MR‐based treatment planning in radiation therapy using a deep learning approach
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Fang Liu | Poonam Yadav | Andrew M. Baschnagel | Alan B. McMillan | F. Liu | P. Yadav | A. Baschnagel | A. McMillan | Fang Liu
[1] Paul Kinahan,et al. Attenuation correction for a combined 3D PET/CT scanner. , 1998, Medical physics.
[2] Anne Bol,et al. Evaluation of a multimodality image (CT, MRI and PET) coregistration procedure on phantom and head and neck cancer patients: accuracy, reproducibility and consistency. , 2003, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[3] B. Dawant,et al. Phantom validation of coregistration of PET and CT for image-guided radiotherapy. , 2004, Medical physics.
[4] David M. Doddrell,et al. Geometric distortion in clinical MRI systems Part II: correction using a 3D phantom. , 2004, Magnetic resonance imaging.
[5] Deming Wang,et al. Geometric distortion in clinical MRI systems Part I: evaluation using a 3D phantom. , 2004, Magnetic resonance imaging.
[6] Marc L Kessler,et al. Use and uncertainties of mutual information for computed tomography/ magnetic resonance (CT/MR) registration post permanent implant of the prostate. , 2005, Medical physics.
[7] B. Schölkopf,et al. Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques , 2009, European Journal of Nuclear Medicine and Molecular Imaging.
[8] Tufve Nyholm,et al. Systematisation of spatial uncertainties for comparison between a MR and a CT-based radiotherapy workflow for prostate treatments , 2009, Radiation oncology.
[9] Juan de Lara,et al. Supporting user-oriented analysis for multi-view domain-specific visual languages , 2009, Inf. Softw. Technol..
[10] B. Zackrisson,et al. Dedicated magnetic resonance imaging in the radiotherapy clinic. , 2009, International journal of radiation oncology, biology, physics.
[11] Nassir Navab,et al. Tissue Classification as a Potential Approach for Attenuation Correction in Whole-Body PET/MRI: Evaluation with PET/CT Data , 2009, Journal of Nuclear Medicine.
[12] B. Fallone,et al. First MR images obtained during megavoltage photon irradiation from a prototype integrated linac-MR system. , 2009, Medical physics.
[13] Paul Kinahan,et al. Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. , 2010, Seminars in ultrasound, CT, and MR.
[14] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[15] Tufve Nyholm,et al. Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions , 2010, Radiation oncology.
[16] Kenneth Ulin,et al. Results of a multi-institutional benchmark test for cranial CT/MR image registration. , 2010, International journal of radiation oncology, biology, physics.
[17] Max A. Viergever,et al. elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.
[18] Aliaksandr Karotki,et al. Comparison of bulk electron density and voxel‐based electron density treatment planning , 2011, Journal of applied clinical medical physics.
[19] S. Mutic,et al. WE-G-BRB-08: TG-51 Calibration of First Commercial MRI-Guided IMRT System in the Presence of 0.35 Tesla Magnetic Field. , 2012, Medical physics.
[20] V. Schulz,et al. Challenges and current methods for attenuation correction in PET/MR , 2013, Magnetic Resonance Materials in Physics, Biology and Medicine.
[21] Olivier Salvado,et al. An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. , 2012, International journal of radiation oncology, biology, physics.
[22] N. Schwenzer,et al. A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning , 2013, Acta oncologica.
[23] Christopher M. Rank,et al. MRI-based simulation of treatment plans for ion radiotherapy in the brain region. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[24] D. Jaffray,et al. Harmonic analysis for the characterization and correction of geometric distortion in MRI. , 2014, Medical physics.
[25] Jinsoo Uh,et al. MRI-based treatment planning with pseudo CT generated through atlas registration. , 2014, Medical physics.
[26] Sasa Mutic,et al. The ViewRay system: magnetic resonance-guided and controlled radiotherapy. , 2014, Seminars in radiation oncology.
[27] 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.
[28] Lei Xing,et al. A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning , 2014, Physics in medicine and biology.
[29] Karin Haustermans,et al. The value of magnetic resonance imaging for radiotherapy planning. , 2014, Seminars in radiation oncology.
[30] John E. Bayouth,et al. A dose homogeneity and conformity evaluation between ViewRay and pinnacle-based linear accelerator IMRT treatment plans , 2014, Journal of medical physics.
[31] H. Kjer,et al. A voxel-based investigation for MRI-only radiotherapy of the brain using ultra short echo times , 2014, Physics in medicine and biology.
[32] K. Smit,et al. Relative dosimetry in a 1.5 T magnetic field: an MR-linac compatible prototype scanning water phantom , 2014, Physics in medicine and biology.
[33] B. Erickson,et al. Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning. , 2014, Medical physics.
[34] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[35] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[36] Adam Johansson,et al. Accuracy of inverse treatment planning on substitute CT images derived from MR data for brain lesions , 2015, Radiation oncology.
[37] Jason Dowling,et al. A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning , 2015, Artif. Intell. Medicine.
[38] Maria A Schmidt,et al. Radiotherapy planning using MRI , 2015, Physics in medicine and biology.
[39] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[40] J. Edmund,et al. Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain. , 2015, Medical physics.
[41] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[42] Charis Kontaxis,et al. A new methodology for inter- and intrafraction plan adaptation for the MR-linac , 2015, Physics in medicine and biology.
[43] H. Zaidi,et al. Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities. , 2016, Medical physics.
[44] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Rabih Hammoud,et al. Characterization of 3D geometric distortion of magnetic resonance imaging scanners commissioned for radiation therapy planning. , 2016, Magnetic resonance imaging.
[46] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[47] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[48] Ronald M. Summers,et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.
[49] Tianyu Zhao,et al. Online Magnetic Resonance Image Guided Adaptive Radiation Therapy: First Clinical Applications. , 2016, International journal of radiation oncology, biology, physics.
[50] Nima Tajbakhsh,et al. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.
[51] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[52] Leonard Wee,et al. Intensity-based dual model method for generation of synthetic CT images from standard T2-weighted MR images - Generalized technique for four different MR scanners. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[53] Xiao Han,et al. MR‐based synthetic CT generation using a deep convolutional neural network method , 2017, Medical physics.
[54] T. Nyholm,et al. A review of substitute CT generation for MRI-only radiation therapy , 2017, Radiation oncology.
[55] N. Kadoya. Current Status of MR-Linac System. , 2017, Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics.
[56] Yasheng Chen,et al. Attenuation Correction of PET/MR Imaging. , 2017, Magnetic resonance imaging clinics of North America.
[57] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[58] 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.
[59] Andrew P. Leynes,et al. Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI , 2017, The Journal of Nuclear Medicine.
[60] Richard Kijowski,et al. Deep convolutional neural network for segmentation of knee joint anatomy , 2018, Magnetic resonance in medicine.
[61] Fang Liu,et al. Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging , 2018, Medical physics.
[62] Richard Kijowski,et al. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging , 2018, Magnetic resonance in medicine.
[63] Stanley J Kruger,et al. Pulmonary ventilation imaging in asthma and cystic fibrosis using oxygen‐enhanced 3D radial ultrashort echo time MRI , 2018, Journal of magnetic resonance imaging : JMRI.
[64] A. McMillan,et al. Deep learning Mr imaging–based attenuation correction for PeT/Mr imaging 1 , 2017 .
[65] Fang Liu,et al. Bayesian convolutional neural network based MRI brain extraction on nonhuman primates , 2018, NeuroImage.
[66] Fang Liu,et al. SUSAN: segment unannotated image structure using adversarial network , 2018, Magnetic resonance in medicine.