3D reconstruction of long bones utilising magnetic resonance imaging (MRI)

3D Reconstruction of Long Bones Utilising Magnetic Resonance Imaging (MRI) Thesis submitted by Kanchana Rathnayaka Rathnayaka Mudiyanselage MBBS This thesis is submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Institute of Health and Biomedical Innovation School of Engineering Systems Faculty of Built Environment and Engineering Queensland University of Technology Brisbane, Australia 2011 Abstract Abstract The design of pre-contoured fracture fixation implants (plates and nails) that correctly fit the anatomy of a patient utilises 3D models of long bones with accurate geometric representation 3D data is usually available from computed tomography (CT) scans of human cadavers that generally represent the above 60 year old age group Thus, despite the fact that half of the seriously injured population comes from the 30 year age group and below, virtually no data exists from these younger age groups to inform the design of implants that optimally fit patients from these groups Hence, relevant bone data from these age groups is required The current gold standard for acquiring such data–CT–involves ionising radiation and cannot be used to scan healthy human volunteers Magnetic resonance imaging (MRI) has been shown to be a potential alternative in the previous studies conducted using small bones (tarsal bones) and parts of the long bones However, in order to use MRI effectively for 3D reconstruction of human long bones, further validations using long bones and appropriate reference standards are required Accurate reconstruction of 3D models from CT or MRI data sets requires an accurate image segmentation method Currently available sophisticated segmentation methods involve complex programming and mathematics that researchers are not trained to perform Therefore, an accurate but relatively simple segmentation method is required for segmentation of CT and MRI data Furthermore, some of the limitations of 1.5T MRI such as very long scanning times and poor contrast in articular regions can potentially be reduced by using higher field 3T MRI imaging However, a quantification of the signal to noise ratio (SNR) gain at the bone - soft tissue interface should be performed; this is not reported in the literature As MRI scanning of long bones has very long scanning times, the acquired images are more prone to motion artefacts due to random movements of the subject‟s limbs One of the artefacts observed is the step artefact that is believed to occur from the random movements of the volunteer during a scan This needs to be corrected before the models can be used for implant design As the first aim, this study investigated two segmentation methods: intensity thresholding and Canny edge detection as accurate but simple segmentation methods for segmentation of MRI and CT data The second aim was to investigate the III Abstract usability of MRI as a radiation free imaging alternative to CT for reconstruction of 3D models of long bones The third aim was to use 3T MRI to improve the poor contrast in articular regions and long scanning times of current MRI The fourth and final aim was to minimise the step artefact using 3D modelling techniques The segmentation methods were investigated using CT scans of five ovine femora The single level thresholding was performed using a visually selected threshold level to segment the complete femur For multilevel thresholding, multiple threshold levels calculated from the threshold selection method were used for the proximal, diaphyseal and distal regions of the femur Canny edge detection was used by delineating the outer and inner contour of 2D images and then combining them to generate the 3D model Models generated from these methods were compared to the reference standard generated using the mechanical contact scans of the denuded bone The second aim was achieved using CT and MRI scans of five ovine femora and segmenting them using the multilevel threshold method A surface geometric comparison was conducted between CT based, MRI based and reference models To quantitatively compare the 1.5T images to the 3T MRI images, the right lower limbs of five healthy volunteers were scanned using scanners from the same manufacturer The images obtained using the identical protocols were compared by means of SNR and contrast to noise ratio (CNR) of muscle, bone marrow and bone In order to correct the step artefact in the final 3D models, the step was simulated in five ovine femora scanned with a 3T MRI scanner The step was corrected using the iterative closest point (ICP) algorithm based aligning method The present study demonstrated that the multi-threshold approach in combination with the threshold selection method can generate 3D models from long bones with an average deviation of 0.18 mm The same was 0.24 mm of the single threshold method There was a significant statistical difference between the accuracy of models generated by the two methods In comparison, the Canny edge detection method generated average deviation of 0.20 mm MRI based models exhibited 0.23 mm average deviation in comparison to the 0.18 mm average deviation of CT based models The differences were not statistically significant 3T MRI improved the contrast in the bone–muscle interfaces of most anatomical regions of femora and tibiae, potentially improving the inaccuracies conferred by poor contrast of the articular regions Using the robust ICP algorithm to align the 3D surfaces, the step IV Abstract artefact that occurred by the volunteer moving the leg was corrected, generating errors of 0.32 ± 0.02 mm when compared with the reference standard The study concludes that magnetic resonance imaging, together with simple multilevel thresholding segmentation, is able to produce 3D models of long bones with accurate geometric representations The method is, therefore, a potential alternative to the current gold standard CT imaging V Keywords Keywords Magnetic resonance imaging Computed tomography Image segmentation 3D models Long bones Thresholding Edge detection Multi thresholding Higher field MRI Musculoskeletal MRI Motion artefacts Validation VI Contents Contents Abstract III Keywords VI List of figures XIII List of tables XV Publications, presentations and awards XVI Authorship XIX Acknowledgement XXI Abbreviations XXII Chapter Introduction Chapter Quantitative imaging of the skeletal system for 3D reconstruction (Background) 2.1 Introduction 2.2 Computed tomography (CT) 2.3 2.2.1 Basic principles of CT 2.2.2 Radiation exposure during CT imaging Magnetic resonance imaging (MRI) .10 2.3.1 Basic principles of MRI .10 2.3.2 How tissue contrast is determined 12 2.3.3 Selection of slice position and thickness 13 2.3.4 Pulse sequences 14 2.3.5 MRI safety .14 2.3.6 Signal to noise ratio of an MRI system .14 2.3.7 Artefacts of MRI 15 2.3.7.1 Motion artefacts 16 2.3.7.2 Magnetic susceptibility difference artefact 16 2.3.7.3 Chemical shift 17 VII Contents 2.3.8 MRI for imaging of the skeletal system 17 2.3.9 Advantages and current limitations of MRI 18 2.4 2.3.9.1 Longer scanning times of MRI 18 2.3.9.2 Poor contrast in certain anatomical regions 18 2.3.9.3 Non-uniformity of the external magnetic field 19 2.3.9.4 Limited accessibility 19 Summary 20 Chapter Image processing and surface reconstruction 21 3.1 Introduction 21 3.2 Acquisition of data for 3D modelling of bones 22 3.2.1 Effect of in plane resolution and slice thickness on accuracy of reconstructed 3D models 23 3.3 Image segmentation 24 3.3.1 Manual segmentation 25 3.3.2 Intensity thresholding 25 3.3.2.1 Selecting an appropriate threshold level 26 3.3.2.2 Multilevel thresholding 26 3.3.3 Edge detection 28 3.3.4 Region growing 28 3.3.5 Sophisticated segmentation methods 29 3.4 Surface generation 29 3.5 Registration (aligning) and comparison of surfaces 30 3.6 A reference standard for validating 3D models of bones 30 3.7 Aims of the study 32 3.8 Methods 32 VIII 3.8.1 Samples 32 3.8.2 Image segmentation 32 Contents 3.8.3 Reference model for validation of the outer 3D models 33 3.8.3.1 Removal of the soft tissues from long bones .33 3.8.3.2 Scanning of the bone‟s outer surface using the contact scanner 34 3.8.3.3 Reconstruction of the 3D model from scanned surfaces 37 3.8.4 Reference model for validation of the medullary canal .39 3.8.5 Basic 3D modelling techniques using Rapidform 2006 41 3.9 3.8.5.1 Registration of 3D surfaces using Rapidform 2006 .41 3.8.5.2 Comparison of the aligned 3D models 43 3.8.5.3 Dividing the 3D models of bones into anatomical regions 44 Results 44 3.10 Summary, discussion and conclusion 45 3.11 Paper 1: Effect of CT image segmentation methods on the accuracy of long bone 3D reconstructions (published) .48 Chapter Application of 3D modelling techniques for orthopaedic implant design and validation 57 4.1 Introduction 57 4.2 3D models for implant design and validation 58 4.3 Aims of the study 59 4.4 Methods .59 4.5 Results 59 4.6 Summary, discussion and conclusion 60 4.7 Paper 2: Quantitative fit assessment of tibial nail designs using 3D computer modelling (published) 61 Chapter Magnetic resonance imaging for 3D reconstruction of long bones 67 5.1 Introduction 67 5.2 Imaging of skeletal system with MRI 68 5.3 Aims of the study 71 5.4 Methods .71 IX Contents 5.5 Results 72 5.6 Summary, discussion and conclusion 72 5.7 Paper 3: Quantification of the accuracy of MRI generated 3D models of long bones compared to CT generated 3D models (in press) 74 Chapter Higher field strength MRI scanning of long bones for generation of 3D models 83 6.1 Introduction 83 6.2 Theoretical consideration of increased SNR at 3T 84 6.3 3T MRI for musculoskeletal system imaging 84 6.3.1 Spin relaxation times and flip angle 85 6.3.2 Fat suppression 86 6.3.3 Magnetic susceptibility at 3T MRI 87 6.3.4 Chemical shift at 3T 87 6.3.5 MRI safety at 3T 88 6.4 Aims of the study 88 6.5 Methods 88 6.5.1 Samples 88 6.5.2 Measuring the quality of MR images 88 6.5.3 Quantification of spin relaxation times 90 6.5.4 Comparison of 1.5T and 3T imaging of musculoskeletal system 91 6.6 Results 93 6.7 Summary, discussion and conclusion 93 6.8 Paper 4: 3T MRI improves bone-soft tissue image contrast compared with 1.5T MRI (Submitted – under review) 96 Chapter Step artefact caused by Magnetic Resonance Imaging of long bone 121 7.1 Introduction 121 7.2 Motion artefact of MRI 122 7.3 Aims of the study 123 X References 20 Martel, A.L., et al., Assessment of 3-dimensional magnetic resonance imaging fast low angle shot images for computer assisted spinal surgery Computer Aided Surgery, 1998 3(1): p 40-44 21 Fassbind, M., et al Quantifying foot bone motion via magnetic resonance imaging in 52nd Annual Meeting of the Orthopaedic Research Society 2006 Chicago, IL: The orthopaedic research society 22 Edelstein, W.A., et al., The intrinsic signal-to-noise ratio in NMR imaging Magnetic Resonance in Medicine, 1986 3(4): p 604-618 23 Ramnath, R.R., 3T MR imaging of the musculoskeletal system (Part I): considerations, coils, and challenges Magnetic Resonance Imaging Clinics of North America, 2006 14(1): p 27-40 24 Hoad, C.L., et al., A 3D MRI sequence for computer assisted surgery of the lumbar spine Physics in Medicine and Biology, 2001 46: p N213-N220 25 Wolf, P., et al., Reliability of tarsal bone segmentation and its contribution to MR kinematic analysis methods Computerized Medical Imaging and Graphics, 2007 31(7): p 523-530 26 Schmutz, B., et al., Using MRI for the imaging of long bones: First Experience Journal of biomechanics, 2008 41(Supplement1): p S188 27 Cogbill, T.H and K.J Zeigelbein, Computed Tomography, Magnetic Resonance, and Ultrasound Imaging: Basic Principles, Glossary of terms, and Patient Safety Surgical Clinics of North America, 2011 91(1): p 1-14 28 Novelline, R.A., et al., Helical CT in Emergency Radiology1 Radiology, 1999 213(2): p 321-339 29 Blackmore, C.C., et al., Cervical Spine Screening with CT in Trauma Patients: A Cost-effectiveness Analysis1 Radiology, 1999 212(1): p 117125 30 HALL, E.J and D.J BRENNER, Cancer risks from diagnostic radiology British Journal of Radiology, 2008 81(965): p 362-378 161 References 31 Smith-Bindman, R., et al., Radiation Dose Associated With Common Computed Tomography Examinations and the Associated Lifetime Attributable Risk of Cancer Archives of Internal Medicine, 2009 169(22): p 2078-2086 32 Berrington de Gonzalez, A., et al., Projected Cancer Risks From Computed Tomographic Scans Performed in the United States in 2007 Archives of Internal Medicine, 2009 169(22): p 2071-2077 33 Griffey, R.T and A Sodickson, Cumulative Radiation Exposure and Cancer Risk Estimates in Emergency Department Patients Undergoing Repeat or Multiple CT American Journal of Roentgenology, 2009 192(4): p 887-892 34 Raeburn, S., Radiation doses in computed tomography: The increasing doses of radiation need to be controlled BMJ, 2000 320: p 593-594 35 Wall, B and D Hart, Revised radiation doses for typical X-ray examinations Report on a recent review of doses to patients from medical X-ray examinations in the UK by NRPB National Radiological Protection Board British Journal of Radiology, 1997 70(833): p 437-439 36 Radiologists, T.R.C.o., Making the best use of department of clinical radiology: guidelines for doctors 5th ed 2003, London: Royal College of Radiologists 37 Semelka, R.C., et al., Imaging strategies to reduce the risk of radiation in CT studies, including selective substitution with MRI Journal of Magnetic Resonance Imaging, 2007 25(5): p 900-909 38 Zannoni, C., A Cappello, and M Viceconti, Optimal CT scanning plan for long-bone 3-D reconstruction Medical Imaging, IEEE Transactions on, 1998 17(4): p 663-666 39 Sint Jan, S.V., et al., Low-dose computed tomography: A solution for in vivo medical imaging and accurate patient-specific 3D bone modeling? Clinical Biomechanics, 2006 21(9): p 992-998 162 References 40 Brown, M.A and R.C Semelka, MRI Basic principles and applications 4th ed 2010, New Jersey: John Wiley & Sons 41 Lipton, M.L., Totally Accessible MRI 2008, New York: Springer 42 Kangarlu, A and P.-M.L Robitaille, Biological effects and health implications in magnetic resonance imaging Concepts in Magnetic Resonance, 2000 12(5): p 321-359 43 Merkle, E.M and B.M Dale, Abdominal MRI at 3.0 T: The Basics Revisited American Journal of Roentgenology, 2006 186(6): p 1524-1532 44 Erasmus, L., et al., A short overview of MRI artefacts South African Journal of Radiology, 2004 8(2): p 13-17 45 Schmutz, B., et al., Fit Assessment of Anatomic Plates for the Distal Medial Tibia Journal of Orthopaedic Trauma, 2008 22(4): p 258-263 46 Goyal, K.S., et al., Analysis of anatomic periarticular tibial plate fit on normal adults Clinical Orthopaedics and Related Research, 2007 461: p 245-257 47 Schmutz, B., et al., Fit optimisation of a distal medial tibia plate Computer Methods in Biomechanics & Biomedical Engineering, 2011 14(4): p 359364 48 Park, A., J DiStefano, and T Nguyen, Congruency of Scapula Locking Plates Implications for Implant Design, in 56th Annual Meeting of the Orthopaedic Research Society 2010: New Orleans, LA p Poster No 1746 49 Schmutz, B., et al., Quantitative fit assessment of tibial nail designs using 3D computer modelling Injury, 2010 41(2): p 216-219 50 Wehner, T., Simulation der Frakturheilung beim Fixateur Externe und bei intramedullarem Nagel., in Medizinische Fakultat 2008, Universitat Ulm 163 References 51 Niu, Q., et al., Image processing, geometric modeling and data management for development of virtual bone surgery system Computer Aided Surgery, 2008 13(1): p 30-40 52 Handels, H., et al., Virtual planning of hip operations and individual adaption of endoprostheses in orthopaedic surgery International Journal of Medical Informatics, 2000 58-59: p 21-28 53 Zachow, S., et al., Draw and cut: intuitive 3D osteotomy planning on polygonal bone models International Congress Series, 2003 1256: p 362369 54 Xia, J., et al., Computer-assisted three-dimensional surgical planning and simulation: 3D color facial model generation International Journal of Oral and Maxillofacial Surgery, 2000 29(1): p 2-10 55 Gomez-Benito, M.J., et al., A 3D Computational Simulation of Fracture Callus Formation: Influence of the Stiffness of the External Fixator Journal of Biomechanical Engineering, 2006 128(3): p 290-299 56 Lacroix, D and P.J Prendergast, Three-dimensional simulation of fracture repair in the human tibia Computer Methods in Biomechanics and Biomedical Engineering, 2002 5(5): p 369-76 57 Anwar, A., A Mekonnen, and P Diamantopoulos, FEA modelling of anatomical structures Journal of Biomechanics, 2006 39(Supplement 1): p S423-S423 58 Trabelsi, N., Z Yosibash, and C Milgrom, Validation of subject-specific automated p-FE analysis of the proximal femur Journal of Biomechanics, 2009 42(3): p 234-241 59 Keyak, J.H and Y Falkinstein, Comparison of in situ and in vitro CT scanbased finite element model predictions of proximal femoral fracture load Medical Engineering & Physics, 2003 25(9): p 781-787 164 References 60 Pérez del Palomar, A and M Doblaré, Dynamic 3D FE modelling of the human temporomandibular joint during whiplash Medical Engineering & Physics, 2008 30(6): p 700-709 61 MacNeil, J.A and S.K Boyd, Bone strength at the distal radius can be estimated from high-resolution peripheral quantitative computed tomography and the finite element method Bone, 2008 42(6): p 1203-1213 62 Viceconti, M., et al., CT data sets surface extraction for biomechanical modeling of long bones Computer Methods and Programs in Biomedicine, 1999 59(3): p 159-166 63 Silva, M.J., et al., Direct and computed tomography thickness measurements of the human, lumbar vertebral shell and endplate Bone, 1994 15(4): p 409414 64 Dougherty, G and D Newman, Measurement of thickness and density of thin structures by computed tomography: A simulation study Medical Physics, 1999 26(7): p 1341-1348 65 Maloul, A., J Fialkov, and C Whyne, The Impact of Voxel Size-Based Inaccuracies on the Mechanical Behavior of Thin Bone Structures Annals of Biomedical Engineering, 2011 39(3): p 1092-1100 66 Prevrhal S, et al., Accuracy of CT-based thickness measurement of thin structures: modeling of limited spatial resolution in all three dimensions Medical Physics, 2003 30(1): p 1-8 67 Souza, A., J.K Udupa, and P.K Saha, Volume rendering in the presence of partial volume effects Medical Imaging, IEEE Transactions on, 2005 24(2): p 223-235 68 Heuscher, D.J and M Vembar, Reduced partial volume artifacts using spiral computed tomography and an integrating interpolator Medical Physics, 1999 26(2): p 276-286 165 References 69 Schmutz, B., M.E Wullschleger, and M.A Schuetz The effect of CT slice spacing on the geometry of 3D models in Proceedings 6th Australasian Biomechanics Conference 2007 Auckland, New Zealand: The University of Auckland 70 Drapikowski, P., Surface modeling-Uncertainty estimation and visualization Computerized Medical Imaging and Graphics, 2008 32(2): p 134-139 71 Kurmis, A.P., J.P Slavotinek, and K.J Reynolds, The influence of slice thickness on the volume measurement accuracy of 3-D MR reconstructions of acrylic phantoms: a precursor to knee imaging Radiography, 2004 10(4): p 277-285 72 Pal, N.R and S.K Pal, A review on image segmentation techniques Pattern Recognition, 1993 26(9): p 1277-1294 73 Olabarriaga, S.D and A.W.M Smeulders, Interaction in the segmentation of medical images: A survey Medical Image Analysis, 2001 5(2): p 127-142 74 Pham, D.L., C Xu, and J.L Prince, Current methods in medical image segmentation Annual Review of Biomedical Engineering, 2000 2(1): p 315337 75 Behiels, G., et al., Evaluation of image features and search strategies for segmentation of bone structures in radiographs using Active Shape Models Medical Image Analysis, 2002 6(1): p 47-62 76 Lee, H., et al., Left Ventricular segmentation using graph searching on intensity and gradiant and a priori knowledge (1vGIGA) for short-axis cardiac mgnetic resonance imaging Journal of Magnetic Resonance Imaging, 2008 28: p 1393-1401 77 Santarelli, M.F., et al., Automated cardiac MR image segmentation: theory and measurement evaluation Medical Engineering & Physics, 2003 25(2): p 149-159 166 References 78 Zhou, X., et al., Automated segmentation of mammary gland regions in noncontrast X-ray CT images Computerized Medical Imaging and Graphics, 2008 32(8): p 699-709 79 Zoroofi, R.A., et al., Automated segmentation of necrotic femoral head from 3D MR data Computerized Medical Imaging and Graphics, 2004 28(5): p 267-278 80 Peter, Z., et al., A constrained region growing approach based on watershed for the segmentation of low contrast structures in bone micro-CT images Pattern Recognition, 2008 41(7): p 2358-2368 81 Hirano, S and Y Hata, Fuzzy expert system for foot CT image segmentation Image and Vision Computing, 2001 19(4): p 207-216 82 Cristoforetti, A., et al., Isolation of the left atrial surface from cardiac multidetector CT images based on marker controlled watershed segmentation Medical Engineering & Physics, 2008 30(1): p 48-58 83 DeVries, N., et al., Validation of phalanx bone three-dimensional surface segmentation from computed tomography images using laser scanning Skeletal Radiology, 2008 37(1): p 35-42 84 Haas, B., et al., Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies Physics in Medicine and Biology, 2008 53(6): p 1751-1771 85 de Bruin, P.W., et al., Interactive 3D segmentation using connected orthogonal contours Computers in Biology and Medicine, 2005 35(4): p 329-346 86 Bokde, A.L.W., et al., Reliable manual segmentation of the frontal, parietal, temporal, and occipital lobes on magnetic resonance images of healthy subjects Brain Research Protocols, 2005 14(3): p 135-145 167 References 87 Stammberger, T., et al., Interobserver reproducibility of quantitative cartilage measurements: comparison of B-spline snakes and manual segmentation Magnetic Resonance Imaging, 1999 17(7): p 1033-1042 88 Bryce, C.D., et al., Validation of three-dimensional models of in situ scapulae Journal of Shoulder and Elbow Surgery, 2008 17(5): p 825-832 89 Ward, K.A., J.E Adams, and T.N Hangartner, Recommendations for Thresholds for Cortical Bone Geometry and Density Measurement by Peripheral Quantitative Computed Tomography Calcified Tissue International, 2005 77(5): p 275-280 90 Jong Bae, K., et al., A Real-Time Region-Based Motion Segmentation Using Adaptive Thresholding and K-Means Clustering Lecture notes in computer science, 2001 2256: p 213-224 91 Wiemker, R and V Pekar, Fast computation of isosurface contour spectra for volume visualization International Congress Series, 2001 1230: p 389394 92 Batenburg, K.J and J Sijbers, Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization Medical Imaging, IEEE Transactions on, 2009 28(5): p 676-686 93 Guo, R and S.M Pandit, Automatic threshold selection based on histogram modes and a discriminant criterion Machine Vision and Applications, 1998 10(5): p 331-338 94 Wang, L and J Bai, Threshold selection by clustering gray levels of boundary Pattern Recognition Letters, 2003 24(12): p 1983-1999 95 Zhang, J., et al., Fast segmentation of bone in CT images using 3D adaptive thresholding Computers in Biology and Medicine, 2010 40(2): p 231-236 96 Marr, D and E Hildreth, Theory of Edge Detection Proceedings of the Royal Society of London Series B Biological Sciences, 1980 207(1167): p 187217 168 References 97 Testi, D., et al., Border-tracing algorithm implementation for the femoral geometry reconstruction Computer Methods and Programs in Biomedicine, 2001 65(3): p 175-182 98 Canny, J., A computational approach to edge detection IEEE Transaction on Pattern Analysis and Machine Intelligence, 1986 8(6): p 679-698 99 Demigny, D., A discrete expression of Canny's criteria for step edge detector performances evaluation 1997 p 1199-1211 100 Bourennane, E., et al., Generalization of Canny-Deriche filter for detection of noisy exponential edge Signal Processing, 2002 82(10): p 1317-1328 101 Heath, M.D., et al., A robust visual method for assessing the relative performance of edge-detection algorithms Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997 19(12): p 1338-1359 102 Adams, R and L Bischof, Seeded region growing Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1994 16(6): p 641-647 103 Yan, K., K Engelke, and W.A Kalender, A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data Medical Imaging, IEEE Transactions on, 2003 22(5): p 586-598 104 Syoji, K., K Katsuya, and H Yutaka, Fully Automated Segmentation of Cerebral Ventricles from 3-D SPGR MR Images using Fuzzy Representative Line Soft computing, 2006 10 105 Ahmadian, A., et al., Correction of oral contrast artifacts in CT-based attenuation correction of PET images using an automated segmentation algorithm European Journal of Nuclear Medicine & Molecular Imaging, 2008 35(10): p 1812-1823 106 Middleton, I and R.I Damper, Segmentation of magnetic resonance images using a combination of neural networks and active contour models Medical Engineering & Physics, 2004 26(1): p 71-86 169 References 107 Ramme, A., et al., Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm Journal of Digital Imaging, 2009 22(5): p 483-491 108 Ramme, A., et al., EM Segmentation of the Distal Femur and Proximal Tibia: A High-Throughput Approach to Anatomic Surface Generation Annals of Biomedical Engineering, 2011 39(5): p 1555-1562 109 Chen, T., L Serra, and H Ng, Surface extraction: dividing voxels International Congress Series, 2004 1268: p 225-230 110 Newman, T.S and H Yi, A survey of the marching cubes algorithm Computers & Graphics, 2006 30(5): p 854-879 111 Lucas, L., D Gillard, and Y Rémion, A new unsupervised cube-based algorithm for iso-surface generation Computer Networks and ISDN Systems, 1997 29(14): p 1737-1744 112 Lorensen, W.E and H.E Cline, Marching cubes: A high resolution 3D surface construction algorithm SIGGRAPH Comput Graph., 1987 21(4): p 163-169 113 Popescu, F., et al., A new method to compare planned and achieved position of an orthopaedic implant Computer Methods and Programs in Biomedicine, 2003 71(2): p 117-127 114 Tocheri, M.W., et al., A 3D quantitative comparison of trapezium and trapezoid relative articular and nonarticular surface areas in modern humans and great apes Journal of Human Evolution, 2005 49(5): p 570586 115 Li, K., et al., Mapping ligament insertion sites onto bone surfaces in knee by co-registration of CT and digitization data Journal of Biomechanics, 2009 42(15): p 2624-2626 170 References 116 Schmutz, B., K.J Reynolds, and J.P Slavotinek, Development and validation of a generic 3D model of the distal femur Computer Methods in Biomechanics & Biomedical Engineering, 2006 9(5): p 305-312 117 Ruedi, T.P and W.M Murphy, AO Principles of Fracture Management Thieme Stuttgart, New York, 2001: p 50-51 118 Haraguchi, K., et al., Comparison of Fit and Fill Between Anatomic Stem and Straight Tapered Stem Using Virtual Implantation on the ORTHODOC Workstation Computer Aided Surgery, 2001 6(5): p 290-296 119 Genant, H and S Majumdar, High-resolution magnetic resonance imaging of trabecular bone structure Osteoporosis International, 1997 7(0): p 135-139 120 Majumdar, S., et al., Magnetic resonance imaging of trabecular bone structure in the distal radius: Relationship with X-ray tomographic microscopy and biomechanics Osteoporosis International, 1996 6(5): p 376385 121 Wehrli, F.W., et al., Role of Magnetic Resonance for Assessing Structure and Function of Trabecular Bone Topics in Magnetic Resonance Imaging, 2002 13(5): p 335-355 122 Liu, J., et al., Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis Medical Physics, 2008 35(8): p 3637-3649 123 Pillai, R.R., et al., MRI-based modeling for evaluation of in vivo contact mechanics in the human wrist during active light grasp Journal of Biomechanics, 2007 40(12): p 2781-2787 124 Moro-oka, T.-a., et al., Can magnetic resonance imaging-derived bone models be used for accurate motion measurement with single-plane threedimensional shape registration? Journal of Orthopaedic Research, 2007 25(7): p 867-872 171 References 125 DeFrate, L.E., et al., In vivo tibiofemoral contact analysis using 3D MRIbased knee models Journal of Biomechanics, 2004 37(10): p 1499-1504 126 Chen, B., et al., Combined MR imaging towards subject-specific knee contact analysis Visual Computer, 2011 27(2): p 121-128 127 Hao, Z., et al., A finite element 3D model of in vivo human knee joint based on MRI for the tibiofemoral joint contact analysis 2007 p 616-622 128 Gold, G.E., et al., Musculoskeletal MRI at 3.0 T: Initial Clinical Experience American Journal of Roentgenology, 2004 183(5): p 1479-1486 129 Tsurusaki, M., et al., Quantitative and qualitative comparison of 3.0 T and 1.5 T MR imaging of the liver in patients with diffuse parenchymal liver disease European Journal of Radiology, 2009 72(2): p 314-320 130 Ramalho, M., et al., Liver MR Imaging: 1.5T versus 3T Magnetic Resonance Imaging Clinics of North America, 2007 15(3): p 321-347 131 Rakow-Penner, R., et al., Relaxation times of breast tissue at 1.5T and 3T measured using IDEAL Journal of Magnetic Resonance Imaging, 2006 23(1): p 87-91 132 Frayne, R., et al., Magnetic resonance imaging at 3.0 Tesla: Challenges and advantages in clinical neurological imaging Investigative Radiology, 2003 38(7): p 385-402 133 Beyersdorff, D., et al., MRI of Prostate Cancer at 1.5 and 3.0 T: Comparison of Image Quality in Tumor Detection and Staging American Journal of Roentgenology, 2005 185(5): p 1214-1220 134 Schindera, S.T., et al., Abdominal Magnetic Resonance Imaging at 3.0 T: What Is the Ultimate Gain in Signal-to-Noise Ratio? Academic Radiology, 2006 13(10): p 1236-1243 135 Bowers, M.E., et al., Effects of ACL interference screws on articular cartilage volume and thickness measurements with 1.5 T and T MRI Osteoarthritis and Cartilage, 2008 16(5): p 572-578 172 References 136 Chen, C.A., et al., Cartilage morphology at 3.0T: Assessment of threedimensional magnetic resonance imaging techniques Journal of Magnetic Resonance Imaging, 2010 32(1): p 173-183 137 Stehling, C., et al., Three-Tesla Magnetic Resonance Imaging of the Wrist: Diagnostic Performance Compared to 1.5-T Journal of Computer Assisted Tomography, 2009 33(6): p 934-939 138 Lambert, A., et al., Rotator cuff tears: value of 3.0T MRI Journal of Radiology, 2009 90: p 583-588 139 Duewell, S.H., et al., Musculoskeletal MR imaging at T and at 1.5 T: comparison of relaxation times and image contrast Radiology, 1995 196(2): p 551-555 140 Bernstein, M.A., J Huston, and H.A Ward, Imaging artifacts at 3.0T Journal of Magnetic Resonance Imaging, 2006 24(4): p 735-746 141 Kuo, R., et al., 3.0 Tesla imaging of the musculoskeletal system Journal of Magnetic Resonance Imaging, 2007 25(2): p 245-261 142 Soher, B.J., B.M Dale, and E.M Merkle, A Review of MR Physics: 3T versus 1.5T Magnetic Resonance Imaging Clinics of North America, 2007 15(3): p 277-290 143 Ramnath, R.R., 3T MR imaging of the musculoskeletal system (Part II): clinical applications Magnetic Resonance Imaging Clinics of North America, 2006 14(1): p 41-62 144 Lawrence, N.T., 3T MRI in clinical practice Applied Radiology, 2005 34(1): p 145 Mosher, T.J., Musculoskeletal Imaging at 3T: Current Techniques and Future Applications Magnetic Resonance Imaging Clinics of North America, 2006 14(1): p 63-76 146 Norris, D.G., High field human imaging Journal of Magnetic Resonance Imaging, 2003 18(5): p 519-529 173 References 147 Gold, G.E., et al., Musculoskeletal MRI at 3.0 T: Relaxation Times and Image Contrast American Journal of Roentgenology, 2004 183(2): p 343-351 148 Stanisz, G.J., et al., T1, T2 relaxation and magnetization transfer in tissue at 3T Magnetic Resonance in Medicine, 2005 54(3): p 507-512 149 Delfaut, E.M., et al., Fat Suppression in MR Imaging: Techniques and Pitfalls Radiographics, 1999 19(2): p 373-382 150 Abduljalil, A.M and P.-M.L Robitaille, Macroscopic Susceptibility in Ultra High Field MRI Journal of Computer Assisted Tomography, 1999 23(6): p 832-841 151 Peh, W.C.G and J.H.M Chan, Artifacts in musculoskeletal magnetic resonance imaging: identification and correction Skeletal Radiology, 2001 30(4): p 179-191 152 Firbank, M.J and et al., A comparison of two methods for measuring the signal to noise ratio on MR images Physics in Medicine and Biology, 1999 44(12): p N261 153 Dietrich, O., et al., Measurement of signal-to-noise ratios in MR images: Influence of multichannel coils, parallel imaging, and reconstruction filters Journal of Magnetic Resonance Imaging, 2007 26(2): p 375-385 154 Henkelman, R.M., Measurement of signal intensities in the presence of noise in MR images Medical Physics, 1985 12(2): p 232-233 155 Edelstein, W.A., P.A Bottomley, and L.M Pfeifer, A signal-to-noise calibration procedure for NMR imaging systems Medical Physics, 1984 11(2): p 180-185 156 Stadler, A., et al., Artifacts in body MR imaging: their appearance and how to eliminate them European Radiology, 2007 17(5): p 1242-1255 157 Odille, F., et al., Generalized MRI reconstruction including elastic physiological motion and coil sensitivity encoding Magnetic Resonance in Medicine, 2008 59(6): p 1401-1411 174 References 158 Besl, P.J and N.D McKay, A Method for Registration of 3-D Shapes IEEE Transaction on Pattern Analysis and Machine Intelligence, 1992 14(2): p 239-256 175 ... small bones Nevertheless, 3D reconstruction of long bones is important as most of the fracture fixation plates and intramedullary nails are used for fixation of long bones When 3D models of long bones. .. Quantification of the accuracy of MRI generated 3D models of long bones compared to CT generated 3D models (in press) 74 Chapter Higher field strength MRI scanning of long bones for generation of 3D. .. Hertz Magnetic resonance Magnetic resonance imaging Transverse component of net magnetisation vector Longitudinal component of net magnetisation vector Number of signal averages Nuclear magnetic resonance