Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation
暂无分享,去创建一个
Jeffrey E Snyder | K. Brock | Jinzhong Yang | J. Sonke | T. Tadic | D. Létourneau | M. Ruschin | D. Thorwarth | J. Christodouleas | M. Naser | C. Fuller | N. Tyagi | C. Zachiu | B. McDonald | D. Hyer | U. Bernchou | X. A. Li | Edyta Bubula-Rehm | E. Bubula-Rehm
[1] E. Yorke,et al. High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic (HyTEC): An Overview. , 2021, International journal of radiation oncology, biology, physics.
[2] A. Bertelsen,et al. Accuracy of automatic structure propagation for daily magnetic resonance image-guided head and neck radiotherapy , 2021, Acta oncologica.
[3] Laurence E. Court,et al. Tissue-specific deformable image registration using a spatial-contextual filter , 2020, Comput. Medical Imaging Graph..
[4] Pengjiang Qian,et al. Dixon-based thorax synthetic CT generation using Generative Adversarial Network , 2020 .
[5] J. Lagendijk,et al. Prostate intrafraction motion during the preparation and delivery of MR-guided radiotherapy sessions on a 1.5T MR-Linac. , 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[6] J. Lagendijk,et al. Delivered dose quantification in prostate radiotherapy using online 3D cine imaging and treatment log files on a combined 1.5T magnetic resonance imaging and linear accelerator system , 2020, Physics and imaging in radiation oncology.
[7] Jinzhong Yang,et al. Initial Feasibility and Clinical Implementation of Daily MR-guided Adaptive Head and Neck Cancer Radiotherapy on a 1.5T MR-Linac System: Prospective R-IDEAL 2a/2b Systematic Clinical Evaluation of Technical Innovation , 2020, medRxiv.
[8] B. Erickson,et al. Dose-Escalated Radiation Therapy for Pancreatic Cancer: A Simultaneous Integrated Boost Approach. , 2020, Practical radiation oncology.
[9] A. Bertelsen,et al. Accuracy of automatic deformable structure propagation for high-field MRI guided prostate radiotherapy , 2020, Radiation Oncology.
[10] R. Louwe,et al. Quantifying the dose accumulation uncertainty after deformable image registration in head-and-neck radiotherapy. , 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[11] Uwe Oelfke,et al. Automatic reconstruction of the delivered dose of the day using MR-linac treatment log files and online MR imaging , 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[12] Cornel Zachiu,et al. Biomechanical quality assurance criteria for deformable image registration algorithms used in radiotherapy guidance , 2020, Physics in medicine and biology.
[13] C. Fuller,et al. Differences between planned and delivered dose for head and neck cancer, and their consequences for normal tissue complication probability and treatment adaptation. , 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[14] Chenbin Liu,et al. Synthetic CT Generation Based on T2 Weighted MRI of Nasopharyngeal Carcinoma (NPC) Using a Deep Convolutional Neural Network (DCNN) , 2019, Front. Oncol..
[15] David A Jaffray,et al. The transformation of radiation oncology using real-time magnetic resonance guidance: A review. , 2019, European journal of cancer.
[16] W. Curran,et al. Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning , 2019, Physics in medicine and biology.
[17] A. Meigooni,et al. Evaluation of deformable image registration algorithm for determination of accumulated dose for brachytherapy of cervical cancer patients , 2019, Journal of contemporary brachytherapy.
[18] S. Senan,et al. End-to-end empirical validation of dose accumulation in MRI-guided adaptive radiotherapy for prostate cancer using an anthropomorphic deformable pelvis phantom. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[19] I. Chetty,et al. Modern Radiation Therapy Planning and Delivery. , 2019, Hematology/oncology clinics of North America.
[20] M F Fast,et al. MRI-guided mid-position liver radiotherapy: Validation of image processing and registration steps. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[21] D. Georg,et al. Image guidance: past and future of radiotherapy , 2019, Der Radiologe.
[22] Clifton David Fuller,et al. Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician. , 2019, Seminars in radiation oncology.
[23] Indrin J Chetty,et al. Deformable Registration for Dose Accumulation. , 2019, Seminars in radiation oncology.
[24] Jan-Jakob Sonke,et al. Adaptive Radiotherapy for Anatomical Changes. , 2019, Seminars in radiation oncology.
[25] Mona Kamal,et al. Prospective quantitative quality assurance and deformation estimation of MRI-CT image registration in simulation of head and neck radiotherapy patients , 2019, Clinical and translational radiation oncology.
[26] Sebastian Klüter,et al. Technical design and concept of a 0.35 T MR-Linac , 2019, Clinical and translational radiation oncology.
[27] Rob H.N. Tijssen,et al. Adaptive radiotherapy: The Elekta Unity MR-linac concept , 2019, Clinical and translational radiation oncology.
[28] X. Li,et al. A Technique to Rapidly Generate Synthetic Computed Tomography for Magnetic Resonance Imaging-Guided Online Adaptive Replanning: An Exploratory Study. , 2019, International journal of radiation oncology, biology, physics.
[29] Pierre Guyomarc'h,et al. Landmark Typology in Applied Morphometrics Studies: What's the Point? , 2018, Anatomical record.
[30] Parag J. Parikh,et al. Stereotactic MR-Guided Online Adaptive Radiation Therapy (SMART) for Ultracentral Thorax Malignancies: Results of a Phase 1 Trial , 2018, Advances in radiation oncology.
[31] Ron Kikinis,et al. Multimodal image registration for liver radioembolization planning and patient assessment , 2018, International Journal of Computer Assisted Radiology and Surgery.
[32] Marco Riboldi,et al. "Patient-specific validation of deformable image registration in radiation therapy: Overview and caveats". , 2018, Medical physics.
[33] Anurag K. Singh,et al. Technical and dosimetric implications of respiratory induced density variations in a heterogeneous lung phantom , 2018, Radiation oncology.
[34] B. Raaymakers,et al. Anatomically plausible models and quality assurance criteria for online mono- and multi-modal medical image registration , 2018, Physics in medicine and biology.
[35] W. Tomé,et al. Towards abdominal MRI-based treatment planning using population-based Hounsfield units for bulk density assignment , 2018, Physics in medicine and biology.
[36] Rojano Kashani,et al. Magnetic Resonance Imaging for Target Delineation and Daily Treatment Modification. , 2018, Seminars in radiation oncology.
[37] K. Brock,et al. A simulation study to assess the potential impact of developing normal tissue complication probability models with accumulated dose , 2018, Advances in radiation oncology.
[38] Martin F Fast,et al. Real-time energy/mass transfer mapping for online 4D dose reconstruction , 2018, Scientific Reports.
[39] Carri K Glide-Hurst,et al. MRI-only treatment planning: benefits and challenges , 2018, Physics in medicine and biology.
[40] Sasa Mutic,et al. Phase I trial of stereotactic MR-guided online adaptive radiation therapy (SMART) for the treatment of oligometastatic or unresectable primary malignancies of the abdomen. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[41] A. Garden,et al. Prospective in silico study of the feasibility and dosimetric advantages of MRI-guided dose adaptation for human papillomavirus positive oropharyngeal cancer patients compared with standard IMRT , 2017, Clinical and translational radiation oncology.
[42] Cornel Zachiu,et al. Non-rigid CT/CBCT to CBCT registration for online external beam radiotherapy guidance , 2017, Physics in medicine and biology.
[43] A. Sahgal,et al. Online Adaptive Radiation Therapy. , 2017, International journal of radiation oncology, biology, physics.
[44] A N T J Kotte,et al. First patients treated with a 1.5 T MRI-Linac: clinical proof of concept of a high-precision, high-field MRI guided radiotherapy treatment , 2017, Physics in Medicine and Biology.
[45] B. Stemkens,et al. Towards fast online intrafraction replanning for free-breathing stereotactic body radiation therapy with the MR-linac , 2017, Physics in medicine and biology.
[46] Jason Vickress,et al. Representing the dosimetric impact of deformable image registration errors , 2017, Physics in medicine and biology.
[47] Elizabeth Gore,et al. Technical Note: Is bulk electron density assignment appropriate for MRI‐only based treatment planning for lung cancer?† , 2017, Medical physics.
[48] K. Brock,et al. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132 , 2017, Medical physics.
[49] K. T. Block,et al. Dosimetric evaluation of synthetic CT for magnetic resonance-only based radiotherapy planning of lung cancer , 2017, Radiation oncology.
[50] Xiao Han,et al. MR‐based synthetic CT generation using a deep convolutional neural network method , 2017, Medical physics.
[51] Raj Shekhar,et al. Deformable registration of CT and cone-beam CT with local intensity matching , 2017, Physics in medicine and biology.
[52] I. Chetty,et al. Caution Must Be Exercised When Performing Deformable Dose Accumulation for Tumors Undergoing Mass Changes During Fractionated Radiation Therapy. , 2016, International journal of radiation oncology, biology, physics.
[53] C. Fuller,et al. The MRI-Linear Accelerator Consortium: Evidence-Based Clinical Introduction of an Innovation in Radiation Oncology Connecting Researchers, Methodology, Data Collection, Quality Assurance, and Technical Development , 2016, Front. Oncol..
[54] Jerry L. Prince,et al. MIND Demons for MR-to-CT deformable image registration in image-guided spine surgery , 2016, SPIE Medical Imaging.
[55] Tianyu Zhao,et al. Online Magnetic Resonance Image Guided Adaptive Radiation Therapy: First Clinical Applications. , 2016, International journal of radiation oncology, biology, physics.
[56] I. Chetty,et al. Dosimetric evaluation of synthetic CT relative to bulk density assignment-based magnetic resonance-only approaches for prostate radiotherapy , 2015, Radiation oncology.
[57] Charis Kontaxis,et al. A new methodology for inter- and intrafraction plan adaptation for the MR-linac , 2015, Physics in medicine and biology.
[58] Allan Hanbury,et al. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool , 2015, BMC Medical Imaging.
[59] Charis Kontaxis,et al. Towards adaptive IMRT sequencing for the MR-linac , 2015, Physics in medicine and biology.
[60] Pascal Haigron,et al. Evaluation of Deformable Image Registration Methods for Dose Monitoring in Head and Neck Radiotherapy , 2015, BioMed research international.
[61] Sébastien Ourselin,et al. Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm. , 2015, Medical physics.
[62] Samuel G Armato,et al. Effect of deformable registration on the dose calculated in radiation therapy planning CT scans of lung cancer patients. , 2014, Medical physics.
[63] Jan J W Lagendijk,et al. The feasibility of utilizing pseudo CT-data for online MRI based treatment plan adaptation for a stereotactic radiotherapy treatment of spinal bone metastases , 2014, Physics in medicine and biology.
[64] Sahar Ahmad,et al. Non rigid image registration by modeling deformations as elastic waves , 2014, 2014 IEEE International Conference on Image Processing (ICIP).
[65] Sasa Mutic,et al. The ViewRay system: magnetic resonance-guided and controlled radiotherapy. , 2014, Seminars in radiation oncology.
[66] Ya Wang,et al. The distance discordance metric---a novel approach to quantifying spatial uncertainties in intra-and inter-patient deformable image registration , 2016 .
[67] João Manuel R S Tavares,et al. Medical image registration: a review , 2014, Computer methods in biomechanics and biomedical engineering.
[68] Carri Glide-Hurst,et al. Direct dose mapping versus energy/mass transfer mapping for 4D dose accumulation: fundamental differences and dosimetric consequences , 2014, Physics in medicine and biology.
[69] Tiina Seppälä,et al. A dual model HU conversion from MRI intensity values within and outside of bone segment for MRI-based radiotherapy treatment planning of prostate cancer. , 2013, Medical physics.
[70] Jeffrey N. Adams,et al. Using patient‐specific phantoms to evaluate deformable image registration algorithms for adaptive radiation therapy , 2013, Journal of applied clinical medical physics.
[71] Adam Johansson,et al. Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information – potential application for MR-only radiotherapy and attenuation correction in positron emission tomography , 2013, Acta oncologica.
[72] Nikos Paragios,et al. Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.
[73] S. Hui,et al. A framework for deformable image registration validation in radiotherapy clinical applications , 2013, Journal of applied clinical medical physics.
[74] Lei Dong,et al. Adaptive radiotherapy for head and neck cancer--dosimetric results from a prospective clinical trial. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[75] Colin G Orton,et al. Point/counterpoint: it is not appropriate to "deform" dose along with deformable image registration in adaptive radiotherapy. , 2012, Medical physics.
[76] Michael Brady,et al. MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration , 2012, Medical Image Anal..
[77] Eduard Schreibmann,et al. A measure to evaluate deformable registration fields in clinical settings , 2012, Journal of applied clinical medical physics.
[78] Jan-Jakob Sonke,et al. Quality assurance for image-guided radiation therapy utilizing CT-based technologies: a report of the AAPM TG-179. , 2012, Medical physics.
[79] Michael Velec,et al. A novel technique to enable experimental validation of deformable dose accumulation. , 2012, Medical physics.
[80] Han Liu,et al. Evaluations of an adaptive planning technique incorporating dose feedback in image-guided radiotherapy of prostate cancer. , 2011, Medical physics.
[81] Hua Yang,et al. Deformable image registration of sliding organs using anisotropic diffusive regularization , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[82] R. Ehman,et al. Magnetic resonance elastography: A review , 2010, Clinical anatomy.
[83] Tufve Nyholm,et al. Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions , 2010, Radiation oncology.
[84] K. Brock,et al. Accurate accumulation of dose for improved understanding of radiation effects in normal tissue. , 2010, International Journal of Radiation Oncology, Biology, Physics.
[85] Joseph O Deasy,et al. Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): an introduction to the scientific issues. , 2010, International journal of radiation oncology, biology, physics.
[86] E. Yorke,et al. Use of normal tissue complication probability models in the clinic. , 2010, International journal of radiation oncology, biology, physics.
[87] Meritxell Bach Cuadra,et al. A multidimensional segmentation evaluation for medical image data , 2009, Comput. Methods Programs Biomed..
[88] Hualiang Zhong,et al. Monte Carlo dose mapping on deforming anatomy , 2009, Physics in medicine and biology.
[89] J G M Kok,et al. Integrating a 1.5 T MRI scanner with a 6 MV accelerator: proof of concept , 2009, Physics in medicine and biology.
[90] N. Burnet,et al. SU‐FF‐T‐602: A New Method to Calculate the Dose Distribution From An Isocenter Shift Without Recalculating Dose Distribution to Evaluate Plan with Geometric Uncertainties , 2009 .
[91] Hualiang Zhong,et al. An energy transfer method for 4D Monte Carlo dose calculation. , 2008, Medical physics.
[92] Lei Xing,et al. Formulating adaptive radiation therapy (ART) treatment planning into a closed-loop control framework , 2007, Physics in medicine and biology.
[93] E. Haber,et al. Intensity Gradient Based Registration and Fusion of Multi-modal Images , 2007, Methods of Information in Medicine.
[94] Eldad Haber,et al. Intensity Gradient Based Registration and Fusion of Multi-modal Images , 2006, MICCAI.
[95] M. Kessler. Image registration and data fusion in radiation therapy. , 2006, The British journal of radiology.
[96] Indrin J Chetty,et al. Dose reconstruction in deforming lung anatomy: dose grid size effects and clinical implications. , 2005, Medical physics.
[97] K. Brock,et al. Accuracy of finite element model-based multi-organ deformable image registration. , 2005, Medical physics.
[98] Fred L. Bookstein,et al. A feature space for edgels in images with landmarks , 1993, Journal of Mathematical Imaging and Vision.
[99] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[100] G S Bauman,et al. Tracking the dose distribution in radiation therapy by accounting for variable anatomy , 2004, Physics in medicine and biology.
[101] David J. Hawkes,et al. Validation of nonrigid image registration using finite-element methods: application to breast MR images , 2003, IEEE Transactions on Medical Imaging.
[102] Marcel van Herk,et al. The effect of set-up uncertainties, contour changes, and tissue inhomogeneities on target dose-volume histograms. , 2002, Medical physics.
[103] A. Nahum,et al. The delta-TCP concept: a clinically useful measure of tumor control probability. , 1999, International journal of radiation oncology, biology, physics.
[104] R K Ten Haken,et al. Estimation of tumor control probability model parameters from 3-D dose distributions of non-small cell lung cancer patients. , 1999, Lung cancer.
[105] J. Ashburner,et al. Nonlinear spatial normalization using basis functions , 1999, Human brain mapping.
[106] Jean-Philippe Thirion,et al. Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..
[107] Christos Davatzikos,et al. Spatial Transformation and Registration of Brain Images Using Elastically Deformable Models , 1997, Comput. Vis. Image Underst..
[108] Calvin R. Maurer,et al. Registration of head volume images using implantable fiducial markers , 1997, IEEE Transactions on Medical Imaging.
[109] Guy Marchal,et al. Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.
[110] Michael I. Miller,et al. Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..
[111] A. V. Cideciyan,et al. Registration of ocular fundus images: an algorithm using cross-correlation of triple invariant image descriptors , 1995 .
[112] Michael Unser,et al. B-spline signal processing. I. Theory , 1993, IEEE Trans. Signal Process..
[113] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[114] P. Jaccard. THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .