Background The evolution of cartilage degeneration is still not fully understood, partly due to its thinness, low radio-opacity and therefore lack of adequately resolving imaging techniques. X-ray phase-contrast imaging (X-PCI) offers increased sensitivity with respect to standard radiography and CT allowing an enhanced visibility of adjoining, low density structures with an almost histological image resolution. This study examined the feasibility of X-PCI for high-resolution (sub-) micrometer analysis of different stages in tissue degeneration of human cartilage samples and compare it to histology and transmission electron microscopy. Methods Ten 10%-formalin preserved healthy and moderately degenerated osteochondral samples, post-mortem extracted from human knee joints, were examined using four different X-PCI tomographic set-ups using synchrotron radiation the European Synchrotron Radiation Facility (France) and the Swiss Light Source (Switzerland). Volumetric datasets were acquired with voxel sizes between 0.7 × 0.7 × 0.7 and 0.1 × 0.1 × 0.1 µm3. Data were reconstructed by a filtered back-projection algorithm, post-processed by ImageJ, the WEKA machine learning pixel classification tool and VGStudio max. For correlation, osteochondral samples were processed for histology and transmission electron microscopy. Results X-PCI provides a three-dimensional visualization of healthy and moderately degenerated cartilage samples down to a (sub-)cellular level with good correlation to histologic and transmission electron microscopy images. X-PCI is able to resolve the three layers and the architectural organization of cartilage including changes in chondrocyte cell morphology, chondrocyte subgroup distribution and (re-)organization as well as its subtle matrix structures. Conclusions X-PCI captures comprehensive cartilage tissue transformation in its environment and might serve as a tissue-preserving, staining-free and volumetric virtual histology tool for examining and chronicling cartilage behavior in basic research/laboratory experiments of cartilage disease evolution.
Recent trends in hard X-ray micro-computed tomography (microCT) aim at increasing both spatial and temporal resolutions. These challenges require intense photon beams. Filtered synchrotron radiation beams, also referred to as `pink beams', which are emitted by wigglers or bending magnets, meet this need, owing to their broad energy range. In this work, the new microCT station installed at the biomedical beamline ID17 of the European Synchrotron is described and an overview of the preliminary results obtained for different biomedical-imaging applications is given. This new instrument expands the capabilities of the beamline towards sub-micrometre voxel size scale and simultaneous multi-resolution imaging. The current setup allows the acquisition of tomographic datasets more than one order of magnitude faster than with a monochromatic beam configuration.
Computed tomography (CT) with hard X-rays is a mature technique that is in regular use to depict the interior of opaque specimens with spatial resolutions up to the micrometre range (microtomography or µCT). Short acquisition times and sophisticated contrast modes are accessible when synchrotron light sources are combined with microtomography—SR-µCT. Both features render SR-µCT as excellent probe to study delicate samples in situ, for example under mechanical load by deploying corresponding sample environments. The so-called TomoPress is such a device available within the public user programme of tomography beamline ID19 of the European Synchrotron Radiation Facility (ESRF). It allows one to study samples under high axial load (up to 500 N) with high spatial resolution up to the micrometer range. Different gauges are installed to allow online monitoring of the applied force. Constant humidity, temperature and wetting are routinely available as well. The article shall outline basic design principles of the press as well as parameters for its utilisation in a descriptive manner. Selected examples underline the potential of the device for such diverse fields as biomedical research, life sciences and materials research.
We applied transfer learning using Convolutional Neuronal Networks to high resolution X-ray phase contrast computed tomography datasets and tested the potential of the systems to accurately classify Computed Tomography images of different stages of two diseases, i.e. osteoarthritis and liver fibrosis. The purpose is to identify a time-effective and observer-independent methodology to identify pathological conditions. Propagation-based X-ray phase contrast imaging WAS used with polychromatic X-rays to obtain a 3D visualization of 4 human cartilage plugs and 6 rat liver samples with a voxel size of 0.7 × 0.7 × 0.7 µm3 and 2.2 × 2.2 × 2.2 µm3, respectively. Images with a size of 224 × 224 pixels are used to train three pre-trained convolutional neuronal networks for data classification, which are the VGG16, the Inception V3, and the Xception networks. We evaluated the performance of the three systems in terms of classification accuracy and studied the effect of the variation of the number of inputs, training images and of iterations. The VGG16 network provides the highest classification accuracy when the training and the validation-test of the network are performed using data from the same samples for both the cartilage (99.8%) and the liver (95.5%) datasets. The Inception V3 and Xception networks achieve an accuracy of 84.7% (43.1%) and of 72.6% (53.7%), respectively, for the cartilage (liver) images. By using data from different samples for the training and validation-test processes, the Xception network provided the highest test accuracy for the cartilage dataset (75.7%), while for the liver dataset the VGG16 network gave the best results (75.4%). By using convolutional neuronal networks we show that it is possible to classify large datasets of biomedical images in less than 25 min on a 8 CPU processor machine providing a precise, robust, fast and observer-independent method for the discrimination/classification of different stages of osteoarthritis and liver diseases.
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