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      Four-dimensional CT as a valid approach to detect and quantify kinematic changes after selective ankle ligament sectioning

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          Abstract

          The objective of the current study was to explore the potential of dynamic computed tomography to detect kinematic changes, induced by sequential sectioning of the lateral collateral ligaments of the ankle, during full motion sequence of the talocrural joint. A custom-made device was used to induce cyclic controlled ankle inversion movement in one fresh frozen cadaver leg. A 256-slice CT scanner was used to investigate four different scenarios. Scenario 1 with all ligaments intact was first investigated followed by sequential section of the anterior talo-fibular ligament (Scenario 2), the calcaneo-fibular ligament (Scenario 3) and posterior talo-fibular ligament (Scenario 4). Off-line image processing based on semi-automatic segmentation and bone rigid registration was performed. Motion parameters such as translation, rotational angles and orientation and position of the axis of rotation were calculated. Differences between scenarios were calculated. Progressive increase of cranio-caudal displacement up to 3.9 mm and flexion up to 10° compared to Scenario 1 were reported. Progressive changes in orientation (up to 20.6°) and position (up to 4.1 mm) of the axis of rotation were also shown. Estimated effective dose of 0.005 mSv (1.9 mGy CTDI vol) was reported. This study demonstrated that kinematic changes due to the absence of ligament integrity can be detected with 4DCT with minimal radiation exposure. Identifying abnormal kinematic patterns could have future application in helping clinicians to choose patients’ optimal treatment. Therefore, further studies with bigger in vitro sample sizes and consequent investigations in vivo are recommended to confirm the current findings.

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          Most cited references18

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          Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease

          Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4–5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15–60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.
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            Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit.

            We present the detailed planning and execution of the Insight Toolkit (ITK), an application programmers interface (API) for the segmentation and registration of medical image data. This public resource has been developed through the NLM Visible Human Project, and is in beta test as an open-source software offering under cost-free licensing. The toolkit concentrates on 3D medical data segmentation and registration algorithms, multimodal and multiresolution capabilities, and portable platform independent support for Windows, Linux/Unix systems. This toolkit was built using current practices in software engineering. Specifically, we embraced the concept of generic programming during the development of these tools, working extensively with C++ templates and the freedom and flexibility they allow. Software development tools for distributed consortium-based code development have been created and are also publicly available. We discuss our assumptions, design decisions, and some lessons learned.
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              The distribution of target registration error in rigid-body point-based registration.

              Guidance systems designed for neurosurgery, hip surgery, spine surgery and for approaches to other anatomy that is relatively rigid can use rigid-body transformations to accomplish image registration. These systems often rely on point-based registration to determine the transformation and many such systems use attached fiducial markers to establish accurate fiducial points for the registration, the points being established by some fiducial localization process. Accuracy is important to these systems, as is knowledge of the level of that accuracy. An advantage of marker-based systems, particularly those in which the markers are bone-implanted, is that registration error depends only on the fiducial localization and is, thus, to a large extent independent of the particular object being registered. Thus, it should be possible to predict the clinical accuracy of marker-based systems on the basis of experimental measurements made with phantoms or previous patients. For most registration tasks, the most important error measure is target registration error (TRE), which is the distance after registration between corresponding points not used in calculating the registration transform. In this paper, we derive an approximation to the distribution of TRE; this is an extension of previous work that gave the expected squared value of TRE. We show the distribution of the squared magnitude of TRE and that of the component of TRE in an arbitrary direction. Using numerical simulations, we show that our theoretical results are a close match to the simulated ones.

                Author and article information

                Contributors
                Luca.Buzzatti@vub.be
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 February 2019
                4 February 2019
                2019
                : 9
                : 1291
                Affiliations
                [1 ]ISNI 0000 0001 2290 8069, GRID grid.8767.e, Department of Physiotherapy, Human Physiology and Anatomy (KIMA), , Vrije Universiteit Brussel, ; Brussel, Belgium
                [2 ]ISNI 0000 0001 2290 8069, GRID grid.8767.e, Faculty of Medicine and Pharmacy, , Vrije Universiteit Brussel, ; Brussel, Belgium
                [3 ]ISNI 0000 0004 0626 3362, GRID grid.411326.3, Department of Radiology, , Universitair Ziekenhuis Brussel, ; Brussel, Belgium
                [4 ]Department of Orthopaedic Surgery and Traumatology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussel, Belgium
                [5 ]ISNI 0000 0001 2290 8069, GRID grid.8767.e, Department of Electronics and Informatics (ETRO), , Vrije Universiteit Brussel, ; Brussel, Belgium
                [6 ]ISNI 0000 0001 2215 0390, GRID grid.15762.37, Imec, ; Leuven, Belgium
                [7 ]ISNI 0000 0001 2290 8069, GRID grid.8767.e, Department of Movement and Sport Sciences, , Vrije Universiteit Brussel, ; Brussel, Belgium
                [8 ]University College of Physiotherapy THIM, Landquart, Switzerland
                Article
                38101
                10.1038/s41598-018-38101-5
                6361967
                30718794
                7bbaa248-ef11-440a-b610-77b2e510d985
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 August 2018
                : 11 December 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004418, Vrije Universiteit Brussel (Université Libre de Bruxelles);
                Award ID: IRP-10
                Award Recipient :
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