|
1
|
Stanescu T, Shessel A, Carpino-Rocca C, Taylor E, Semeniuk O, Li W, Barry A, Lukovic J, Dawson L, Hosni A. MRI-Guided Online Adaptive Stereotactic Body Radiation Therapy of Liver and Pancreas Tumors on an MR-Linac System. Cancers (Basel) 2022; 14:cancers14030716. [PMID: 35158984 PMCID: PMC8833602 DOI: 10.3390/cancers14030716] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary The hybrid magnetic resonance imaging and medical linear accelerator (MR-Linac) systems are expected to revolutionize radiation therapy, uniquely offering high quality soft-tissue contrast and fast imaging to facilitate the online re-planning and guidance of the treatment delivery. While the clinical procedures for stereotactic body radiotherapy are well-established for conventional linacs (with their strengths and weaknesses), they still require significant development and refinement for the MR-Linac systems. Adjustment of fractionation schemes including clinical goals, patient selection, organ motion management, treatment session length, staff logistics, and overall complexity of the online re-planning sessions are key factors that drive the adoption of MR-Linac technologies. In this report, we present the clinical implementation of an MRI-guided radiation therapy workflow, which was used to treat 16 upper gastro-intestinal cancer patients on a 1.5 T MR-Linac platform. The workflow was proven to be feasible for a wide range of clinical scenarios, and the overall treatment session time was significantly reduced as tasks were optimized and the clinical team gradually gained expertise. Abstract Purpose: To describe a comprehensive workflow for MRI-guided online adaptive stereotactic body radiation therapy (SBRT) specific to upper gastrointestinal cancer patients with abdominal compression on a 1.5T MR-Linac system. Additionally, we discuss the workflow’s clinical feasibility and early experience in the case of 16 liver and pancreas patients. Methods: Eleven patients with liver cancer and five patients with pancreas cancer were treated with online adaptive MRI-guidance under abdominal compression. Two liver patients received single-fraction treatments; the remainder plus all pancreas cancer patients received five fractions. A total of 65 treatment sessions were investigated to provide analytics relevant to the online adaptive processes. The quantification of target and organ motion as well as definition and validation of internal target volume (ITV) margins were performed via multi-contrast imaging provided by three different 2D cine sequences. The plan generation was driven by full re-optimization strategies and using T2-weighted 3D image series acquired by means of a respiratory-triggered exhale phase or a time-averaged imaging protocol. As a pre-requisite for the clinical development of the procedure, the image quality was thoroughly investigated via phantom measurements and numerical simulations specific to upper abdominal sites. The delivery of the online adaptive treatments was facilitated by real-time monitoring with 2D cine imaging. Results: Liver 1-fraction and 5-fraction online adaptive session time were on average 80 and 67.5 min, respectively. The total session length varied between 70–90 min for a single fraction and 55–90 min for five fractions. The pancreas sessions were 54–85 min long with an average session time of 68.2 min. Target visualization on the 2D cine image data varied per patient, with at least one of the 2D cine sequences providing sufficient contrast to confidently identify its location and confirm reproducibility of ITV margins. The mean/range of absolute and relative dose values for all treatment sessions evaluated with ArcCheck were 90.6/80.9–96.1% and 99/95.4–100%, respectively. Conclusion: MR-guidance is feasible for liver and pancreas tumors when abdominal compression is used to reduce organ motion, improve imaging quality, and achieve a robust intra- and inter-fraction patient setup. However, the treatment length is significantly longer than for the conventional linac, and patient compliance is paramount for the successful completion of the treatment. Opportunities for reducing the online adaptive session time should be explored. As the next steps, dose-of-the-day and dose accumulation analysis and tools are needed to enhance the workflow and to help further refine the online re-planning processes.
Collapse
Affiliation(s)
- Teo Stanescu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
- Correspondence:
| | - Andrea Shessel
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
| | - Cathy Carpino-Rocca
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
| | - Edward Taylor
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Oleksii Semeniuk
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
| | - Winnie Li
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
| | - Aisling Barry
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Jelena Lukovic
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Laura Dawson
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Ali Hosni
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; (A.S.); (C.C.-R.); (E.T.); (O.S.); (W.L.); (A.B.); (J.L.); (L.D.); (A.H.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| |
Collapse
|
|
2
|
Stanescu T, Mousavi SH, Cole M, Barberi E, Wachowicz K. Quantification of magnetic susceptibility fingerprint of a 3D linearity medical device. Phys Med 2021; 87:39-48. [PMID: 34116316 DOI: 10.1016/j.ejmp.2021.05.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/13/2021] [Accepted: 05/14/2021] [Indexed: 02/03/2023] Open
Abstract
PURPOSE The study investigates the numerical modelling as well as experimental validation of magnetic susceptibility effects with respect to a 3D linearity phantom used for the quantification of MR image distortions. METHODS Magnetic field numerical simulations based on finite difference methods were conducted to generate the susceptibility (χ) model of the MRID3D phantom. Experimental data was acquired and analyzed for eight different MR scanners to include a wide range of scanning parameters. Distortion vector fields were generated by applying a harmonic analysis based on finite elements methods. Phantom scans for the same setup but with opposite polarities of the frequency encoding gradient were processed in conjunction with the susceptibility modelling to separately quantify three field components due to gradient non-linearities (GNL), B0 inhomogeneities and χ perturbations. RESULTS The numerical modelling showed a significant range of χ value of up to 8.23 ppm, with a mean value of 2.9 ppm. The χ perturbations were found to be mostly present at the end plates of the cylindrical phantom design. The simulations also showed that setup rotations of up to 10° introduced only negligible variations in the χ model of less than 0.1 ppm. This allows for a straightforward practical implementation of the modelling as a single lookup table. After correcting for the χ perturbations, the B0 inhomogeneities were derived and found to be in good agreement with either the MR system manufacturer specifications or experimental data available in the literature. CONCLUSIONS It is possible to accurately model the magnetic susceptibility signature of a 3D linearity device and remove it as a post-processing correction step. This is important as the procedure unlocks the ability of determining both the GNL field and B0 map of the scanner without the need of extra acquisitions or phantoms.
Collapse
Affiliation(s)
- T Stanescu
- Princess Margaret Cancer Centre, University Health Network, Department of Radiation Oncology, University of Toronto, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada.
| | - S H Mousavi
- Princess Margaret Cancer Centre, University Health Network, Department of Radiation Oncology, University of Toronto, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada
| | - M Cole
- Modus QA, London, Ontario N6H 5L6, Canada
| | - E Barberi
- Modus QA, London, Ontario N6H 5L6, Canada
| | - K Wachowicz
- Cross Cancer Institute, Alberta Health Services, Department of Radiation Oncology, University of Alberta, 11560 University Avenue, Edmonton, AB T6G 1Z2, Canada
| |
Collapse
|