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Efficient full Monte Carlo modelling and multi-energy generative model development of an advanced X-ray device. Z Med Phys 2022:S0939-3889(22)00061-7. [DOI: 10.1016/j.zemedi.2022.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/28/2022] [Accepted: 04/28/2022] [Indexed: 11/22/2022]
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Virtual monoenergetic micro-CT imaging in mice with artificial intelligence. Sci Rep 2022; 12:2324. [PMID: 35149703 PMCID: PMC8837804 DOI: 10.1038/s41598-022-06172-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 01/23/2022] [Indexed: 11/26/2022] Open
Abstract
Micro cone-beam computed tomography (µCBCT) imaging is of utmost importance for carrying out extensive preclinical research in rodents. The imaging of animals is an essential step prior to preclinical precision irradiation, but also in the longitudinal assessment of treatment outcomes. However, imaging artifacts such as beam hardening will occur due to the low energetic nature of the X-ray imaging beam (i.e., 60 kVp). Beam hardening artifacts are especially difficult to resolve in a ‘pancake’ imaging geometry with stationary source and detector, where the animal is rotated around its sagittal axis, and the X-ray imaging beam crosses a wide range of thicknesses. In this study, a seven-layer U-Net based network architecture (vMonoCT) is adopted to predict virtual monoenergetic X-ray projections from polyenergetic X-ray projections. A Monte Carlo simulation model is developed to compose a training dataset of 1890 projection pairs. Here, a series of digital anthropomorphic mouse phantoms was derived from the reference DigiMouse phantom as simulation geometry. vMonoCT was trained on 1512 projection pairs (= 80%) and tested on 378 projection pairs (= 20%). The percentage error calculated for the test dataset was 1.7 ± 0.4%. Additionally, the vMonoCT model was evaluated on a retrospective projection dataset of five mice and one frozen cadaver. It was found that beam hardening artifacts were minimized after image reconstruction of the vMonoCT-corrected projections, and that anatomically incorrect gradient errors were corrected in the cranium up to 15%. Our results disclose the potential of Artificial Intelligence to enhance the µCBCT image quality in biomedical applications. vMonoCT is expected to contribute to the reproducibility of quantitative preclinical applications such as precision irradiations in X-ray cabinets, and to the evaluation of longitudinal imaging data in extensive preclinical studies.
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Pauwels R, Pittayapat P, Sinpitaksakul P, Panmekiate S. Scatter-to-primary ratio in dentomaxillofacial cone-beam CT: effect of field of view and beam energy. Dentomaxillofac Radiol 2021; 50:20200597. [PMID: 33882256 DOI: 10.1259/dmfr.20200597] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE The aim of this study was to evaluate the effect of field of view (FOV) and beam energy on the scatter-to-primary ratio (SPR) in dental cone-beam CT (CBCT). METHODS An anthropomorphic phantom representing an adult male (ATOM Max 711-HN, Norfolk, VA, USA) was scanned using the 3D Accuitomo 170 CBCT (J. Morita, Kyoto, Japan) using 11 FOVs. During each scan, half of the X-ray beam was blocked. Each scan was performed at three exposure settings with varying beam energy and equal radiation dose: 90 kV 5 mA, 77 kV 7.5 mA and 69 kV 10 mA. The SPR was estimated by measuring the grey values in the blocked and non-blocked regions of the RAW data. The effect of FOV on SPR was evaluated using Dunn's multiple comparison test, and the effect of the exposure settings was compared using a Wilcoxon signed rank test. RESULTS Larger FOVs showed increased scatter. FOVs with a shorter isocenter-detector distance showed a particularly high SPR. Most intercomparisons between FOVs were statistically significant. The largest difference was found between 17 × 12 cm and 6 × 6 cm (lower jaw), with the former showing a 4.9-fold higher SPR. The effect of beam energy was relatively small and varied between FOV sizes and positions. CONCLUSION While the choice of FOV size and position is determined by the diagnostic region of interest, the image quality deterioration for large FOVs due to scatter provides another incentive to limit the FOV size as much as possible.
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Affiliation(s)
- Ruben Pauwels
- Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark.,Department of Radiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Pisha Pittayapat
- Department of Radiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Phonkit Sinpitaksakul
- Department of Radiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Soontra Panmekiate
- Department of Radiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
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Rossi M, Belotti G, Paganelli C, Pella A, Barcellini A, Cerveri P, Baroni G. Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning. Med Phys 2021; 48:7112-7126. [PMID: 34636429 PMCID: PMC9297981 DOI: 10.1002/mp.15282] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 09/29/2021] [Accepted: 10/01/2021] [Indexed: 11/21/2022] Open
Abstract
Purpose: Cone beam computed tomography (CBCT) is a standard solution for in‐room image guidance for radiation therapy. It is used to evaluate and compensate for anatomopathological changes between the dose delivery plan and the fraction delivery day. CBCT is a fast and versatile solution, but it suffers from drawbacks like low contrast and requires proper calibration to derive density values. Although these limitations are even more prominent with in‐room customized CBCT systems, strategies based on deep learning have shown potential in improving image quality. As such, this article presents a method based on a convolutional neural network and a novel two‐step supervised training based on the transfer learning paradigm for shading correction in CBCT volumes with narrow field of view (FOV) acquired with an ad hoc in‐room system. Methods: We designed a U‐Net convolutional neural network, trained on axial slices of corresponding CT/CBCT couples. To improve the generalization capability of the network, we exploited two‐stage learning using two distinct data sets. At first, the network weights were trained using synthetic CBCT scans generated from a public data set, and then only the deepest layers of the network were trained again with real‐world clinical data to fine‐tune the weights. Synthetic data were generated according to real data acquisition parameters. The network takes a single grayscale volume as input and outputs the same volume with corrected shading and improved HU values. Results: Evaluation was carried out with a leave‐one‐out cross‐validation, computed on 18 unique CT/CBCT pairs from six different patients from a real‐world dataset. Comparing original CBCT to CT and improved CBCT to CT, we obtained an average improvement of 6 dB on peak signal‐to‐noise ratio (PSNR), +2% on structural similarity index measure (SSIM). The median interquartile range (IQR) Hounsfield unit (HU) difference between CBCT and CT improved from 161.37 (162.54) HU to 49.41 (66.70) HU. Region of interest (ROI)‐based HU difference was narrowed by 75% in the spongy bone (femoral head), 89% in the bladder, 85% for fat, and 83% for muscle. The improvement in contrast‐to‐noise ratio for these ROIs was about 67%. Conclusions: We demonstrated that shading correction obtaining CT‐compatible data from narrow‐FOV CBCTs acquired with a customized in‐room system is possible. Moreover, the transfer learning approach proved particularly beneficial for such a shading correction approach.
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Affiliation(s)
- Matteo Rossi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Gabriele Belotti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Andrea Pella
- Bioengineering Unit, Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Amelia Barcellini
- Radiation Oncology Unit, Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Pietro Cerveri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.,Bioengineering Unit, Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy
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Sarrut D, Bała M, Bardiès M, Bert J, Chauvin M, Chatzipapas K, Dupont M, Etxebeste A, M Fanchon L, Jan S, Kayal G, S Kirov A, Kowalski P, Krzemien W, Labour J, Lenz M, Loudos G, Mehadji B, Ménard L, Morel C, Papadimitroulas P, Rafecas M, Salvadori J, Seiter D, Stockhoff M, Testa E, Trigila C, Pietrzyk U, Vandenberghe S, Verdier MA, Visvikis D, Ziemons K, Zvolský M, Roncali E. Advanced Monte Carlo simulations of emission tomography imaging systems with GATE. Phys Med Biol 2021; 66:10.1088/1361-6560/abf276. [PMID: 33770774 PMCID: PMC10549966 DOI: 10.1088/1361-6560/abf276] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/26/2021] [Indexed: 12/13/2022]
Abstract
Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.
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Affiliation(s)
- David Sarrut
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | | | - Manuel Bardiès
- Cancer Research Institute of Montpellier, U1194 INSERM/ICM/Montpellier University, 208 Av des Apothicaires, F-34298 Montpellier cedex 5, France
| | - Julien Bert
- LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, F-29238, Brest, France
| | - Maxime Chauvin
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
| | | | | | - Ane Etxebeste
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | - Louise M Fanchon
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, United States of America
| | - Sébastien Jan
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Service Hospitalier Frédéric Joliot, F-91401, Orsay, France
| | - Gunjan Kayal
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, Mol 2400, Belgium
| | - Assen S Kirov
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, United States of America
| | - Paweł Kowalski
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
| | - Wojciech Krzemien
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
| | - Joey Labour
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | - Mirjam Lenz
- FH Aachen University of Applied Sciences, Forschungszentrum Jülich, Jülich, Germany
- Faculty of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany
| | - George Loudos
- Bioemission Technology Solutions (BIOEMTECH), Alexandras Av. 116, Athens, Greece
| | | | - Laurent Ménard
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, F-91405 Orsay, France
- Université de Paris, IJCLab, F-91405 Orsay France
| | | | | | - Magdalena Rafecas
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Julien Salvadori
- Department of Nuclear Medicine and Nancyclotep molecular imaging platform, CHRU-Nancy, Université de Lorraine, F-54000, Nancy, France
| | - Daniel Seiter
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, 53705, United States of America
| | - Mariele Stockhoff
- Medical Image and Signal Processing (MEDISIP), Ghent University, Ghent, Belgium
| | - Etienne Testa
- Univ. Lyon, Univ. Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, F-69622, Villeurbanne, France
| | - Carlotta Trigila
- Department of Biomedical Engineering, University of California, Davis, CA 95616 United States of America
| | - Uwe Pietrzyk
- Faculty of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany
| | | | - Marc-Antoine Verdier
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, F-91405 Orsay, France
- Université de Paris, IJCLab, F-91405 Orsay France
| | - Dimitris Visvikis
- LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, F-29238, Brest, France
| | - Karl Ziemons
- FH Aachen University of Applied Sciences, Forschungszentrum Jülich, Jülich, Germany
| | - Milan Zvolský
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Emilie Roncali
- Department of Biomedical Engineering, University of California, Davis, CA 95616 United States of America
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Fonseca GP, van Wagenberg T, Voncken R, Podesta M, van Beveren C, van Limbergen E, Lutgens L, Vanneste B, Berbee M, Reniers B, Verhaegen F. Brachytherapy treatment verification using gamma radiation from the internal treatment source combined with an imaging panel-a phantom study. Phys Med Biol 2021; 66. [PMID: 33831856 DOI: 10.1088/1361-6560/abf605] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 04/08/2021] [Indexed: 12/15/2022]
Abstract
Brachytherapy has an excellent clinical outcome for different treatment sites. However,in vivotreatment verification is not performed in the majority of hospitals due to the lack of proper monitoring systems. This study investigates the use of an imaging panel (IP) and the photons emitted by a high dose rate (HDR)192Ir source to track source motion and obtain some information related to the patient anatomy. The feasibility of this approach was studied by monitoring the treatment delivery to a 3D printed phantom that mimicks a prostate patient. A 3D printed phantom was designed with a template for needle insertion, a cavity ('rectum') to insert an ultrasound probe, and lateral cavities used to place tissue-equivalent materials. CT images were acquired to create HDR192Ir treatment plans with a range of dwell times, interdwell distances and needle arrangements. Treatment delivery was verified with an IP placed at several positions around the phantom using radiopaque markers on the outer surface to register acquired IP images with the planning CT. All dwell positions were identified using acquisition times ≤0.11 s (frame rates ≥ 9 fps). Interdwell distances and dwell positions (in relation to the IP) were verified with accuracy better than 0.1 cm. Radiopaque markers were visible in the acquired images and could be used for registration with CT images. Uncertainties for image registration (IP and planning CT) between 0.1 and 0.4 cm. The IP is sensitive to tissue-mimicking insert composition and showed phantom boundaries that could be used to improve treatment verification. The IP provided sufficient time and spatial resolution for real-time source tracking and allows for the registration of the planning CT and IP images. The results obtained in this study indicate that several treatment errors could be detected including swapped catheters, incorrect dwell times and dwell positions.
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Affiliation(s)
- G P Fonseca
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
| | - T van Wagenberg
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
| | - R Voncken
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
| | - M Podesta
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
| | - C van Beveren
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
| | - E van Limbergen
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
| | - L Lutgens
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
| | - B Vanneste
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
| | - M Berbee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
| | - B Reniers
- Research group NuTeC, Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium
| | - F Verhaegen
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, The Netherlands
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van der Heyden B, Cohilis M, Souris K, de Freitas Nascimento L, Sterpin E. Artificial intelligence supported single detector multi-energy proton radiography system. Phys Med Biol 2021; 66. [PMID: 33621962 DOI: 10.1088/1361-6560/abe918] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Abstract
Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specific CT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus®One accelerator was built at 200 and 220 MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4% ± 0.4% in our test dataset. For one example patient, detector dose to WET conversions were performed based on the total dose (ITotal), the primary proton dose (IPrimary), and the ProtonDSE corrected detector dose (ICorrected). The determined WET accuracy was compared with respect to the reference WET by idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was determined 4.3% ± 4.1% forWET(ITotal),2.2% ± 1.4% forWET(IPrimary),and 2.5% ± 2.0% forWET(ICorrected).
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Affiliation(s)
- Brent van der Heyden
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
| | - Marie Cohilis
- UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging Radiotherapy and Oncology Lab, Brussels, Belgium
| | - Kevin Souris
- UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging Radiotherapy and Oncology Lab, Brussels, Belgium
| | | | - Edmond Sterpin
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium.,UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging Radiotherapy and Oncology Lab, Brussels, Belgium
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Park Y, Alexeev T, Miller B, Miften M, Altunbas C. Evaluation of scatter rejection and correction performance of 2D antiscatter grids in cone beam computed tomography. Med Phys 2021; 48:1846-1858. [PMID: 33554377 DOI: 10.1002/mp.14756] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 01/18/2021] [Accepted: 02/01/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE We have been investigating two-dimensional (2D) antiscatter grids (2D ASGs) to reduce scatter fluence and improve image quality in cone beam computed tomography (CBCT). In this work, two different aspects of 2D ASGs, their scatter rejection and correction capability, were investigated in CBCT experiments. To correct residual scatter transmitted through the 2D ASG, it was used as a scatter measurement device with a novel method: grid-based scatter sampling. METHODS Three focused 2D ASG prototypes with grid ratios of 8, 12, and 16 were developed for linac-mounted offset detector CBCT geometry. In the first phase, 2D ASGs were used as a scatter rejection device, and the effect of grid ratio on CT number accuracy and contrast-to-noise ratio (CNR) evaluated in CBCT images. In the second phase, a grid-based scatter sampling method which exploits the signal modulation characteristics of the 2D ASG's septal shadows to measure and correct residual scatter transmitted through the grid was implemented. To evaluate CT number accuracy, the percent change in CT numbers was measured by changing the phantom from head to pelvis size and configuration. RESULTS When 2D ASG was used as a scatter rejection device, CT number accuracy increased and the CT number variation due to change in phantom dimensions was reduced from 23% to 2-6%. A grid ratio of 16 yielded the lowest CT number variation. All three 2D ASGs yielded improvement in CNR, up to a factor of two in pelvis-sized phantoms. When 2D ASG prototypes were used for both scatter rejection and correction, CT number variations were reduced further, to 1.3-2.6%. In comparisons with a clinical CBCT system and a high-performance radiographic ASG, 2D ASG provided higher CT number accuracy under the same imaging conditions. CONCLUSIONS When 2D ASG is used solely as a scatter rejection device, substantial improvement in CT number accuracy can be achieved by increasing the grid ratio. Two-dimensional ASGs also provided significant CNR improvement even at lower grid ratios. Two-dimensional ASGs used in conjunction with the grid-based scatter sampling method provided further improvement in CT number accuracy, irrespective of the grid ratio, while preserving 2D ASGs' capacity to improve CNR. The combined effect of scatter rejection and residual scatter correction by 2D ASG may accelerate implementation of new techniques in CBCT that require high quantitative accuracy, such as radiotherapy dose calculation and dual energy CBCT.
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Affiliation(s)
- Yeonok Park
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706, Aurora, CO, 80045, USA
| | - Timur Alexeev
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706, Aurora, CO, 80045, USA
| | - Brian Miller
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706, Aurora, CO, 80045, USA
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706, Aurora, CO, 80045, USA
| | - Cem Altunbas
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706, Aurora, CO, 80045, USA
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