Murine models for rheumatoid arthritis (RA) research can provide important insights for understanding RA pathogenesis and evaluating the efficacy of novel treatments. However, simultaneously imaging both murine articular cartilage and subchondral bone using conventional techniques is challenging because of low spatial resolution and poor soft tissue contrast. X-ray phase-contrast imaging (XPCI) is a new technique that offers high spatial resolution for the visualisation of cartilage and skeletal tissues. The purpose of this study was to utilise XPCI to observe articular cartilage and subchondral bone in a collagen-induced arthritis (CIA) murine model and quantitatively assess changes in the joint microstructure. XPCI was performed on the two treatment groups (the control group and CIA group, n = 9 per group) to monitor the progression of damage to the femur from the knee joint in a longitudinal study (at 0, 4 and 8 weeks after primary injection). For quantitative assessment, morphologic parameters were measured in three-dimensional (3D) images using appropriate image analysis software. Our results showed that the average femoral cartilage volume, surface area and thickness were significantly decreased (P<0.05) in the CIA group compared to the control group. Meanwhile, these decreases were accompanied by obvious destruction of the surface of subchondral bone and a loss of trabecular bone in the CIA group. This study confirms that XPCI technology has the ability to qualitatively and quantitatively evaluate microstructural changes in mouse joints. This technique has the potential to become a routine analysis method for accurately monitoring joint damage and comprehensively assessing treatment efficacy.

BackgroundIn recent years, X-ray phase-contrast imaging techniques have been extensively studied to visualize weakly absorbing objects. One of the most popular methods for phase-contrast imaging is in-line phase-contrast imaging (ILPCI). Combined with computed tomography (CT), phase-contrast CT can produce 3D volumetric images of samples. To date, the most common reconstruction method for phase-contrast X-ray CT imaging has been filtered back projection (FBP). However, because of the impact of respiration, lung slices cannot be reconstructed in vivo for a mouse using this method. Methods for reducing the radiation dose and the sampling time must also be considered.MethodsThis paper proposes a novel method of in vivo mouse lung in-line phase-contrast imaging that has two primary improvements compared with recent methods: 1) using a compressed sensing (CS) theory-based CT reconstruction method for the in vivo in-line phase-contrast imaging application and 2) using the breathing phase extraction method to address the lung and rib cage movement caused by a live mouse’s breathing.ResultsExperiments were performed to test the breathing phase extraction method as applied to the lung and rib cage movement of a live mouse. Results with a live mouse specimen demonstrate that our method can reconstruct images of in vivo mouse lung.ConclusionsThe results demonstrate that our method could deal with vivo mouse’s breathing and movements, meanwhile, using less sampling data than FBP while maintaining the same high quality.

PurposeTo analyse the diagnostic signs present in slices of human breast tumour specimens using synchrotron radiation phase-contrast imaging computed tomography (PCI-CT) for the first time and assess the feasibility of this technique for clinical applications.Materials and MethodsThe ethics committee of our university and relevant clinical hospital approved this prospective study, and written informed consent was obtained from all patients. PCI-CT of human breast tumour specimens with synchrotron radiation was performed at the Shanghai Synchrotron Radiation Facility (SSRF). A total of 14 specimens of early-stage carcinomas and 8 specimens of adenomas were enrolled. Based on raw data reconstruction, the diagnostic signs present in the slices were analysed and correlated with histopathology. We proposed a criterion for clinical diagnosis according to the evaluated signs and the Breast Imaging Reporting and Data System (BI-RADS) for reference. The criterion was then assessed by clinicians in a double-blind method. Finally, descriptive statistics were evaluated, depending on the assessment results.ResultsThe 14 carcinoma specimens and 8 adenoma specimens were diagnosed as malignant and benign tumours, respectively. The total coincidence rate was 100%.ConclusionOur study results demonstrate that the X-ray diagnostic signs observed in the specimen slices and the criterion used for clinical diagnosis were accurate and reliable. The criterion based on signs analysis can be used to differentiate early-stage benign or malignant tumours. As a promising imaging method, PCI-CT can serve as a possible and feasible supplement to BI-RADS in the future.

BackgroundChronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide, and emphysema is a common component of COPD. Currently, it is very difficult to detect early stage emphysema using conventional radiographic imaging without contrast agents, because the change in X-ray attenuation is not detectable with absorption-based radiography. Compared with the absorption-based CT, phase contrast imaging has more advantages in soft tissue imaging, because of its high spatial resolution and contrast.MethodsIn this article, we used diffraction enhanced imaging (DEI) method to get the images of early stage emphysematous and healthy samples, then extract X-ray absorption, refraction, and ultra-small-angle X-ray scattering (USAXS) information from DEI images using multiple image radiography (MIR). We combined the absorption image with the USAXS image by a scatter plot. The critical threshold in the scatter plot was calibrated using the linear discriminant function in the pattern recognition.ResultsUSAXS image was sensitive to the change of tissue micro-structure, it could show the lesions which were invisible in the absorption image. Combined with the absorption-based image, the USAXS information enabled better discrimination between healthy and emphysematous lung tissue in a mouse model. The false-color images demonstrated that our method was capable of classifying healthy and emphysematous tissues.ConclusionHere we present USAXS images of early stage emphysematous and healthy samples, where the dependence of the USAXS signal on micro-structures of biomedical samples leads to improved diagnosis of emphysema in lung radiographs.

BackgroundMechanical system with imperfect or misalignment of X-ray phase contrast imaging (XPCI) components causes projection data misplaced, and thus result in the reconstructed slice images of computed tomography (CT) blurred or with edge artifacts. So the features of biological microstructures to be investigated are destroyed unexpectedly, and the spatial resolution of XPCI image is decreased. It makes data correction an essential pre-processing step for CT reconstruction of XPCI.MethodsTo remove unexpected blurs and edge artifacts, a mathematics model for in-line XPCI is built by considering primary geometric parameters which include a rotation angle and a shift variant in this paper. Optimal geometric parameters are achieved by finding the solution of a maximization problem. And an iterative approach is employed to solve the maximization problem by using a two-step scheme which includes performing a composite geometric transformation and then following a linear regression process. After applying the geometric transformation with optimal parameters to projection data, standard filtered back-projection algorithm is used to reconstruct CT slice images.ResultsNumerical experiments were carried out on both synthetic and real in-line XPCI datasets. Experimental results demonstrate that the proposed method improves CT image quality by removing both blurring and edge artifacts at the same time compared to existing correction methods.ConclusionsThe method proposed in this paper provides an effective projection data correction scheme and significantly improves the image quality by removing both blurring and edge artifacts at the same time for in-line XPCI. It is easy to implement and can also be extended to other XPCI techniques.