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3DReconstructionofLongBonesUtilisingMagneticResonanceImaging(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 oflongbones 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 Magneticresonanceimaging(MRI) has been shown to be a potential alternative in the previous studies conducted using small bones (tarsal bones) and parts of the longbones However, in order to use MRI effectively for 3Dreconstructionof human long bones, further validations using longbones and appropriate reference standards are required Accurate reconstructionof3D 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 oflongbones 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 reconstructionof3D models oflongbones 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 longbones 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 magneticresonance imaging, together with simple multilevel thresholding segmentation, is able to produce 3D models oflongbones with accurate geometric representations The method is, therefore, a potential alternative to the current gold standard CT imaging V Keywords Keywords Magneticresonanceimaging Computed tomography Image segmentation 3D models Longbones 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 imagingof the skeletal system for 3Dreconstruction (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 imagingMagneticresonanceimaging(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 imagingof 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 ofbones 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 ofbones 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 longbones .33 3.8.3.2 Scanning of the bone‟s outer surface using the contact scanner 34 3.8.3.3 Reconstructionof 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 of3D surfaces using Rapidform 2006 .41 3.8.5.2 Comparison of the aligned 3D models 43 3.8.5.3 Dividing the 3D models ofbones 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 oflong bone 3D reconstructions (published) .48 Chapter Application of3D 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 Magneticresonanceimaging for 3Dreconstructionoflongbones 67 5.1 Introduction 67 5.2 Imagingof 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 oflongbones compared to CT generated 3D models (in press) 74 Chapter Higher field strength MRI scanning oflongbones for generation of3D 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 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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