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Muren LP. Physics and Imaging in Radiation Oncology comes of age. Phys Imaging Radiat Oncol 2024; 29:100559. [PMID: 38434208 PMCID: PMC10906384 DOI: 10.1016/j.phro.2024.100559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024] Open
Affiliation(s)
- Ludvig P Muren
- Physics and Imaging in Radiation Oncology, Danish Centre for Particle Therapy, Aarhus University Hospital / Aarhus University, Aarhus, Denmark
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Bogowicz M, Lustermans D, Taasti VT, Hazelaar C, Verhaegen F, Fonseca GP, van Elmpt W. Evaluation of a cone-beam computed tomography system calibrated for accurate radiotherapy dose calculation. Phys Imaging Radiat Oncol 2024; 29:100566. [PMID: 38487622 PMCID: PMC10937948 DOI: 10.1016/j.phro.2024.100566] [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: 11/03/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/17/2024] Open
Abstract
Background and purpose Dose calculation on cone-beam computed tomography (CBCT) images has been less accurate than on computed tomography (CT) images due to lower image quality and discrepancies in CT numbers for CBCT. As increasing interest arises in offline and online re-planning, dose calculation accuracy was evaluated for a novel CBCT imager integrated into a ring gantry treatment machine. Materials and methods The new CBCT system allowed fast image acquisition (5.9 s) by using new hardware, including a large-size flat panel detector, and incorporated image-processing algorithms with iterative reconstruction techniques, leading to accurate CT numbers allowing dose calculation. In this study, CBCT- and CT-based dose calculations were compared based on three anthropomorphic phantoms, after CBCT-to-mass-density calibration was performed. Six plans were created on the CT scans covering various target locations and complexities, followed by CBCT to CT registrations, copying of contours, and re-calculation of the plans on the CBCT scans. Dose-volume histogram metrics for target volumes and organs-at-risk (OARs) were evaluated, and global gamma analyses were performed. Results Target coverage differences were consistently below 1.2 %, demonstrating the agreement between CT and re-calculated CBCT dose distributions. Differences in Dmean for OARs were below 0.5 Gy for all plans, except for three OARs, which were below 0.8 Gy (<1.1 %). All plans had a 3 %/1mm gamma pass rate > 97 %. Conclusions This study demonstrated comparable results between dose calculations performed on CBCT and CT acquisitions. The new CBCT system with enhanced image quality and CT number accuracy opens possibilities for off-line and on-line re-planning.
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Affiliation(s)
| | - Didier Lustermans
- Corresponding author at: Postbox 3035, 6202 NA Maastricht, The Netherlands.
| | - Vicki Trier Taasti
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Colien Hazelaar
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Frank Verhaegen
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Gabriel Paiva Fonseca
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
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van Elmpt W, Trier Taasti V, Redalen KR. Current and future developments of synthetic computed tomography generation for radiotherapy. Phys Imaging Radiat Oncol 2023; 28:100521. [PMID: 38058591 PMCID: PMC10696097 DOI: 10.1016/j.phro.2023.100521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023] Open
Affiliation(s)
- Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Vicki Trier Taasti
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
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Martins JC, Maier J, Gianoli C, Neppl S, Dedes G, Alhazmi A, Veloza S, Reiner M, Belka C, Kachelrieß M, Parodi K. Towards real-time EPID-based 3D in vivo dosimetry for IMRT with Deep Neural Networks: A feasibility study. Phys Med 2023; 114:103148. [PMID: 37801811 DOI: 10.1016/j.ejmp.2023.103148] [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: 03/12/2023] [Revised: 08/17/2023] [Accepted: 09/22/2023] [Indexed: 10/08/2023] Open
Abstract
We investigate the potential of the Deep Dose Estimate (DDE) neural network to predict 3D dose distributions inside patients with Monte Carlo (MC) accuracy, based on transmitted EPID signals and patient CTs. The network was trained using as input patient CTs and first-order dose approximations (FOD). Accurate dose distributions (ADD) simulated with MC were given as training targets. 83 pelvic CTs were used to simulate ADDs and respective EPID signals for subfields of prostate IMRT plans (gantry at 0∘). FODs were produced as backprojections from the EPID signals. 581 ADD-FOD sets were produced and divided into training and test sets. An additional dataset simulated with gantry at 90∘ (lateral set) was used for evaluating the performance of the DDE at different beam directions. The quality of the FODs and DDE-predicted dose distributions (DDEP) with respect to ADDs, from the test and lateral sets, was evaluated with gamma analysis (3%,2 mm). The passing rates between FODs and ADDs were as low as 46%, while for DDEPs the passing rates were above 97% for the test set. Meaningful improvements were also observed for the lateral set. The high passing rates for DDEPs indicate that the DDE is able to convert FODs into ADDs. Moreover, the trained DDE predicts the dose inside a patient CT within 0.6 s/subfield (GPU), in contrast to 14 h needed for MC (CPU-cluster). 3D in vivo dose distributions due to clinical patient irradiation can be obtained within seconds, with MC-like accuracy, potentially paving the way towards real-time EPID-based in vivo dosimetry.
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Affiliation(s)
- Juliana Cristina Martins
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Joscha Maier
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.
| | - Chiara Gianoli
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Sebastian Neppl
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, Munich, 81377, Germany.
| | - George Dedes
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Abdulaziz Alhazmi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Stella Veloza
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, Munich, 81377, Germany.
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, Munich, 81377, Germany.
| | - Marc Kachelrieß
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany; Heidelberg University, Grabengasse 1, Heidelberg, 69117, Germany.
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching b. München, 85748, Germany.
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Taasti VT, Hattu D, Peeters S, van der Salm A, van Loon J, de Ruysscher D, Nilsson R, Andersson S, Engwall E, Unipan M, Canters R. Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients. Phys Imaging Radiat Oncol 2023; 27:100459. [PMID: 37397874 PMCID: PMC10314284 DOI: 10.1016/j.phro.2023.100459] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 07/04/2023] Open
Abstract
Background and purpose Efficient workflows for adaptive proton therapy are of high importance. This study evaluated the possibility to replace repeat-CTs (reCTs) with synthetic CTs (sCTs), created based on cone-beam CTs (CBCTs), for flagging the need of plan adaptations in intensity-modulated proton therapy (IMPT) treatment of lung cancer patients. Materials and methods Forty-two IMPT patients were retrospectively included. For each patient, one CBCT and a same-day reCT were included. Two commercial sCT methods were applied; one based on CBCT number correction (Cor-sCT), and one based on deformable image registration (DIR-sCT). The clinical reCT workflow (deformable contour propagation and robust dose re-computation) was performed on the reCT as well as the two sCTs. The deformed target contours on the reCT/sCTs were checked by radiation oncologists and edited if needed. A dose-volume-histogram triggered plan adaptation method was compared between the reCT and the sCTs; patients needing a plan adaptation on the reCT but not on the sCT were denoted false negatives. As secondary evaluation, dose-volume-histogram comparison and gamma analysis (2%/2mm) were performed between the reCT and sCTs. Results There were five false negatives, two for Cor-sCT and three for DIR-sCT. However, three of these were only minor, and one was caused by tumour position differences between the reCT and CBCT and not by sCT quality issues. An average gamma pass rate of 93% was obtained for both sCT methods. Conclusion Both sCT methods were judged to be of clinical quality and valuable for reducing the amount of reCT acquisitions.
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Affiliation(s)
- Vicki Trier Taasti
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Djoya Hattu
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Stephanie Peeters
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Anke van der Salm
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Judith van Loon
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Dirk de Ruysscher
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | | | | | - Mirko Unipan
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Richard Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
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Knäusl B, Taasti VT, Poulsen P, Muren LP. Surveying the clinical practice of treatment adaptation and motion management in particle therapy. Phys Imaging Radiat Oncol 2023; 27:100457. [PMID: 37361612 PMCID: PMC10285555 DOI: 10.1016/j.phro.2023.100457] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023] Open
Affiliation(s)
- Barbara Knäusl
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Vicki T Taasti
- Department of Radiation Oncology (Maastro), GROW - School for Oncology and Reproduction, Maastricht, University Medical Centre+, Maastricht, The Netherlands
| | - Per Poulsen
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University, Aarhus, Denmark
| | - Ludvig P Muren
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Ozaki S, Kaji S, Nawa K, Imae T, Aoki A, Nakamoto T, Ohta T, Nozawa Y, Yamashita H, Haga A, Nakagawa K. Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion. Med Phys 2022; 49:3769-3782. [PMID: 35315529 DOI: 10.1002/mp.15626] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 02/21/2022] [Accepted: 03/14/2022] [Indexed: 11/06/2022] Open
Abstract
PURPOSE In recent years, deep-learning-based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep-learning-based methods heavily relies on the amount of training data; the high cost of acquiring a large dataset is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images. METHODS The proposed method is based on CycleGAN with several extensions tailored for CT images, which aims at preserving the structure in the processed images and reducing the amount of training data. This method was applied to realize the conversion of megavoltage computed tomography (MVCT) to kilovoltage computed tomography (kVCT) images. Training was conducted using several datasets acquired from patients with head and neck cancer. The size of the datasets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT. RESULTS The required size of the training data was found to be as small as a few hundred slices. By statistical and visual evaluations, the quality improvement and structure preservation of the MVCT images converted by the proposed model were investigated. As a clinical benefit, it was observed by medical doctors that the converted images enhanced the precision of contouring. CONCLUSIONS We developed an MVCT to kVCT conversion model based on deep learning, which can be trained using only a few hundred unpaired images. The stability of the model against changes in data size was demonstrated. This study promotes the reliable use of deep learning in clinical medicine by partially answering commonly asked questions, such as "Is our data sufficient?" and "How much data should we acquire?" This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Sho Ozaki
- Graduate School of Medicine, University of Tokyo, Tokyo, 113-8655, Japan
| | - Shizuo Kaji
- Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Kanabu Nawa
- Department of Radiology, University of Tokyo Hospital, Tokyo, 113-8655, Japan
| | - Toshikazu Imae
- Department of Radiology, University of Tokyo Hospital, Tokyo, 113-8655, Japan
| | - Atsushi Aoki
- Department of Radiology, University of Tokyo Hospital, Tokyo, 113-8655, Japan
| | - Takahiro Nakamoto
- Department of Biological Science and Engineering, Faculty of Health Sciences, Hokkaido University, N12-W5, Kita-ku, Sapporo, Hokkaido, 060-0812, Japan
| | - Takeshi Ohta
- Department of Radiology, University of Tokyo Hospital, Tokyo, 113-8655, Japan
| | - Yuki Nozawa
- Department of Radiology, University of Tokyo Hospital, Tokyo, 113-8655, Japan
| | - Hideomi Yamashita
- Department of Radiology, University of Tokyo Hospital, Tokyo, 113-8655, Japan
| | - Akihiro Haga
- Graduate School of Biomedical Science, Tokushima University, Tokushima, 770-8503, Japan
| | - Keiichi Nakagawa
- Graduate School of Medicine, University of Tokyo, Tokyo, 113-8655, Japan
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Muren LP, Redalen KR, Thorwarth D. Five years, 20 volumes and 300 publications of Physics and Imaging in Radiation Oncology. Phys Imaging Radiat Oncol 2022; 21:123-125. [PMID: 35265751 PMCID: PMC8899405 DOI: 10.1016/j.phro.2022.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
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Spadea MF, Maspero M, Zaffino P, Seco J. Deep learning based synthetic-CT generation in radiotherapy and PET: A review. Med Phys 2021; 48:6537-6566. [PMID: 34407209 DOI: 10.1002/mp.15150] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/06/2021] [Accepted: 07/13/2021] [Indexed: 01/22/2023] Open
Abstract
Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.
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Affiliation(s)
- Maria Francesca Spadea
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Matteo Maspero
- Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands
| | - Paolo Zaffino
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Joao Seco
- Division of Biomedical Physics in Radiation Oncology, DKFZ German Cancer Research Center, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
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Niyoteka S, Berger T, Fokdal LU, Petersen JBB, Zolnay A, Hoogeman M, Tanderup K, Nystrom HU. Impact of interfractional target motion in locally advanced cervical cancer patients treated with spot scanning proton therapy using an internal target volume strategy. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 17:84-90. [PMID: 33898784 PMCID: PMC8058016 DOI: 10.1016/j.phro.2021.01.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 01/23/2021] [Accepted: 01/28/2021] [Indexed: 01/08/2023]
Abstract
Background and purpose The more localized dose deposition of proton therapy (PT) compared to photon therapy might allow a reduction in treatment-related side effects but induces additional challenges to address. The aim of this study was to evaluate the impact of interfractional motion on the target and organs at risk (OARs) in cervical cancer patients treated with spot scanning PT using an internal target volume (ITV) strategy. Methods and materials For ten locally advanced cervical cancer patients, empty and full bladder planning computed tomography (pCT) as well as 25 daily cone beam CTs (CBCTs) were available. The Clinical Target Volume (CTV), the High Risk CTV (CTVHR) (gross tumor volume and whole cervix), the non-involved uterus as well as the OARs (bowel, bladder and rectum) were contoured on the daily CBCTs and transferred to the pCT through rigid bony match. Using synthetic CTs derived from pCTs, four-beam spot scanning PT plans were generated to target the patient-specific ITV with 45 Gy(RBE) in 25 fractions. This structure was defined based on pre-treatment MRI and CT to anticipate potential target motion throughout the treatment. D98% of the targets and V40Gy(RBE) of the OARs were extracted from the daily anatomies, accumulated and analyzed. In addition, the impact of bladder volume deviations from planning values on target and bowel dose was investigated. Results The ITV strategy ensured a total accumulated dose >42.75 Gy(RBE) to the CTVHR for all ten patients. Two patients with large bladder-related uterus motion had accumulated dose to the non-involved uterus of 35.7 Gy(RBE) and 41.1 Gy(RBE). Variations in bowel V40Gy(RBE) were found to be correlated (Pearson r = −0.55; p-value <0.0001) with changes in bladder volume during treatment. Conclusion The ITV concept ensured adequate dose to the CTVHR, but was insufficient for the non-involved uterus of patients subject to large target interfractional motion. CBCT monitoring and occasional replanning is recommended along the same lines as with photon radiotherapy in cervical cancer.
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Affiliation(s)
| | - Thomas Berger
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | | | | | - Andras Zolnay
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, The Netherlands
| | - Mischa Hoogeman
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, The Netherlands
- Holland PTC, Delft, The Netherlands
| | - Kari Tanderup
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
- Corresponding author.
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Andersen AG, Park YK, Elstrøm UV, Petersen JBB, Sharp GC, Winey B, Dong L, Muren LP. Evaluation of an a priori scatter correction algorithm for cone-beam computed tomography based range and dose calculations in proton therapy. Phys Imaging Radiat Oncol 2020; 16:89-94. [PMID: 33458349 PMCID: PMC7807858 DOI: 10.1016/j.phro.2020.09.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/22/2020] [Accepted: 09/30/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND AND PURPOSE Scatter correction of cone-beam computed tomography (CBCT) projections may enable accurate online dose-delivery estimations in photon and proton-based radiotherapy. This study aimed to evaluate the impact of scatter correction in CBCT-based proton range/dose calculations, in scans acquired in both proton and photon gantries. MATERIAL AND METHODS CBCT projections of a Catphan and an Alderson phantom were acquired on both a proton and a photon gantry. The scatter corrected CBCTs (corrCBCTs) and the clinical reconstructions (stdCBCTs) were compared against CTs rigidly registered to the CBCTs (rigidCTs). The CBCTs of the Catphan phantom were segmented by materials for CT number analysis. Water equivalent path length (WEPL) maps were calculated through the Alderson phantom while proton plans optimized on the rigidCT and recalculated on all CBCTs were compared in a gamma analysis. RESULTS In medium and high-density materials, the corrCBCT CT numbers were much closer to those of the rigidCT than the stdCBCTs. E.g. in the 50% bone segmentations the differences were reduced from above 300 HU (with stdCBCT) to around 60-70 HU (with corrCBCT). Differences in WEPL from the rigidCT were typically well below 5 mm for the corrCBCTs, compared to well above 10 mm for the stdCBCTs with the largest deviations in the head and thorax regions. Gamma pass rates (2%/2mm) when comparing CBCT-based dose re-calculations to rigidCT calculations were improved from around 80% (with stdCBCT) to mostly above 90% (with corrCBCT). CONCLUSION Scatter correction leads to substantial artefact reductions, improving accuracy of CBCT-based proton range/dose calculations.
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Affiliation(s)
| | | | - Ulrik Vindelev Elstrøm
- Danish Centre for Particle Therapy, Aarhus University Hospital/Aarhus University, Aarhus, Denmark
| | | | - Gregory C. Sharp
- Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Brian Winey
- Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Lei Dong
- University of Pennsylvania, Philadelphia, PA, USA
| | - Ludvig Paul Muren
- Danish Centre for Particle Therapy, Aarhus University Hospital/Aarhus University, Aarhus, Denmark
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