X-ray CT geometrical calibration via locally linear embedding

Locally linear embedding and plantar pressure-time graph selection in heel pain classification: An observational, case-control study

Plantar heel pain mainly manifests during the gait cycle when the whole foot is in contact with the floor, which corresponds to the second rocker of the gait. This moment can be studied through the analysis of pressure-time graphs obtained using plantar pressure plate systems. However, these graphs are complex, and a dimensionality reduction method, such as locally linear embedding (LLE), greatly assists in their comprehension. This observational, case-control pilot study included 45 subjects divided into case (n = 21) and control (n = 24) groups, depending on the presence/absence of plantar heel pain. The second rocker pressure-time graphs of the 45 subjects were obtained using the Footwork Pro® plantar pressure plate system. These graphs were analyzed and defined as the dynamic simultaneity surfaces (DSSs). This complex structure was composed of four dimensions: the dynamic simultaneity time (DST), slope upward grade (α), slope downward grade (β), and height (h), and were reduced into one dimension and classified into pathological and non-pathological subjects using the LLE method. All 45 DSSs were successfully reduced and classified to distinguish between the case (plantar heel pain) and control (non-plantar heel pain) subjects. This study is the first to use the LLE method for gait analysis. This method serves as a novel and promising tool for the study and classification of pathological and non-pathological gait cycles. This method opens the door for future research and analysis, with significant potential to assess diagnosis, treatment follow-up, and injury prevention in physical medicine consultations.

Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning

While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary application. The structural details of the cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 mm) provided by current medical CT scanners. In this paper, we propose a clinical micro-CT (CMCT) system design integrating conventional spiral cone-beam CT, contemporary interior tomography, deep learning techniques, and the technologies of a micro-focus X-ray source, a photon-counting detector (PCD), and robotic arms for ultrahigh-resolution localized tomography of a freely-selected volume of interest (VOI) at a minimized radiation dose level. The whole system consists of a standard CT scanner for a clinical CT exam and VOI specification, and a robotic micro-CT scanner for a local scan of high spatial and spectral resolution at minimized radiation dose. The prior information from the global scan is also fully utilized for background compensation of the local scan data for accurate and stable VOI reconstruction. Our results and analysis show that the proposed hybrid reconstruction algorithm delivers accurate high-resolution local reconstruction, and is insensitive to the misalignment of the isocenter position, initial view angle and scale mismatch in the data/image registration. These findings demonstrate the feasibility of our system design. We envision that deep learning techniques can be leveraged for optimized imaging performance. With high-resolution imaging, high dose efficiency and low system cost synergistically, our proposed CMCT system has great promise in temporal bone imaging as well as various other clinical applications.

The Experimental Study on Geometric Calibration and Material Discrimination for In Vivo Dual-Energy CT Imaging

Photon-counting detector (PCD) can identify absorption features in the multiple ranges of photon energies, which has a great potential in material discrimination. In this paper, we focused on in vivo dual-energy CT imaging to characterize different biomedical compositions. The precision of material decomposition in post-reconstruction space depends on the quality of reconstructed CT images; we used the locally linear embedding (LLE) based online geometric calibration method and GPU-based reconstruction toolbox to reconstruct high-quality CT images. Then, we performed the real experiment and studied materials decomposition with basis material model to discriminate soft tissue and cortical bone of small animal. Finally, the experimental results demonstrated that the proposed method could reconstruct small animal CT images with more slim structures and details, and improve the precision of materials decomposition in dual-energy CT imaging.

Geometric artifacts reduction for cone-beam CT via L0-norm minimization without dedicated phantoms

For cone-beam computed tomography (CBCT), transversal shifts of the rotation center exist inevitably, which will result in geometric artifacts in CT images. In this work, we propose a novel geometric calibration method for CBCT, which can also be used in micro-CT. The symmetry property of the sinogram is used for the first calibration, and then L0-norm of the gradient image from the reconstructed image is used as the cost function to be minimized for the second calibration. An iterative search method is adopted to pursue the local minimum of the L0-norm minimization problem. The transversal shift value is updated with affirmatory step size within a search range determined by the first calibration. In addition, graphic processing unit (GPU)-based FDK algorithm and acceleration techniques are designed to accelerate the calibration process of the presented new method. In simulation experiments, the mean absolute difference (MAD) and the standard deviation (SD) of the transversal shift value were less than 0.2 pixels between the noise-free and noisy projection images, which indicated highly accurate calibration applying the new calibration method. In real data experiments, the smaller entropies of the corrected images also indicated that higher resolution image was acquired using the corrected projection data and the textures were well protected. Study results also support the feasibility of applying the proposed method to other imaging modalities.

A novel method of micro-tomography geometric angle calibration with random phantom

The objective of this study is to develop and test the feasibility of applying a machine learning method for geometry calibration of angles in micro-tomography systems. Increasing importance of micro-tomography systems are manifested with escalating applications in various scenarios including but not limited to oral and maxillofacial surgery, vascular and intervention radiology, among other specific applications for purposes of diagnosis and treatments planning. There is possibility, however, actual pathology is confused by artifact of tissue structures after volume reconstruction as a result of CT construction errors. A Kernel Ridge Regression algorithm for micro-tomography geometry estimation and its corresponding phantom is developed and tested in this study. Several projection images of a rotating Random Phantom of some steel ball bearings in an unknown geometry with gantry angle information were utilized to calibrate both in-plane and out-plane rotation of the detector. The described method can also be expanded to calibrate other parameters of CT construction effortlessly. Using computer simulation, the study results validated that geometry parameters of micro-tomography system were accurately calibrated.

Detector-unit-dependent calibration for polychromatic projections of rock core CT

Computed tomography (CT) plays an important role in digital rock analysis, which is a new prospective technique for oil and gas industry. But the artifacts in CT images will influence the accuracy of the digital rock model. In this study, we proposed and demonstrated a novel method to restore detector-unit-dependent functions for polychromatic projection calibration by scanning some simple shaped reference samples. As long as the attenuation coefficients of the reference samples are similar to the scanned object, the size or position is not needed to be exactly known. Both simulated and real data were used to verify the proposed method. The results showed that the new method reduced both beam hardening artifacts and ring artifacts effectively. Moreover, the method appeared to be quite robust.

Synchronous scanning mode of industrial computed tomography for multiple objects test

When testing small size and high density objects with a broad field-of-view (FOV) industrial CT system, multiple objects are always assembled evenly onto the turntable for detecting to improve test efficiency. However, the maximum X-ray's penetrating path through the materials increases, which means the CT system should collocate with a high energy X-ray source and wide-dynamic range detectors to complete the CT scanning. In this study, we proposed and tested a novel and efficient CT scanning method based on linear-arrangement and synchronous-rotating multi-turntables without enhancing the energy of X-ray source and wide-dynamic range of detectors for the CT system. With this modality, multiple objects are assembled onto multiple synchronous-rotating turntables respectively, and X-rays within the FOV merely penetrate one single object when scanning. The corresponding filtered back projection algorithm for image reconstruction is deduced. The computer simulation and experimental results verified the feasibility of this novel method and the scanning time was reduced to 5-8 minutes when completing the scanning of 3 to 5 group objects.

Fully 3D geometrical calibration for an X-ray grating-based imaging system

The Precision of geometric parameters is the key requirement for grating-based phase-contrast CT and attenuation-based CT in cone-beam geometry. By extending our fan-beam geometric calibration work via Locally Linear Embedding (LLE) into the cone-beam case, here we calibrate the geometric parameters of our in-house phase-contrast CT system with attenuation projection data. Numerical and experimental studies show that the LLE-based calibration method is feasible to calibrate 8 parameters of the cone-beam geometry that improves reconstruction quality significantly.

An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction

Compressive Sensing (CS) theory has great potential for reconstructing Computed Tomography (CT) images from sparse-views projection data and Total Variation- (TV-) based CT reconstruction method is very popular. However, it does not directly incorporate prior images into the reconstruction. To improve the quality of reconstructed images, this paper proposed an improved TV minimization method using prior images and Split-Bregman method in CT reconstruction, which uses prior images to obtain valuable previous information and promote the subsequent imaging process. The images obtained asynchronously were registered via Locally Linear Embedding (LLE). To validate the method, two studies were performed. Numerical simulation using an abdomen phantom has been used to demonstrate that the proposed method enables accurate reconstruction of image objects under sparse projection data. A real dataset was used to further validate the method.