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Keeler A, Lehmann M, Luce J, Kaur M, Roeske J, Kang H. Technical note: TIGRE-DE for the creation of virtual monoenergetic images from dual-energy cone-beam CT. Med Phys 2024; 51:2975-2982. [PMID: 38408013 PMCID: PMC10994758 DOI: 10.1002/mp.17002] [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: 09/27/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND Dual-energy (DE)-CBCT represents a promising imaging modality that can produce virtual monoenergetic (VM) CBCT images. VM images, which provide enhanced contrast and reduced imaging artifacts, can be used to assist in soft-tissue visualization during image-guided radiotherapy. PURPOSE This work reports the development of TIGRE-DE, a module in the open-source TIGRE toolkit for the performance of DE-CBCT and the production of VM CBCT images. This module is created to make DE-CBCT tools accessible in a wider range of clinical and research settings. METHODS We developed an add-on (TIGRE-DE) to the TIGRE toolkit that performs DE material decomposition. To verify its performance, sequential CBCT scans at 80 and 140 kV of a Catphan 604 phantom were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl-methylacrylate (PMMA) basis materials. These basis material projections were used to synthesize VM projections for a range of x-ray energies, which were then reconstructed using the Feldkamp-Davis-Kress (FDK) algorithm. Image quality was assessed by computing Hounsfield units (HU) and contrast-to-noise ratios (CNR) for the material inserts of the phantom and comparing with the constituent 80 and 140 kV images. RESULTS All VM images generated using TIGRE-DE showed good general agreement with the theoretical HU values of the material inserts of the phantom. Apart from the highest-density inserts imaged at the extremes of the energy range, the measured HU values agree with theoretical HUs within the clinical tolerance of ±50 HU. CNR measurements for the various inserts showed that, of the energies selected, 60 keV provided the highest CNR values. Moreover, 60 keV VM images showed average CNR enhancements of 63% and 66% compared to the 80 and 140 kV full-fan protocols. CONCLUSIONS TIGRE-DE successfully implements DE-CBCT material decomposition and VM image creation in an accessible, open-source platform.
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Affiliation(s)
- Andrew Keeler
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer Center, Loyola University of Chicago, Maywood, Illinois, USA
| | | | - Jason Luce
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer Center, Loyola University of Chicago, Maywood, Illinois, USA
| | - Mandeep Kaur
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer Center, Loyola University of Chicago, Maywood, Illinois, USA
| | - John Roeske
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer Center, Loyola University of Chicago, Maywood, Illinois, USA
| | - Hyejoo Kang
- Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernardin Cancer Center, Loyola University of Chicago, Maywood, Illinois, USA
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Hoffmans-Holtzer N, Magallon-Baro A, de Pree I, Slagter C, Xu J, Thill D, Olofsen-van Acht M, Hoogeman M, Petit S. Evaluating AI-generated CBCT-based synthetic CT images for target delineation in palliative treatments of pelvic bone metastasis at conventional C-arm linacs. Radiother Oncol 2024; 192:110110. [PMID: 38272314 DOI: 10.1016/j.radonc.2024.110110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 01/11/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
PURPOSE One-table treatments with treatment imaging, preparation and delivery occurring at one treatment couch, could increase patients' comfort and throughput for palliative treatments. On regular C-arm linacs, however, cone-beam CT (CBCT) imaging quality is currently insufficient. Therefore, our goal was to assess the suitability of AI-generated CBCT based synthetic CT (sCT) images for target delineation and treatment planning for palliative radiotherapy. MATERIALS AND METHODS CBCTs and planning CT-scans of 22 female patients with pelvic bone metastasis were included. For each CBCT, a corresponding sCT image was generated by a deep learning model in ADMIRE 3.38.0. Radiation oncologists delineated 23 target volumes (TV) on the sCTs (TVsCT) and scored their delineation confidence. The delineations were transferred to planning CTs and manually adjusted if needed to yield gold standard target volumes (TVclin). TVsCT were geometrically compared to TVclin using Dice coefficient (DC) and Hausdorff Distance (HD). The dosimetric impact of TVsCT inaccuracies was evaluated for VMAT plans with different PTV margins. RESULTS Radiation oncologists scored the sCT quality as sufficient for 13/23 TVsCT (median: DC = 0.9, HD = 11 mm) and insufficient for 10/23 TVsCT (median: DC = 0.7, HD = 34 mm). For the sufficient category, remaining inaccuracies could be compensated by +1 to +4 mm additional margin to achieve coverage of V95% > 95% and V95% > 98%, respectively in 12/13 TVsCT. CONCLUSION The evaluated sCT quality allowed for accurate delineation for most targets. sCTs with insufficient quality could be identified accurately upfront. A moderate PTV margin expansion could address remaining delineation inaccuracies. Therefore, these findings support further exploration of CBCT based one-table treatments on C-arm linacs.
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Affiliation(s)
- Nienke Hoffmans-Holtzer
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
| | - Alba Magallon-Baro
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
| | - Ilse de Pree
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Cleo Slagter
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Jiaofeng Xu
- Elekta Inc, St. Charles office, 1450 Beale St, St. Charles, MO 63303, USA
| | - Daniel Thill
- Elekta Inc, St. Charles office, 1450 Beale St, St. Charles, MO 63303, USA
| | - Manouk Olofsen-van Acht
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Mischa Hoogeman
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Steven Petit
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
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Zhuang T, Parsons D, Desai N, Gibbard G, Keilty D, Lin MH, Cai B, Nguyen D, Chiu T, Godley A, Pompos A, Jiang S. Simulation and pre-planning omitted radiotherapy (SPORT): a feasibility study for prostate cancer. Biomed Phys Eng Express 2024; 10:025019. [PMID: 38241733 DOI: 10.1088/2057-1976/ad20aa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 01/19/2024] [Indexed: 01/21/2024]
Abstract
This study explored the feasibility of on-couch intensity modulated radiotherapy (IMRT) planning for prostate cancer (PCa) on a cone-beam CT (CBCT)-based online adaptive RT platform without an individualized pre-treatment plan and contours. Ten patients with PCa previously treated with image-guided IMRT (60 Gy/20 fractions) were selected. In contrast to the routine online adaptive RT workflow, a novel approach was employed in which the same preplan that was optimized on one reference patient was adapted to generate individual on-couch/initial plans for the other nine test patients using Ethos emulator. Simulation CTs of the test patients were used as simulated online CBCT (sCBCT) for emulation. Quality assessments were conducted on synthetic CTs (sCT). Dosimetric comparisons were performed between on-couch plans, on-couch plans recomputed on the sCBCT and individually optimized plans for test patients. The median value of mean absolute difference between sCT and sCBCT was 74.7 HU (range 69.5-91.5 HU). The average CTV/PTV coverage by prescription dose was 100.0%/94.7%, and normal tissue constraints were met for the nine test patients in on-couch plans on sCT. Recalculating on-couch plans on the sCBCT showed about 0.7% reduction of PTV coverage and a 0.6% increasing of hotspot, and the dose difference of the OARs was negligible (<0.5 Gy). Hence, initial IMRT plans for new patients can be generated by adapting a reference patient's preplan with online contours, which had similar qualities to the conventional approach of individually optimized plan on the simulation CT. Further study is needed to identify selection criteria for patient anatomy most amenable to this workflow.
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Affiliation(s)
- Tingliang Zhuang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - David Parsons
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Neil Desai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Grant Gibbard
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Dana Keilty
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Mu-Han Lin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Bin Cai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Dan Nguyen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Tsuicheng Chiu
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Andrew Godley
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Arnold Pompos
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
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Singhrao K, Dugan CL, Calvin C, Pelayo L, Yom SS, Chan JW, Scholey JE, Singer L. Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck. J Appl Clin Med Phys 2024; 25:e14239. [PMID: 38128040 PMCID: PMC10795453 DOI: 10.1002/acm2.14239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/22/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Magnetic resonance image only (MRI-only) simulation for head and neck (H&N) radiotherapy (RT) could allow for single-image modality planning with excellent soft tissue contrast. In the MRI-only simulation workflow, synthetic computed tomography (sCT) is generated from MRI to provide electron density information for dose calculation. Bone/air regions produce little MRI signal which could lead to electron density misclassification in sCT. Establishing the dosimetric impact of this error could inform quality assurance (QA) procedures using MRI-only RT planning or compensatory methods for accurate dosimetric calculation. PURPOSE The aim of this study was to investigate if Hounsfield unit (HU) voxel misassignments from sCT images result in dosimetric errors in clinical treatment plans. METHODS Fourteen H&N cancer patients undergoing same-day CT and 3T MRI simulation were retrospectively identified. MRI was deformed to the CT using multimodal deformable image registration. sCTs were generated from T1w DIXON MRIs using a commercially available deep learning-based generator (MRIplanner, Spectronic Medical AB, Helsingborg, Sweden). Tissue voxel assignment was quantified by creating a CT-derived HU threshold contour. CT/sCT HU differences for anatomical/target contours and tissue classification regions including air (<250 HU), adipose tissue (-250 HU to -51 HU), soft tissue (-50 HU to 199 HU), spongy (200 HU to 499 HU) and cortical bone (>500 HU) were quantified. t-test was used to determine if sCT/CT HU differences were significant. The frequency of structures that had a HU difference > 80 HU (the CT window-width setting for intra-cranial structures) was computed to establish structure classification accuracy. Clinical intensity modulated radiation therapy (IMRT) treatment plans created on CT were retrospectively recalculated on sCT images and compared using the gamma metric. RESULTS The mean ratio of sCT HUs relative to CT for air, adipose tissue, soft tissue, spongy and cortical bone were 1.7 ± 0.3, 1.1 ± 0.1, 1.0 ± 0.1, 0.9 ± 0.1 and 0.8 ± 0.1 (value of 1 indicates perfect agreement). T-tests (significance set at t = 0.05) identified differences in HU values for air, spongy and cortical bone in sCT images compared to CT. The structures with sCT/CT HU differences > 80 HU of note were the left and right (L/R) cochlea and mandible (>79% of the tested cohort), the oral cavity (for 57% of the tested cohort), the epiglottis (for 43% of the tested cohort) and the L/R TM joints (occurring > 29% of the cohort). In the case of the cochlea and TM joints, these structures contain dense bone/air interfaces. In the case of the oral cavity and mandible, these structures suffer the additional challenge of being positionally altered in CT versus MRI simulation (due to a non-MR safe immobilizing bite block requiring absence of bite block in MR). Finally, the epiglottis HU assignment suffers from its small size and unstable positionality. Plans recalculated on sCT yielded global/local gamma pass rates of 95.5% ± 2% (3 mm, 3%) and 92.7% ± 2.1% (2 mm, 2%). The largest mean differences in D95, Dmean , D50 dose volume histogram (DVH) metrics for organ-at-risk (OAR) and planning tumor volumes (PTVs) were 2.3% ± 3.0% and 0.7% ± 1.9% respectively. CONCLUSIONS In this cohort, HU differences of CT and sCT were observed but did not translate into a reduction in gamma pass rates or differences in average PTV/OAR dose metrics greater than 3%. For sites such as the H&N where there are many tissue interfaces we did not observe large scale dose deviations but further studies using larger retrospective cohorts are merited to establish the variation in sCT dosimetric accuracy which could help to inform QA limits on clinical sCT usage.
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Affiliation(s)
- Kamal Singhrao
- Department of Radiation OncologyBrigham and Women's Hospital, Dana‐Farber Cancer Institute, Harvard Medical SchoolBostonMassachusettsUSA
| | - Catherine Lu Dugan
- Department of Radiation OncologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Christina Calvin
- Department of Radiation OncologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Luis Pelayo
- Department of Radiation OncologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Sue Sun Yom
- Department of Radiation OncologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Jason Wing‐Hong Chan
- Department of Radiation OncologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | | | - Lisa Singer
- Department of Radiation OncologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
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Allen C, Yeo AU, Hardcastle N, Franich RD. Evaluating synthetic computed tomography images for adaptive radiotherapy decision making in head and neck cancer. Phys Imaging Radiat Oncol 2023; 27:100478. [PMID: 37655123 PMCID: PMC10465931 DOI: 10.1016/j.phro.2023.100478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 09/02/2023] Open
Abstract
Background and purpose Adaptive radiotherapy (ART) decision-making benefits from dosimetric information to supplement image inspection when assessing the significance of anatomical changes. This study evaluated a dosimetry-based clinical decision workflow for ART utilizing deformable registration of the original planning computed tomography (CT) image to the daily Cone Beam CT (CBCT) to replace the need for a replan CT for dose estimation. Materials and methods We used 12 retrospective Head & Neck patient cases having a ground truth - a replan CT (rCT) in response to anatomical changes apparent in the daily CBCT - to evaluate the accuracy of dosimetric assessment conducted on synthetic CTs (sCT) generated by deforming the original planning CT Hounsfield Units to the daily CBCT anatomy.The original plan was applied to the sCT and dosimetric accuracy of the sCT was assessed by analyzing plan objectives for targets and organs-at-risk compared to calculations on the ground-truth rCT. Three commercial DIR algorithms were compared. Results For the best-performing algorithms, the majority of dose metrics calculated on the sCTs differed by less than 4 Gy (5.7% of 70 Gy prescription dose). An uncertainty of ±2.5 Gy (3.6% of 70 Gy prescription) is recommended as a conservative tolerance when evaluating dose metrics on sCTs for head and neck. Conclusions Synthetic CTs present a valuable addition to the adaptive radiotherapy workflow, and synthetic CT dose estimates can be effectively used in addition to the current practice of visually inspecting the overlay of the planning CT and CBCT to assess the significance of anatomical change.
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Affiliation(s)
- Caitlin Allen
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Adam U. Yeo
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Centre for Medical Radiation Physics, University of Wollongong, NSW, Australia
| | - Rick D. Franich
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- School of Science, RMIT University, Melbourne, Victoria, Australia
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Jassim H, Nedaei HA, Geraily G, Banaee N, Kazemian A. The geometric and dosimetric accuracy of kilovoltage cone beam computed tomography images for adaptive treatment: a systematic review. BJR Open 2023; 5:20220062. [PMID: 37389008 PMCID: PMC10301728 DOI: 10.1259/bjro.20220062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/24/2023] [Indexed: 07/01/2023] Open
Abstract
Objectives To provide an overview and meta-analysis of different techniques adopted to accomplish kVCBCT for dose calculation and automated segmentation. Methods A systematic review and meta-analysis were performed on eligible studies demonstrating kVCBCT-based dose calculation and automated contouring of different tumor features. Meta-analysis of the performance was accomplished on the reported γ analysis and dice similarity coefficient (DSC) score of both collected results as three subgroups (head and neck, chest, and abdomen). Results After the literature scrutinization (n = 1008), 52 papers were recognized for the systematic review. Nine studies of dosimtric studies and eleven studies of geometric analysis were suitable for inclusion in meta-analysis. Using kVCBCT for treatment replanning depends on a method used. Deformable Image Registration (DIR) methods yielded small dosimetric error (≤2%), γ pass rate (≥90%) and DSC (≥0.8). Hounsfield Unit (HU) override and calibration curve-based methods also achieved satisfactory yielded small dosimetric error (≤2%) and γ pass rate ((≥90%), but they are prone to error due to their sensitivity to a vendor-specific variation in kVCBCT image quality. Conclusions Large cohorts of patients ought to be undertaken to validate methods achieving low levels of dosimetric and geometric errors. Quality guidelines should be established when reporting on kVCBCT, which include agreed metrics for reporting on the quality of corrected kVCBCT and defines protocols of new site-specific standardized imaging used when obtaining kVCBCT images for adaptive radiotherapy. Advances in knowledge This review gives useful knowledge about methods making kVCBCT feasible for kVCBCT-based adaptive radiotherapy, simplifying patient pathway and reducing concomitant imaging dose to the patient.
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Affiliation(s)
| | | | | | - Nooshin Banaee
- Medical Radiation Research Center, Islamic Azad University, Tehran, Iran
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Suwanraksa C, Bridhikitti J, Liamsuwan T, Chaichulee S. CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer. Cancers (Basel) 2023; 15:cancers15072017. [PMID: 37046678 PMCID: PMC10093508 DOI: 10.3390/cancers15072017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient’s anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dynamically estimate the correct labels, allowing supervised learning with noisy labels. The study developed and evaluated the approach using imaging data from 146 patients with head and neck cancer. The results showed that GAN trained with RegNet performed better than those trained without RegNet. Specifically, in the UNIT model trained with RegNet, the mean absolute error (MAE) was reduced from 40.46 to 37.21, the root mean-square error (RMSE) was reduced from 119.45 to 108.86, the peak signal-to-noise ratio (PSNR) was increased from 28.67 to 29.55, and the structural similarity index (SSIM) was increased from 0.8630 to 0.8791. The sCT generated from the model had fewer artifacts and retained the anatomical information as in CBCT.
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O'Hara CJ, Bird D, Al-Qaisieh B, Speight R. Assessment of CBCT-based synthetic CT generation accuracy for adaptive radiotherapy planning. J Appl Clin Med Phys 2022; 23:e13737. [PMID: 36200179 DOI: 10.1002/acm2.13737] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/26/2022] [Accepted: 07/04/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Cone-beam CT (CBCT)-based synthetic CT (sCT) dose calculation has the potential to make the adaptive radiotherapy (ART) pathway more efficient while removing subjectivity. This study assessed four sCT generation methods using 15 head-and-neck rescanned ART patients. Each patient's planning CT (pCT), rescan CT (rCT), and CBCT post-rCT was acquired with the CBCT deformably registered to the rCT (dCBCT). METHODS The four methods investigated were as follows: method 1-deformably registering the pCT to the dCBCT. Method 2-assigning six mass density values to the dCBCT. Method 3-iteratively removing artifacts and correcting the dCBCT Hounsfield units (HU). Method 4-using a cycle general adversarial network machine learning model (trained with 45 paired pCT and CBCT). Treatment plans were created on the rCT and recalculated on each sCT. Planning target volume (PTV) and organ-at-risk (OAR) structures were contoured by clinicians on the rCT (high-dose PTV, low-dose PTV, spinal canal, larynx, brainstem, and parotids) to allow the assessment of dose-volume histogram statistics at clinically relevant points. RESULTS The HU mean absolute error (MAE) and minimum dose gamma index pass rate (2%/2 mm) were calculated, and the generation time was measured for 15 patients using the rCT as the comparator. For methods 1-4 the MAE, gamma index analysis, and generation time were as follows: 59.7 HU, 100.0%, and 143 s; 164.2 HU, 95.2%, and 232 s; 75.7 HU, 99.9%, and 153 s; and 79.4 HU, 99.8%, and 112 s, respectively. Dose differences for PTVs and OARs were all <0.3 Gy except for method 2 (<0.5 Gy). CONCLUSION All methods were considered clinically viable. The machine learning method was found to be most suitable for clinical implementation due to its high dosimetric accuracy and short generation time. Further investigation is required for larger anatomical changes between the CBCT and pCT and for other anatomical sites.
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Affiliation(s)
| | - David Bird
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | - Richard Speight
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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Rusanov B, Hassan GM, Reynolds M, Sabet M, Kendrick J, Farzad PR, Ebert M. Deep learning methods for enhancing cone-beam CT image quality towards adaptive radiation therapy: A systematic review. Med Phys 2022; 49:6019-6054. [PMID: 35789489 PMCID: PMC9543319 DOI: 10.1002/mp.15840] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 05/21/2022] [Accepted: 06/16/2022] [Indexed: 11/11/2022] Open
Abstract
The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up-to-date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings - with emphasis on study design and deep learning techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarised, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state of the art methods utilized in radiation oncology. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Branimir Rusanov
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia
| | - Mark Reynolds
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia
| | - Mahsheed Sabet
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Jake Kendrick
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Pejman Rowshan Farzad
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Martin Ebert
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
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10
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Bojechko C, Hua P, Sumner W, Guram K, Atwood T, Sharabi A. Adaptive replanning using cone beam CT for deformation of original CT simulation. J Med Radiat Sci 2022; 69:267-272. [PMID: 34704381 PMCID: PMC9163453 DOI: 10.1002/jmrs.549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 08/16/2021] [Accepted: 09/03/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND During a course of radiation therapy, anatomical changes such as a decrease in tumour size or weight loss can trigger the need for repeating a computed tomography (CT) simulation scan in order to generate a new treatment plan. This adaptive approach requires a separate appointment for an additional CT scan which generates additional burden, cost, and radiation exposure for patients. CASE PRESENTATION Here, we present a case of a head and neck cancer patient who required palliative radiation for a large neck mass. During treatment, he had a remarkable response which required a replan due to rapid tumour downsizing. In this case, we used a novel technique to avoid repeating the planning CT simulation by using a mid-treatment high-quality cone beam CT (CBCT) to deform the secondary image (plan CT) of the original planning CT and generate a new adapted treatment plan. CONCLUSION This is the first report to our knowledge using a Halcyon CBCT to deform the original planning CT in order to generate a new radiation treatment plan, and this novel technique represents a new potential method of adaptive replanning for select patients.
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Affiliation(s)
- Casey Bojechko
- Department of Radiation Medicine and Applied SciencesUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Patricia Hua
- Department of Radiation Medicine and Applied SciencesUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Whitney Sumner
- Department of Radiation Medicine and Applied SciencesUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Kripa Guram
- Department of Radiation Medicine and Applied SciencesUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Todd Atwood
- Department of Radiation Medicine and Applied SciencesUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Andrew Sharabi
- Department of Radiation Medicine and Applied SciencesUniversity of California San DiegoLa JollaCaliforniaUSA
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Adaptive radiation therapy: When, how and what are the benefits that literature provides? Cancer Radiother 2021; 26:622-636. [PMID: 34688548 DOI: 10.1016/j.canrad.2021.08.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE To identify from the current literature when is the right time to replan and to assign thresholds for the optimum process of replanning. Nowadays, adaptive radiotherapy (ART) for head and neck cancer plays an exceptional role consisting of an evaluation procedure of the prominent anatomical and dosimetric variations. By performing complex radiotherapy methods, the credibility of the therapeutic result is crucial. Image guided radiotherapy (IGRT) was developed to ensure locoregional control and thus changes that might occur during radiotherapy be dealt with. MATERIALS AND METHODS An electronic research of articles published in PubMed/MEDLINE and Science Direct databases from January 2004 to October 2020 was performed. Among a total of 127 studies assessed for eligibility, 85 articles were ultimately retained for the review. RESULTS The most noticeable changes have been reported in the middle fraction of the treatment. Therefore, the suggested optimal time to replan is between the third and the fourth week. Anatomical deviations>1cm in the external contour, average weight loss>10%, violation in the dose coverage of the targets>5%, and violation in the dose of the peripherals were some of the thresholds that are currently used, and which lead to replanning. CONCLUSION ART may decrease toxicity and improve local-control. Whether it is beneficial or not, depends ultimately on each patient. However, more investigation of the changes should be performed in future prospective studies to obtain more accurate results.
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Duffton A, Moore K, Williamson A. Diversity in radiation therapist/therapeutic radiographer (RTT) advanced practice (AP) roles delivering on the four domains. Tech Innov Patient Support Radiat Oncol 2021; 17:102-107. [PMID: 34007915 PMCID: PMC8111037 DOI: 10.1016/j.tipsro.2021.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/18/2021] [Accepted: 02/02/2021] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Advanced practice roles are well documented, and continue to respond to the changing landscape in radiotherapy and oncology. In the UK the highest level of AP for the therapeutic radiographer/radiation therapist (RTT) is the consultant radiographer. These posts should meet the four domains of practice, as set out in national guidance. Here we aim to describe well established roles that meet this criteria, and provide subgroups of examples. METHODOLOGY Three AP post holders with over 10 years AP experience completed a questionnaire adapted from the consultant radiographer toolkit. These were completed in conjunction with guidance and framework documents. The examples were to demonstrate how they achieve a high level of practice in clinical and expert practice; professional leadership and consultancy; education, training and development; and practice and service development, research and evaluation. Participants then categorised results to add subgroups to each domain. RESULTS The questionnaire was completed by three RTTs specialising as a lung consultant radiographer (LCR), a neuro-oncology consultant radiographer (NCR) and a lead research radiographer (RR). Each post holder described how they meet the criteria by discussing the benefit they make to their profession, department and patients. All posts had examples for all criteria, achieving consultant practice. Clinical and expert practice was the dominant domain for the clinical specialist posts, and professional leadership and research evaluation was the strongest domains for the RR. CONCLUSION All three consultant RTTs have demonstrated expert practice with clear and transparent examples of their professional practice which evidence the four domains of consultant practice. Following two decades of AP practice for RTTs there is a need to be strategic in the development of future posts with a prospective view on succession planning that safeguards their longevity.
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Affiliation(s)
- Aileen Duffton
- Department of Radiotherapy, Beatson West of Scotland Cancer Centre, Glasgow, UK
| | - Karen Moore
- Department of Radiotherapy, Beatson West of Scotland Cancer Centre, Glasgow, UK
| | - Aoife Williamson
- Department of Radiotherapy, Beatson West of Scotland Cancer Centre, Glasgow, UK
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Duffton A, Li W, Forde E. The Pivotal Role of the Therapeutic Radiographer/Radiation Therapist in Image-guided Radiotherapy Research and Development. Clin Oncol (R Coll Radiol) 2020; 32:852-860. [PMID: 33087296 DOI: 10.1016/j.clon.2020.09.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/21/2020] [Accepted: 09/22/2020] [Indexed: 12/24/2022]
Abstract
The ability to personalise radiotherapy to fit the individual patient and their diagnosis has been realised through technological advancements. There is now more opportunity to utilise these technologies and deliver precision radiotherapy for more patients. Image-guided radiotherapy (IGRT) has enabled users to safely and accurately plan, treat and verify complex cases; and deliver a high dose to the target volume, while minimising dose to normal tissue. Rapid changes in IGRT have required a multidisciplinary team (MDT) approach, carefully deciding optimum protocols to achieve clinical benefit. Therapeutic radiographer/radiation therapists (RTTs) play a pivotal role in this MDT. There is already a great deal of evidence that illustrates the contribution of RTTs in IGRT development; implementation; quality assurance; and maintaining training and competency programmes. Often this has required the RTT to undertake additional roles and responsibilities. These publications show how the profession has evolved, expanding the scope of practice. There are now more opportunities for RTT-led IGRT research. This is not only undertaken in the more traditional aspects of practice, but in recent times, more RTTs are becoming involved in imaging biomarkers research and radiomic analysis. The aim of this overview is to describe the RTT contribution to the ongoing development of IGRT and to showcase some of the profession's involvement in IGRT research.
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Affiliation(s)
- A Duffton
- Department of Radiotherapy, Beatson West of Scotland Cancer Centre, Glasgow, UK.
| | - W Li
- University of Toronto, Toronto, Ontario, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - E Forde
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College Dublin, The University of Dublin, Dublin, Ireland
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Bjarnason TA, Rees R, Kainz J, Le LH, Stewart EE, Preston B, Elbakri I, Fife IAJ, Lee T, Gagnon IMB, Arsenault C, Therrien P, Kendall E, Tonkopi E, Cottreau M, Aldrich JE. An international survey on the clinical use of rigid and deformable image registration in radiotherapy. J Appl Clin Med Phys 2020; 21:10-24. [PMID: 32915492 PMCID: PMC7075391 DOI: 10.1002/acm2.12957] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/13/2020] [Accepted: 05/14/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES Rigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration. METHODS A survey was designed to identify variations in use, resources, implementation, and decision-making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018. RESULTS There were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)-CT registration (96% and 51%, respectively), followed by CT-PET (81% and 47%), CT-CBCT (84% and 19%), CT-MR (93% and 19%), MR-MR (49% and 5%), and CT-US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas-based segmentation (47%), multi-modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multi-modality treatment planning and accounting for retreatments was considered to have the highest benefit-to-risk ratio (69% and 67%, respectively). CONCLUSIONS This survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT-CT (51%) and CT-PET (47%) for the head and neck (43-57% over all use cases) region. The highest benefit-risk ratio for clinical use of DIR was for multi-modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlas-based segmentation.
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Affiliation(s)
- Thorarin A. Bjarnason
- Medical ImagingInterior Health AuthorityKelownaBCCanada
- RadiologyUniversity of British ColumbiaVancouverBCCanada
- PhysicsUniversity of British Columbia OkanaganKelownaBCCanada
| | - Robert Rees
- Occupational Health & SafetyYukon Workers' Compensation Health and Safety BoardWhitehorseYKCanada
| | - Judy Kainz
- Workers' Safety and Compensation Commission for Northwest Territories and NunavutYellowknifeNTCanada
| | - Lawrence H. Le
- Diagnostic ImagingAlberta Health ServicesCalgaryABCanada
- Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonABCanada
| | | | - Brent Preston
- Radiation Safety UnitGovernment of SaskatchewanSaskatoonSKCanada
| | - Idris Elbakri
- Cancer Care ManitobaWinnipegMBCanada
- Physics and AstronomyUniversity of ManitobaWinnipegMBCanada
- RadiologyUniversity of ManitobaWinnipegMBCanada
| | - Ingvar A. J. Fife
- Cancer Care ManitobaWinnipegMBCanada
- Physics and AstronomyUniversity of ManitobaWinnipegMBCanada
- RadiologyUniversity of ManitobaWinnipegMBCanada
| | - Ting‐Yim Lee
- St Joseph’s Health Care LondonLondonONCanada
- Lawson Research InstituteLondonONCanada
- Medical ImagingMedical Biophysics, OncologyRobarts Research InstituteUniversity of Western OntarioLondonONCanada
| | | | - Clément Arsenault
- Hôpital Dr Georges–L. DumontCentre d'Oncologie Dr Léon–RichardMonctonNBCanada
| | | | | | - Elena Tonkopi
- Nova Scotia Health AuthorityHalifaxNSCanada
- Diagnostic RadiologyDalhousie UniversityHalifaxNSCanada
- Radiation OncologyDalhousie UniversityHalifaxNSCanada
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