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Samanci Y, Askeroglu MO, Düzkalir AH, Peker S. Assessing the impact of distortion correction on Gamma Knife radiosurgery for multiple metastasis: Volumetric and dosimetric analysis. BRAIN & SPINE 2024; 4:102791. [PMID: 38584868 PMCID: PMC10995810 DOI: 10.1016/j.bas.2024.102791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 03/11/2024] [Accepted: 03/19/2024] [Indexed: 04/09/2024]
Abstract
Introduction Magnetic resonance imaging (MRI) is a robust neuroimaging technique and is the preferred method for stereotactic radiosurgery (SRS) planning. However, MRI data always contain distortions caused by hardware and patient factors. Research question Can these distortions potentially compromise the effectiveness and safety of SRS treatments? Material and methods Twenty-six MR datasets with multiple metastatic brain tumors (METs) used for Gamma Knife radiosurgery (GKRS) were retrospectively evaluated. A commercially available software was used for distortion correction. Geometrical agreement between corrected and uncorrected tumor volumes was evaluated using MacDonald criteria, Euclidian distance, and Dice similarity coefficient (DSC). SRS plans were generated using uncorrected tumor volumes, which were assessed to determine their coverage of the corrected tumor volumes. Results The median target volume was 0.38 cm3 (range,0.01-12.38 cm3). A maximum displacement of METs of up to 2.87 mm and a median displacement of 0.55 mm (range,0.1-2.87 mm) were noted. The median DSC between uncorrected and corrected MRI was 0.92, and the most concerning case had a DSC of 0.46. Although all plans met the optimization criterion of at least 98% of the uncorrected tumor volume (median 99.55%, range 98.1-100%) receiving at least 100% of the prescription dose, the percent of the corrected tumor volume receiving the total prescription dose was a median of 95.45% (range,23.1-99.5%). Discussion and conclusion MRI distortion, though visually subtle, has significant implications for SRS planning. Regular utilization of corrected MRI is recommended for SRS planning as distortion is sometimes enough to cause a volumetric miss of SRS targets.
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Affiliation(s)
- Yavuz Samanci
- Department of Neurosurgery, Koc University School of Medicine, Istanbul, Turkey
- Gamma Knife Center, Department of Neurosurgery, Koc University Hospital, Istanbul, Turkey
| | - M. Orbay Askeroglu
- Gamma Knife Center, Department of Neurosurgery, Koc University Hospital, Istanbul, Turkey
| | - Ali Haluk Düzkalir
- Gamma Knife Center, Department of Neurosurgery, Koc University Hospital, Istanbul, Turkey
- Department of Neurosurgery, Koc University Hospital, Istanbul, Turkey
| | - Selcuk Peker
- Department of Neurosurgery, Koc University School of Medicine, Istanbul, Turkey
- Gamma Knife Center, Department of Neurosurgery, Koc University Hospital, Istanbul, Turkey
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Grigo J, Szkitsak J, Höfler D, Fietkau R, Putz F, Bert C. "sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy. Radiat Oncol 2024; 19:33. [PMID: 38459584 PMCID: PMC10924348 DOI: 10.1186/s13014-024-02428-3] [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: 10/31/2023] [Accepted: 02/29/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data. METHODS A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery. DISCUSSION Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow. TRIAL REGISTRATION NCT06106997.
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Affiliation(s)
- Johanna Grigo
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Juliane Szkitsak
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Daniel Höfler
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany.
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
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Putz F, Bock M, Schmitt D, Bert C, Blanck O, Ruge MI, Hattingen E, Karger CP, Fietkau R, Grigo J, Schmidt MA, Bäuerle T, Wittig A. Quality requirements for MRI simulation in cranial stereotactic radiotherapy: a guideline from the German Taskforce "Imaging in Stereotactic Radiotherapy". Strahlenther Onkol 2024; 200:1-18. [PMID: 38163834 PMCID: PMC10784363 DOI: 10.1007/s00066-023-02183-6] [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: 09/01/2023] [Accepted: 11/06/2023] [Indexed: 01/03/2024]
Abstract
Accurate Magnetic Resonance Imaging (MRI) simulation is fundamental for high-precision stereotactic radiosurgery and fractionated stereotactic radiotherapy, collectively referred to as stereotactic radiotherapy (SRT), to deliver doses of high biological effectiveness to well-defined cranial targets. Multiple MRI hardware related factors as well as scanner configuration and sequence protocol parameters can affect the imaging accuracy and need to be optimized for the special purpose of radiotherapy treatment planning. MRI simulation for SRT is possible for different organizational environments including patient referral for imaging as well as dedicated MRI simulation in the radiotherapy department but require radiotherapy-optimized MRI protocols and defined quality standards to ensure geometrically accurate images that form an impeccable foundation for treatment planning. For this guideline, an interdisciplinary panel including experts from the working group for radiosurgery and stereotactic radiotherapy of the German Society for Radiation Oncology (DEGRO), the working group for physics and technology in stereotactic radiotherapy of the German Society for Medical Physics (DGMP), the German Society of Neurosurgery (DGNC), the German Society of Neuroradiology (DGNR) and the German Chapter of the International Society for Magnetic Resonance in Medicine (DS-ISMRM) have defined minimum MRI quality requirements as well as advanced MRI simulation options for cranial SRT.
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Affiliation(s)
- Florian Putz
- Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
| | - Michael Bock
- Klinik für Radiologie-Medizinphysik, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Daniela Schmitt
- Klinik für Strahlentherapie und Radioonkologie, Universitätsmedizin Göttingen, Göttingen, Germany
| | - Christoph Bert
- Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Oliver Blanck
- Klinik für Strahlentherapie, Universitätsklinikum Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Maximilian I Ruge
- Klinik für Stereotaxie und funktionelle Neurochirurgie, Zentrum für Neurochirurgie, Universitätsklinikum Köln, Cologne, Germany
| | - Elke Hattingen
- Institut für Neuroradiologie, Universitätsklinikum Frankfurt, Frankfurt am Main, Germany
| | - Christian P Karger
- Abteilung Medizinische Physik in der Strahlentherapie, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
- Nationales Zentrum für Strahlenforschung in der Onkologie (NCRO), Heidelberger Institut für Radioonkologie (HIRO), Heidelberg, Germany
| | - Rainer Fietkau
- Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Johanna Grigo
- Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Manuel A Schmidt
- Neuroradiologisches Institut, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Tobias Bäuerle
- Radiologisches Institut, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Andrea Wittig
- Klinik und Poliklinik für Strahlentherapie und Radioonkologie, Universitätsklinikum Würzburg, Würzburg, Germany
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Khaledi N, Khan R, Gräfe JL. Historical Progress of Stereotactic Radiation Surgery. J Med Phys 2023; 48:312-327. [PMID: 38223793 PMCID: PMC10783188 DOI: 10.4103/jmp.jmp_62_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/24/2023] [Accepted: 09/27/2023] [Indexed: 01/16/2024] Open
Abstract
Radiosurgery and stereotactic radiotherapy have established themselves as precise and accurate areas of radiation oncology for the treatment of brain and extracranial lesions. Along with the evolution of other methods of radiotherapy, this type of treatment has been associated with significant advances in terms of a variety of modalities and techniques to improve the accuracy and efficacy of treatment. This paper provides a comprehensive overview of the progress in stereotactic radiosurgery (SRS) over several decades, and includes a review of various articles and research papers, commencing with the emergence of stereotactic techniques in radiotherapy. Key clinical aspects of SRS, such as fixation methods, radiobiology considerations, quality assurance practices, and treatment planning strategies, are presented. In addition, the review highlights the technological advancements in treatment modalities, encompassing the transition from cobalt-based systems to linear accelerator-based modalities. By addressing these topics, this study aims to offer insights into the advancements that have shaped the field of SRS, that have ultimately enhanced the accuracy and effectiveness of treatment.
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Affiliation(s)
- Navid Khaledi
- Department of Medical Physics, Cancer Care Manitoba, Winnipeg, MB, Canada
| | - Rao Khan
- Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada
- Department of Physics and Astronomy and Department of Radiation Oncology, Howard University, Washington, District of Columbia, USA
| | - James L. Gräfe
- Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada
- Cancer Care Program, Dr. H. Bliss Murphy Cancer Center. 300 Prince Philip Drive St. John’s, NL, Canada
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5
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Kaushik SS, Bylund M, Cozzini C, Shanbhag D, Petit SF, Wyatt JJ, Menzel MI, Pirkl C, Mehta B, Chauhan V, Chandrasekharan K, Jonsson J, Nyholm T, Wiesinger F, Menze B. Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network. Phys Med Biol 2023; 68:195003. [PMID: 37567235 DOI: 10.1088/1361-6560/acefa3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 08/10/2023] [Indexed: 08/13/2023]
Abstract
Objective. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.Approach. We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.Main results. We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.Significance. We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.
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Affiliation(s)
- Sandeep S Kaushik
- GE Healthcare, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Mikael Bylund
- Department of Radiation Sciences, UmeåUniversity, Umea, Sweden
| | | | | | - Steven F Petit
- Department of Radiotherapy, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Jonathan J Wyatt
- Translational and Clinical Research Institute, Newcastle University and Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, United Kingdom
| | - Marion I Menzel
- GE Healthcare, Munich, Germany
- Dept. of Physics, Technical University of Munich, Munich, Germany
| | | | | | - Vikas Chauhan
- Sree Chitra Tirunal Institute of Medical Sciences and Technology (SCTIMST), Trivandrum, India
| | | | - Joakim Jonsson
- Department of Radiation Sciences, UmeåUniversity, Umea, Sweden
| | - Tufve Nyholm
- Department of Radiation Sciences, UmeåUniversity, Umea, Sweden
| | | | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
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6
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Zhou X, Cai W, Cai J, Xiao F, Qi M, Liu J, Zhou L, Li Y, Song T. Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation. Comput Biol Med 2023; 162:107054. [PMID: 37290389 DOI: 10.1016/j.compbiomed.2023.107054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/24/2023] [Accepted: 05/20/2023] [Indexed: 06/10/2023]
Abstract
Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting multimodality MRI data can provide sufficient information for accurate CT synthesis: however, obtaining the necessary number of MRI modalities is clinically expensive and time-consuming. In this study, we propose a multimodality MRI synchronous construction based deep learning framework from a single T1-weight (T1) image for MRIgRT synthetic CT (sCT) image generation. The network is mainly based on a generative adversarial network with sequential subtasks of intermediately generating synthetic MRIs and jointly generating the sCT image from the single T1 MRI. It contains a multitask generator and a multibranch discriminator, where the generator consists of a shared encoder and a splitted multibranch decoder. Specific attention modules are designed within the generator for feasible high-dimensional feature representation and fusion. Fifty patients with nasopharyngeal carcinoma who had undergone radiotherapy and had CT and sufficient MRI modalities scanned (5550 image slices for each modality) were used in the experiment. Results showed that our proposed network outperforms state-of-the-art sCT generation methods well with the least MAE, NRMSE, and comparable PSNR and SSIM index measure. Our proposed network exhibits comparable or even superior performance than the multimodality MRI-based generation method although it only takes a single T1 MRI image as input, thereby providing a more effective and economic solution for the laborious and high-cost generation of sCT images in clinical applications.
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Affiliation(s)
- Xuanru Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Wenwen Cai
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Jiajun Cai
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Fan Xiao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Mengke Qi
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Jiawen Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Yongbao Li
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
| | - Ting Song
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
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7
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Lane SA, Slater JM, Yang GY. Image-Guided Proton Therapy: A Comprehensive Review. Cancers (Basel) 2023; 15:cancers15092555. [PMID: 37174022 PMCID: PMC10177085 DOI: 10.3390/cancers15092555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Image guidance for radiation therapy can improve the accuracy of the delivery of radiation, leading to an improved therapeutic ratio. Proton radiation is able to deliver a highly conformal dose to a target due to its advantageous dosimetric properties, including the Bragg peak. Proton therapy established the standard for daily image guidance as a means of minimizing uncertainties associated with proton treatment. With the increasing adoption of the use of proton therapy over time, image guidance systems for this modality have been changing. The unique properties of proton radiation present a number of differences in image guidance from photon therapy. This paper describes CT and MRI-based simulation and methods of daily image guidance. Developments in dose-guided radiation, upright treatment, and FLASH RT are discussed as well.
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Affiliation(s)
- Shelby A Lane
- James M. Slater, MD Proton Treatment and Research Center, Loma Linda University, Loma Linda, CA 92354, USA
| | - Jason M Slater
- James M. Slater, MD Proton Treatment and Research Center, Loma Linda University, Loma Linda, CA 92354, USA
| | - Gary Y Yang
- James M. Slater, MD Proton Treatment and Research Center, Loma Linda University, Loma Linda, CA 92354, USA
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Masitho S, Szkitsak J, Grigo J, Fietkau R, Putz F, Bert C. Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: two-way dose validation and 2D/2D kV-image-based positioning. Phys Imaging Radiat Oncol 2022; 24:111-117. [DOI: 10.1016/j.phro.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/12/2022] [Accepted: 10/17/2022] [Indexed: 11/05/2022] Open
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Tang B, Liu M, Wang B, Diao P, Li J, Feng X, Wu F, Yao X, Liao X, Hou Q, Orlandini LC. Improving the clinical workflow of a MR-Linac by dosimetric evaluation of synthetic CT. Front Oncol 2022; 12:920443. [PMID: 36106119 PMCID: PMC9464932 DOI: 10.3389/fonc.2022.920443] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 08/04/2022] [Indexed: 11/13/2022] Open
Abstract
Adaptive radiotherapy performed on the daily magnetic resonance imaging (MRI) is an option to improve the treatment quality. In the adapt-to-shape workflow of 1.5-T MR-Linac, the contours of structures are adjusted on the basis of patient daily MRI, and the adapted plan is recalculated on the MRI-based synthetic computed tomography (syCT) generated by bulk density assignment. Because dosimetric accuracy of this strategy is a priority and requires evaluation, this study aims to explore the usefulness of adding an assessment of dosimetric errors associated with recalculation on syCT to the clinical workflow. Sixty-one patients, with various tumor sites, treated using a 1.5-T MR-Linac were included in this study. In Monaco V5.4, the target and organs at risk (OARs) were contoured, and a reference CT plan that contains information about the outlined contours, their average electron density (ED), and the priority of ED assignment was generated. To evaluate the dosimetric error of syCT caused by the inherent approximation within bulk density assignment, the reference CT plan was recalculated on the syCT obtained from the reference CT by forcing all contoured structures to their mean ED defined on the reference plan. The dose–volume histogram (DVH) and dose distribution of the CT and syCT plan were compared. The causes of dosimetric discrepancies were investigated, and the reference plan was reworked to minimize errors if needed. For 54 patients, gamma analysis of the dose distribution on syCT and CT show a median pass rate of 99.7% and 98.5% with the criteria of 3%/3 mm and 2%/2 mm, respectively. DVH difference of targets and OARs remained less than 1.5% or 1 Gy. For the remaining patients, factors (i.e., inappropriate ED assignments) influenced the dosimetric agreement of the syCT vs. CT reference DVH by up to 21%. The causes of the errors were promptly identified, and the DVH dosimetry was realigned except for two lung treatments for which a significant discrepancy remained. The recalculation on the syCT obtained from the planning CT is a powerful tool to assess and decrease the minimal error committed during the adaptive plan on the MRI-based syCT.
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Affiliation(s)
- Bin Tang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Research Institute, affiliated to University of Electronic Science and Technology of China (UESTC), Chengdu, China
- Key Laboratory of Radiation Physics and Technology of the Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, China
| | - Min Liu
- Department of Radiation Oncology, Sichuan Cancer Hospital and Research Institute, affiliated to University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Bingjie Wang
- Faculty of Arts and Science, University of Toronto, Toronto, ON, Canada
| | - Peng Diao
- Department of Radiation Oncology, Sichuan Cancer Hospital and Research Institute, affiliated to University of Electronic Science and Technology of China (UESTC), Chengdu, China
- *Correspondence: Peng Diao,
| | - Jie Li
- Department of Radiation Oncology, Sichuan Cancer Hospital and Research Institute, affiliated to University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Xi Feng
- Department of Radiation Oncology, Sichuan Cancer Hospital and Research Institute, affiliated to University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Fan Wu
- Department of Radiation Oncology, Sichuan Cancer Hospital and Research Institute, affiliated to University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Xinghong Yao
- Department of Radiation Oncology, Sichuan Cancer Hospital and Research Institute, affiliated to University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Xiongfei Liao
- Department of Radiation Oncology, Sichuan Cancer Hospital and Research Institute, affiliated to University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Qing Hou
- Key Laboratory of Radiation Physics and Technology of the Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, China
| | - Lucia Clara Orlandini
- Department of Radiation Oncology, Sichuan Cancer Hospital and Research Institute, affiliated to University of Electronic Science and Technology of China (UESTC), Chengdu, China
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10
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Abu-Qasmieh IF, Masad IS, Al-Quran HH, Alawneh KZ. Generation of Synthetic-Pseudo MR Images from Real CT Images. Tomography 2022; 8:1244-1259. [PMID: 35645389 PMCID: PMC9149978 DOI: 10.3390/tomography8030103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
This study aimed to generate synthetic MR images from real CT images. CT# mean and standard deviation of a moving window across every pixel in the reconstructed CT images were mapped to their corresponding tissue-mimicking types. Identification of the tissue enabled remapping it to its corresponding intrinsic parameters: T1, T2, and proton density (ρ). Lastly, synthetic weighted MR images of a selected slice were generated by simulating a spin-echo sequence using the intrinsic parameters and proper contrast parameters (TE and TR). Experiments were performed on a 3D multimodality abdominal phantom and on human knees at different TE and TR parameters to confirm the clinical effectiveness of the approach. Results demonstrated the validity of the approach of generating synthetic MR images at different weightings using only CT images and the three predefined mapping functions. The slope of the fitting line and percentage root-mean-square difference (PRD) between real and synthetic image vector representations were (0.73, 10%), (0.9, 18%), and (0.2, 8.7%) for T1-, T2-, and ρ-weighted images of the phantom, respectively. The slope and PRD for human knee images, on average, were 0.89% and 18.8%, respectively. The generated MR images provide valuable guidance for physicians with regard to deciding whether acquiring real MR images is crucial.
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Affiliation(s)
- Isam F. Abu-Qasmieh
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan; (I.F.A.-Q.); (H.H.A.-Q.)
| | - Ihssan S. Masad
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan; (I.F.A.-Q.); (H.H.A.-Q.)
- Correspondence:
| | - Hiam H. Al-Quran
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan; (I.F.A.-Q.); (H.H.A.-Q.)
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Khaled Z. Alawneh
- Faculty of Medicine, Jordan University of Science and Technology, King Abdullah University Hospital, Irbid 22110, Jordan;
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11
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Sreeja S, Muhammad Noorul Mubarak D. Pseudo computed tomography image generation from brain magnetic resonance image using integration of PCA & DCNN-UNET: A comparative analysis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
MRI-Only Radiation (RT) now avoids some of the issues associated with employing Computed Tomography(CT) in RT chains, such as MRI registration to a separate CT, excess dosage administration, and the cost of recurrent imaging. The fact that MRI signal intensities are unrelated to the biological tissue’s attenuation coefficient poses a problem. This raises workloads, creates uncertainty as a result of the required inter-modality image registrations, and exposes patients to needless radiation. While using only MRI would be preferable, a method for estimating a pseudo-CT (pCT)or synthetic-CT(sCT) for producing electron density maps and patient positioning reference images is required. As Deep Learning(DL) is revolutionized in so many fields these days, an effective and accurate model is required for generating pCT from MRI. So, this paper depicts an efficient DL model in which the following are the stages: a) Data Acquisition where CT and MRI images are collected b) preprocessing these to avoid the anomalies and noises using techniques like outlier elimination, data smoothening and data normalizing c) feature extraction and selection using Principal Component Analysis (PCA) & regression method d) generating pCT from MRI using Deep Convolutional Neural Network and UNET (DCNN-UNET). We here compare both feature extraction (PCA) and classification model (DCNN-UNET) with other methods such as Discrete Wavelet Tranform(DWT), Independent Component Analysis(ICA), Fourier Transform and VGG16, ResNet, AlexNet, DenseNet, CNN (Convolutional Neural Network)respectively. The performance measures used to evaluate these models are Dice Coefficient(DC), Structured Similarity Index Measure(SSIM), Mean Absolute Error(MAE), Mean Squared Error(MSE), Accuracy, Computation Time in which our proposed system outperforms better with 0.94±0.02 over other state-of-art models.
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Affiliation(s)
- S Sreeja
- Department of Computer Science, University of Kerala, Karyavattom Campus, Trivandrum, Kerala, India
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12
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Lerner M, Medin J, Jamtheim Gustafsson C, Alkner S, Olsson LE. Prospective Clinical Feasibility Study for MRI-Only Brain Radiotherapy. Front Oncol 2022; 11:812643. [PMID: 35083159 PMCID: PMC8784680 DOI: 10.3389/fonc.2021.812643] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/20/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES MRI-only radiotherapy (RT) provides a workflow to decrease the geometric uncertainty introduced by the image registration process between MRI and CT data and to streamline the RT planning. Despite the recent availability of validated synthetic CT (sCT) methods for the head region, there are no clinical implementations reported for brain tumors. Based on a preceding validation study of sCT, this study aims to investigate MRI-only brain RT through a prospective clinical feasibility study with endpoints for dosimetry and patient setup. MATERIAL AND METHODS Twenty-one glioma patients were included. MRI Dixon images were used to generate sCT images using a CE-marked deep learning-based software. RT treatment plans were generated based on MRI delineated anatomical structures and sCT for absorbed dose calculations. CT scans were acquired but strictly used for sCT quality assurance (QA). Prospective QA was performed prior to MRI-only treatment approval, comparing sCT and CT image characteristics and calculated dose distributions. Additional retrospective analysis of patient positioning and dose distribution gamma evaluation was performed. RESULTS Twenty out of 21 patients were treated using the MRI-only workflow. A single patient was excluded due to an MRI artifact caused by a hemostatic substance injected near the target during surgery preceding radiotherapy. All other patients fulfilled the acceptance criteria. Dose deviations in target were within ±1% for all patients in the prospective analysis. Retrospective analysis yielded gamma pass rates (2%, 2 mm) above 99%. Patient positioning using CBCT images was within ± 1 mm for registrations with sCT compared to CT. CONCLUSION We report a successful clinical study of MRI-only brain radiotherapy, conducted using both prospective and retrospective analysis. Synthetic CT images generated using the CE-marked deep learning-based software were clinically robust based on endpoints for dosimetry and patient positioning.
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Affiliation(s)
- Minna Lerner
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Joakim Medin
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Medical Radiation Physics, Clinical Sciences, Lund, Lund University, Lund, Sweden
| | - Christian Jamtheim Gustafsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Sara Alkner
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
| | - Lars E. Olsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
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13
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Petti PL, Rivard MJ, Alvarez PE, Bednarz G, Daniel Bourland J, DeWerd LA, Drzymala RE, Johansson J, Kunugi K, Ma L, Meltsner SG, Neyman G, Seuntjens JP, Shiu AS, Goetsch SJ. Recommendations on the practice of calibration, dosimetry, and quality assurance for gamma stereotactic radiosurgery: Report of AAPM Task Group 178. Med Phys 2021; 48:e733-e770. [DOI: 10.1002/mp.14831] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 02/21/2021] [Accepted: 02/24/2021] [Indexed: 12/25/2022] Open
Affiliation(s)
- Paula L. Petti
- Gamma Knife Center Washington Hospital Fremont CA 94538 USA
| | - Mark J. Rivard
- Department of Radiation Oncology Alpert Medical School of Brown University Providence RI 02903 USA
| | - Paola E. Alvarez
- Radiological Physics Center University of Texas MD Anderson Cancer Center Houston TX 77054 USA
| | - Greg Bednarz
- Department of Radiation Oncology University of Pittsburgh Medical Center Pittsburgh PA 15232 USA
| | - J. Daniel Bourland
- Department of Radiation Oncology Wake Forest University Winston‐Salem NC 27157 USA
| | - Larry A. DeWerd
- Accredited Dosimetry and Calibration Laboratory University of Wisconsin Madison WI 53705 USA
| | - Robert E. Drzymala
- Department of Radiation Oncology Washington University Saint Louis MO 63119 USA
| | | | - Keith Kunugi
- Accredited Dosimetry and Calibration Laboratory University of Wisconsin Madison WI 53705 USA
| | - Lijun Ma
- Department of Radiation Oncology University California–San Francisco San Francisco CA 94143 USA
| | - Sheridan G. Meltsner
- Department of Radiation Oncology Duke University Medical Center Durham NC 27713 USA
| | - Gennady Neyman
- Department of Radiation Oncology The Cleveland Clinic Cleveland OH 44195 USA
| | - Jan P. Seuntjens
- Department of Medical Physics McGill University Montreal QC H4A3J1 Canada
| | - Almon S. Shiu
- Department of Radiation Oncology University of Southern California Los Angeles CA 90033 USA
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14
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Yoo D, Choi YA, Rah CJ, Lee E, Cai J, Min BJ, Kim EH. Signal Enhancement of Low Magnetic Field Magnetic Resonance Image Using a Conventional- and Cyclic-Generative Adversarial Network Models With Unpaired Image Sets. Front Oncol 2021; 11:660284. [PMID: 34046353 PMCID: PMC8144640 DOI: 10.3389/fonc.2021.660284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/08/2021] [Indexed: 11/23/2022] Open
Abstract
In this study, the signal enhancement ratio of low-field magnetic resonance (MR) images was investigated using a deep learning-based algorithm. Unpaired image sets (0.06 Tesla and 1.5 Tesla MR images for different patients) were used in this study following three steps workflow. In the first step, the deformable registration of a 1.5 Tesla MR image into a 0.06 Tesla MR image was performed to ensure that the shapes of the unpaired set matched. In the second step, a cyclic-generative adversarial network (GAN) was used to generate a synthetic MR image of the original 0.06 Tesla MR image based on the deformed or original 1.5 Tesla MR image. Finally, an enhanced 0.06 Tesla MR image could be generated using the conventional-GAN with the deformed or synthetic MR image. The results from the optimized flow and enhanced MR images showed significant signal enhancement of the anatomical view, especially in the nasal septum, inferior nasal choncha, nasopharyngeal fossa, and eye lens. The signal enhancement ratio, signal-to-noise ratio (SNR) and correlation factor between the original and enhanced MR images were analyzed for the evaluation of the image quality. A combined method using conventional- and cyclic-GANs is a promising approach for generating enhanced MR images from low-magnetic-field MR.
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Affiliation(s)
- Denis Yoo
- Artificial Intelligence Research Lab, Talos, Sheung Wan, Hong Kong
| | | | - C J Rah
- Artificial Intelligence Research Lab, Talos, Sheung Wan, Hong Kong
| | - Eric Lee
- Artificial Intelligence Research Lab, Talos, Sheung Wan, Hong Kong
| | - Jing Cai
- Department of Health Technology & Informatics, Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Byung Jun Min
- Department of Radiation Oncology, Chungbuk National University Hospital, Cheongju, South Korea
| | - Eun Ho Kim
- Department of Biochemistry, School of Medicine, Daegu Catholic University, Daegu, South Korea
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15
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Touati R, Le WT, Kadoury S. A feature invariant generative adversarial network for head and neck MRI/CT image synthesis. Phys Med Biol 2021; 66. [PMID: 33761478 DOI: 10.1088/1361-6560/abf1bb] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 03/24/2021] [Indexed: 12/12/2022]
Abstract
With the emergence of online MRI radiotherapy treatments, MR-based workflows have increased in importance in the clinical workflow. However proper dose planning still requires CT images to calculate dose attenuation due to bony structures. In this paper, we present a novel deep image synthesis model that generates in an unsupervised manner CT images from diagnostic MRI for radiotherapy planning. The proposed model based on a generative adversarial network (GAN) consists of learning a new invariant representation to generate synthetic CT (sCT) images based on high frequency and appearance patterns. This new representation encodes each convolutional feature map of the convolutional GAN discriminator, leading the training of the proposed model to be particularly robust in terms of image synthesis quality. Our model includes an analysis of common histogram features in the training process, thus reinforcing the generator such that the output sCT image exhibits a histogram matching that of the ground-truth CT. This CT-matched histogram is embedded then in a multi-resolution framework by assessing the evaluation over all layers of the discriminator network, which then allows the model to robustly classify the output synthetic image. Experiments were conducted on head and neck images of 56 cancer patients with a wide range of shape sizes and spatial image resolutions. The obtained results confirm the efficiency of the proposed model compared to other generative models, where the mean absolute error yielded by our model was 26.44(0.62), with a Hounsfield unit error of 45.3(1.87), and an overall Dice coefficient of 0.74(0.05), demonstrating the potential of the synthesis model for radiotherapy planning applications.
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Affiliation(s)
- Redha Touati
- MedICAL Laboratory, Polytechnique Montreal, Montreal, QC, Canada
| | - William Trung Le
- MedICAL Laboratory, Polytechnique Montreal, Montreal, QC, Canada
| | - Samuel Kadoury
- MedICAL Laboratory, Polytechnique Montreal, Montreal, QC, Canada.,CHUM Research Center, Montreal, QC, Canada
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16
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McCallum HM, Andersson S, Wyatt JJ, Richmond N, Walker CP, Svensson S. Technical Note: Efficient and accurate MRI-only based treatment planning of the prostate using bulk density assignment through atlas-based segmentation. Med Phys 2020; 47:4758-4762. [PMID: 32682337 DOI: 10.1002/mp.14406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 06/05/2020] [Accepted: 07/02/2020] [Indexed: 01/20/2023] Open
Abstract
PURPOSE This study investigates the dosimetric accuracy as well as the robustness of a bulk density assignment approach to magnetic resonance imaging (MRI)-only based treatment planning of the prostate, with bulk density regions automatically identified using atlas-based segmentation (ABS). METHODS Twenty prostate radiotherapy patients received planning computed tomography (CT) and MRI scans and were treated with volumetric modulated arc therapy (VMAT). Two bulk densities were set, one for bone and one for soft tissue. The bone contours were created by using ABS followed by manual modification if considered necessary. A range of soft tissue and bone density pairs, between 0.95 and 1.03 g/cm3 with increments of 0.01 for soft tissue, and between 1.15 and 1.65 g/cm3 with increments of 0.05 for bone, were evaluated. Using the density pair giving the lowest dose difference compared to the CT-based dose, dose differences were calculated using both the manually modified bone contours and the bone contours from ABS. Contour overlap measurements between the ABS contours and the manually modified contours were calculated. RESULTS The dose comparison shows a very good agreement with the CT when using 0.98 g/cm3 for soft tissue and 1.20 g/cm3 for bone, with a dose difference less than 1 % in average dose in all regions of interest. The mean Dice similarity coefficient for bone was 0.94 and the Mean Distance to Agreement was <1 mm in most cases. CONCLUSIONS Using bulk density assignment on MR images with suitable densities for bone and soft tissue results in clinically acceptable dose differences compared to dose calculated on the CT, for both atlas-based and manual bone contours. This demonstrates that an integrated MRI-only pathway utilizing a bulk density assignment for two tissue types is a feasible and robust approach for patients with prostate cancer treated with VMAT.
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Affiliation(s)
- Hazel Mhairi McCallum
- Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, NE7 7DN, UK
| | | | - Jonathan James Wyatt
- Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, NE7 7DN, UK
| | - Neil Richmond
- Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, NE7 7DN, UK
| | - Christopher Paul Walker
- Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, NE7 7DN, UK
| | - Stina Svensson
- RaySearch Laboratories AB (PUBL), PO Box 3297, Stockholm, SE-103 65, Sweden
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17
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Yousefi Moteghaed N, Mostaar A, Maghooli K, Houshyari M, Ameri A. Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning. Rep Pract Oncol Radiother 2020; 25:738-745. [PMID: 32684863 DOI: 10.1016/j.rpor.2020.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 05/05/2020] [Accepted: 05/25/2020] [Indexed: 10/23/2022] Open
Abstract
Aim The aim of this study is to construct and evaluate Pseudo-CT images (P-CTs) for electron density calculation to facilitate external radiotherapy treatment planning. Background Despite numerous benefits, computed tomography (CT) scan does not provide accurate information on soft tissue contrast, which often makes it difficult to precisely differentiate target tissues from the organs at risk and determine the tumor volume. Therefore, MRI imaging can reduce the variability of results when registering with a CT scan. Materials and methods In this research, a fuzzy clustering algorithm was used to segment images into different tissues, also linear regression methods were used to design the regression model based on the feature extraction method and the brightness intensity values. The results of the proposed algorithm for dose-volume histogram (DVH), Isodose curves, and gamma analysis were investigated using the RayPlan treatment planning system, and VeriSoft software. Furthermore, various statistical indices such as Mean Absolute Error (MAE), Mean Error (ME), and Structural Similarity Index (SSIM) were calculated. Results The MAE of a range of 45-55 was found from the proposed methods. The relative difference error between the PTV region of the CT and the Pseudo-CT was 0.5, and the best gamma rate was 95.4% based on the polar coordinate feature and proposed polynomial regression model. Conclusion The proposed method could support the generation of P-CT data for different parts of the brain region from a collection of MRI series with an acceptable average error rate by different evaluation criteria.
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Affiliation(s)
- Niloofar Yousefi Moteghaed
- Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Mostaar
- Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
| | - Mohammad Houshyari
- Department of Radiation Oncology, Shohada-e-Tajrish Hospital, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Ameri
- Department of Radiation Oncology, Imam Hossein Hospital, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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18
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Tafreshi AR, Peng T, Yu C, Kramer DR, Gogia AS, Lee MB, Barbaro MF, Sebastian R, Del Campo-Vera RM, Chen KH, Kellis SS, Lee B. A Phantom Study of the Spatial Precision and Accuracy of Stereotactic Localization Using Computed Tomography Imaging with the Leksell Stereotactic System. World Neurosurg 2020; 139:e297-e307. [DOI: 10.1016/j.wneu.2020.03.204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/27/2020] [Accepted: 03/29/2020] [Indexed: 11/17/2022]
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19
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Magnetic resonance imaging for brain stereotactic radiotherapy : A review of requirements and pitfalls. Strahlenther Onkol 2020; 196:444-456. [PMID: 32206842 PMCID: PMC7182639 DOI: 10.1007/s00066-020-01604-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 03/03/2020] [Indexed: 12/29/2022]
Abstract
Due to its superior soft tissue contrast, magnetic resonance imaging (MRI) is essential for many radiotherapy treatment indications. This is especially true for treatment planning in intracranial tumors, where MRI has a long-standing history for target delineation in clinical practice. Despite its routine use, care has to be taken when selecting and acquiring MRI studies for the purpose of radiotherapy treatment planning. Requirements on MRI are particularly demanding for intracranial stereotactic radiotherapy, where accurate imaging has a critical role in treatment success. However, MR images acquired for routine radiological assessment are frequently unsuitable for high-precision stereotactic radiotherapy as the requirements for imaging are significantly different for radiotherapy planning and diagnostic radiology. To assure that optimal imaging is used for treatment planning, the radiation oncologist needs proper knowledge of the most important requirements concerning the use of MRI in brain stereotactic radiotherapy. In the present review, we summarize and discuss the most relevant issues when using MR images for target volume delineation in intracranial stereotactic radiotherapy.
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20
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Kim MJ, Lee SR, Song KH, Baek HM, Choe BY, Suh TS. Development of a hybrid magnetic resonance/computed tomography-compatible phantom for magnetic resonance guided radiotherapy. JOURNAL OF RADIATION RESEARCH 2020; 61:314-324. [PMID: 32030420 PMCID: PMC7246062 DOI: 10.1093/jrr/rrz094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 05/12/2019] [Accepted: 12/10/2019] [Indexed: 06/10/2023]
Abstract
The purpose of the present study was to develop a hybrid magnetic resonance/computed tomography (MR/CT)-compatible phantom and tissue-equivalent materials for each MR and CT image. Therefore, the essential requirements necessary for the development of a hybrid MR/CT-compatible phantom were determined and the development process is described. A total of 12 different tissue-equivalent materials for each MR and CT image were developed from chemical components. The uniformity of each sample was calculated. The developed phantom was designed to use 14 plugs that contained various tissue-equivalent materials. Measurement using the developed phantom was performed using a 3.0-T scanner with 32 channels and a Somatom Sensation 64. The maximum percentage difference of the signal intensity (SI) value on MR images after adding K2CO3 was 3.31%. Additionally, the uniformity of each tissue was evaluated by calculating the percent image uniformity (%PIU) of the MR image, which was 82.18 ±1.87% with 83% acceptance, and the average circular-shaped regions of interest (ROIs) on CT images for all samples were within ±5 Hounsfield units (HU). Also, dosimetric evaluation was performed. The percentage differences of each tissue-equivalent sample for average dose ranged from -0.76 to 0.21%. A hybrid MR/CT-compatible phantom for MR and CT was investigated as the first trial in this field of radiation oncology and medical physics.
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Affiliation(s)
- Min-Joo Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, 120-752, Korea
- Department of Biomedical Engineering, Research Institute of Biomedical Engineering, The Catholic University of Korea College of Medicine, Seoul, 137-701, Korea
| | - Seu-Ran Lee
- Department of Biomedical Engineering, Research Institute of Biomedical Engineering, The Catholic University of Korea College of Medicine, Seoul, 137-701, Korea
| | - Kyu-Ho Song
- Department of Radiology, Washington University, Saint Louis, Missouri, 63130, United States
| | - Hyeon-Man Baek
- Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, Korea
| | - Bo-Young Choe
- Department of Biomedical Engineering, Research Institute of Biomedical Engineering, The Catholic University of Korea College of Medicine, Seoul, 137-701, Korea
| | - Tae Suk Suh
- Department of Biomedical Engineering, Research Institute of Biomedical Engineering, The Catholic University of Korea College of Medicine, Seoul, 137-701, Korea
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21
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Hsu SH, DuPre P, Peng Q, Tomé WA. A technique to generate synthetic CT from MRI for abdominal radiotherapy. J Appl Clin Med Phys 2020; 21:136-143. [PMID: 32043812 PMCID: PMC7020981 DOI: 10.1002/acm2.12816] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 11/20/2019] [Accepted: 12/12/2019] [Indexed: 12/22/2022] Open
Abstract
Purpose To investigate a method to classify tissues types for synthetic CT generation using MRI for treatment planning in abdominal radiotherapy. Methods An institutional review board approved volunteer study was performed on a 3T MRI scanner. In‐phase, fat and water images were acquired for five volunteers with breath‐hold using an mDixon pulse sequence. A method to classify different tissue types for synthetic CT generation in the abdomen was developed. Three tissue clusters (fat, high‐density tissue, and spine/air/lungs) were generated using a fuzzy‐c means clustering algorithm. The third cluster was further segmented into three sub‐clusters that represented spine, air, and lungs. Therefore, five segments were automatically generated. To evaluate segmentation accuracy using the method, the five segments were manually contoured on MRI images as the ground truth, and the volume ratio, Dice coefficient, and Hausdorff distance metric were calculated. The dosimetric effect of segmentation accuracy was evaluated on simulated targets close to air, lungs, and spine using a two‐arc volumetric modulated arc therapy (VMAT) technique. Results The volume ratio of auto‐segmentation to manual segmentation was 0.88–2.1 for the air segment and 0.72–1.13 for the remaining segments. The range of the Dice coefficient was 0.24–0.83, 0.84–0.93, 0.94–0.98, 0.93–0.96, and 0.76–0.79 for air, fat, lungs, high‐density tissue, and spine, respectively. The range of the mean Hausdorff distance was 3–29.1 mm, 0.5–1.3 mm, 0.4–1 mm, 0.7–1.6 mm, and 1.2–1.4 mm for air, fat, lungs, high‐density tissue, and spine, respectively. Despite worse segmentation accuracy in air and spine, the dosimetric effect was 0.2% ± 0.2%, with a maximum difference of 0.8% for all target locations. Conclusion A method to generate synthetic CT in the abdomen was developed, and segmentation accuracy and its dosimetric effect were evaluated. Our results demonstrate the potential of using MRI alone for treatment planning in the abdomen.
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Affiliation(s)
- Shu-Hui Hsu
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY, USA.,Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Pamela DuPre
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY, USA
| | - Qi Peng
- Gruss Magnetic Resonance Research Center, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Radiology, Montefiore Medical Center, Bronx, NY, USA
| | - Wolfgang A Tomé
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY, USA.,Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY, USA
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22
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Ranta I, Kemppainen R, Keyriläinen J, Suilamo S, Heikkinen S, Kapanen M, Saunavaara J. Quality assurance measurements of geometric accuracy for magnetic resonance imaging-based radiotherapy treatment planning. Phys Med 2019; 62:47-52. [PMID: 31153398 DOI: 10.1016/j.ejmp.2019.04.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 03/29/2019] [Accepted: 04/24/2019] [Indexed: 10/26/2022] Open
Abstract
BACKGROUND Using magnetic resonance imaging (MRI) as the only imaging method for radiotherapy treatment planning (RTP) is becoming more common as MRI-only RTP solutions have evolved. The geometric accuracy of MR images is an essential factor of image quality when determining the suitability of MRI for RTP. The need is therefore clear for clinically feasible quality assurance (QA) methods for the geometric accuracy measurement. MATERIALS AND METHODS This work evaluates long-term stability of geometric accuracy and the validity of a 2D geometric accuracy QA method compared to a prototype 3D method and analysis software in routine QA. The long-term follow-up measurements were conducted on one of the 1.5 T scanners over a period of 19 months using both methods. Inter-scanner variability of geometric distortions was also evaluated in three 1.5 T and three 3 T MRI scanners from a single vendor by using the prototype 3D QA method. RESULTS The geometric accuracy of the magnetic resonance for radiotherapy (MR-RT) platform remained stable within 2 mm at distances of <250 mm from isocenter. All scanners achieved good geometric accuracy with mean geometric distortions of <1 mm at <150 mm and <2 mm at <250 mm from the isocenter. Both measurement methods provided relevant information about geometric distortions. CONCLUSIONS Geometric distortions are often considered a limitation of MRI-only RTP. Results indicate that geometric accuracy of modern scanners remain within acceptable limits by default even after many years of clinical use based on the 3D QA evaluation.
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Affiliation(s)
- Iiro Ranta
- Department of Physics and Astronomy, University of Turku, Vesilinnantie 5, FI-20014 Turku, Finland; Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland; Department of Oncology and Radiotherapy, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland.
| | | | - Jani Keyriläinen
- Department of Physics and Astronomy, University of Turku, Vesilinnantie 5, FI-20014 Turku, Finland; Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland; Department of Oncology and Radiotherapy, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland
| | - Sami Suilamo
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland; Department of Oncology and Radiotherapy, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland
| | - Samuli Heikkinen
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland
| | - Mika Kapanen
- Department of Medical Physics, Medical Imaging Center, Tampere University Hospital, P.O. Box 2000, FI-33521 Tampere, Finland; Department of Oncology, Unit of Radiotherapy, Tampere University Hospital, P.O. Box 2000, FI-33521 Tampere, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland
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Fatemi A, Kanakamedala MR, Yang CC, Morris B, Duggar WN, Vijayakumar S. Evaluation of the Geometric and Dosimetric Accuracy of Synthetic Computed Tomography Images for Magnetic Resonance Imaging-only Stereotactic Radiosurgery. Cureus 2019; 11:e4404. [PMID: 31245194 PMCID: PMC6559689 DOI: 10.7759/cureus.4404] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Introduction Stereotactic radiosurgery (SRS) plans created using synthetic computed tomography (CT) images derived from magnetic resonance imaging (MRI) data may offer the advantage of inhomogeneity correction by convolution algorithms, as is done for CT-based plans. We sought to determine and validate the clinical significance and accuracy of synthetic CT images for inhomogeneity correction in MRI-only stereotactic radiosurgery plans for treatment of brain tumors. Methods In this retrospective study, data from two patients with brain metastases and one with meningioma who underwent imaging with multiple modalities and received frameless SRS treatment were analyzed. The SRS plans were generated using a convolution algorithm to account for brain inhomogeneity using CT and synthetic CT images and compared with the original clinical TMR10 plans created using MRI images. Results Synthetic CT-derived SRS plans are comparable with CT-based plans using convolution algorithm, and for some targets, based on location, they provided better coverage and a lower maximum dose. Conclusions The results suggest similar dose delivery results for CT and synthetic CT-based treatment plans. Synthetic CT plans offered a noticeable improvement in target dose coverage and a more gradual dose fall-off relative to TMR10 MRI-based plans. The major disadvantage is a slightly increased dose (by 0.37%) to nearby healthy tissue (brainstem) for synthetic CT-based plans relative to those created using clinical MRI images, which may be a problem for patients undergoing high-dose treatment.
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Affiliation(s)
- Ali Fatemi
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | | | - Claus Chunli Yang
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | - Bart Morris
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | - William N Duggar
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
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Guerreiro F, Koivula L, Seravalli E, Janssens GO, Maduro JH, Brouwer CL, Korevaar EW, Knopf AC, Korhonen J, Raaymakers BW. Feasibility of MRI-only photon and proton dose calculations for pediatric patients with abdominal tumors. Phys Med Biol 2019; 64:055010. [PMID: 30669135 DOI: 10.1088/1361-6560/ab0095] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The purpose of this study was to develop a method enabling synthetic computed tomography (sCT) generation of the whole abdomen using magnetic resonance imaging (MRI) scans of pediatric patients with abdominal tumors. The proposed method relies on an automatic atlas-based segmentation of bone and lungs followed by an MRI intensity to synthetic Hounsfield unit conversion. Separate conversion algorithms were used for bone, lungs and soft-tissue. Rigidly registered CT and T2-weighted MR images of 30 patients in treatment position and with the same field of view were used for the evaluation of the atlas and the conversion algorithms. The dose calculation accuracy of the generated sCTs was verified for volumetric modulated arc therapy (VMAT) and pencil beam scanning (PBS). VMAT and PBS plans were robust optimized on an internal target volume (ITV) against a patient set-up uncertainty of 5 mm. Average differences between CT and sCT dose calculations for the ITV V 95% were 0.5% (min 0.0%; max 5.0%) and 0.0% (min -0.1%; max 0.1%) for VMAT and PBS dose distributions, respectively. Average differences for the mean dose to the organs at risk were <0.2% (min -0.6%; max 1.2%) and <0.2% (min -2.0%; max 2.6%) for VMAT and PBS dose distributions, respectively. Results show that MRI-only photon and proton dose calculations are feasible for children with abdominal tumors.
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Affiliation(s)
- Filipa Guerreiro
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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Paštyková V, Novotný J, Veselský T, Urgošík D, Liščák R, Vymazal J. Assessment of MR stereotactic imaging and image co-registration accuracy for 3 different MR scanners by 3 different methods/phantoms: phantom and patient study. J Neurosurg 2018; 129:125-132. [DOI: 10.3171/2018.7.gks181527] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/31/2018] [Indexed: 11/06/2022]
Abstract
OBJECTIVEThe aim of this study was to compare 3 different methods to assess the geometrical distortion of two 1.5-T and one 3-T magnetic resonance (MR) scanners and to evaluate co-registration accuracy. The overall uncertainty of each particular method was also evaluated.METHODSThree different MR phantoms were used: 2 commercial CIRS skull phantoms and PTGR known target phantom and 1 custom cylindrical Perspex phantom made in-house. All phantoms were fixed in the Leksell stereotactic frame and examined by a Siemens Somatom CT unit, two 1.5-T Siemens (Avanto and Symphony) MRI systems, and one 3-T Siemens (Skyra) MRI system. The images were evaluated using Leksell GammaPlan software, and geometrical deviation of the selected points from the reference values were determined. The deviations were further investigated for both definitions including fiducial-based and co-registration–based in the case of the CIRS phantom images. The same co-registration accuracy assessment was also performed for a clinical case. Patient stereotactic imaging was done on 3-T Skyra, 1.5-T Avanto, and CT scanners.RESULTSThe accuracy of the CT scanner was determined as 0.10, 0.30, and 0.30 mm for X, Y, and Z coordinates, respectively. The total estimated uncertainty in distortion measurement in one coordinate was determined to be 0.32 mm and 0.14 mm, respectively, for methods using and not using CT as reference imaging. Slightly more significant distortions were observed when using the 3-T than either 1.5-T MR units. However, all scanners were comparable within the estimated measurement error. Observed deviation/distortion for individual X, Y, and Z stereotactic coordinates was typically within 0.50 mm for all 3 scanners and all 3 measurement methods employed. The total radial deviation/distortion was typically within 1.00 mm. Maximum total radial distortion was observed when the CIRS phantom was used; 1.08 ± 0.49 mm, 1.15 ± 0.48 mm, and 1.35 ± 0.49 mm for Symphony, Avanto, and Skyra, respectively. The co-registration process improved image stereotactic definition in a clinical case in which fiducial-based stereotactic definition was not accurate; this was demonstrated for 3-T stereotactic imaging in this study. The best results were shown for 3-T MR image co-registration with CT images improving image stereotactic definition by about 0.50 mm. The results obtained with patient data provided a similar trend of improvement in stereotactic definition by co-registration.CONCLUSIONSAll 3 methods/phantoms used were evaluated as satisfactory for the image distortion measurement. The method using the PTGR phantom had the lowest uncertainty as no reference CT imaging was needed. Image co-registration can improve stereotactic image definition when fiducial-based definition is not accurate.
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Affiliation(s)
| | | | | | | | | | - Josef Vymazal
- 3Radiodiagnostics, Na Homolce Hospital, Prague, Czech Republic
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Advanced Multimodal Methods for Cranial Pseudo-CT Generation Validated by IMRT and VMAT Radiation Therapy Plans. Int J Radiat Oncol Biol Phys 2018; 102:792-800. [DOI: 10.1016/j.ijrobp.2018.06.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 06/05/2018] [Accepted: 06/14/2018] [Indexed: 12/27/2022]
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Largent A, Barateau A, Nunes JC, Lafond C, Greer PB, Dowling JA, Saint-Jalmes H, Acosta O, de Crevoisier R. Pseudo-CT Generation for MRI-Only Radiation Therapy Treatment Planning: Comparison Among Patch-Based, Atlas-Based, and Bulk Density Methods. Int J Radiat Oncol Biol Phys 2018; 103:479-490. [PMID: 30336265 DOI: 10.1016/j.ijrobp.2018.10.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 08/15/2018] [Accepted: 10/01/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE Methods have been recently developed to generate pseudo-computed tomography (pCT) for dose calculation in magnetic resonance imaging (MRI)-only radiation therapy. This study aimed to propose an original nonlocal mean patch-based method (PBM) and to compare this PBM to an atlas-based method (ABM) and to a bulk density method (BDM) for prostate MRI-only radiation therapy. MATERIALS AND METHODS Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer. In addition to the planning computed tomography (CT) scans, T2-weighted MRI scans were acquired. pCTs were generated from MRIs using 3 methods: an original nonlocal mean PBM, ABM, and BDM. The PBM was performed using feature extraction and approximate nearest neighbor search in a training cohort. The PBM accuracy was evaluated in a validation cohort by using imaging and dosimetric endpoints. Imaging endpoints included mean absolute error and mean error between Hounsfield units of the pCT and the reference CT (CTref). Dosimetric endpoints were based on dose-volume histograms calculated from the CTref and the pCTs for various volumes of interest and on 3-dimensional gamma analyses. The PBM uncertainties were compared with those of the ABM and BDM. RESULTS The mean absolute error and mean error obtained from the PBM were 41.1 and -1.1 Hounsfield units. The PBM dose-volume histogram differences were 0.7% for prostate planning target volume V95%, 0.5% for rectum V70Gy, and 0.2% for bladder V50Gy. Compared with ABM and BDM, PBM provided significantly lower dose uncertainties for the prostate planning target volume (70-78 Gy), the rectum (8.5-29 Gy, 40-48 Gy, and 61-73 Gy), and the bladder (12-78 Gy). The PBM mean gamma pass rate (99.5%) was significantly higher than that of ABM (94.9%) or BDM (96.1%). CONCLUSIONS The proposed PBM provides low uncertainties with dose planned on CTref. These uncertainties were smaller than those of ABM and BDM and are unlikely to be clinically significant.
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Affiliation(s)
- Axel Largent
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
| | - Anaïs Barateau
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Jean-Claude Nunes
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Caroline Lafond
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Peter B Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, Australia
| | - Jason A Dowling
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia
| | - Hervé Saint-Jalmes
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Oscar Acosta
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Renaud de Crevoisier
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
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Magnetic resonance imaging in radiotherapy treatment target volumes definition for brain tumours: a systematic review and meta-analysis. JOURNAL OF RADIOTHERAPY IN PRACTICE 2018. [DOI: 10.1017/s1460396917000693] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
AbstractPurposeThe aim of this study is to establish clinical evidence regarding the use of magnetic resonance imaging (MRI) in target volume definition for radiotherapy treatment planning of brain tumours.MethodsPrimary studies were systematically retrieved from six electronic databases and other sources. Studies included were only those that quantitatively compared computed tomography (CT) and MRI in target volume definition for radiotherapy of brain tumours. Study characteristics and quality were assessed and the data were extracted from eligible studies. Effect estimates for each study was computed as mean percentage difference based on individual patient data where available. The included studies were then combined in meta-analysis using Review Manager (RevMan) software version 5.0.ResultFive studies with a total number of 72 patients were included in this review. The quality of the studies was rated strong. The percentages mean differences of the studies were 7·47, 11·36, 30·70, 41·69 and −24·6% using CT as the baseline. The result of statistical analysis showed small-to-moderate heterogeneity; τ2=36·8; χ2=6·23; df=4 (p=0·18); I2=36%. The overall effect estimate was −1·85 [95% confidence interval (CI); −7·24, 10·94], Z=0·40 (p=0·069>0·5).ConclusionBrain tumour volumes measured using MRI-based method for radiotherapy treatment planning were larger compared with CT defined volumes but the difference lacks statistical significance.
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Hsu SH, Zawisza I, O’Grady K, Peng Q, Tomé WA. Towards abdominal MRI-based treatment planning using population-based Hounsfield units for bulk density assignment. ACTA ACUST UNITED AC 2018; 63:155003. [DOI: 10.1088/1361-6560/aacfb1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Dinkla AM, Wolterink JM, Maspero M, Savenije MHF, Verhoeff JJC, Seravalli E, Išgum I, Seevinck PR, van den Berg CAT. MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network. Int J Radiat Oncol Biol Phys 2018; 102:801-812. [PMID: 30108005 DOI: 10.1016/j.ijrobp.2018.05.058] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 05/15/2018] [Accepted: 05/22/2018] [Indexed: 01/01/2023]
Abstract
PURPOSE This work aims to facilitate a fast magnetic resonance (MR)-only workflow for radiation therapy of intracranial tumors. Here, we evaluate whether synthetic computed tomography (sCT) images generated with a dilated convolutional neural network (CNN) enable accurate MR-based dose calculations in the brain. METHODS AND MATERIALS We conducted a retrospective study of 52 patients with brain tumors who underwent both computed tomography (CT) and MR imaging for radiation therapy treatment planning. To generate the sCTs, a T1-weighted gradient echo MR sequence was selected from the clinical protocol for multiple types of brain tumors. sCTs were created for all 52 patients with a dilated CNN using 2-fold cross validation; in each fold, 26 patients were used for training and the remaining 26 patients were used for evaluation. For each patient, the clinical CT-based treatment plan was recalculated on sCT. We calculated dose differences and gamma pass rates between CT- and sCT-based plans inside body and planning target volume. Geometric fidelity of the sCT and differences in beam depth and equivalent path length were assessed between both treatment plans. RESULTS sCT generation took 1 minute per patient. Over the patient population, the mean absolute error of the sCT within the intersection of body contours was 67 ± 11 HU (±1 standard deviation [SD], range: 51-117 HU), and the mean error was 13 ± 9 HU (±1 SD, range: -2 to 38 HU). Dosimetric analysis showed mean deviations of 0.00% ± 0.02% (±1 SD, range: -0.05 to 0.03) for dose within the body contours and -0.13% ± 0.39% (±1 SD, range: -1.43 to 0.80) inside the planning target volume. Mean γ1mm/1% was 98.8% ± 2.2% for doses >50% of the prescribed dose. CONCLUSIONS The presented dilated CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate and can therefore be used for MR-only intracranial radiation therapy treatment planning.
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Affiliation(s)
- Anna M Dinkla
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Jelmer M Wolterink
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Matteo Maspero
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mark H F Savenije
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joost J C Verhoeff
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Enrica Seravalli
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter R Seevinck
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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Kim SW, Shin HJ, Hwang JH, Shin JS, Park SK, Kim JY, Kim KJ, Kay CS, Kang YN. Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment. PLoS One 2017; 12:e0185082. [PMID: 28926610 PMCID: PMC5605009 DOI: 10.1371/journal.pone.0185082] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 09/06/2017] [Indexed: 11/30/2022] Open
Abstract
Objective Various methods for radiation-dose calculation have been investigated over previous decades, focusing on the use of magnetic resonance imaging (MRI) only. The bulk-density-assignment method based on manual segmentation has exhibited promising results compared to dose-calculation with computed tomography (CT). However, this method cannot be easily implemented in clinical practice due to its time-consuming nature. Therefore, we investigated an automatic anatomy segmentation method with the intention of providing the proper methodology to evaluate synthetic CT images for a radiation-dose calculation based on MR images. Methods CT images of 20 brain cancer patients were selected, and their MR images including T1-weighted, T2-weighted, and PETRA were retrospectively collected. Eight anatomies of the patients, such as the body, air, eyeball, lens, cavity, ventricle, brainstem, and bone, were segmented for bulk-density-assigned CT image (BCT) generation. In addition, water-equivalent CT images (WCT) with only two anatomies—body and air—were generated for a comparison with BCT. Histogram comparison and gamma analysis were performed by comparison with the original CT images, after the evaluation of automatic segmentation performance with the dice similarity coefficient (DSC), false negative dice (FND) coefficient, and false positive dice (FPD) coefficient. Results The highest DSC value was 99.34 for air segmentation, and the lowest DSC value was 73.50 for bone segmentation. For lens segmentation, relatively high FND and FPD values were measured. The cavity and bone were measured as over-segmented anatomies having higher FPD values than FND. The measured histogram comparison results of BCT were better than those of WCT in all cases. In gamma analysis, the averaged improvement of BCT compared to WCT was measured. All the measured results of BCT were better than those of WCT. Therefore, the results of this study show that the introduced methods, such as histogram comparison and gamma analysis, are valid for the evaluation of the synthetic CT generation from MR images. Conclusions The image similarity results showed that BCT has superior results compared to WCT for all measurements performed in this study. Consequently, more accurate radiation treatment for the intracranial regions can be expected when the proper image similarity evaluation introduced in this study is performed.
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Affiliation(s)
- Shin-Wook Kim
- Department of Radiation Oncology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hun-Joo Shin
- Department of Radiation Oncology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jin-Ho Hwang
- Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jin-Sol Shin
- Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sung-Kwang Park
- Department of Radiation Oncology, Busan Paik Hospital, Inje University, Busan, Korea
| | - Jin-Young Kim
- Department of Radiation Oncology, Haeundae Paik Hospital, Inje University, Busan, Korea
| | - Ki-Jun Kim
- Department of Radiology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Chul-Seung Kay
- Department of Radiation Oncology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Young-Nam Kang
- Department of Radiation Oncology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- * E-mail:
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Johnstone E, Wyatt JJ, Henry AM, Short SC, Sebag-Montefiore D, Murray L, Kelly CG, McCallum HM, Speight R. Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imaging-Only Radiation Therapy. Int J Radiat Oncol Biol Phys 2017; 100:199-217. [PMID: 29254773 DOI: 10.1016/j.ijrobp.2017.08.043] [Citation(s) in RCA: 207] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 07/07/2017] [Accepted: 08/30/2017] [Indexed: 10/18/2022]
Abstract
Magnetic resonance imaging (MRI) offers superior soft-tissue contrast as compared with computed tomography (CT), which is conventionally used for radiation therapy treatment planning (RTP) and patient positioning verification, resulting in improved target definition. The 2 modalities are co-registered for RTP; however, this introduces a systematic error. Implementing an MRI-only radiation therapy workflow would be advantageous because this error would be eliminated, the patient pathway simplified, and patient dose reduced. Unlike CT, in MRI there is no direct relationship between signal intensity and electron density; however, various methodologies for MRI-only RTP have been reported. A systematic review of these methods was undertaken. The PRISMA guidelines were followed. Embase and Medline databases were searched (1996 to March, 2017) for studies that generated synthetic CT scans (sCT)s for MRI-only radiation therapy. Sixty-one articles met the inclusion criteria. This review showed that MRI-only RTP techniques could be grouped into 3 categories: (1) bulk density override; (2) atlas-based; and (3) voxel-based techniques, which all produce an sCT scan from MR images. Bulk density override techniques either used a single homogeneous or multiple tissue override. The former produced large dosimetric errors (>2%) in some cases and the latter frequently required manual bone contouring. Atlas-based techniques used both single and multiple atlases and included methods incorporating pattern recognition techniques. Clinically acceptable sCTs were reported, but atypical anatomy led to erroneous results in some cases. Voxel-based techniques included methods using routine and specialized MRI sequences, namely ultra-short echo time imaging. High-quality sCTs were produced; however, use of multiple sequences led to long scanning times increasing the chances of patient movement. Using nonroutine sequences would currently be problematic in most radiation therapy centers. Atlas-based and voxel-based techniques were found to be the most clinically useful methods, with some studies reporting dosimetric differences of <1% between planning on the sCT and CT and <1-mm deviations when using sCTs for positional verification.
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Affiliation(s)
- Emily Johnstone
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom.
| | - Jonathan J Wyatt
- The Northern Centre for Cancer Care, The Newcastle-upon-Tyne NHS Foundation Trust, Newcastle-upon-Tyne, United Kingdom
| | - Ann M Henry
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom; Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Susan C Short
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom; Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - David Sebag-Montefiore
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom; Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Louise Murray
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom; Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Charles G Kelly
- The Northern Centre for Cancer Care, The Newcastle-upon-Tyne NHS Foundation Trust, Newcastle-upon-Tyne, United Kingdom
| | - Hazel M McCallum
- The Northern Centre for Cancer Care, The Newcastle-upon-Tyne NHS Foundation Trust, Newcastle-upon-Tyne, United Kingdom
| | - Richard Speight
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
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Wang H, Chandarana H, Block KT, Vahle T, Fenchel M, Das IJ. Dosimetric evaluation of synthetic CT for magnetic resonance-only based radiotherapy planning of lung cancer. Radiat Oncol 2017. [PMID: 28651599 PMCID: PMC5485621 DOI: 10.1186/s13014-017-0845-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Background Interest in MR-only treatment planning for radiation therapy is growing rapidly with the emergence of integrated MRI/linear accelerator technology. The purpose of this study was to evaluate the feasibility of using synthetic CT images generated from conventional Dixon-based MRI scans for radiation treatment planning of lung cancer. Methods Eleven patients who underwent whole-body PET/MR imaging following a PET/CT exam were randomly selected from an ongoing prospective IRB-approved study. Attenuation maps derived from the Dixon MR Images and atlas-based method was used to create CT data (synCT). Treatment planning for radiation treatment of lung cancer was optimized on the synCT and subsequently copied to the registered CT (planCT) for dose calculation. Planning target volumes (PTVs) with three sizes and four different locations in the lung were planned for irradiation. The dose-volume metrics comparison and 3D gamma analysis were performed to assess agreement between the synCT and CT calculated dose distributions. Results Mean differences between PTV doses on synCT and CT across all the plans were −0.1% ± 0.4%, 0.1% ± 0.5%, and 0.4% ± 0.5% for D95, D98 and D100, respectively. Difference in dose between the two datasets for organs at risk (OARs) had average differences of −0.14 ± 0.07 Gy, 0.0% ± 0.1%, and −0.1% ± 0.2% for maximum spinal cord, lung V20, and heart V40 respectively. In patient groups based on tumor size and location, no significant differences were observed in the PTV and OARs dose-volume metrics (p > 0.05), except for the maximum spinal-cord dose when the target volumes were located at the lung apex (p = 0.001). Gamma analysis revealed a pass rate of 99.3% ± 1.1% for 2%/2 mm (dose difference/distance to agreement) acceptance criteria in every plan. Conclusions The synCT generated from Dixon-based MRI allows for dose calculation of comparable accuracy to the standard CT for lung cancer treatment planning. The dosimetric agreement between synCT and CT calculated doses warrants further development of a MR-only workflow for radiotherapy of lung cancer.
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Affiliation(s)
- Hesheng Wang
- Department of Radiation Oncology, New York University School of Medicine, Langone Medical Center, New York, NY, USA.
| | - Hersh Chandarana
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Kai Tobias Block
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Thomas Vahle
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.,Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Indra J Das
- Department of Radiation Oncology, New York University School of Medicine, Langone Medical Center, New York, NY, USA
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Persson E, Gustafsson C, Nordström F, Sohlin M, Gunnlaugsson A, Petruson K, Rintelä N, Hed K, Blomqvist L, Zackrisson B, Nyholm T, Olsson LE, Siversson C, Jonsson J. MR-OPERA: A Multicenter/Multivendor Validation of Magnetic Resonance Imaging-Only Prostate Treatment Planning Using Synthetic Computed Tomography Images. Int J Radiat Oncol Biol Phys 2017; 99:692-700. [PMID: 28843375 DOI: 10.1016/j.ijrobp.2017.06.006] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 05/12/2017] [Accepted: 06/07/2017] [Indexed: 10/19/2022]
Abstract
PURPOSE To validate the dosimetric accuracy and clinical robustness of a commercially available software for magnetic resonance (MR) to synthetic computed tomography (sCT) conversion, in an MR imaging-only workflow for 170 prostate cancer patients. METHODS AND MATERIALS The 4 participating centers had MriPlanner (Spectronic Medical), an atlas-based sCT generation software, installed as a cloud-based service. A T2-weighted MR sequence, covering the body contour, was added to the clinical protocol. The MR images were sent from the MR scanner workstation to the MriPlanner platform. The sCT was automatically returned to the treatment planning system. Four MR scanners and 2 magnetic field strengths were included in the study. For each patient, a CT-treatment plan was created and approved according to clinical practice. The sCT was rigidly registered to the CT, and the clinical treatment plan was recalculated on the sCT. The dose distributions from the CT plan and the sCT plan were compared according to a set of dose-volume histogram parameters and gamma evaluation. Treatment techniques included volumetric modulated arc therapy, intensity modulated radiation therapy, and conventional treatment using 2 treatment planning systems and different dose calculation algorithms. RESULTS The overall (multicenter/multivendor) mean dose differences between sCT and CT dose distributions were below 0.3% for all evaluated organs and targets. Gamma evaluation showed a mean pass rate of 99.12% (0.63%, 1 SD) in the complete body volume and 99.97% (0.13%, 1 SD) in the planning target volume using a 2%/2-mm global gamma criteria. CONCLUSIONS Results of the study show that the sCT conversion method can be used clinically, with minimal differences between sCT and CT dose distributions for target and relevant organs at risk. The small differences seen are consistent between centers, indicating that an MR imaging-only workflow using MriPlanner is robust for a variety of field strengths, vendors, and treatment techniques.
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Affiliation(s)
- Emilia Persson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden; Department of Medical Physics, Lund University, Malmö, Sweden.
| | - Christian Gustafsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden; Department of Medical Physics, Lund University, Malmö, Sweden
| | - Fredrik Nordström
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Maja Sohlin
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Adalsteinn Gunnlaugsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Karin Petruson
- Department of Oncology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Niina Rintelä
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Kristoffer Hed
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Lennart Blomqvist
- Department of Radiation Sciences, Umeå University, Umeå, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm, Sweden
| | | | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden; Department of Immunology, Genetics, and Pathology, Uppsala University, Uppsala, Sweden
| | - Lars E Olsson
- Department of Medical Physics, Lund University, Malmö, Sweden
| | | | - Joakim Jonsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
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Arivarasan I, Anuradha C, Subramanian S, Anantharaman A, Ramasubramanian V. Magnetic resonance image guidance in external beam radiation therapy planning and delivery. Jpn J Radiol 2017; 35:417-426. [DOI: 10.1007/s11604-017-0656-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 05/29/2017] [Indexed: 12/14/2022]
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Farjam R, Tyagi N, Veeraraghavan H, Apte A, Zakian K, Hunt MA, Deasy JO. Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy. Med Phys 2017; 44:3706-3717. [PMID: 28444772 DOI: 10.1002/mp.12303] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Revised: 04/03/2017] [Accepted: 04/19/2017] [Indexed: 11/06/2022] Open
Abstract
PURPOSE The growing use of magnetic resonance imaging (MRI) as a substitute for computed tomography-based treatment planning requires the development of effective algorithms to generate electron density maps for treatment planning and patient setup verification. The purpose of this work was to develop a method to synthesize computerized tomography (CT) for MR-only radiotherapy of head and neck cancer patients. METHODS The algorithm is based on registration of multiple patient datasets containing both MRI and CT images (a "multiatlas" algorithm). Twelve matched pairs of good quality CT and MRI scans (those without apparent motion and blurring artifacts) were selected from a pool of head and neck cancer patients to form the atlas. All atlas MRI scans were preprocessed to reduce scanner- and patient-induced intensity inhomogeneities and to standardize their intensity histograms. Atlas CT and MRIs were coregistered using a novel bone-to-air replacement technique applied to the CT scans that improves the similarity between CTs and MRIs and facilitates the registration process. For each new patient, all atlas MRIs are deformed initially onto the new patients' MRI. We introduce a generalized registration error (GRE) metric that automatically measures the goodness of local registration between MRI pairs. The final synthetic CT value at each point is a nonlinear GRE-weighted average of the atlas CTs. For evaluation, the leave-one-out technique was used for synthetic CT generation and the mean absolute error (MAE) between the original and synthetic CT was computed over the entire CT image. The impact of our proposed CT-MR registration scheme on the accuracy of the final synthetic CT was also studied. The original treatment plans were also recomputed on the new synthetic CTs and dose-volume histogram metrics were compared. In addition, the two-dimensional (2D) gamma analysis at 1%/1 mm and 2%/2 mm dose difference/distance to agreement was also performed to study the dose distribution at the isocenter. RESULTS MAE error (± standard deviation) between the original and the synthetic CTs was 64 ± 10, 113 ± 12, and 130 ± 28 Hounsfield Unit (HU) for the entire image, air, and bone regions respectively. Our results showed that our proposed bone-suppression based CT-MR fusion and GRE-weighted strategy could lower the overall MAE error between the original and synthetic CTs by ~69% and ~34% respectively. Dose recalculation comparison showed highly consistent results between plans based on the synthetic vs. the original CTs. The 2D gamma analysis revealed the pass rate of 95.44 ± 2.5 and 99.36 ± 0.71 for 1%/1 mm and 2%/2 mm criteria respectively. Due to local registration weighting, the method is robust with respect to MRI imaging artifacts. CONCLUSION We developed a novel image analysis technique to synthesize CT for head and neck anatomy. Novel methods were introduced to accurately register atlas CTs and MRIs as well as to weight the final electron density maps using local registration goodness estimates. The resulting accuracy is clinically acceptable, at least for these atlas patients.
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Affiliation(s)
- Reza Farjam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Aditya Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Kristen Zakian
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Margie A Hunt
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Prior P, Chen X, Gore E, Johnstone C, Li XA. Technical Note: Is bulk electron density assignment appropriate for MRI-only based treatment planning for lung cancer? Med Phys 2017; 44:3437-3443. [DOI: 10.1002/mp.12267] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 03/27/2017] [Accepted: 03/30/2017] [Indexed: 11/06/2022] Open
Affiliation(s)
- Phil Prior
- Department of Radiation Oncology; Medical College of Wisconsin; Milwaukee WI USA
| | - Xinfeng Chen
- Department of Radiation Oncology; Medical College of Wisconsin; Milwaukee WI USA
| | - Elizabeth Gore
- Department of Radiation Oncology; Medical College of Wisconsin; Milwaukee WI USA
| | - Candice Johnstone
- Department of Radiation Oncology; Medical College of Wisconsin; Milwaukee WI USA
| | - X. Allen Li
- Department of Radiation Oncology; Medical College of Wisconsin; Milwaukee WI USA
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Demol B, Boydev C, Korhonen J, Reynaert N. Dosimetric characterization of MRI-only treatment planning for brain tumors in atlas-based pseudo-CT images generated from standard T1-weighted MR images. Med Phys 2017; 43:6557. [PMID: 27908187 DOI: 10.1118/1.4967480] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI)-only radiotherapy treatment planning requires accurate pseudo-CT (pCT) images for precise dose calculation. The current work introduced an atlas-based method combined with MR intensity information. pCT analyses and Monte Carlo dose calculations for intracranial stereotactic treatments were performed. METHODS Twenty-two patients, representing 35 tumor targets, were scanned using a 3D T1-weighted MRI sequence according to the clinical protocol. The MR atlas image was registered to the MR patient image using a deformable algorithm, and the deformation was then applied to the atlas CT. Two methods were applied. The first method (MRdef) was based on deformations only, while the second (MRint) also used the actual MR intensities. pCT analysis was performed using the mean (absolute) error, as well as an in-house tool based on a gamma index. Dose differences between pCT and true CT were analyzed using dose-volume histogram (DVH) parameters, statistical tests, the gamma index, and probability density functions. An unusual case, where the patient underwent an operation (part of the skull bone was removed), was studied in detail. RESULTS Soft tissues presented a mean error inferior to 50 HUs, while low-density tissues and bones presented discrepancies up to 600 HUs for hard bone. The MRdef method led to significant dose differences compared with the true CT (p-value < 0.05; Wilcoxon-signed-rank test). The MRint method performed better. The DVH parameter differences compared with CT were between -2.9% and 3.1%, except for two cases where the tumors were located within the sphenoid bone. For these cases, the dose errors were up to 6.6% and 5.4% (D98 and D95). Furthermore, for 85% of the tested patients, the mean dose to the planning target volume agreed within 2% with the calculation using the actual CT. Fictitious bone was generated in the unusual case using atlas-based methods. CONCLUSIONS Generally, the atlas-based method led to acceptable dose distributions. The use of common T1 sequences allows the implementation of this method in clinical routine. However, unusual patient anatomy may produce large dose calculation errors. The detection of large anatomic discrepancies using MR image subtraction can be realized, but an alternative way to produce synthetic CT numbers in these regions is still required.
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Affiliation(s)
- Benjamin Demol
- Department of Radiotherapy, Centre Oscar Lambret, Lille 59000, France; Department of Research and Development, AQUILAB SAS, Loos Les Lille 59120, France; and Department of Research, IEMN, UMR CNRS 8520, Villeneuve d'Ascq 59650, France
| | - Christine Boydev
- Department of Radiotherapy, Centre Oscar Lambret, Lille 59000, France
| | - Juha Korhonen
- Department of Radiation Oncology, Comprehensive Cancer Center, Helsinki University Central Hospital, Helsinki FI-00029, Finland and Department of Radiology, Helsinki University Central Hospital, Helsinki FI-00029, Finland
| | - Nick Reynaert
- Department of Radiotherapy, Centre Oscar Lambret, Lille 59000, France
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Koivula L, Wee L, Korhonen J. Feasibility of MRI-only treatment planning for proton therapy in brain and prostate cancers: Dose calculation accuracy in substitute CT images. Med Phys 2017; 43:4634. [PMID: 27487880 DOI: 10.1118/1.4958677] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI) is increasingly used for radiotherapy target delineation, image guidance, and treatment response monitoring. Recent studies have shown that an entire external x-ray radiotherapy treatment planning (RTP) workflow for brain tumor or prostate cancer patients based only on MRI reference images is feasible. This study aims to show that a MRI-only based RTP workflow is also feasible for proton beam therapy plans generated in MRI-based substitute computed tomography (sCT) images of the head and the pelvis. METHODS The sCTs were constructed for ten prostate cancer and ten brain tumor patients primarily by transforming the intensity values of in-phase MR images to Hounsfield units (HUs) with a dual model HU conversion technique to enable heterogeneous tissue representation. HU conversion models for the pelvis were adopted from previous studies, further extended in this study also for head MRI by generating anatomical site-specific conversion models (a new training data set of ten other brain patients). This study also evaluated two other types of simplified sCT: dual bulk density (for bone and water) and homogeneous (water only). For every clinical case, intensity modulated proton therapy (IMPT) plans robustly optimized in standard planning CTs were calculated in sCT for evaluation, and vice versa. Overall dose agreement was evaluated using dose-volume histogram parameters and 3D gamma criteria. RESULTS In heterogeneous sCTs, the mean absolute errors in HUs were 34 (soft tissues: 13, bones: 92) and 42 (soft tissues: 9, bones: 97) in the head and in the pelvis, respectively. The maximum absolute dose differences relative to CT in the brain tumor clinical target volume (CTV) were 1.4% for heterogeneous sCT, 1.8% for dual bulk sCT, and 8.9% for homogenous sCT. The corresponding maximum differences in the prostate CTV were 0.6%, 1.2%, and 3.6%, respectively. The percentages of dose points in the head and pelvis passing 1% and 1 mm gamma index criteria were over 91%, 85%, and 38% with heterogeneous, dual bulk, and homogeneous sCTs, respectively. There were no significant changes to gamma index pass rates for IMPT plans first optimized in CT and then calculated in heterogeneous sCT versus IMPT plans first optimized in heterogeneous sCT and then calculated on standard CT. CONCLUSIONS This study demonstrates that proton therapy dose calculations on heterogeneous sCTs are in good agreement with plans generated with standard planning CT. An MRI-only based RTP workflow is feasible in IMPT for brain tumors and prostate cancers.
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Affiliation(s)
- Lauri Koivula
- Department of Radiation Oncology, Comprehensive Cancer Center, Helsinki University Central Hospital, P.O. Box 180, Helsinki 00029 HUS, Finland and Department of Medical Physics, Oncology Services, Vejle Hospital, Kabbeltoft 25, Vejle DK-7100, Denmark
| | - Leonard Wee
- Department of Medical Physics, Oncology Services, Vejle Hospital, Kabbeltoft 25, Vejle DK-7100, Denmark and Danish Colorectal Cancer Centre South, Vejle Hospital, Kabbeltoft 25, Vejle DK-7100, Denmark
| | - Juha Korhonen
- Department of Radiation Oncology, Comprehensive Cancer Center, Helsinki University Central Hospital, P.O. Box 180, Helsinki 00029 HUS, Finland; Danish Colorectal Cancer Centre South, Vejle Hospital, Kabbeltoft 25, Vejle DK-7100, Denmark; and Department of Radiology, Helsinki University Central Hospital, P.O. Box 180, Helsinki 00029 HUS, Finland
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Jia X, Tian Z, Xi Y, Jiang SB, Wang G. New concept on an integrated interior magnetic resonance imaging and medical linear accelerator system for radiation therapy. J Med Imaging (Bellingham) 2017; 4:015004. [PMID: 28331888 DOI: 10.1117/1.jmi.4.1.015004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 02/13/2017] [Indexed: 12/25/2022] Open
Abstract
Image guidance plays a critical role in radiotherapy. Currently, cone-beam computed tomography (CBCT) is routinely used in clinics for this purpose. While this modality can provide an attenuation image for therapeutic planning, low soft-tissue contrast affects the delineation of anatomical and pathological features. Efforts have recently been devoted to several MRI linear accelerator (LINAC) projects that lead to the successful combination of a full diagnostic MRI scanner with a radiotherapy machine. We present a new concept for the development of the MRI-LINAC system. Instead of combining a full MRI scanner with the LINAC platform, we propose using an interior MRI (iMRI) approach to image a specific region of interest (RoI) containing the radiation treatment target. While the conventional CBCT component still delivers a global image of the patient's anatomy, the iMRI offers local imaging of high soft-tissue contrast for tumor delineation. We describe a top-level system design for the integration of an iMRI component into an existing LINAC platform. We performed numerical analyses of the magnetic field for the iMRI to show potentially acceptable field properties in a spherical RoI with a diameter of 15 cm. This field could be shielded to a sufficiently low level around the LINAC region to avoid electromagnetic interference. Furthermore, we investigate the dosimetric impacts of this integration on the radiotherapy beam.
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Affiliation(s)
- Xun Jia
- University of Texas Southwestern Medical Center , Department of Radiation Oncology, Dallas, Texas, United States
| | - Zhen Tian
- University of Texas Southwestern Medical Center , Department of Radiation Oncology, Dallas, Texas, United States
| | - Yan Xi
- Biomedical Imaging Center , Rensselaer Polytechnic Institute, Troy, New York, United States
| | - Steve B Jiang
- University of Texas Southwestern Medical Center , Department of Radiation Oncology, Dallas, Texas, United States
| | - Ge Wang
- Biomedical Imaging Center , Rensselaer Polytechnic Institute, Troy, New York, United States
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Wood AM, Shea SM, Medved M, Karczmar GS, Surucu M, Gros S, Small W, Roeske J. Spectral characterization of tissues in high spectral and spatial resolution MR images: Implications for a classification-based synthetic CT algorithm. Med Phys 2017; 44:1865-1875. [PMID: 28236649 DOI: 10.1002/mp.12173] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Revised: 02/09/2017] [Accepted: 02/16/2017] [Indexed: 01/17/2023] Open
Abstract
PURPOSE To characterize the spectral parameters of tissues with high spectral and spatial resolution magnetic resonance images to be used as a foundation for a classification-based synthetic CT algorithm. METHODS A phantom was constructed consisting of a section of fresh beef leg with bone embedded in 1% agarose gel. The high spectral and spatial (HiSS) resolution MR imaging sequence used had 1.0 mm in-plane resolution and 11.1 Hz spectral resolution. This sequence was used to image the phantom and one patient. Post-processing was performed off-line with IDL and included Fourier transformation of the time-domain data, labeling of fat and water peaks, and fitting the magnitude spectra with Lorentzian functions. Images of the peak height and peak integral of both the water and fat resonances were generated and analyzed. Several regions-of-interest (ROIs) were identified in phantom: bone marrow, cortical bone, adipose tissue, muscle, agar gel, and air; in the patient, no agar gel was present but an ROI of saline in the bladder was analyzed. All spectra were normalized by the noise within each voxel; thus, all parameters are reported in terms of signal-to-noise (SNR). The distributions of tissue spectral parameters were analyzed and scatterplots generated. Water peak height in cortical bone was compared to air using a nonparametric t-test. Composition of the various ROIs in terms of water, fat, or fat and water was also reported. RESULTS In phantom, the scatterplot of peak height (water versus fat) showed good separation of bone marrow and adipose tissue. Water versus fat integral scatterplot showed better separation of muscle and cortical bone than the peak height scatterplot. In the patient data, the distributions of water and fat peak heights were similar to that in phantom, with more overlap of bone marrow and cortical bone than observed in phantom. The relationship between bone marrow and cortical bone for peak integral was better separated than those of peak heights in the patient data. For both the phantom and patient, there was a significant amount of overlap in spectral parameters of cortical bone versus air. CONCLUSION These results show promising results for utilizing HiSS imaging in a classification-based synthetic CT algorithm. Cortical bone and air overlap was expected due to the short T2* of bone; reducing early echo times would improve the SNR in bone and image data from these early echoes could help differentiate these tissue types. Further studies need to be done with the goal of better separation of air and bone, and to extend the concept to volumetric imaging before it can be clinically applied.
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Affiliation(s)
- Abbie M Wood
- Division of Medical Physics, Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, 60153, USA
| | - Steven M Shea
- Department of Radiology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, 60153, USA
| | - Milica Medved
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
| | | | - Murat Surucu
- Division of Medical Physics, Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, 60153, USA
| | - Sebastien Gros
- Division of Medical Physics, Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, 60153, USA
| | - William Small
- Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, 60153, USA
| | - John Roeske
- Division of Medical Physics, Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, 60153, USA
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Yang X, Lei Y, Shu HK, Rossi P, Mao H, Shim H, Curran WJ, Liu T. Pseudo CT Estimation from MRI Using Patch-based Random Forest. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10133. [PMID: 31607771 DOI: 10.1117/12.2253936] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.
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Affiliation(s)
- Xiaofeng Yang
- Department of Radiation Oncology, Winship Cancer Institute
| | - Yang Lei
- Department of Radiation Oncology, Winship Cancer Institute
| | - Hui-Kuo Shu
- Department of Radiation Oncology, Winship Cancer Institute
| | - Peter Rossi
- Department of Radiation Oncology, Winship Cancer Institute
| | - Hui Mao
- Department of Radiology and Imaging Sciences, Winship Cancer Institute, Emory University, Atlanta, GA
| | - Hyunsuk Shim
- Department of Radiation Oncology, Winship Cancer Institute.,Department of Radiology and Imaging Sciences, Winship Cancer Institute, Emory University, Atlanta, GA
| | | | - Tian Liu
- Department of Radiation Oncology, Winship Cancer Institute
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Edmund JM, Nyholm T. A review of substitute CT generation for MRI-only radiation therapy. Radiat Oncol 2017; 12:28. [PMID: 28126030 PMCID: PMC5270229 DOI: 10.1186/s13014-016-0747-y] [Citation(s) in RCA: 235] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 12/21/2016] [Indexed: 12/13/2022] Open
Abstract
Radiotherapy based on magnetic resonance imaging as the sole modality (MRI-only RT) is an area of growing scientific interest due to the increasing use of MRI for both target and normal tissue delineation and the development of MR based delivery systems. One major issue in MRI-only RT is the assignment of electron densities (ED) to MRI scans for dose calculation and a similar need for attenuation correction can be found for hybrid PET/MR systems. The ED assigned MRI scan is here named a substitute CT (sCT). In this review, we report on a collection of typical performance values for a number of main approaches encountered in the literature for sCT generation as compared to CT. A literature search in the Scopus database resulted in 254 papers which were included in this investigation. A final number of 50 contributions which fulfilled all inclusion criteria were categorized according to applied method, MRI sequence/contrast involved, number of subjects included and anatomical site investigated. The latter included brain, torso, prostate and phantoms. The contributions geometric and/or dosimetric performance metrics were also noted. The majority of studies are carried out on the brain for 5–10 patients with PET/MR applications in mind using a voxel based method. T1 weighted images are most commonly applied. The overall dosimetric agreement is in the order of 0.3–2.5%. A strict gamma criterion of 1% and 1mm has a range of passing rates from 68 to 94% while less strict criteria show pass rates > 98%. The mean absolute error (MAE) is between 80 and 200 HU for the brain and around 40 HU for the prostate. The Dice score for bone is between 0.5 and 0.95. The specificity and sensitivity is reported in the upper 80s% for both quantities and correctly classified voxels average around 84%. The review shows that a variety of promising approaches exist that seem clinical acceptable even with standard clinical MRI sequences. A consistent reference frame for method benchmarking is probably necessary to move the field further towards a widespread clinical implementation.
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Affiliation(s)
- Jens M Edmund
- Radiotherapy Research Unit, Department of Oncology, Herlev & Gentofte Hospital, Copenhagen University, Herlev, Denmark. .,Niels Bohr Institute, Copenhagen University, Copenhagen, Denmark.
| | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, SE-901 87, Sweden.,Medical Radiation Physics, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
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Ren S, Hara W, Wang L, Buyyounouski MK, Le QT, Xing L, Li R. Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach. Int J Radiat Oncol Biol Phys 2016; 97:849-857. [PMID: 28244422 DOI: 10.1016/j.ijrobp.2016.11.053] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 11/23/2016] [Accepted: 11/29/2016] [Indexed: 11/26/2022]
Abstract
PURPOSE To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. METHODS AND MATERIALS We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. RESULTS The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). CONCLUSION The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.
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Affiliation(s)
- Shangjie Ren
- Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin, China; Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California
| | - Wendy Hara
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California
| | - Lei Wang
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California
| | - Mark K Buyyounouski
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California.
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45
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Guo L, Wang G, Feng Y, Yu T, Guo Y, Bai X, Ye Z. Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors. Radiat Oncol 2016; 11:123. [PMID: 27655356 PMCID: PMC5031292 DOI: 10.1186/s13014-016-0702-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 09/13/2016] [Indexed: 12/12/2022] Open
Abstract
Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images.
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Affiliation(s)
- Lu Guo
- Department of Biomedical Engineering, Tianjin University, Tianjin, 300072, China
| | - Gang Wang
- Department of Biomedical Engineering, Tianjin University, Tianjin, 300072, China
| | - Yuanming Feng
- Department of Biomedical Engineering, Tianjin University, Tianjin, 300072, China. .,Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China. .,Department of Radiation Oncology, East Carolina University, 600 Moye Blvd, Greenville, NC, 27834, USA.
| | - Tonggang Yu
- Department of Radiology, Huashan hospital, Fudan University, Shanghai, 200040, China
| | - Yu Guo
- Department of Biomedical Engineering, Tianjin University, Tianjin, 300072, China
| | - Xu Bai
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China
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46
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Jutras JD, Wachowicz K, Gilbert G, De Zanche N. SNR efficiency of combined bipolar gradient echoes: Comparison of three-dimensional FLASH, MPRAGE, and multiparameter mapping with VFA-FLASH and MP2RAGE. Magn Reson Med 2016; 77:2186-2202. [DOI: 10.1002/mrm.26306] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 05/18/2016] [Accepted: 05/19/2016] [Indexed: 11/08/2022]
Affiliation(s)
- Jean-David Jutras
- Department of Oncology; University of Alberta; Edmonton Alberta Canada
| | - Keith Wachowicz
- Department of Oncology; University of Alberta; Edmonton Alberta Canada
- Department of Medical Physics; Cross Cancer Institute; Edmonton Alberta Canada
| | - Guillaume Gilbert
- MR Clinical Science; Philips Healthcare Canada; Markham Ontario Canada
| | - Nicola De Zanche
- Department of Oncology; University of Alberta; Edmonton Alberta Canada
- Department of Medical Physics; Cross Cancer Institute; Edmonton Alberta Canada
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47
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Price RG, Kadbi M, Kim J, Balter J, Chetty IJ, Glide-Hurst CK. Technical Note: Characterization and correction of gradient nonlinearity induced distortion on a 1.0 T open bore MR-SIM. Med Phys 2016; 42:5955-60. [PMID: 26429270 DOI: 10.1118/1.4930245] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Distortions in magnetic resonance imaging (MRI) compromise spatial fidelity, potentially impacting delineation and dose calculation. We characterized 2D and 3D large field of view (FOV), sequence-independent distortion at various positions in a 1.0 T high-field open MR simulator (MR-SIM) to implement correction maps for MRI treatment planning. METHODS A 36 × 43 × 2 cm(3) phantom with 255 known landmarks (∼1 mm(3)) was scanned using 1.0 T high-field open MR-SIM at isocenter in the transverse, sagittal, and coronal axes, and a 465 × 350 × 168 mm(3) 3D phantom was scanned by stepping in the superior-inferior direction in three overlapping positions to achieve a total 465 × 350 × 400 mm(3) sampled FOV yielding >13 800 landmarks (3D Gradient-Echo, TE/TR/α = 5.54 ms/30 ms/28°, voxel size = 1 × 1 × 2 mm(3)). A binary template (reference) was generated from a phantom schematic. An automated program converted MR images to binary via masking, thresholding, and testing for connectivity to identify landmarks. Distortion maps were generated by centroid mapping. Images were corrected via warping with inverse distortion maps, and temporal stability was assessed. RESULTS Over the sampled FOV, non-negligible residual gradient distortions existed as close as 9.5 cm from isocenter, with a maximum distortion of 7.4 mm as close as 23 cm from isocenter. Over six months, average gradient distortions were -0.07 ± 1.10 mm and 0.10 ± 1.10 mm in the x and y directions for the transverse plane, 0.03 ± 0.64 and -0.09 ± 0.70 mm in the sagittal plane, and 0.4 ± 1.16 and 0.04 ± 0.40 mm in the coronal plane. After implementing 3D correction maps, distortions were reduced to <1 pixel width (1 mm) for all voxels up to 25 cm from magnet isocenter. CONCLUSIONS Inherent distortion due to gradient nonlinearity was found to be non-negligible even with vendor corrections applied, and further corrections are required to obtain 1 mm accuracy for large FOVs. Statistical analysis of temporal stability shows that sequence independent distortion maps are consistent within six months of characterization.
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Affiliation(s)
- Ryan G Price
- Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202 and Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, Michigan 48201
| | - Mo Kadbi
- Philips Healthcare, Cleveland, Ohio 44143
| | - Joshua Kim
- Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202
| | - James Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202 and Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, Michigan 48201
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202 and Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, Michigan 48201
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48
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Seibert TM, White NS, Kim GY, Moiseenko V, McDonald CR, Farid N, Bartsch H, Kuperman J, Karunamuni R, Marshall D, Holland D, Sanghvi P, Simpson DR, Mundt AJ, Dale AM, Hattangadi-Gluth JA. Distortion inherent to magnetic resonance imaging can lead to geometric miss in radiosurgery planning. Pract Radiat Oncol 2016; 6:e319-e328. [PMID: 27523440 DOI: 10.1016/j.prro.2016.05.008] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 05/24/2016] [Accepted: 05/26/2016] [Indexed: 11/18/2022]
Abstract
PURPOSE Anatomic distortion is present in all magnetic resonance imaging (MRI) data because of nonlinearity of gradient fields; it measures up to several millimeters. We evaluated the potential for uncorrected MRI to lead to geometric miss of the target volume in stereotactic radiosurgery (SRS). METHODS AND MATERIALS Twenty-eight SRS cases were studied retrospectively. MRI scans were corrected for gradient nonlinearity distortion in 3 dimensions, and gross tumor volumes (GTVs) were contoured. The manufacturer-specified distortion field was then reapplied to GTV masks to allow measurement of GTV displacement in uncorrected images. The uncorrected GTV was used for SRS planning, and the dose received by the true (corrected) GTV was measured. RESULTS Median displacement of the GTV resulting from gradient distortion was 1.2 mm (interquartile range, 0.1-2.3 mm), with a minimum of 0 mm and a maximum of 3.9 mm. Eight of the 28 cases met a priori criteria for "geometric miss." CONCLUSIONS Although MRI distortion is often subtle on visual inspection, there is a significant clinical impact of this distortion on SRS planning. Distortion-corrected MRI should uniformly be used for intracranial radiosurgery planning because uncorrected MRI can lead to potential geometric miss.
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Affiliation(s)
- Tyler M Seibert
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California
| | - Nathan S White
- Department of Radiology, University of California, San Diego, La Jolla, California
| | - Gwe-Ya Kim
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California
| | - Vitali Moiseenko
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California
| | - Carrie R McDonald
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California; Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Nikdokht Farid
- Department of Radiology, University of California, San Diego, La Jolla, California
| | - Hauke Bartsch
- Department of Radiology, University of California, San Diego, La Jolla, California
| | - Joshua Kuperman
- Department of Radiology, University of California, San Diego, La Jolla, California
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California
| | - Deborah Marshall
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California
| | - Dominic Holland
- Department of Radiology, University of California, San Diego, La Jolla, California
| | - Parag Sanghvi
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California
| | - Daniel R Simpson
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California
| | - Arno J Mundt
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, California; Department of Neurosciences, University of California, San Diego, La Jolla, California
| | - Jona A Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California.
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Ahn KH, Ozturk N, Smith B, Slavin KV, Koshy M, Aydogan B. A preplanning method for stereotactic radiosurgery to improve treatment workflow. J Appl Clin Med Phys 2016; 17:171-179. [PMID: 27167274 PMCID: PMC5690936 DOI: 10.1120/jacmp.v17i3.6031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2015] [Revised: 01/24/2016] [Accepted: 01/19/2016] [Indexed: 11/23/2022] Open
Abstract
Frame‐based stereotactic radiosurgery (SRS) requires fixation of an invasive head ring to ensure accurate targeting. Minimizing waiting time with a fixed head ring is important for patient comfort and satisfaction. We report a practical preplanning solution for the Brainlab iPlan treatment planning system that reduces waiting time by expediting the planning process on treatment day. A water‐filled anthropomorphic head phantom was used to acquire a surrogate CT image set for preplanning and fused with patient's MRI, which was obtained before the day of treatment. Once an acceptable preplan was obtained, it was saved as a plan template and the phantom image set was removed from the Brainlab database to prevent any confusion and mix‐up of image sets. On the treatment day, the patient's CT and MRI were fused, and the customized beam settings of the preplan template were then applied and optimized. Up to 10‐fold of reduction in treatment plan time was demonstrated by bench testing with multiple planners and a variety of cases. Loading the plan template and fine‐tuning the preconfigured beam settings took only a small fraction of the preplan time to restore the conformity and dose falloff comparable to those of the preplan. For instance, preplan time was 2 hr for a two‐isocenter case, whereas, it took less than 20 min for a less experienced planner to plan it on the day of treatment using the preplan method. The SRS preplanning technique implemented in this study for the Brainlab iPlan treatment planning system offers an opportunity to explore possible beam configurations thoroughly, optimize planning parameters, resolve gantry angle clearance issues, and communicate and address challenges with physicians before the treatment day. Preplanning has been proven to improve plan quality and to improve efficiency in our clinic, especially for multiple‐isocenter and dosimetrically challenging cases. PACS number(s): 87.53.Ly, 87.55.D‐, 87.55.Gh, 87.55.tm
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50
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Applying radiobiological plan ranking methodology to VMAT prostate SBRT. Phys Med 2016; 32:636-41. [DOI: 10.1016/j.ejmp.2016.03.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2015] [Revised: 02/19/2016] [Accepted: 03/26/2016] [Indexed: 11/23/2022] Open
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