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A computed tomography scan (usually abbreviated to CT scan; formerly called computed axial tomography scan or CAT scan) is a medical imaging technique used to obtain detailed internal images of the body. The personnel that perform CT scans are called radiographers or radiology technologists. CT scanners use a rotating X-ray tube and a row of detectors placed in a gantry to measure X-ray attenuations by different tissues inside the body. The multiple X-ray measurements taken from different angles are then processed on a computer using tomographic reconstruction algorithms to produce tomographic (cross-sectional) images (virtual "slices") of a body. CT scan can be used in patients with metallic implants or pacemakers, for whom magnetic resonance imaging (MRI) is contraindicated. Since its development in the 1970s, CT scanning has proven to be a versatile imaging technique. While CT is most prominently used in medical diagnosis, it can also be used to form images of non-living objects.

A numerical model to reproduce an experimental design to detect strain in cement

Christina Maria Bauer

An experimental set-up consisting of a loading device inserted in a μCT scanner was numerically simulated to predict the strain at the bone-implant interface of the scapula after applying various loading conditions. The aim of the project was to determine which applied force is required to induce strain measurable by the μCT scanner. First, a 3D model of the bone was obtained by scanning a cadaveric scapula using CT scanner. Second, cortical and trabecular bone were segmented according to Hounsfield Units using Amira software. Extracted 3D scapula was assembled with a glenoid implant and cement and was cut to fit into the loading device. Final, a finite element model was created using Abaqus software. A mesh convergence study was undertaken to set the local element size to 2 mm around the peri-implant area. Strain – volume distribution in the cement was investigated. For an axial loading of 2.4 kN, 60% of the volume achieved a maximum principal strain value lying between 0.15% - 0.29% strain. Previous studies on DVC approach showed strain errors of 6 to 8 με. Such a low error percentage would be suitable for this study and hence the strain level should be detectable.

2016

Relationship between interfacial micromotion and bone density around cementless hip implant

Valérie Parvex

The hip is one of the most injured joint in the body with ageing. Osteoarthritis and degenerative diseases often lead to the necessity of a total hip arthroplasty in order to reduce pain and increase mobility. The advantage of cementless implants resides in the natural stem anchorage by osseointegration instead of the cement use. Nevertheless, the related stability is highly dependent on the micromotion between the implant and the bone. As more and more arthroplasties are performed each year, it is important to control the outcome and to reduce the risks linked with implant loosening due to excessive micromotion. The goal of this study is to improve a micromotion measurement method based on compression and torsion tests on cadaveric femurs. Moreover, in order to help the surgeon to position the implant to limit high micromotion, density around the implant is studied in order to compare with micromovements distribution. Markers made of stainless steel are put on the internal bone surface. The compression and torsion tests are performed under micro-computed tomography environment. The locations of markers are visible on scans performed before, during and after the tests. Post-processing based on micro-CT scans allows the extraction of micromotion, subsidence and gap values between loading and rest states. Contrary to other studies, which use LVDT to measure micromotion on the outer bone surface, this method allows local measurement. Thanks to micromotion values projection on the implant, it is possible to see the local distribution around the stem. The amplitude of measured micromotion remains under the limit value of 150 [µm] that is supposed to lead to the formation of brous tissue, except when bone quality is damaged. The improved method is accurate and fast. Nevertheless, it is highly dependent on the image acquisition quality. However, no general statistical correlation has been found between density and micromotion. Nonetheless, based on the rendering of the postoperative planning software HipPlan (Symbios, Switzerland), it seems that few support zones present similarities between low micromotion and high bone density.

2013

The use of texture-based radiomics CT analysis to predict outcomes in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy

Adrien Raphaël Depeursinge, Pierre Starkov

Objective: Stereotactic ablative radiotherapy (SABR) is being increasingly used as a non-invasive treatment for early-stage non-small cell lung cancer (NSCLC). A non-invasive method to estimate treatment outcomes in these patients would be valuable, especially since access to tissue specimens is often difficult in these cases. Methods: We developed a method to predict survival following SABR in NSCLC patients using analysis of quantitative image features on pre-treatment CT images. We developed a Cox Lasso model based on two-dimensional Riesz wavelet quantitative texture features on CT scans with the goal of separating patients based on survival. Results: The median log-rank p-value for 1000 cross-validations was 0.030. Our model was able to separate patients based upon predicted survival. When we added tumor size into the model, the p-value lost its significance, demonstrating that tumor size is not a key feature in the model but rather decreases significance likely due to the relatively small number of events in the dataset. Furthermore, running the model using Riesz features extracted either from the solid component of the tumor or from the ground glass opacity (GGO) component of the tumor maintained statistical significance. However, the p-value improved when combining features from the solid and the GGO components, demonstrating that there are important data that can be extracted from the entire tumor. Conclusions: The model predicting patient survival following SABR in NSCLC may be useful in future studies by enabling prediction of survival-based outcomes using radiomics features in CT images. Advances in knowledge: Quantitative image features from NSCLC nodules on CT images have been found to significantly separate patient populations based on overall survival (p = 0.04). In the long term, a non-invasive method to estimate treatment outcomes in patients undergoing SABR would be valuable, especially since access to tissue specimens is often difficult in these cases.

BRITISH INST RADIOLOGY2019