1
|
Iramina H, Nakamura M, Sasaki M, Mizowaki T. Performance of cone-beam computed tomography imaging during megavoltage beam irradiation under phase-gated conditions. Phys Med 2024; 123:103409. [PMID: 38870644 DOI: 10.1016/j.ejmp.2024.103409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 05/23/2024] [Accepted: 06/10/2024] [Indexed: 06/15/2024] Open
Abstract
PURPOSE Target positions should be acquired during beam delivery for accurate lung stereotactic body radiotherapy. We aimed to perform kilovoltage (kV) imaging during beam irradiation (intra-irradiation imaging) under phase-gated conditions and evaluate its performance. METHODS Catphan 504 and QUASAR respiratory motion phantoms were used to evaluate image quality and target detectability, respectively. TrueBeam STx linac and the Developer Mode was used. The imaging parameters were 125 kVp and 1.2 mAs/projection. Flattened megavoltage (MV) X-ray beam energies 6, 10 and 15 MV and un-flattened beam energies 6 and 10 MV were used with field sizes of 5 × 5 and 15 × 15 cm2 and various frame rates for intra-irradiation imaging. In addition, using a QUASAR phantom, intra-irradiation imaging was performed during intensity-modulated plan delivery. The root-mean-square error (RMSE) of the CT-number for the inserted rods, image noise, visual assessment, and contrast-to-noise ratio (CNR) were evaluated. RESULTS The RMSEs of intra-irradiation cone-beam computed tomography (CBCT) images under gated conditions were 50-230 Hounsfield Unit (HU) (static < 30 HU). The noise of the intra-irradiation CBCT images under gated conditions was 15-35 HU, whereas that of the standard CBCT images was 8.8-27.2 HU. Lower frame rates exhibited large RMSEs and noise; however, the iterative reconstruction algorithm (IR) was effective at improving these values. Approximately 7 fps with the IR showed an equivalent CNR of 15 fps without the IR. The target was visible on all the gated intra-irradiation CBCT images. CONCLUSION Several image quality improvements are required; however, intra-irradiated CBCT images showed good visual target detection.
Collapse
Affiliation(s)
- Hiraku Iramina
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Mitsuhiro Nakamura
- Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Makoto Sasaki
- Division of Clinical Radiology Service, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| |
Collapse
|
2
|
Renner A, Gulyas I, Buschmann M, Heilemann G, Knäusl B, Heilmann M, Widder J, Georg D, Trnková P. Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data. Phys Imaging Radiat Oncol 2024; 30:100594. [PMID: 38883146 PMCID: PMC11176922 DOI: 10.1016/j.phro.2024.100594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/17/2024] [Accepted: 05/25/2024] [Indexed: 06/18/2024] Open
Abstract
Background and purpose Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers. Material and methods Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7) mm. Results Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11) mm with a prediction horizon of 500 ms. Conclusion It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.
Collapse
Affiliation(s)
- Andreas Renner
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Ingo Gulyas
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Martin Buschmann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gerd Heilemann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Barbara Knäusl
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Martin Heilmann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Joachim Widder
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Petra Trnková
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
3
|
Persson E, Goodwin E, Eiben B, Wetscherek A, Nill S, Oelfke U. Real-time motion-including dose estimation of simulated multi-leaf collimator-tracked magnetic resonance-guided radiotherapy. Med Phys 2024; 51:2221-2229. [PMID: 37898109 DOI: 10.1002/mp.16798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 09/28/2023] [Accepted: 09/28/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND Real-time dose estimation is a key-prerequisite to enable online intra-fraction treatment adaptation in magnetic resonance (MR)-guided radiotherapy (MRgRT). It is an essential component for the assessment of the dosimetric benefits and risks of online adaptive treatments, such as multi-leaf collimator (MLC)-tracking. PURPOSE We present a proof-of-concept for a software workflow for real-time dose estimation of MR-guided adaptive radiotherapy based on real-time data-streams of the linac delivery parameters and target positions. METHODS A software workflow, combining our in-house motion management software DynaTrack, a real-time dose calculation engine that connects to a research version of the treatment planning software (TPS) Monaco (v.6.09.00, Elekta AB, Stockholm, Sweden) was developed and evaluated. MR-guided treatment delivery on the Elekta Unity MR-linac was simulated with and without MLC-tracking for three prostate patients, previously treated on the Elekta Unity MR-linac (36.25 Gy/five fractions). Three motion scenarios were used: no motion, regular motion, and erratic prostate motion. Accumulated monitor units (MUs), centre of mass target position and MLC-leaf positions, were forwarded from DynaTrack at a rate of 25 Hz to a Monte Carlo (MC) based dose calculation engine which utilises the research GPUMCD-library (Elekta AB, Stockholm, Sweden). A rigid isocentre shift derived from the selected motion scenarios was applied to a bulk density-assigned session MR-image. The respective electron density used for treatment planning was accessed through the research Monaco TPS. The software workflow including the online dose reconstruction was validated against offline dose reconstructions. Our investigation showed that MC-based real-time dose calculations that account for all linac states (including MUs, MLC positions and target position) were infeasible, hence states were randomly sampled and used for calculation as follows; Once a new linac state was received, a dose calculation with 106 photons was started. Linac states that arrived during the time of the ongoing calculation were put into a queue. After completion of the ongoing calculation, one new linac state was randomly picked from the queue and assigned the MU accumulated from the previous state until the last sample in the queue. The queue was emptied, and the process repeated throughout treatment simulation. RESULTS On average 27% (23%-30%) of received samples were used in the real-time calculation, corresponding to a calculation time for one linac state of 148 ms. Median gamma pass rate (2%/3 mm local) was 100.0% (99.9%-100%) within the PTV volume and 99.1% (90.1%-99.4.0%) with a 15% dose cut off. Differences in PTVDmean , CTVDmean , RectumD2% , and BladderD2% (offline-online, % of prescribed dose) were below 0.64%. Beam-by-beam comparisons showed deviations below 0.07 Gy. Repeated simulations resulted in standard deviations below 0.31% and 0.12 Gy for the investigated volume and dose criteria respectively. CONCLUSIONS Real-time dose estimation was successfully performed using the developed software workflow for different prostate motion traces with and without MLC-tracking. Negligible dosimetric differences were seen when comparing online and offline reconstructed dose, enabling online intra-fraction treatment decisions based on estimates of the delivered dose.
Collapse
Affiliation(s)
- Emilia Persson
- Joint Department of Physics, The Royal Marsden Hospital and The Institute of Cancer Research, Sutton, UK
| | - Edmund Goodwin
- Joint Department of Physics, The Royal Marsden Hospital and The Institute of Cancer Research, Sutton, UK
| | - Björn Eiben
- Joint Department of Physics, The Royal Marsden Hospital and The Institute of Cancer Research, Sutton, UK
| | - Andreas Wetscherek
- Joint Department of Physics, The Royal Marsden Hospital and The Institute of Cancer Research, Sutton, UK
| | - Simeon Nill
- Joint Department of Physics, The Royal Marsden Hospital and The Institute of Cancer Research, Sutton, UK
| | - Uwe Oelfke
- Joint Department of Physics, The Royal Marsden Hospital and The Institute of Cancer Research, Sutton, UK
| |
Collapse
|
4
|
Sui Z, Palaniappan P, Brenner J, Paganelli C, Kurz C, Landry G, Riboldi M. Intra-frame motion deterioration effects and deep-learning-based compensation in MR-guided radiotherapy. Med Phys 2024; 51:1899-1917. [PMID: 37665948 DOI: 10.1002/mp.16702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/07/2023] [Accepted: 07/31/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra-frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. PURPOSE The aim of this work is to investigate intra-frame motion deterioration effects in MR-guided radiotherapy (MRgRT) by simulating the motion-corrupted image acquisition, and to explore the feasibility of deep-learning-based compensation approaches, relying on the intra-frame motion information which is spatially and temporally encoded in the raw data (k-space). METHODS An intra-frame motion model was defined to simulate motion-corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra-frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra-frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U-Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss-L1 , NNFloss-L1 and NNL2-Loss were trained to extract information from the motion-corrupted image and used to estimate the ground truth final-position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean-squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical-flow image registration. RESULTS Image degradation caused by intra-frame motion was observed: for a linearly and fully acquired Cartesian readout k-space trajectory, intra-frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion-corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95 ) dropped from 4.8 mm to 2.1 mm. Different models showed slight performance variations across different intra-frame motion amplitude categories: NNImgLoss-L1 excelled for small/medium amplitudes, whereas NNFloss-L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion-corrupted image highlighted the major contribution of the later acquired k-space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. CONCLUSIONS Our results demonstrate the deep-learning-based approaches have the potential to compensate for intra-frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k-space components.
Collapse
Affiliation(s)
- Zhuojie Sui
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Prasannakumar Palaniappan
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Jakob Brenner
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| |
Collapse
|
5
|
Wu TC, Smith LM, Woolf D, Faivre-Finn C, Lee P. Exploring the Advantages and Challenges of MR-Guided Radiotherapy in Non-Small-Cell Lung Cancer: Who are the Optimal Candidates? Semin Radiat Oncol 2024; 34:56-63. [PMID: 38105094 DOI: 10.1016/j.semradonc.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The landscape of lung radiotherapy (RT) has rapidly evolved over the past decade with modern RT and surgical techniques, systemic therapies, and expanding indications for RT. To date, 2 MRI-guided RT (MRgRT) units, 1 using a 0.35T magnet and 1 using a 1.5T magnet, are available for commercial use with more systems in the pipeline. MRgRT offers distinct advantages such as real-time target tracking, margin reduction, and on-table treatment adaptation, which may help overcome many of the common challenges associated with thoracic RT. Nonetheless, the use of MRI for image guidance and the current MRgRT units also have intrinsic limitations. In this review article, we will discuss clinical experiences to date, advantages, challenges, and future directions of MRgRT to the lung.
Collapse
Affiliation(s)
- Trudy C Wu
- Department of Radiation Oncology, University of California, Los Angeles, CA
| | - Lauren M Smith
- Department of Radiation Oncology, University of California, Los Angeles, CA
| | - David Woolf
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom.; Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom
| | - Corinne Faivre-Finn
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom.; Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom
| | - Percy Lee
- Department of Radiation Oncology, City of Hope National Medical Center, Los Angeles, CA..
| |
Collapse
|
6
|
Lombardo E, Dhont J, Page D, Garibaldi C, Künzel LA, Hurkmans C, Tijssen RHN, Paganelli C, Liu PZY, Keall PJ, Riboldi M, Kurz C, Landry G, Cusumano D, Fusella M, Placidi L. Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects. Radiother Oncol 2024; 190:109970. [PMID: 37898437 DOI: 10.1016/j.radonc.2023.109970] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 10/30/2023]
Abstract
MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.
Collapse
Affiliation(s)
- Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Jennifer Dhont
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium
| | - Denis Page
- University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom
| | - Cristina Garibaldi
- IEO, Unit of Radiation Research, European Institute of Oncology IRCCS, Milan, Italy
| | - Luise A Künzel
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Rob H N Tijssen
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Paul Z Y Liu
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Paul J Keall
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, a Partnership between DKFZ and LMU University Hospital Munich, Germany; Bavarian Cancer Research Center (BZKF), Partner Site Munich, Munich, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy.
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy
| |
Collapse
|
7
|
Grimbergen G, Hackett SL, van Ommen F, van Lier ALHMW, Borman PTS, Meijers LTC, de Groot-van Breugel EN, de Boer JCJ, Raaymakers BW, Intven MPW, Meijer GJ. Gating and intrafraction drift correction on a 1.5 T MR-Linac: Clinical dosimetric benefits for upper abdominal tumors. Radiother Oncol 2023; 189:109932. [PMID: 37778533 DOI: 10.1016/j.radonc.2023.109932] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/18/2023] [Accepted: 09/23/2023] [Indexed: 10/03/2023]
Abstract
This work reports on the first seven patients treated with gating and baseline drift correction on the high-field MR-Linac system. Dosimetric analysis showed that the active motion management system improved congruence to the planned dose, efficiently mitigating detrimental effects of intrafraction motion in the upper abdomen.
Collapse
Affiliation(s)
- Guus Grimbergen
- Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands.
| | - Sara L Hackett
- Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands
| | - Fasco van Ommen
- Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands
| | | | - Pim T S Borman
- Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands
| | - Lieke T C Meijers
- Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands
| | | | - Johannes C J de Boer
- Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands
| | - Bas W Raaymakers
- Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands
| | - Martijn P W Intven
- Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands
| | - Gert J Meijer
- Department of Radiation Oncology, University Medical Center Utrecht, the Netherlands
| |
Collapse
|
8
|
Bernchou U, Schytte T, Bertelsen A, Lorenzen EL, Brink C, Mahmood F. Impact of abdominal compression on intra-fractional motion and delivered dose in magnetic resonance image-guided adaptive radiation ablation of adrenal gland metastases. Phys Med 2023; 114:102682. [PMID: 37717398 DOI: 10.1016/j.ejmp.2023.102682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/08/2023] [Accepted: 09/11/2023] [Indexed: 09/19/2023] Open
Abstract
PURPOSE The current study investigated the impact of abdominal compression on motion and the delivered dose during non-gated, magnetic resonance image (MRI)-guided radiation ablation of adrenal gland metastases. METHODS Thirty-one patients with adrenal gland metastases treated to 45-60 Gy in 3-8 fractions on a 1.5 T MRI-linac were included in the study. The patients were breathing freely (n = 14) or with motion restricted by using an abdominal compression belt (n = 17). The time-resolved position of the target in online 2D cine MR images acquired during treatment was assessed and used to estimate the dose delivered to the GTV and abutting luminal organs at risk (OAR). RESULTS The median (range) 3D root-mean-square target position error was significantly higher in patients treated without a compression belt [2.9 (1.9-5.6) mm] compared to patients using the belt [2.1 (1.2-3.5) mm] (P < 0.01). The median (range) GTV V95% was significantly reduced from planned 98.6 (65.9-100) % to delivered 96.5 (64.5-99.9) % due to motion (P < 0.01). Most prominent dose reductions were found in patients showing either large target drift or respiration motion and were mainly treated without abdominal compression. Motion did not lead to an increased number of constraint violations for luminal OAR. CONCLUSIONS Acceptable target coverage and dose to OAR was observed in the vast majority of patients despite intra-fractional motion during adaptive MRI-guided radiation ablation. The use of abdominal compression significantly reduced the target position error and prevented the most prominent target coverage degradations and is, therefore, recommended as motion management at MRI-linacs.
Collapse
Affiliation(s)
- Uffe Bernchou
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19 3., 5000 Odense C, Denmark.
| | - Tine Schytte
- Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19 3., 5000 Odense C, Denmark; Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark.
| | - Anders Bertelsen
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark.
| | - Ebbe Laugaard Lorenzen
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark.
| | - Carsten Brink
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19 3., 5000 Odense C, Denmark.
| | - Faisal Mahmood
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19 3., 5000 Odense C, Denmark.
| |
Collapse
|
9
|
Klavsen MF, Ankjærgaard C, Boye K, Behrens CP, Vogelius IR, Ehrbar S, Baumgartl M, Rippke C, Buchele C, Renkamp CK, Santurio GV, Andersen CE. Accumulated dose implications from systematic dose-rate transients in gated treatments with Viewray MRIdian accelerators. Biomed Phys Eng Express 2023; 9:065001. [PMID: 37591227 DOI: 10.1088/2057-1976/acf138] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 08/17/2023] [Indexed: 08/19/2023]
Abstract
The combination of magnetic resonance (MR) imaging and linear accelerators (linacs) into MR-Linacs enables continuous MR imaging and advanced gated treatments of patients. Previously, a dose-rate transient (∼8% reduced dose rate during the initial 0.5 s of each beam) was identified for a Viewray MRIdian MR-Linac (Klavsenet al2022Radiation Measurement106759). Here, the dose-rate transient is studied in more detail at four linacs of the same type at different hospitals. The implications of dose-rate transients were examined for gated treatments. The dose-rate transients were investigated using dose-per pulse measurements with organic plastic scintillators in three experiments: (i) A gated treatment with the scintillator placed in a moving target in a dynamic phantom, (ii) a gated treatment with the same dynamic conditions but with the scintillator placed in a stationary target, and (iii) measurements in a water-equivalent material to examine beam quality deviations at a dose-per-pulse basis. Gated treatments (i) compared with non-gated treatments with a static target in the same setup showed a broadening of accumulated dose profiles due to motion (dose smearing). The linac with the largest dose-rate transient had a reduced accumulated dose of up to (3.1 ± 0.65) % in the center of the PTV due to the combined dose smearing and dose-rate transient effect. Dose-rate transients were found to vary between different machines. Two MR-Linacs showed initial dose-rate transients that could not be identified from conventional linearity tests. The source of the transients includes an initial change in photon fluence rate and an initial change in x-ray beam quality. For gated treatments, this caused a reduction of more than 1% dose delivered at the central part of the beam for the studied, cyclic-motion treatment plan. Quality assurance of this effect should be considered when gated treatment with the Viewray MRIdian is implemented clinically.
Collapse
Affiliation(s)
- M F Klavsen
- DTU Health Tech, Technical University of Denmark, Roskilde, Denmark
| | - C Ankjærgaard
- DTU Health Tech, Technical University of Denmark, Roskilde, Denmark
| | - K Boye
- Dept. of Oncology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - C P Behrens
- DTU Health Tech, Technical University of Denmark, Roskilde, Denmark
- Dept. of Oncology, Copenhagen University Hospital-Herlev and Gentofte, Copenhagen, Denmark
| | - I R Vogelius
- Dept. of Oncology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen- Copenhagen, Denmark
| | - S Ehrbar
- Dept. of Radiation Oncology, University Hospital Zürich and University of Zürich, Zürich, Switzerland
| | - M Baumgartl
- Dept. of Radiation Oncology, University Hospital Zürich and University of Zürich, Zürich, Switzerland
| | - C Rippke
- Dept. of Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - C Buchele
- Dept. of Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - C K Renkamp
- Dept. of Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - G V Santurio
- Dept. of Oncology, Copenhagen University Hospital-Herlev and Gentofte, Copenhagen, Denmark
| | - C E Andersen
- DTU Health Tech, Technical University of Denmark, Roskilde, Denmark
| |
Collapse
|
10
|
Palacios MA, Gerganov G, Cobussen P, Tetar SU, Finazzi T, Slotman BJ, Senan S, Haasbeek CJ, Kawrakow I. Accuracy of deformable image registration-based intra-fraction motion management in Magnetic Resonance-guided radiotherapy. Phys Imaging Radiat Oncol 2023; 26:100437. [PMID: 37089906 PMCID: PMC10113900 DOI: 10.1016/j.phro.2023.100437] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023] Open
Abstract
Background and Purpose Intra-fraction motion management is key in Stereotactic Ablative Radiotherapy (SABR) gated delivery. This study assessed the accuracy of automatic tumor segmentation in the delivery of MR-guided radiotherapy (MRgRT) by comparing it to manual delineations performed by experienced observers. Materials and Methods Twenty patients previously treated with MR-guided SABR for thoracic and abdominal tumors were included. Five observers with at least two years of experience in MRgRT manually delineated the gross tumor volume (GTV) for 20 patients on 240 frames of a cine MRI on a sagittal plane. Deformable Image Registration (DIR) based GTV contours were propagated using four different algorithms from a reference frame to subsequent frames.Geometrical analysis based on the Dice Similarity Coefficient (DSC), centroid distance and Hausdorff Distance (HDD) were performed to assess the inter-observer variability and the accuracy of automatic segmentation. A Confidence Value (CV) metric for the reliability of the tumor auto-contouring was also calculated. Results Inter-observer delineation variability resulted in mean DSC of 0.89, HDD of 5.8 mm and centroid distance of 1.7 mm. Tumor auto-contouring by the four DIR algorithms resulted in an excellent agreement with the manual delineations by the experienced observers. Mean DSC for each algorithm across all patients was greater than 0.90, whereas the HDD and centroid distances were below 4.0 mm and 1.5 mm, respectively. The CV showed a strong correlation with the DSC. Conclusions DIR-based auto-contouring in MRgRT exhibited a high level of agreement with the manual contouring performed by experts, allowing accurate gated delivery.
Collapse
|
11
|
Price AT, Schiff JP, Zhu T, Mazur T, Kavanaugh JA, Maraghechi B, Green O, Kim H, Spraker MB, Henke LE. First treatments for Lattice stereotactic body radiation therapy using magnetic resonance image guided radiation therapy. Clin Transl Radiat Oncol 2023; 39:100577. [PMID: 36718251 PMCID: PMC9883196 DOI: 10.1016/j.ctro.2023.100577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 12/29/2022] [Accepted: 01/04/2023] [Indexed: 01/09/2023] Open
Abstract
Two abdominal patients were treated with Lattice stereotactic body radiation therapy (SBRT) using magnetic resonance guided radiation therapy (MRgRT). This is one of the first reported treatments of Lattice SBRT with the use of MRgRT. A description of the treatment approach and planning considerations were incorporated into this report. MRgRT Lattice SBRT delivered similar planning quality metrics to established dosimetric parameters for Lattice SBRT. Increased signal intensity were seen in the MRI treatments for one of the patients during the course of treatment.
Collapse
Affiliation(s)
- Alex T. Price
- Department of Radiation Oncology, University Hospitals, Cleveland, OH, USA
- Corresponding author.
| | - Joshua P. Schiff
- Department of Radiation Oncology, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Tong Zhu
- Department of Radiation Oncology, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Thomas Mazur
- Department of Radiation Oncology, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | | | - Borna Maraghechi
- Department of Radiation Oncology, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | - Olga Green
- Varian Medical Systems, Inc., Palo Alto, CA, USA
| | - Hyun Kim
- Department of Radiation Oncology, Washington University in St Louis School of Medicine, St. Louis, MO, USA
| | | | - Lauren E. Henke
- Department of Radiation Oncology, University Hospitals, Cleveland, OH, USA
| |
Collapse
|
12
|
Rammohan N, Randall JW, Yadav P. History of Technological Advancements towards MR-Linac: The Future of Image-Guided Radiotherapy. J Clin Med 2022; 11:jcm11164730. [PMID: 36012969 PMCID: PMC9409689 DOI: 10.3390/jcm11164730] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/27/2022] [Accepted: 08/05/2022] [Indexed: 11/16/2022] Open
Abstract
Image-guided radiotherapy (IGRT) enables optimal tumor targeting and sparing of organs-at-risk, which ultimately results in improved outcomes for patients. Magnetic resonance imaging (MRI) revolutionized diagnostic imaging with its superior soft tissue contrast, high spatiotemporal resolution, and freedom from ionizing radiation exposure. Over the past few years there has been burgeoning interest in MR-guided radiotherapy (MRgRT) to overcome current challenges in X-ray-based IGRT, including but not limited to, suboptimal soft tissue contrast, lack of efficient daily adaptation, and incremental exposure to ionizing radiation. In this review, we present an overview of the technologic advancements in IGRT that led to MRI-linear accelerator (MRL) integration. Our report is organized in three parts: (1) a historical timeline tracing the origins of radiotherapy and evolution of IGRT, (2) currently available MRL technology, and (3) future directions and aspirations for MRL applications.
Collapse
|