Three-dimensional visualization of microvasculature from few-projection data using a novel CT reconstruction algorithm for propagation-based X-ray phase-contrast imaging

Non-invasive evaluation of endometrial microvessels via in vivo intrauterine photoacoustic endoscopy

The endometrium microvessel system, responsible for supplying oxygen and nutrients to the embryo, holds significant importance in evaluating endometrial receptivity (ER). Visualizing this system directly can significantly enhance ER evaluation. Currently, clinical methods like Narrow-band hysteroscopy and Color Doppler ultrasound are commonly used for uterine blood vessel examination, but they have limitations in depth or resolution. Endoscopic Photoacoustic Imaging (PAE) has proven effective in visualizing microvessels in the digestive tract, while its adaptation to uterine imaging faces challenges due to the uterus's unique physiological characteristics. This paper for the first time that uses high-resolution PAE in vivo to capture a comprehensive network of endometrial microvessels non-invasively. Followed by continuous observation and quantitative analysis in the endometrial injury model, we further corroborated that PAE detection of endometrial microvessels stands as a valuable indicator for evaluating ER. The PAE system showcases its promising potential for integration into reproductive health assessments.

Full-Field Strain Measurements of the Muscle-Tendon Junction Using X-ray Computed Tomography and Digital Volume Correlation

We report, for the first time, the full-field 3D strain distribution of the muscle-tendon junction (MTJ). Understanding the strain distribution at the junction is crucial for the treatment of injuries and to predict tear formation at this location. Three-dimensional full-field strain distribution of mouse MTJ was measured using X-ray computer tomography (XCT) combined with digital volume correlation (DVC) with the aim of understanding the mechanical behavior of the junction under tensile loading. The interface between the Achilles tendon and the gastrocnemius muscle was harvested from adult mice and stained using 1% phosphotungstic acid in 70% ethanol. In situ XCT combined with DVC was used to image and compute strain distribution at the MTJ under a tensile load (2.4 N). High strain measuring 120,000 µε, 160,000 µε, and 120,000 µε for the first principal stain (εp1), shear strain (γ), and von Mises strain (εVM), respectively, was measured at the MTJ and these values reduced into the body of the muscle or into the tendon. Strain is concentrated at the MTJ, which is at risk of being damaged in activities associated with excessive physical activity.

Application of 3D reconstruction technology based on an improved MC algorithm in a shotcreting robot

A shotcreting robot needs to reconstruct the arch surface in three dimensions (3D) during the process of spraying a tunnel. To solve this problem, we propose an improved marching cube (MC) reconstruction method based on a point cloud splice and normal re-orient. First, we use the explosion-proof LIDAR to acquire the point cloud data of the tunnel arch, followed by the use of the iterative closest point algorithm, a PassThrough filter, and a StatisticalOutlierRemoval filter for point cloud splicing, data segmentation, and simplification, respectively. In order to improve the reconstruction accuracy, we adjusted the estimated point cloud normal for normal consistency and obtained the geometric features of the complex point cloud surface. Furthermore, combined with the improved MC algorithm, the 3D reconstruction of the tunnel arch is realized. The experimental results show that the proposed method can reconstruct the 3D model of the tunnel arch surface quickly and accurately, which lays a foundation for further research on a trajectory plan, spraying status monitors, and control strategies.

Suppressing multi-material and streak artifacts with an accelerated 3D iterative image reconstruction algorithm for in-line X-ray phase-contrast computed tomography

In-line X-ray phase-contrast computed tomography typically contains two independent procedures: phase retrieval and computed tomography reconstruction, in which multi-material and streak artifacts are two important problems. To address these problems simultaneously, an accelerated 3D iterative image reconstruction algorithm is proposed. It merges the above-mentioned two procedures into one step, and establishes the data fidelity term in raw projection domain while introducing 3D total variation regularization term in image domain. Specifically, a transport-of-intensity equation (TIE)-based phase retrieval method is updated alternately for different areas of the multi-material sample. Simulation and experimental results validate the effectiveness and efficiency of the proposed algorithm.

Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network

Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people. Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images. Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation. Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604. Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.

Improving spatial resolution with an edge-enhancement model for low-dose propagation-based X-ray phase-contrast computed tomography

Propagation-based X-ray phase-contrast computed tomography (PB-PCCT) has been increasingly popular for distinguishing low contrast tissues. Phase retrieval is an important step to quantitatively obtain the phase information before the tomographic reconstructions, while typical phase retrieval methods in PB-PCCT, such as homogenous transport of intensity equation (TIE-Hom), are essentially low-pass filters and thus improve the signal to noise ratio at the expense of the reduced spatial resolution of the reconstructed image. To improve the reconstructed spatial resolution, measured phase contrast projections with high edge enhancement and the phase projections retrieved by TIE-Hom were weighted summed and fed into an iterative tomographic algorithm within the framework of the adaptive steepest descent projections onto convex sets (ASD-POCS), which was employed for suppressing the image noise in low dose reconstructions because of the sparse-view scanning strategy or low exposure time for single phase contrast projection. The merging strategy decreases the accuracy of the linear model of PB-PCCT and would finally lead to the reconstruction failure in iterative reconstructions. Therefore, the additive median root prior is also introduced in the algorithm to partly increase the model accuracy. The reconstructed spatial resolution and noise performance can be flexibly balanced by a pair of antagonistic hyper-parameters. Validations were performed by the established phase-contrast Feldkamp-Davis-Kress, phase-retrieved Feldkamp-Davis-Kress, conventional ASD-POCS and the proposed enhanced ASD-POCS with a numerical phantom dataset and experimental biomaterial dataset. Simulation results show that the proposed algorithm outperforms the conventional ASD-POCS in spatial evaluation assessments such as root mean square error (a ratio of 9.78%), contrast to noise ratio (CNR) (a ratio of 7.46%), and also frequency evaluation assessments such as modulation transfer function (a ratio of 66.48% of MTF_50% (50% MTF value)), noise power spectrum (a ratio of 35.25% of f_50% (50% value of the Nyquist frequency)) and noise equivalent quanta (1-2 orders of magnitude at high frequencies). Experimental results again confirm the superiority of proposed strategy relative to the conventional one in terms of edge sharpness and CNR (an average increase of 67.35%).

Iterative reconstruction algorithm based on discriminant adaptive-weighted TV regularization for fibrous biological tissues using in-line X-ray phase-contrast imaging

In-line X-ray phase-contrast computed tomography (IL-PCCT) can produce high-contrast and high-resolution images of biological samples, and it has a great advantage with regard to imaging the microstructures and morphologies of fibrous biological tissues (FBTs). Filtered back projection (FBP) is widely used in ILPCCT. However, it requires long scanning times and high radiation doses to produce high-quality CT images, and this restricts its applicability in biomedical and preclinical studies on FBTs. To solve this problem, a novel IL-PCCT reconstruction algorithm is proposed to decrease the radiation dose by reducing the number of projections and reconstruct high-quality CT images of FBTs. The proposed algorithm incorporates the FBP method into the iterative reconstruction framework. Considering the area types and anisotropic edge properties of FBTs, a discriminant adaptive-weighted total variation model is introduced to optimize the intermediate reconstructed images. A fibrous phantom simulation and real experiment were performed to assess the performance of the proposed algorithm. Simulation and experimental results demonstrated that the proposed algorithm is an effective IL-PCCT reconstruction method for FBTs with incomplete projection data, and it has a great ability to suppress artifacts and preserve the edges of fibrous structures.