X-ray fluorescence computed tomography (XFCT) is an emerging imaging modality that enables quantification of the distribution of high-Z elements, including gold, gadolinium, and iodine, in diverse biomedical applications, by specifically detecting the x-ray fluorescence (XRF) emitted from the target element. Pixelated semiconductor detectors such as Cadmium Telluride (CdTe) and Cadmium Zinc Telluride (CZT) sensors are particularly suited for XFCT imaging due to their high energy and spatial resolution capabilities. However, their performance degrades because of multi-pixel events, which occur when an incident photon deposits energy across multiple adjacent pixels. In this study, we implement corrections for the energy loss during charge sharing. Furthermore, for bi-pixel events occurring within the gadolinium K α energy and caused by the escape and re-capture of detector elements’ x-ray fluorescence, we correct the interaction location. To validate the efficacy of the charge sharing energy loss correction and fluorescence escape events location correction, we utilized a PMMA phantom filled with Gadolinium saline solutions at concentrations ranging from 0 to 1.2mg/ml for XFCT imaging. The implemented corrections enhanced the contrast noise ratio in the gadolinium region. These improvements in XFCT imaging quality are useful for the preclinical investigation of precise tumor diagnosis and treatment using high atomic number element nanoparticles, and for other semiconductor detector-based imaging modalities.
This paper introduces and discusses the development of an interesting multimodal CT imaging technique, called full x-ray particle information CT (PI-CT), which combines x-ray transmission, fluorescence, and scattering tomography using a polychromatic x-ray source. The PI-CT allows for the simultaneous reconstruction of high-resolution tissue structure images, quantitative imaging of high-Z element concentrations, and electron density distributions. During x-ray photons passing through an object, photoelectric effects and Compton scattering may occur, resulting in x-ray attenuation and the generation of scattering and fluorescent photons. All these interaction information is innovatively utilized in PI-CT to detect and image different physical quantities inside the object. X-ray transmission CT could image the object’s high-resolution structure. X-ray fluorescence CT could realize the quantitative imaging of high-Z agents. Compton scattering CT could reconstruct the electron density information, which may have better contrast in weak absorption radiation imaging cases, such as lung imaging. Therefore, with the help of functional imaging nanoparticles, PI-CT can provide both high-resolution tissue structure images and highly sensitive molecular functional images of living animals, which provides a new multimodal tool for tumor diagnosis and treatment. Experimental results demonstrate the potential of PI-CT in enhancing multimodal CT imaging, particularly in tumor diagnosis and treatment applications.
This research explores the use of x-ray induced acoustic computed tomography (XACT) in vascular imaging to assess parameters like oxygen content, blood flow, and velocity. XACT’s high-resolution imaging capabilities could revolutionize diagnostics and monitoring of vascular conditions by enabling non-invasive, real-time evaluations. The study will investigate the feasibility of obtaining quantitative measurements of blood oxygenation, along with flow rates and velocities, using XACT, potentially enhancing our understanding of vascular physiology and improving clinical outcomes.
In cone-beam computed tomography (CBCT), the presence of metal implants causes significant artifacts during imaging, leading to adverse effects on the clinical structure and information of the teeth. This results in reduced imaging quality and can ultimately impact subsequent clinical treatment. Despite numerous metal artifact reduction (MAR) methods available, they still fall short in preserving tooth structure. To address these challenges, our proposed MAR method integrates CBCT data with intra-oral scan data. This approach provides improved guidance for the segmentation of metal areas and enables the comprehensive utilization of projection domain and image domain data to effectively eliminate metal artifacts. Experimental results are presented to demonstrate the feasibility and effectiveness of our proposed approach, which notably introduces intraoral scan data for the first time in the analysis and processing of projection domain data. This advancement allows for the accurate reconstruction of 3D dental images, promising enhanced diagnostic and clinical applications.
Because of the ability to present molecular and functional information in organisms, nuclear medical imaging (NMI) is attracting more and more attention. Among NMI modalities, X-ray fluorescence computed tomography (XFCT) has the advantage that the tracers used in XFCT are not spontaneously decayed. The synthesis, storage of contrast agents is more convenient, the price of XFCT is much lower as well. However, XFCT usually has mechanical collimation to tell the incident photon direction, which results in the reduction of the detection efficiency. The Compton camera is an imaging modality, which does not need mechanical collimators in its structure, which makes Compton cameras have high detection efficiency. Therefore, it is a great idea to use Compton camera-based imaging systems to realize X-ray fluorescence (XF) imaging. In this work, the first XFCC imaging system in the laboratory environment is established, which consists of a 150keV X-ray tube and a single-layer Compton camera system based on the Timepix3 photon-counting detector (PCD). The element Gd (43keV) is used as the XF element. The first imaging reconstruction results of the XFCC system are represented.
Multi-energy CT conducted by photon-counting detectors has a wide range of applications, especially in multiple contrast agent imaging. However, multi-energy CT imaging suffers from higher statistical noise because of increased energy bin numbers. Our team has proposed the dynamic dual-energy CT imaging mode and the corresponding iterative imaging algorithms to solve this problem. The multi-energy projections and reconstructions calculated from the dynamic dual-energy CT data are less noisy than the static multi-energy CT, which has been verified by sufficient numerical simulations and experiments. However, a rigorous mathematical derivation has not been conducted to explain why dynamic dual-energy CT is better than static multi-energy CT in reducing statistical noise. In this work, we drive the noise model of the dynamic dual-energy CT to explain the reason. The reason is: compared to the multi-energy projections that are directly measured from a static multi-energy CT, the multi-energy projections, which are calculated from the dynamic dual-energy CT data, have the same expectation, but the variance is lower.
The development of energy-resolving photon-counting detectors provides a new approach for obtaining spectral information in computed tomography. However, the non-uniformity between different photon-counting detector pixels can cause stripe artefacts in projection domain and concentric ring artefacts in image domain. Here we propose a nonuniformity correction method based on two generative adversarial nets (GANs). The first GAN is a conditional GAN and is responsible for ring artifacts estimation in image domain. The first GAN is trained on 2016 AAPM Grand Challenge dataset, with ring artifacts artificially introduced. The second GAN is an ordinary GAN and is responsible for experimental ring artifacts auto-modeling. The second GAN is trained on real ring artifacts removed from experimental images by the first GAN and aims to provide ample and realistic training labels for re-training the first GAN. Experimental results show that GAN can accurately extract the characteristics of ring artefacts and get them removed from original images to get clear images. Besides, GAN can also be used for realistic training label generation thus better improve the performance of ring artifacts estimation network on experimental datasets.
This work proposes an attenuation correction method for x-ray fluorescence computed tomography (XFCT). The phantom is irradiated by a polychromatic cone-beam source produced by a conventional x-ray tube. X-ray fluorescence (XRF) photons are stimulated by the incident beam and are then collected by a photon counting detector placed on one side of the beamline. A flat-panel detector is placed along the beamline for detection of attenuation information. For quantitative reconstruction of XFCT images, the attenuation of incident photons as well as XRF photons in the phantom are estimated utilizing the transmission CT images. Simulation results show that the attenuation correction method proposed in this work significantly improves the accuracy of image reconstruction for XFCT, which enables quantitative identification of fluorescence materials in the objects.
Spectral computed tomography (CT) can reconstruct scanned objects at different energy-bins and thus solve the multimaterial decomposition (MMD) problem. Because the linear attenuation coefficients of different basis materials may be extremely close, the decomposition problem is often ill-conditioned. Meanwhile, traditional material decompositions with image-domain algorithms are usually voxelwise based. Therefore, these algorithms rely heavily on image quality. Ring artifacts often exist in the reconstructed images of spectral CT due to the inconsistency feature of energy-resolved detectors and beam-hardening effect. Considering the enlargement of the receptive field and taking advantage of the modeling ability of convolutional neural networks in deep learning, we proposed a convolutional material decomposition algorithm to solve the MMD problem through a basis of patches instead of pixels of the spectral CT images. Simulations and physical experiments were performed to validate the proposed algorithm, and its quality was compared with a traditional MMD algorithm in the image domain. Results show that the proposed method achieves good accuracy, reduces mean squared errors by one to two orders, and exhibits robustness in the MMD of spectral CT images even in the case that obvious ring artifacts is presented.
KEYWORDS: Sensors, Monte Carlo methods, Gadolinium, Gold, Imaging systems, X-ray fluorescence spectroscopy, Computed tomography, In vivo imaging, Luminescence, Computing systems
We present the design concept and initial simulations for a polychromatic full-field fan-beam x-ray fluorescence computed tomography (XFCT) device with pinhole collimators and linear-array photon counting detectors. The phantom is irradiated by a fan-beam polychromatic x-ray source filtered by copper. Fluorescent photons are stimulated and then collected by two linear-array photon counting detectors with pinhole collimators. The Compton scatter correction and the attenuation correction are applied in the data processing, and the maximum-likelihood expectation maximization algorithm is applied for the image reconstruction of XFCT. The physical modeling of the XFCT imaging system was described, and a set of rapid Monte Carlo simulations was carried out to examine the feasibility and sensitivity of the XFCT system. Different concentrations of gadolinium (Gd) and gold (Au) solutions were used as contrast agents in simulations. Results show that 0.04% of Gd and 0.065% of Au can be well reconstructed with the full scan time set at 6 min. Compared with using the XFCT system with a pencil-beam source or a single-pixel detector, using a full-field fan-beam XFCT device with linear-array detectors results in significant scanning time reduction and may satisfy requirements of rapid imaging, such as in vivo imaging experiments.
Rapid development of the X-ray phonon-counting detection technology brings tremendous research and application
opportunities. In addition to improvements in conventional X-ray imaging performance such as radiation dose utilization
and beam hardening correction, photon-counting detectors allows significantly more efficient X-ray fluorescence (XRF)
and K-edge imaging, and promises a great potential of X-ray functional, cellular and molecular imaging. XRF is the
characteristic emission of secondary X-ray photons from a material excited by initial X-rays. The phenomenon is widely
used for chemical and elemental analysis. K-edge imaging identifies a material based on its chemically-specific
absorption discontinuity over X-ray photon energy. In this paper, we try to combine XRF and K-edge signals from the
contrast agents (e.g., iodine, gadolinium, gold nanoparticles) to simultaneously realize XFCT and K-edge CT imaging
for superior image performance. As a prerequisite for this dual-modality imaging, the accurate energy calibration of
multi-energy-bin photon-counting detectors is critically important. With the measured XRF data of different materials,
we characterize the energy response function of a CZT detector for energy calibration and spectrum reconstruction,
which can effectively improve the energy resolution and decrease the inconsistence of the photon counting detectors.
Then, a simultaneous K-edge and X-ray fluorescence CT imaging (SKYFI) experimental setup is designed which
includes a cone-beam X-ray tube, two separate photon counting detector arrays, a pin-hole collimator and a rotation
stage. With a phantom containing gold nanoparticles the two types of XFCT and K-edge CT datasets are collected
simultaneously. Then, XFCT and K-edge CT images are synergistically reconstructed in a same framework. Simulation
results are presented and quantitative analyzed and compared with the separate XFCT and K-edge CT results.
It is essential for accurate image reconstruction to obtain a set of parameters that describes the x-ray scanning geometry. A geometric estimation method is presented for x-ray digital intraoral tomosynthesis (DIT) in which the detector remains stationary while the x-ray source rotates. The main idea is to estimate the three-dimensional (3-D) coordinates of each shot position using at least two small opaque balls adhering to the detector surface as the positioning markers. From the radiographs containing these balls, the position of each x-ray focal spot can be calculated independently relative to the detector center no matter what kind of scanning trajectory is used. A 3-D phantom which roughly simulates DIT was designed to evaluate the performance of this method both quantitatively and qualitatively in the sense of mean square error and structural similarity. Results are also presented for real data acquired with a DIT experimental system. These results prove the validity of this geometric estimation method.
At present, there are mainly three x-ray imaging modalities for dental clinical diagnosis: radiography, panorama and computed tomography (CT). We develop a new x-ray digital intra-oral tomosynthesis (IDT) system for quasi-three-dimensional dental imaging which can be seen as an intermediate modality between traditional radiography and CT. In addition to normal x-ray tube and digital sensor used in intra-oral radiography, IDT has a specially designed mechanical device to complete the tomosynthesis data acquisition. During the scanning, the measurement geometry is such that the sensor is stationary inside the patient’s mouth and the x-ray tube moves along an arc trajectory with respect to the intra-oral sensor. Therefore, the projection geometry can be obtained without any other reference objects, which makes it be easily accepted in clinical applications. We also present a compressed sensing-based iterative reconstruction algorithm for this kind of intra-oral tomosynthesis. Finally, simulation and experiment were both carried out to evaluate this intra-oral imaging modality and algorithm. The results show that IDT has its potentiality to become a new tool for dental clinical diagnosis.
This work gives a new Compressed Sensing (CS) based Computed Tomography (CT) reconstruction method for limited angle problem. Currently CS based reconstruction methods are achieved by a minimizing process on the total variation (TV) of CT image under data consistency constraint. For limited-angle problem due to the missing range of projection views the strength of data consistency constraint becomes direction relevant. In our work a new anisotropic total variation (ATV) minimization method is proposed. Instead of using image TV as the minimization objective, an ATV objective is designed which is combined of multiple 1D directional TV with different weights according to the actual scanned angular range. Experiments with simulated data demonstrate the advantages of our approach relative to the standard CS based reconstruction methods.
Nowadays a famous way to solve Computed Tomography (CT) inverse problems is to consider a constrained minimization problem following the Compressed Sensing (CS) theory. The CS theory proves the possibility of sparse signal recovery using under sampled measurements which gives a powerful tool for CT problems that have incomplete measurements or contain heavy noise. Among current CS reconstruction methods, one widely accepted reconstruction framework is to perform a total variation (TV) minimization process and a data fidelity constraint process in an alternative way by two separate iteration loops. However because the two processes are done independently certain misbalance may occur which leads to either over-smoothed or noisy reconstructions. Moreover, such misbalance is usually difficult to adjust as it varies according to the scanning objects and protocols. In our work we try to make good balance between the minimization and the constraint processes by estimating the variance of image noise. First, considering that the noise of projection data follows a Poisson distribution, the Anscombe transform (AT) and its inversion is utilized to calculate the unbiased variance of the projections. Second, an estimation of image noise is given through a noise transform model from projections to the image. Finally a modified CS reconstruction method is proposed which guarantees the desired variance on the reconstructed image thus prevents the block-wising or over-noised caused by misbalanced constrained minimizations. Results show the advantage in both image quality and convergence speed.
Human head motion has been experimentally measured for high-resolution computed tomography (CT) design using a Canon digital camera. Our goal is to identify the minimal movements of the human head under ideal conditions without rigid fixation. In our experiments, all the 19 healthy volunteers were lying down with strict self-control. All of them were asked to be calm without pressures. Our results showed that the mean absolute value of the measured translation excursion was about 0.35 mm, which was much less than the measurements on real patients. Furthermore, the head motions in different directions were correlated. These results are useful for the design of the new instant CT system for in vivo high-resolution imaging (about 40 µm).
In this paper we present a backprojection filtered type (BPF-type) reconstruction algorithm for cone-beam circular scans based on Zou and Pan's work. The algorithm could use all the projection data passing through the PI-line segments in 2π scanning range. Because all the projection data in 2π is used, the algorithm has a good quality for practical noisy projection data. The algorithm is implemented using numerical and practical experiments. The practical experiments were done on our X-ray CT system with a flat-panel detector. We also compare the results with FDK reconstructions. From the experimental results, we deem that the BPF algorithm could satisfy the requirement of the X-ray CT inspection.
In this paper we discuss image reconstruction algorithms in super-short-scan fan-beam and cone-beam computed tomography (CT). We propose a new fan-beam filtered back-projection algorithm which can obtain exact region of interest (ROI) reconstruction if and only if every projecting line passing through the ROI intersects the source trajectory, even if the scanning range is smaller than the half-scan. And we prove the algorithm is approximate when the projections are truncated. Furthermore, we expand the algorithm to cone-beam reconstruction. Then we simulate the algorithm on the computer and evaluate the noise properties of the new algorithm and the other algorithms. Numerical results in our work suggest that the new algorithm is generally less susceptible to data noise and less artifacts than the before algorithms. In particular, the new algorithm is easily and successfully expanded to cone-beam tomography when the source trajectory is a short-arc on the single circle or on the helical trajectory.
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