Surface-based rigid registration using a global optimization algorithm for assessment of MRI knee cartilage thickness changes

Guo, Cheng; Cheng, Yuanzhi; Guo, Haoyan; Wang, Jinke; Wang, Yadong; Tamura, Shinichi

doi:10.1016/j.bspc.2015.02.007

Cited by 3 publications

(2 citation statements)

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“…Their location tracked by specific navigation devices is referred to the pre-operative image data [ 5 , 16 ]. Those systems require, however, a well defined and repeatable landmark specification and placement, and promise the better results, the more rigid the anatomical object of interest is [ 17 ]. The common problem of fiducial markers attachment is its physically invasive character, always causing some level of danger during the treatment.…”

Section: Introductionmentioning

confidence: 99%

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

et al. 2016

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PurposeA growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed.MethodsWe present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm.ResultsThe experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera.ConclusionThe obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers.

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Section: Introductionmentioning

confidence: 99%

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

et al. 2016

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“…Currently, more advanced techniques have been developed for the knee joint allowing pointwise intra-and inter-subject comparisons of the cartilage and further detect localized morphological changes in the knee cartilage (Carballido-Gamio et al 2008a;Guo et al 2015;Williams et al 2003). For the hip joint, our preliminary work on subregional analyses of the hip joint cartilages showed great potential to effectively detect focal cartilage defects, to elucidate spatial pattern of cartilage thinning and to potentially identify subregions with increased rates of (and sensitivity to) cartilage loss in intervention trials.…”

Section: Discussionmentioning

confidence: 99%

Automated segmentation and quantitative analysis of the hip joint from magnetic resonance images

Xia¹

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The aim of this thesis is to develop a novel computer-aided system with advanced medical image processing approaches. This will allow automatic segmentation and quantification of the osteochondral elements (i.e. the articulating bones and cartilages) from high-resolution three-dimensional (3D) magnetic resonance (MR) images of the hip joint.This research is motivated by the importance of early detection of structural changes and degeneration of the bones and articular cartilages for good patient outcomes, particularly for early and pre-osteoarthritic conditions such as cam-type femoroacetabular impingement (FAI). MR imaging provides an optimal tool for in vivo assessment of the hip joint structure, including the bones and cartilages. This has generated extensive interest in the development of MR technologies to analyse cartilage morphology and assess biochemical compositions of the hyaline cartilage to facilitate early diagnostic and treatment for hip osteoarthritis (OA) and FAI.Quantitative analyses can provide useful morphometric data from complex MR data. In the hip joint, the segmentation of bones and cartilages is an essential prerequisite, which must be accurate, reliable and reproducible, for quantitative measurements. However, this is difficult and is traditionally performed using time-and expert-intensive manual or semi-automatic methods.The hypothesis behind this research is that accurate and reproducible quantitative data can be automatically obtained from high-resolution 3D MR images of the hip joint, through the use of advanced image processing techniques. To this end, this research focuses on two specific aims: Aim 1 -to develop and evaluate a fully automated segmentation approach to deliver accurate and reproducible bone and cartilage segmentations from high-resolution 3D MR images of the hip joint and Aim 2 -to automatically extract reliable and reproducible morphometric data based on the segmented subchondral bones and articular cartilages.The development of an automatic segmentation scheme for the bones and cartilages with high precision and reproducibility is firstly needed in order to provide a basis for subsequent quantitative measurements, which deliver reliable morphometric data of the segmented bones and cartilages for the use in early OA and FAI studies. To attain these aims, images were acquired using different MR sequences from a mixed demographic (male or female and young adult to elderly) of participants with a variety of femoral head-neck junction presentations but no apparent hip OA. Different sequence scans were used to image the same participant for the associated reproducibility experiments.Two state-of-the-art methods (multi-atlas-based and active shape model (ASM) based algorithms) i were developed and evaluated for automatic segmentation of proximal femurs and innominate bones from large field of view (FOV) MR images (water-excitation dual echo steady state (DESS) and multi echo data image combination (MEDIC)). The validation results have indicated accurate and robust...

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Efficient contact-based registration for minimally invasive anterior hip arthroplasty

Xie,

Zhu,

Chen

et al. 2025

Biomedical Signal Processing and Control

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Surface-based rigid registration using a global optimization algorithm for assessment of MRI knee cartilage thickness changes

Cited by 3 publications

References 27 publications

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

Automated segmentation and quantitative analysis of the hip joint from magnetic resonance images

Efficient contact-based registration for minimally invasive anterior hip arthroplasty

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