Review of 2-D/3-D Reconstruction Using Statistical Shape and Intensity Models and X-Ray Image Synthesis: Toward a Unified Framework
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Tania S. Douglas | Valérie Burdin | Thomas Vetter | Tinashe Mutsvangwa | Marcel Lüthi | Cornelius Johannes Frederik Reyneke | T. Vetter | V. Burdin | M. Lüthi | Tinashe Ernest Mutsvangwa | T. Douglas | C. Reyneke
[1] Agnes Grünerbl,et al. 3D image segmentation using combined shape-intensity prior models , 2007, International Journal of Computer Assisted Radiology and Surgery.
[2] R. Siddon. Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.
[3] Max Mignotte,et al. 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.
[4] Simon R. Arridge,et al. A survey of hierarchical non-linear medical image registration , 1999, Pattern Recognit..
[5] Guoyan Zheng,et al. 3D reconstruction of a patient-specific surface model of the proximal femur from calibrated x-ray radiographs: a validation study. , 2009, Medical physics.
[6] J. Jurvelin,et al. Estimation of 3D shape, internal density and mechanics of proximal femur by combining bone mineral density images with shape and density templates , 2012, Biomechanics and modeling in mechanobiology.
[7] Wolfgang Birkfellner,et al. Wobbled splatting—a fast perspective volume rendering method for simulation of x-ray images from CT , 2005, Physics in medicine and biology.
[8] Cristian Lorenz,et al. Temporal subtraction of chest radiographs compensating pose differences , 2011, Medical Imaging.
[9] Wolfgang Birkfellner,et al. Fast DRR splat rendering using common consumer graphics hardware. , 2007, Medical physics.
[10] Guoyan Zheng,et al. Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition , 2014, Comput. Vis. Image Underst..
[11] Laura Caponetti,et al. 3D Bone Reconstruction From Two X-ray Views , 1990, [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[12] Marleen de Bruijne,et al. 2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models , 2011, Medical Image Anal..
[13] Bostjan Likar,et al. A review of 3D/2D registration methods for image-guided interventions , 2012, Medical Image Anal..
[14] Thierry Cresson,et al. 3D shape reconstruction of bone from two x-ray images using 2D/3D non-rigid registration based on moving least-squares deformation , 2010, Medical Imaging.
[15] Wolfgang Birkfellner,et al. The Zernike Expansion - An Example of a Merit Function for 2D/3D Registration Based on Orthogonal Functions , 2008, MICCAI.
[16] Wee Kheng Leow,et al. Recovery of 3D Pose of Bones in Single 2D X-ray Images , 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).
[17] P. King. Medical imaging systems , 1986, Proceedings of the IEEE.
[18] L. Joskowicz,et al. Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT , 2003, IEEE Transactions on Medical Imaging.
[19] Russell H. Taylor,et al. Deformable 2D-3D Registration of the Pelvis with a Limited Field of View, Using Shape Statistics , 2007, MICCAI.
[20] D. Felson,et al. Magnetic resonance imaging-based three-dimensional bone shape of the knee predicts onset of knee osteoarthritis: data from the osteoarthritis initiative. , 2013, Arthritis and rheumatism.
[21] J. S. Marron,et al. Nested Sphere Statistics of Skeletal Models , 2013, Innovations for Shape Analysis, Models and Algorithms.
[22] Hans-Christian Hege,et al. Fast Generation of Virtual X-ray Images for Reconstruction of 3D Anatomy , 2013, IEEE Transactions on Visualization and Computer Graphics.
[23] Iasonas Kokkinos,et al. 3D Model-based Reconstruction of the Proximal Femur from Low-dose Biplanar X-Ray Images , 2011, BMVC.
[24] Graeme P. Penney,et al. Fully automated 2D-3D registration and verification , 2015, Medical Image Anal..
[25] Guoyan Zheng,et al. Statistical Shape and Deformation Models Based 2D–3D Reconstruction , 2017 .
[26] Guoyan Zheng,et al. Scaled, patient-specific 3D vertebral model reconstruction based on 2D lateral fluoroscopy , 2011, International Journal of Computer Assisted Radiology and Surgery.
[27] Lok Ming Lui,et al. Landmark constrained genus-one surface Teichmüller map applied to surface registration in medical imaging , 2015, Medical Image Anal..
[28] Guoyan Zheng,et al. Effective incorporating spatial information in a mutual information based 3D-2D registration of a CT volume to X-ray images , 2010, Comput. Medical Imaging Graph..
[29] Andriy Myronenko,et al. Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] F Lavaste,et al. [Geometrical modeling of the spine and the thorax for the biomechanical analysis of scoliotic deformities using the finite element method]. , 1995, Annales de chirurgie.
[31] Hans-Christian Hege,et al. Atlas-based 3D-Shape Reconstruction from X-Ray Images , 2006, 18th International Conference on Pattern Recognition (ICPR'06).
[32] Swapna Banerjee,et al. 2D/3D Non-rigid Image Registration by an Efficient Demons Approach , 2014, 2014 IEEE 27th International Symposium on Computer-Based Medical Systems.
[33] Lei Wang,et al. A novel mutual information-based similarity measure for 2D/3D registration in image guided intervention , 2013, 2013 1st International Conference on Orange Technologies (ICOT).
[34] Eric Nectoux,et al. Three-dimensional measurements of the lower extremity in children and adolescents using a low-dose biplanar X-ray device , 2012, European Radiology.
[35] Rüdiger Westermann,et al. Accelerated volume ray-casting using texture mapping , 2001, Proceedings Visualization, 2001. VIS '01..
[36] Diego Borro,et al. Study of a Ray Casting Technique for the Visualization of Deformable Volumes , 2014, IEEE Transactions on Visualization and Computer Graphics.
[37] David J. Hawkes,et al. A Comparison of 2D-3D Intensity-Based Registration and Feature-Based Registration for Neurointerventions , 2002, MICCAI.
[38] Nikos Paragios,et al. Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.
[39] D Mitton,et al. A Biplanar Reconstruction Method Based on 2D and 3D Contours: Application to the Distal Femur , 2003, Computer methods in biomechanics and biomedical engineering.
[40] Randy E. Ellis,et al. 2D/3D Deformable Registration Using a Hybrid Atlas , 2005, MICCAI.
[41] J. D. De Guise,et al. 3D reconstruction of the proximal femur with low-dose digital stereoradiography , 2004, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.
[42] Ingo Rechenberg,et al. Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .
[43] Timothy F. Cootes,et al. Active Appearance Models , 1998, ECCV.
[44] Elena De Momi,et al. Statistical shape models based 2D/3D registration methods for knee orthopaedic surgery , 2016 .
[45] Omar Ahmad,et al. Volumetric DXA (VXA): A new method to extract 3D information from multiple in vivo DXA images , 2010, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.
[46] Russell H. Taylor,et al. Construction and simplification of bone density models , 2001, SPIE Medical Imaging.
[47] Richard D. Komistek,et al. A robust method for registration of three-dimensional knee implant models to two-dimensional fluoroscopy images , 2003, IEEE Transactions on Medical Imaging.
[48] Z. Jane Wang,et al. A CNN Regression Approach for Real-Time 2D/3D Registration , 2016, IEEE Transactions on Medical Imaging.
[49] Mauricio Reyes,et al. Image-based vs. mesh-based statistical appearance models of the human femur: implications for finite element simulations. , 2014, Medical engineering & physics.
[50] J. Jurvelin,et al. Assessment of the 3-D shape and mechanics of the proximal femur using a shape template and a bone mineral density image , 2011, Biomechanics and modeling in mechanobiology.
[51] Kenji Shimada,et al. Cost‐ and time‐effective three‐dimensional bone‐shape reconstruction from X‐ray images , 2007, The international journal of medical robotics + computer assisted surgery : MRCAS.
[52] Carolyn Anglin,et al. Towards Robust Measurement of Pelvic Parameters from AP Radiographs using Articulated 3D Models , 2015 .
[53] Guoyan Zheng,et al. Reconstruction of Patient-Specific 3D Bone Model from Biplanar X-Ray Images and Point Distribution Models , 2006, 2006 International Conference on Image Processing.
[54] P J Prendergast,et al. A method to reconstruct patient-specific proximal femur surface models from planar pre-operative radiographs. , 2010, Medical engineering & physics.
[55] Joyoni Dey,et al. Targeted 2D/3D registration using ray normalization and a hybrid optimizer. , 2006, Medical physics.
[56] Jürgen Weese,et al. A comparison of similarity measures for use in 2-D-3-D medical image registration , 1998, IEEE Transactions on Medical Imaging.
[57] Chengwen Chu,et al. Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images , 2016, International Journal of Computer Assisted Radiology and Surgery.
[58] Guoyan Zheng. Personalized X-Ray Reconstruction of the Proximal Femur via Intensity-Based Non-rigid 2D-3D Registration , 2011, MICCAI.
[59] Daniel Rueckert,et al. Fast calculation of digitally reconstructed radiographs using light fields , 2003, SPIE Medical Imaging.
[60] D Mitton,et al. 3D reconstruction of the pelvis from bi-planar radiography , 2006, Computer methods in biomechanics and biomedical engineering.
[61] Zhiping Mu. A Fast DRR Generation Scheme for 3D-2D Image Registration Based on the Block Projection Method , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[62] Hanna Isaksson,et al. Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image , 2015, Medical Image Anal..
[63] Pavel Zemcík,et al. Intensity-based femoral atlas 2D/3D registration using Levenberg-Marquardt optimisation , 2016, SPIE Medical Imaging.
[64] Randy E. Ellis,et al. Hardware-Assisted 2D/3D Intensity-Based Registration for Assessing Patellar Tracking , 2004, MICCAI.
[65] Baba C. Vemuri,et al. Real-Time DRR Generation Using Cylindrical Harmonics , 2002, MICCAI.
[66] Martin Styner,et al. Evaluation of 3D Correspondence Methods for Model Building , 2003, IPMI.
[67] Alejandro F. Frangi,et al. 3D bone mineral density distribution and shape reconstruction of the proximal femur from a single simulated DXA image: an in vitro study , 2010, Medical Imaging.
[68] P. Hu,et al. Method for registration of 3D shapes without overlap for known 3D priors , 2021, Electronics Letters.
[69] W Skalli,et al. 3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences. , 2009, Medical engineering & physics.
[70] C. Anglin,et al. Radiological method for measuring patellofemoral tracking and tibiofemoral kinematics before and after total knee replacement , 2012, Bone & joint research.
[71] Russell H. Taylor,et al. Statistical Atlases of Bone Anatomy: Construction, Iterative Improvement and Validation , 2007, MICCAI.
[72] Alejandro F. Frangi,et al. 3D reconstruction of both shape and Bone Mineral Density distribution of the femur from DXA images , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[73] David Mitton,et al. Fast accurate stereoradiographic 3D-reconstruction of the spine using a combined geometric and statistic model. , 2004, Clinical biomechanics.
[74] Michel A. Audette,et al. Statistical Shape Model Construction of Lumbar Vertebrae and Intervertebral Discs in Segmentation for Discectomy Surgery Simulation , 2015, CSI@MICCAI.
[75] Xun Jia,et al. Graphics Processing Unit-Based High Performance Computing in Radiation Therapy , 2015 .
[76] Bhallamudi Ravi,et al. 3D femur model reconstruction from biplane X-ray images: a novel method based on Laplacian surface deformation , 2015, International Journal of Computer Assisted Radiology and Surgery.
[77] Pietro Cerveri,et al. 2D/3D reconstruction of the distal femur using statistical shape models addressing personalized surgical instruments in knee arthroplasty: A feasibility analysis , 2017, The international journal of medical robotics + computer assisted surgery : MRCAS.
[78] Robert B. Fisher,et al. A Comparison of Four Algorithms for Estimating 3-D Rigid Transformations , 1995, BMVC.
[79] Guoyan Zheng,et al. Non-rigid free-form 2D-3D registration using a B-spline-based statistical deformation model , 2017, Pattern Recognit..
[80] Russell H. Taylor,et al. Integrating Statistical Models of Bone Density into Shape Based 2 D-3 D Registration Framework , 2009 .
[81] Yuemin Zhu,et al. Digitally reconstructed radiograph generation by an adaptive Monte Carlo method , 2006, Physics in medicine and biology.
[82] W. Eric L. Grimson,et al. 2D-3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely sampled histogram estimators , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[83] Nahmkeon Hur,et al. The Study of Femoral 3D Reconstruction Process Based on Anatomical Parameters Using a Numerical Method , 2008 .
[84] Alejandro F. Frangi,et al. Reconstructing the 3D Shape and Bone Mineral Density Distribution of the Proximal Femur From Dual-Energy X-Ray Absorptiometry , 2011, IEEE Transactions on Medical Imaging.
[85] Russell H. Taylor,et al. Simultaneous pose estimation and patient-specific model reconstruction from single image using maximum penalized likelihood estimation (MPLE) , 2016, Pattern Recognit..
[86] Stephen D. Laycock,et al. GPU Accelerated Generation of Digitally Reconstructed Radiographs for 2-D/3-D Image Registration , 2012, IEEE Transactions on Biomedical Engineering.
[87] Wafa Skalli,et al. 3D reconstruction of rib cage geometry from biplanar radiographs using a statistical parametric model approach , 2016, Comput. methods Biomech. Biomed. Eng. Imaging Vis..
[88] Nicholas Ayache,et al. Articulated Spine Models for 3-D Reconstruction From Partial Radiographic Data , 2008, IEEE Transactions on Biomedical Engineering.
[89] Frank Sauer,et al. Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy , 2006, Medical Image Anal..
[90] Stéphane Lavallée,et al. Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery , 1999, Medical Image Anal..
[91] Leo Joskowicz,et al. Gradient-Based 2D/3D Rigid Registration of Fluoroscopic X-ray to CT , 2003, IEEE Trans. Medical Imaging.
[92] Max A. Viergever,et al. Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.
[93] M. Wybier,et al. Musculoskeletal imaging in progress: the EOS imaging system. , 2013, Joint, bone, spine : revue du rhumatisme.
[94] Tim Cootes,et al. Detection of vertebral fractures in DXA VFA images using statistical models of appearance and a semi-automatic segmentation , 2010, Osteoporosis International.
[95] Stefan Klein,et al. Simultaneous Multiresolution Strategies for Nonrigid Image Registration , 2013, IEEE Transactions on Image Processing.
[96] M. Levoy,et al. Fast volume rendering using a shear-warp factorization of the viewing transformation , 1994, SIGGRAPH.
[97] Farida Cheriet,et al. A Novel Method for the 3-D Reconstruction of Scoliotic Ribs From Frontal and Lateral Radiographs , 2011, IEEE Transactions on Biomedical Engineering.
[98] Alejandro F. Frangi,et al. 3D reconstruction of the lumbar vertebrae from anteroposterior and lateral dual-energy X-ray absorptiometry , 2013, Medical Image Anal..
[99] S. Bierma-Zeinstra,et al. Total hip replacement but not clinical osteoarthritis can be predicted by the shape of the hip: a prospective cohort study (CHECK). , 2012, Osteoarthritis and cartilage.
[100] Leo Joskowicz,et al. Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences , 2008, International Journal of Computer Assisted Radiology and Surgery.
[101] Zhe Chen,et al. Automated 2D-3D registration of portal images and CT data using line-segment enhancement. , 2008, Medical physics.
[102] Alejandro F. Frangi,et al. Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration , 2001, MICCAI.
[103] A. A. Zadpoor,et al. Statistical shape and appearance models of bones. , 2014, Bone.
[104] Simon Baker,et al. Active Appearance Models Revisited , 2004, International Journal of Computer Vision.
[105] Jinwei Sun,et al. 3D Reconstruction Method from Biplanar Radiography Using DLT Algorithm: Application to the Femur , 2010, 2010 First International Conference on Pervasive Computing, Signal Processing and Applications.
[106] Leo Joskowicz,et al. Effective Intensity-Based 2D/3D Rigid Registration between Fluoroscopic X-Ray and CT , 2003, MICCAI.
[107] Marco Viceconti,et al. Evaluation of the generality and accuracy of a new mesh morphing procedure for the human femur. , 2011, Medical engineering & physics.
[108] Bjorn De Sutter,et al. A Fast Algorithm to Calculate the Exact Radiological Path through a Pixel or Voxel Space , 1998 .
[109] Guoyan Zheng,et al. Statistical shape model-based reconstruction of a scaled, patient-specific surface model of the pelvis from a single standard AP x-ray radiograph. , 2010, Medical physics.
[110] R. Aspden,et al. Femoral geometry as a risk factor for osteoporotic hip fracture in men and women. , 2008, Medical engineering & physics.
[111] J. Keyak,et al. Generation of a 3D proximal femur shape from a single projection 2D radiographic image , 2009, Osteoporosis International.
[112] Kebin Jia,et al. A GPU-Based DRR Generation Method Using Cubic Window , 2012, 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.
[113] Farida Cheriet,et al. Personalized X-Ray 3-D Reconstruction of the Scoliotic Spine From Hybrid Statistical and Image-Based Models , 2009, IEEE Transactions on Medical Imaging.
[114] Zhonglin Zhu,et al. Construction of 3D human distal femoral surface models using a 3D statistical deformable model. , 2011, Journal of biomechanics.
[115] Marleen de Bruijne,et al. Statistical Shape Model-Based Femur Kinematics From Biplane Fluoroscopy , 2012, IEEE Transactions on Medical Imaging.
[116] David R. Haynor,et al. PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.
[117] Robert T. Schultz,et al. Registration of Cortical Anatomical Structures via Robust 3D Point Matching , 1999, IPMI.
[118] David Staub,et al. A digitally reconstructed radiograph algorithm calculated from first principles. , 2012, Medical physics.
[119] Bernhard Schölkopf,et al. Kernels, regularization and differential equations , 2008, Pattern Recognit..
[120] Paul A. Yushkevich,et al. Deformable M-Reps for 3D Medical Image Segmentation , 2003, International Journal of Computer Vision.
[121] Allan Hanbury,et al. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool , 2015, BMC Medical Imaging.
[122] Takeo Kanade,et al. Iterative x-ray/ct registration using accelerated volume rendering , 2001 .
[123] Natalie N. Braun,et al. Strategies for reducing radiation dose in CT. , 2009, Radiologic clinics of North America.
[124] Prasanth B. Nair,et al. Statistical modelling of the whole human femur incorporating geometric and material properties. , 2010, Medical engineering & physics.
[125] S. Bierma-Zeinstra,et al. Variation in joint shape of osteoarthritic knees. , 2011, Arthritis and rheumatism.
[126] Christian Roux,et al. 2-D–3-D Frequency Registration Using a Low-Dose Radiographic System for Knee Motion Estimation , 2013, IEEE Transactions on Biomedical Engineering.
[127] Hans-Peter Meinzer,et al. Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..
[128] Tania S. Douglas,et al. Interactive patient-specific 3D approximation of scapula bone shape from 2D X-ray images using landmark-constrained statistical shape model fitting , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[129] Raphaël Dumas,et al. A semi-automated method using interpolation and optimisation for the 3D reconstruction of the spine from bi-planar radiography: a precision and accuracy study , 2007, Medical & Biological Engineering & Computing.
[130] Thomas Malzbender,et al. Fourier volume rendering , 1993, TOGS.
[131] W Skalli,et al. Fast 3D reconstruction of the lower limb using a parametric model and statistical inferences and clinical measurements calculation from biplanar X-rays , 2012, Computer methods in biomechanics and biomedical engineering.
[132] E. Ntasis,et al. Real time digital reconstructed radiograph (DRR) rendering in frequency domain , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.
[133] Russell H. Taylor,et al. Assessing accuracy factors in deformable 2D/3D medical image registration using a statistical pelvis model , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[134] Cedric Schwartz,et al. An Automated Statistical Shape Model Developmental Pipeline: Application to the Human Scapula and Humerus , 2015, IEEE Transactions on Biomedical Engineering.
[135] Paul J. Besl,et al. A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[136] Guoyan Zheng. 3D volumetric intensity reconsturction from 2D x-ray images using partial least squares regression , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.
[137] Johan Thunberg,et al. Shape‐aware surface reconstruction from sparse 3D point‐clouds , 2016, Medical Image Anal..
[138] Benedikt Schuler,et al. Comparison of Different Metrics for Appearance-Model-Based 2D/3D-registration with X-ray Images , 2008, Bildverarbeitung für die Medizin.
[139] Thomas Gerig,et al. Gaussian Process Morphable Models , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[140] Bostjan Likar,et al. Evaluation of similarity measures for reconstruction-based registration in image-guided radiotherapy and surgery. , 2006, International journal of radiation oncology, biology, physics.