncol. 2017;12:1-15.

Persson E, Gustafsson CJ, Ambolt P, Engelholm S, Ceberg S, Bäck S, et al. MR-PROTECT: Clinical feasibility of a prostate MRI-only radiotherapy treatment workflow and investigation of acceptance criteria. Radiat Oncol. 2020;15:1-13.

Johnstone E, Wyatt JJ, Henry AM, Short SC, Sebag-Montefiore D, Murray L, et al. Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imaging-Only Radiation Therapy. Int J Radiat Oncol Biol Phys. 2018;100:199-217.

Spadea MF, Maspero M, Zaffino P, Seco J. Deep learning based synthetic-CT generation in radiotherapy and PET: A review. Med Phys. 2021;48:6537-66.

Cronholm RO, Karlsson A, Siversson C. Whitepaper: MRI only radiotherapy planning using the transfer function estimation algorithm. 2020:1-7.

Siversson C, Nordström F, Nilsson T, Nyholm T, Jonsson J, Gunnlaugsson A, et al. Technical note: MRI only prostate radiotherapy planning using the statistical decomposition algorithm. Med Phys. 2015;42:6090-7.

Ding S, Liu H, Li Y, Wang B, Li R, Huang X. Dosimetric accuracy of MR-guided online adaptive planning for nasopharyngeal carcinoma radiotherapy on 1.5 T MR-linac. Front Oncol. 2022;12:1-11.

Dinkla AM, Florkow MC, Maspero M, Savenije MHF, Zijlstra F, Doornaert PAH, et al. Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based three-dimensional convolutional neural network. Med Phys. 2019;46:4095-104.

Guerreiro F, Burgos N, Dunlop A, Wong K, Petkar I, Nutting C, et al. Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning. Phys Med. 2017;35:7-17.

Wang H, Du K, Qu J, Chandarana H, Das IJ. Dosimetric evaluation of magnetic resonance-generated synthetic CT for radiation treatment of rectal cancer. PLoS One. 2018;13:1-15.

Kemppainen R, Suilamo S, Tuokkola T, Lindholm P, Deppe MH, Keyriläinen J. Magnetic resonance-only simulation and dose calculation in external beam radiation therapy: a feasibility study for pelvic cancers. Acta Oncol. 2017;56:792-8.

Ma X, Chen X, Li J, Wang Y, Men K, Dai J. MRI-only radiotherapy planning for nasopharyngeal carcinoma using deep learning. Front Oncol. 2021;11:1-8.

Lerner M, Medin J, Gustafsson CJ, Alkner S, Siversson C, Olsson LE. Clinical validation of a commercially available deep learning software for synthetic CT generation for brain. Radiat Oncol. 2021;16:1-11.

Jurkovic IA, Papanikolaou N, Stathakis S, Kirby N, Mavroidis P. Objective Assessment of the quality and accuracy of deformable image registration. J Med Phys. 2020;45:156-67.

Brock KK, Mutic S, McNutt TR, Li H, Kessler ML. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132. Med Phys. 2017;44:e43-76.

Chourak H, Barateau A, Tahri S, Cadin C, Lafond C, Nunes JC, et al. Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods. Front Oncol. 2022;12:1-17.

Brock KK. Image registration in intensity- modulated, image-guided and stereotactic body radiation therapy. Front Radiat Ther Oncol. 2007;40:94-115.

Fu J, Yang Y, Singhrao K, Ruan D, Chu FI, Low DA, et al. Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging. Med Phys. 2019;46:3788-98.

Peng Y, Chen S, Qin A, Chen M, Gao X, Liu Y, et al. Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning. Radiother Oncol. 2020;150:217-24.

Wang Y, Liu C, Zhang X, Deng W. Synthetic CT generation based on T2 weighted MRI of nasopharyngeal carcinoma (NPC) using a deep convolutional neural network (DCNN). Front Oncol. 2019;9:1-10.

Elsayed O, Mahar K, Kholief M, Khater H. Automatic detection of the pulmonary nodules from CT images. 2015:742-6.

Bo Y, Chang Y, Liang Y, Wang Z, Pei X, Xu XG, et al. A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality. Front oncol. 2022;12:1-12.

Korsholm ME, Waring LW, Edmund JM. A criterion for the reliable use of MRI-only radiotherapy. Radiat Oncol. 2014;9:1-7.

Rong Y, Rosu-Bubulac M, Benedict SH, Cui Y, Ruo R, Connell T, et al. Rigid and deformable image registration for radiation therapy: A self-study evaluation guide for NRG oncology clinical trial participation. Pract Radiat Oncol. 2021;11:282-98.

Tang B, Wu F, Fu Y, Wang X, Wang P, Orlandini LC, et al. Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy. J Appl Clin Med Phys. 2021;22:55-62.

G Gonzalez-Moya A, Dufreneix S, Ouyessad N, Guillerminet C, Autret D. Evaluation of a commercial synthetic computed tomography generation solution for magnetic resonance imaging-only radiotherapy. J Appl Clin Med Phys. 2021;22:191-7.

Bratova I, Paluska P, Grepl J, Sykorova P, Jansa J, Hodek M, et al. Validation of dose distribution computation on sCT images generated from MRI scans by Philips MRCAT. Rep Pract Oncol Radiother. 2019;24:245-50.

Downloads

Published

2023-06-28

How to Cite

1.
Earwong P, Suphaphong S, Worapruekjaru L, Thumyongkit N, Jiarpinitnun C, Puttanawarut C, Asavaphatiboon S, Khachonkham S. Assessing image registration method for synthetic CT evaluation in MRI-only radiotherapy treatment planning. J Thai Assn of Radiat Oncol [Internet]. 2023 Jun. 28 [cited 2024 Dec. 21];29(1):R121-R138. Available from: https://he01.tci-thaijo.org/index.php/jtaro/article/view/261917

Issue

Section

Original articles