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Fu Y, Zhang P, Fan Q, Cai W, Pham H, Rimner A, Cuaron J, Cervino L, Moran JM, Li T, Li X. Deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy. Med Phys 2024; 51:4271-4282. [PMID: 38507259 DOI: 10.1002/mp.17039] [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: 08/02/2023] [Revised: 02/07/2024] [Accepted: 03/04/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND In radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challenging due to the low tumor visibility caused by tumor-obscuring structures. Developing a new method to enhance tumor visibility for real-time tumor tracking is essential. PURPOSE To introduce a novel method for markerless kV image-based tracking of lung tumors via deep learning-based target decomposition. METHODS We utilized a conditional Generative Adversarial Network (cGAN), known as Pix2Pix, to build a patient-specific model and generate the synthetic decomposed target image (sDTI) to enhance tumor visibility on the real-time kV projection images acquired by the onboard kV imager equipped on modern linear accelerators. We used 4DCT simulation images to generate the digitally reconstructed radiograph (DRR) and DTI image pairs for model training. We augmented the training dataset by randomly shifting the 4DCT in the superior-inferior, anterior-posterior, and left-right directions during the DRR and DTI generation process. We performed real-time 2D tumor tracking via template matching between the DTI generated from the CT simulation and the sDTI generated from the real-time kV projection images. We validated the proposed method using nine patients' datasets with implanted beacons near the tumor. RESULTS The sDTI can effectively improve the image contrast around the lung tumors on the kV projection images for the nine patients. With the beacon motion as ground truth, the tracking errors were on average 0.8 ± 0.7 mm in the superior-inferior (SI) direction and 0.9 ± 0.8 mm in the in-plane left-right (IPLR) direction. The percentage of successful tracking, defined as a tracking error less than 2 mm in the SI direction, is 92.2% on the 4312 tested images. The patient-specific model took approximately 12 h to train. During testing, it took approximately 35 ms to generate one sDTI, and 13 ms to perform the tumor tracking using template matching. CONCLUSIONS Our method offers the potential solution for nearly real-time markerless lung tumor tracking. It achieved a high level of accuracy and an impressive tracking rate. Further development of 3D lung tumor tracking is warranted.
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Affiliation(s)
- Yabo Fu
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Qiyong Fan
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Weixing Cai
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Hai Pham
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - John Cuaron
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Jean M Moran
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Tianfang Li
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Xiang Li
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
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Grama D, Dahele M, van Rooij W, Slotman B, Gupta DK, Verbakel WFAR. Deep learning-based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks. Med Phys 2023; 50:6881-6893. [PMID: 37219823 DOI: 10.1002/mp.16470] [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: 10/27/2022] [Revised: 03/27/2023] [Accepted: 04/27/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Radiotherapy (RT) is involved in about 50% of all cancer patients, making it a very important treatment modality. The most common type of RT is external beam RT, which consists of delivering the radiation to the tumor from outside the body. One novel treatment delivery method is volumetric modulated arc therapy (VMAT), where the gantry continuously rotates around the patient during the radiation delivery. PURPOSE Accurate tumor position monitoring during stereotactic body radiotherapy (SBRT) for lung tumors can help to ensure that the tumor is only irradiated when it is inside the planning target volume. This can maximize tumor control and reduce uncertainty margins, lowering organ-at-risk dose. Conventional tracking methods are prone to errors, or have a low tracking rate, especially for small tumors that are in close vicinity to bony structures. METHODS We investigated patient-specific deep Siamese networks for real-time tumor tracking, during VMAT. Due to lack of ground truth tumor locations in the kilovoltage (kV) images, each patient-specific model was trained on synthetic data (DRRs), generated from the 4D planning CT scans, and evaluated on clinical data (x-rays). Since there are no annotated datasets with kV images, we evaluated the model on a 3D printed anthropomorphic phantom but also on six patients by computing the correlation coefficient with the breathing-related vertical displacement of the surface-mounted marker (RPM). For each patient/phantom, we used 80% of DRRs for training and 20% for validation. RESULTS The proposed Siamese model outperformed the conventional benchmark template matching-based method (RTR): (1) when evaluating both methods on the 3D phantom, the Siamese model obtained a 0.57-0.79-mm mean absolute distance to the ground truth tumor locations, compared to 1.04-1.56 mm obtained by RTR; (2) on patient data, the Siamese-determined longitudinal tumor position had a correlation coefficient of 0.71-0.98 with the RPM, compared to 0.07-0.85 for RTR; (3) the Siamese model had a 100% tracking rate, compared to 62%-82% for RTR. CONCLUSIONS Based on these results, we argue that Siamese-based real-time 2D markerless tumor tracking during radiation delivery is possible. Further investigation and development of 3D tracking is warranted.
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Affiliation(s)
- Dragos Grama
- Department of Radiation Oncology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Max Dahele
- Department of Radiation Oncology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ward van Rooij
- Department of Radiation Oncology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ben Slotman
- Department of Radiation Oncology, Amsterdam UMC, Amsterdam, The Netherlands
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Zhong J, Huang T, Qiu M, Guan Q, Luo N, Deng Y. A markerless beam's eye view tumor tracking algorithm based on unsupervised deformable registration learning framework of VoxelMorph in medical image with partial data. Phys Med 2023; 105:102510. [PMID: 36535237 DOI: 10.1016/j.ejmp.2022.12.002] [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: 05/19/2022] [Revised: 10/18/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To propose an unsupervised deformable registration learning framework-based markerless beam's eye view (BEV) tumor tracking algorithm for the inferior quality megavolt (MV) images with occlusion and deformation. METHODS Quality assurance (QA) plans for thorax phantom were delivered to the linear accelerator with artificially treatment offsets. Electronic portal imaging device (EPID) images (682 in total) and corresponding digitally reconstructed radiograph (DRR) were gathered as the moving and fixed image pairs, which were randomly divided into training and testing set in a ratio of 0.7:0.3 to train a non-rigid registration model with Voxelmorph. Simultaneously, 533 pairs of patient images from 21 lung tumor plans were acquired for tumor tracking investigation to offer quantifiable tumor motion data. Tracking error and image similarity measures were employed to evaluate the algorithm's accuracy. RESULTS The tracking algorithm can handle image registration with non-rigid deformation and losses ranging from 10 % to 80 %. The tracking errors in the phantom study were below 3 mm in about 86.8 % of cases, and below 2 mm in about 80 % of cases. The normalized mutual information (NMI) changes from 1.182 ± 0.024 to 1.198 ± 0.024 (p < 0.005). The patient target had an average translation of 3.784 mm and a maximum comprehensive displacement value of 7.455 mm. NMI of patient images changes from 1.209 ± 0.027 to 1.217 ± 0.026 (p < 0.005), indicating the presence of non-negligible non-rigid deformation. CONCLUSIONS The study provides a robust markerless tumor tracking algorithm for multi-modal, partial data and inferior quality image processing.
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Affiliation(s)
- Jiajian Zhong
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Taiming Huang
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Minmin Qiu
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Qi Guan
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Ning Luo
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China.
| | - Yongjin Deng
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China.
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Kaur M, Wagstaff P, Mostafavi H, Lehmann M, Morf D, Zhu L, Kang H, Walczak M, Harkenrider MM, Roeske JC. Effect of different noise reduction techniques and template matching parameters on markerless tumor tracking using dual-energy imaging. J Appl Clin Med Phys 2022; 23:e13821. [PMID: 36350280 PMCID: PMC9797162 DOI: 10.1002/acm2.13821] [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: 03/15/2022] [Revised: 06/16/2022] [Accepted: 09/28/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To evaluate the impact of various noise reduction algorithms and template matching parameters on the accuracy of markerless tumor tracking (MTT) using dual-energy (DE) imaging. METHODS A Varian TrueBeam linear accelerator was used to acquire a series of alternating 60 and 120 kVp images (over a 180° arc) using fast kV switching, on five early-stage lung cancer patients. Subsequently, DE logarithmic weighted subtraction was performed offline on sequential images to remove bone. Various noise reduction techniques-simple smoothing, anticorrelated noise reduction (ACNR), noise clipping (NC), and NC-ACNR-were applied to the resultant DE images. Separately, tumor templates were generated from the individual planning CT scans, and band-pass parameter settings for template matching were varied. Template tracking was performed for each combination of noise reduction techniques and templates (based on band-pass filter settings). The tracking success rate (TSR), root mean square error (RMSE), and missing frames (percent unable to track) were evaluated against the estimated ground truth, which was obtained using Bayesian inference. RESULTS DE-ACNR, combined with template band-pass filter settings of σlow = 0.4 mm and σhigh = 1.6 mm resulted in the highest TSR (87.5%), RMSE (1.40 mm), and a reasonable amount of missing frames (3.1%). In comparison to unprocessed DE images, with optimized band-pass filter settings of σlow = 0.6 mm and σhigh = 1.2 mm, the TSR, RMSE, and missing frames were 85.3%, 1.62 mm, and 2.7%, respectively. Optimized band-pass filter settings resulted in improved TSR values and a lower missing frame rate for both unprocessed DE and DE-ACNR as compared to the use previously published band-pass parameters based on single energy kV images. CONCLUSION Noise reduction strategies combined with the optimal selection of band-pass filter parameters can improve the accuracy and TSR of MTT for lung tumors when using DE imaging.
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Affiliation(s)
- Mandeep Kaur
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer CenterLoyola University ChicagoMaywoodIllinoisUSA
| | - Peter Wagstaff
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer CenterLoyola University ChicagoMaywoodIllinoisUSA
| | | | | | - Daniel Morf
- Varian Medical SystemsPalo AltoCaliforniaUSA
| | | | - Hyejoo Kang
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer CenterLoyola University ChicagoMaywoodIllinoisUSA,Department of Radiation OncologyLoyola University Medical CenterMaywoodIllinoisUSA
| | | | - Matthew M. Harkenrider
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer CenterLoyola University ChicagoMaywoodIllinoisUSA,Department of Radiation OncologyLoyola University Medical CenterMaywoodIllinoisUSA
| | - John C. Roeske
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer CenterLoyola University ChicagoMaywoodIllinoisUSA,Department of Radiation OncologyLoyola University Medical CenterMaywoodIllinoisUSA
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Gulyas I, Trnkova P, Knäusl B, Widder J, Georg D, Renner A. A novel bone suppression algorithm in intensity‐based 2D/3D image registration for real‐time tumour motion monitoring: development and phantom‐based validation. Med Phys 2022; 49:5182-5194. [PMID: 35598307 PMCID: PMC9540269 DOI: 10.1002/mp.15716] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 04/28/2022] [Accepted: 05/05/2022] [Indexed: 11/25/2022] Open
Abstract
Background Real‐time tumor motion monitoring (TMM) is a crucial process for intra‐fractional respiration management in lung cancer radiotherapy. Since the tumor can be partly or fully located behind the ribs, the TMM is challenging. Purpose The aim of this work was to develop a bone suppression (BS) algorithm designed for real‐time 2D/3D marker‐less TMM to increase the visibility of the tumor when overlapping with bony structures and consequently to improve the accuracy of TMM. Method A BS method was implemented in the in‐house developed software for ultrafast intensity‐based 2D/3D tumor registration (Fast Image‐based Registration [FIRE]). The method operates on both, digitally reconstructed radiograph (DRR) and intra‐fractional X‐ray images. The bony structures are derived from computed tomography data by thresholding during ray‐casting, and the resulting bone DRR is subtracted from intra‐fractional X‐ray images to obtain a soft‐tissue‐only image for subsequent tumor registration. The accuracy of TMM utilizing BS was evaluated within a retrospective phantom study with nine different 3D‐printed tumor phantoms placed in the in‐house developed Advanced Radiation DOSimetry (ARDOS) breathing phantom. A 24 mm craniocaudal tumor motion, including rib eclipses, was simulated, and X‐ray images were acquired on the Elekta Versa HD Linac in the lateral and posterior–anterior directions. An error assessment for BS images was evaluated with respect to the ground truth tumor position. Results A total error (root mean square error) of 0.87 ± 0.23 mm and 1.03 ± 0.26 mm was found for posterior–anterior and lateral imaging; the mean time for BS was 8.03 ± 1.54 ms. Without utilizing BS, TMM failed in all X‐ray images since the registration algorithm focused on the rib position due to the predominant intensity of this tissue within DRR and X‐ray images. Conclusion The BS algorithm developed and implemented improved the accuracy, robustness, and stability of real‐time TMM in lung cancer in a phantom study, even in the case of rib interlude where normal tumor registration fails.
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Affiliation(s)
- Ingo Gulyas
- Division of Medical Radiation Physics Department of Radiation Oncology Medical University of Vienna
| | - Petra Trnkova
- Division of Medical Radiation Physics Department of Radiation Oncology Medical University of Vienna
| | - Barbara Knäusl
- Division of Medical Radiation Physics Department of Radiation Oncology Medical University of Vienna
- MedAustron Ion Therapy Center Wiener Neustadt Austria
| | - Joachim Widder
- Division of Medical Radiation Physics Department of Radiation Oncology Medical University of Vienna
| | - Dietmar Georg
- Division of Medical Radiation Physics Department of Radiation Oncology Medical University of Vienna
- MedAustron Ion Therapy Center Wiener Neustadt Austria
| | - Andreas Renner
- Division of Medical Radiation Physics Department of Radiation Oncology Medical University of Vienna
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Romadanov I, Abeywardhana R, Sattarivand M. Adaptive dual-energy algorithm based on pre-calibrated weighting factors for chest radiography. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 03/29/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. To develop a dual-energy (DE) algorithm with spatially varying weighting factors for material selection and noise suppression. Approach. Calibration step-phantoms, with overlapping slabs of solid water and bone with different thicknesses, were used to obtain the pre-calibrated material selection and noise reduction weighting factors. The Material selection weighting factors were calculated by finding a zero of contrast-to-noise-ratio (CNR) between regions with two overlapping materials and regions of only target material, while noise suppression weighting factors were determined by maximizing signal-to-noise ratio for overlapping regions. The pre-calibrated weighting factors were fitted with low and high energy radiograph of two Rando phantoms to create maps of material selection and noise suppression weighting factors, which used with DE algorithm and anti-correlated noise reduction (ACNR) algorithm to generate DE images. Three different implementations, including two different sizes of Rando phantoms and two different orientations (oblique and anterior-posterior), were investigated. Soft-tissue and bone only images of Rando phantoms were obtained with five combinations of DE algorithms and CNR, contrast, and noise values of selected regions of interest were compared to evaluate the performance of the novel method: simple log subtraction (SLS), SLS with uniform ACNR, adaptive DE (aDE), aDE with uniform ACNR, and aDE and adaptive ACNR (aACNR). Main results. Compared to SLS, the aDE algorithm demonstrated improved image quality in all three orientations. CNR increased with better contrast for both soft-tissue and bone images. Implementation of aACNR algorithm resulted in further reduction of image noise and improvements in CNR at the cost of contrast. However, aACNR algorithm showed better contrast compared to ACNR method. Significance. A novel DE algorithm was proposed, which showed improved material selection and noise suppression as compared to the conventional DE techniques and can be easily implemented in a clinical environment for real-time DE image generation.
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Mueller M, Poulsen P, Hansen R, Verbakel W, Berbeco R, Ferguson D, Mori S, Ren L, Roeske JC, Wang L, Zhang P, Keall P. The markerless lung target tracking AAPM Grand Challenge (MATCH) results. Med Phys 2022; 49:1161-1180. [PMID: 34913495 PMCID: PMC8828678 DOI: 10.1002/mp.15418] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/16/2021] [Accepted: 12/06/2021] [Indexed: 02/03/2023] Open
Abstract
PURPOSE Lung stereotactic ablative body radiotherapy (SABR) is a radiation therapy success story with level 1 evidence demonstrating its efficacy. To provide real-time respiratory motion management for lung SABR, several commercial and preclinical markerless lung target tracking (MLTT) approaches have been developed. However, these approaches have yet to be benchmarked using a common measurement methodology. This knowledge gap motivated the MArkerless lung target Tracking CHallenge (MATCH). The aim was to localize lung targets accurately and precisely in a retrospective in silico study and a prospective experimental study. METHODS MATCH was an American Association of Physicists in Medicine sponsored Grand Challenge. Common materials for the in silico and experimental studies were the experiment setup including an anthropomorphic thorax phantom with two targets within the lungs, and a lung SABR planning protocol. The phantom was moved rigidly with patient-measured lung target motion traces, which also acted as ground truth motion. In the retrospective in silico study a volumetric modulated arc therapy treatment was simulated and a dataset consisting of treatment planning data and intra-treatment kilovoltage (kV) and megavoltage (MV) images for four blinded lung motion traces was provided to the participants. The participants used their MLTT approach to localize the moving target based on the dataset. In the experimental study, the participants received the phantom experiment setup and five patient-measured lung motion traces. The participants used their MLTT approach to localize the moving target during an experimental SABR phantom treatment. The challenge was open to any participant, and participants could complete either one or both parts of the challenge. For both the in silico and experimental studies the MLTT results were analyzed and ranked using the prospectively defined metric of the percentage of the tracked target position being within 2 mm of the ground truth. RESULTS A total of 30 institutions registered and 15 result submissions were received, four for the in silico study and 11 for the experimental study. The participating MLTT approaches were: Accuray CyberKnife (2), Accuray Radixact (2), BrainLab Vero, C-RAD, and preclinical MLTT (5) on a conventional linear accelerator (Varian TrueBeam). For the in silico study the percentage of the 3D tracking error within 2 mm ranged from 50% to 92%. For the experimental study, the percentage of the 3D tracking error within 2 mm ranged from 39% to 96%. CONCLUSIONS A common methodology for measuring the accuracy of MLTT approaches has been developed and used to benchmark preclinical and commercial approaches retrospectively and prospectively. Several MLTT approaches were able to track the target with sub-millimeter accuracy and precision. The study outcome paves the way for broader clinical implementation of MLTT. MATCH is live, with datasets and analysis software being available online at https://www.aapm.org/GrandChallenge/MATCH/ to support future research.
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Affiliation(s)
- Marco Mueller
- Corresponding author; Room 221, ACRF Image X institute, 1 Central Ave, Eveleigh NSW 2015, Australia; +61 2 8627 1106,
| | - Per Poulsen
- Danish Center for Particle Therapy and Department of Oncology, Aarhus University Hospital, Aarhus 8200, Denmark
| | - Rune Hansen
- Department of Medical Physics, Aarhus University Hospital, Aarhus 8200, Denmark
| | - Wilko Verbakel
- Amsterdam University Medical Centers, location VUmc, Amsterdam 1081 HV, Netherlands
| | - Ross Berbeco
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | | | - Shinichiro Mori
- Research Center for Charged Particle Therapy, National Institute of Radiological Sciences, Chiba 263-0024, Japan
| | - Lei Ren
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
| | - John C. Roeske
- Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL 60153, USA
| | - Lei Wang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center New York, NY, USA
| | - Paul Keall
- ACRF Image X Institute, The University of Sydney, Sydney, NSW 2015, Australia
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Remmerts de Vries IF, Dahele M, Mostafavi H, Slotman B, Verbakel W. Markerless 3D tumor tracking during single-fraction free-breathing 10MV flattening-filter-free stereotactic lung radiotherapy. Radiother Oncol 2021; 164:6-12. [PMID: 34506828 DOI: 10.1016/j.radonc.2021.08.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE Positional verification during single fraction lung SBRT could increase confidence and reduce the chance of geographic miss. As planar 2DkV imaging during VMAT irradiation is already available on current linear accelerators, markerless tracking based on these images could offer widely available and low-cost verification. We evaluated treatment delivery data and template matching and triangulation for 3D-positional verification during free-breathing, single fraction (34 Gy), 10 MV flattening-filter-free VMAT lung SBRT. METHODS AND MATERIALS Tumor tracking based on kV imaging at 7 frames/second was performed during irradiation in 6 consecutive patients (7 lesions). Tumor characteristics, tracking ability, comparison of tracking displacements with CBCT-based shifts, tumor position relative to the PTV margin, and treatment times are reported. RESULTS For all 7 lesions combined, 3D tumor position could be determined for, on average, 71% (51-84%) of the total irradiation time. Visually estimated tracked and automated match +/- manually-corrected CBCT-derived displacements generally agreed within 1 mm. During the tracked period, the longitudinal, lateral and vertical position of the tumor was within a 5 mm/3 mm PTV margin 95.5/85.3% of the time. The PTV was derived from the ITV including all tumor motion. The total time from first set-up imaging to end of the last arc was 18.3-31.4 min (mean = 23.4, SD = 4.1). CONCLUSION 3D positional verification during irradiation of small lung targets with limited motion, was feasible. However, tumor position could not be determined for on average 29% of the time. Improvements are needed. Margin reduction may be feasible. Imaging and delivery of a single 34 Gy fraction was fast.
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Affiliation(s)
- I F Remmerts de Vries
- Department of Radiation Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands.
| | - Max Dahele
- Department of Radiation Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | | | - Ben Slotman
- Department of Radiation Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Wilko Verbakel
- Department of Radiation Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands
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