Zhiqiang Chen

Multi-energy spectral CT has a broader range of applications with the recent development of photon-counting detectors. However, the photons counted in each energy bin decrease when the number of energy bins increases, which causes a higher statistical noise level of the CT image. In this work, we propose a novel iterative dynamic dual-energy CT algorithm to reduce the statistical noise. In the proposed algorithm, the multi-energy projections are estimated from the dynamic dual-energy CT data during the iterative process. The proposed algorithm is verified on sufficient numerical simulations and a laboratory two-energy-threshold PCD system. By applying the same reconstruction algorithm, the dynamic dual-energy CT’s final reconstruction results have a much lower statistical noise level than the conventional multi-energy CT. Moreover, based on the analysis of the simulation results, we explain why the dynamic dual-energy CT has a lower statistical noise level than the conventional multi-energy CT. The underlying idea is to sample sparse in the energy dimension, which can be done because there is a high correlation between projection data of different energy bins.

Chest X-ray fluoroscopy is a commonly used medical imaging method, which has a wide range of applications in the diagnosis of lung diseases and other fields. However, due to low contrast and relatively close linear attenuation coefficients, some early small lesions are difficult to detect in time. Using the X-ray fluorescent effect of high atomic number metal elements and metal atom-containing agents that can be enriched in the lesion, the fluoroscopy signal and the fluorescent signal emitted by the metal atoms can be detected at the same time during the fluoroscopy, and the images of the two can be integrated, which can theoretically enhance the contrast between the lesion and the surrounding tissue. Based on GEANT4, this paper conducts Monte Carlo simulations to explore the feasibility and enhancement effects of three enhancement schemes: the pencil beam spot scanning method, cone-beam collimation method, and slit scanning method, and discusses the specific geometric structure and material selection.

In this paper, we propose a novel static CT system: triple planes CT (TPCT) system. Three source-detector planes in different horizontal directions are placed in the system. Line-array carbon nanotube sources with different voltages and sandwich detectors are used. Compared to conventional cone-beam CT and common inverse-geometry CT, the TPCT enables fast scanning and six-energy imaging. 1-D U-Net is applied to correct the severe scatter caused by the special geometry. The limited-view problem is solved by the hybrid reconstruction algorithm. A Monte-Carlo simulation is performed on a thorax phantom. Both the reconstruction results and decomposition results have good image quality and show the feasibility of our proposed TPCT imaging system.

X-ray fluorescence computed tomography (XFCT) is a high sensitivity imaging modality for high-atomic elements such as gadolinium (Gd) or gold (Au). In order to improve the contrast of x-ray fluorescent (XRF) signal in the raw projection data, common XFCT systems use the single-pixel x-ray spectrometer to record XRF photons stimulated by the pencil-beam x-ray source and achieve line-by-line scan of the whole object by translating the x-ray source or the object. However, this kind of design results in waiting time of translation device. One improvement would be to replace the traditional x-ray tube with the spatially distributed multi-beam x-ray source and scan the whole object by switching the exposure of each focus. In this study, we present a design of XFCT system using the spatially distributed multi-beam source and a XFCT imaging experiment was performed to investigate its feasibility. Each cold cathode of the x-ray source used in this study was made of carbon nanotubes and each focus was independently controlled by the electronic control system. The incident beam was collimated by a pinhole array to produce pencil-beam source. The object scanned by the system was a PMMA cylinder (8cm in diameter) with Gd (20mg/mL) and I (100mg/mL) insertions. Results show that the distribution of I and Gd in a PMMA phantom was successfully reconstructed but the imaging performance was limited by the collimation and exposure mode of current distributed x-ray source. The practicality of current distributed x-ray source used for XFCT scan and further optimization of the proposed system are discussed according to the experimental results.

Purpose: Inverse‐geometry computed tomography (IGCT) could have great potential in medical applications and security inspections, and has been actively investigated in recent years. In this work, we explore a special architecture of IGCT in a stationary configuration: symmetric‐geometry computed tomography (SGCT), where the x‐ray source and detector are linearly distributed in a symmetric design. A direct filtered backprojection (FBP)‐type algorithm is developed to analytically reconstruct images from the SGCT projections.Methods: In our proposed SGCT system, a big number of x‐ray source points equally distributed along a straight‐line trajectory will sequentially fire in an ultra‐fast manner in one side, and an equispaced detector whose total length is comparable to that of the source will continuously collect data in the opposite side, as the object to be scanned moves into the imaging plane. We firstly present the overall design of SGCT. An FBP‐type reconstruction algorithm is then derived for this unique imaging configuration. With finite length of x‐ray source and detector arrays, projection data from one segment of SGCT scan are insufficient for an exact reconstruction. As a result, in practical applications, dual‐SGCT scan whose detector segments are placed perpendicular to each other, is of particular interest and is proposed. Two segments of SGCT together can make sure that the passing rays cover at least 180 degrees for each and every point if carefully designed. In general, however, there exists a data redundancy problem for a dual‐SGCT. So a weighting strategy is developed to maximize the use of projection data collected while avoid image artifacts. In addition, we further extend the fan‐beam SGCT to cone beam and obtain a Feldkamp–Davis–Kress (FDK)‐type reconstruction algorithm. Finally, we conduct a set of experimental studies both in simulation and on a prototype SGCT system and validate our proposed methods.Results: A simulation study using the Shepp–Logan head phantom confirms that CT images can be exactly reconstructed from dual‐SGCT scan and that our proposed weighting strategy is able to handle the data redundancy properly. Compared with the rebinning‐to‐parallel‐beam method using the forward projection of an abdominal CT dataset, our proposed method is seen to be less sensitive to data truncation. Our algorithm can achieve 10.64 lp/cm of spatial resolution at 50% modulation transfer functions point, higher than that of the rebinning method which can only reach at 9.42 lp/cm even with extremely fine interpolation. Real experiments of a cylindrical object on a prototype SGCT further prove the effectiveness and practicability of the direct FBP method proposed, with similar level of noise performance to rebinning algorithm.Conclusions: A new concept of SGCT with linearly distributed source and detector is investigated in this work, in which spinning of sources and detectors is no longer needed during data acquisition, simplifying its system design, development, and manufacturing. A direct FBP‐type algorithm is developed for analytical reconstruction from SGCT projection data. Numerical and real experiments validate our method and show that exact CT image can be reconstructed from dual‐SGCT scan, where data redundancy problem can be solved by our proposed weighting function.

Current benchtop x-ray fluorescence computed tomography (XFCT) devices, which use x-ray tubes to stimulate x-ray fluorescence (XRF) photons, suffer from the contamination of Compton scatter background produced by the polychromatic incident beam. The conventional maximum-likelihood expectation-maximization (ML-EM) algorithm only considers the noise model of the XRF signal, which results in high statistical noise in reconstructed images caused by scattered photons. In this study, we proposed a scattering noise model enhanced EM-TV algorithm for benchtop XFCT image reconstruction in order to reduce the noise of scatter background and improve the sensitivity of XFCT images. The statistical noise of scattered photons was considered in the likelihood function and the EM iteration step was modified correspondingly to suppress the statistical noise caused by Compton scattered photons. The robustness of the EM iteration was improved by applying the reweighted total variation (TV) norm as the penalty function. Numerical simulations and imaging experiments of a PMMA phantom consisting of gadolinium (Gd) solutions were performed to validate the proposed algorithm. The phantom was irradiated by a cone-beam polychromatic source and the projection was recorded by a linear-array photon counting detector. For comparison, the XFCT images of Gd were reconstructed using different algorithms. Results indicate that compared with the conventional ML-EM algorithm, the proposed algorithm can obtain XFCT images with lower background noise and higher contrast, which may further improve the sensitivity and image performance of current benchtop XFCT systems.

Nanoparticles (NPs) are currently under intensive research for their application in tumor diagnosis and therapy. X-ray fluorescence computed tomography (XFCT) is considered a promising non-invasive imaging technique to obtain the bio-distribution of nanoparticles which include high-Z elements (e.g., gadolinium (Gd) or gold (Au)). In the present work, a set of experiments with quantitative imaging of GdNPs in mice were performed using our benchtop XFCT device. GdNPs solution which consists of 20 mg/mL NaGdF4 was injected into a nude mouse and two tumor-bearing mice. Each mouse was then irradiated by a cone-beam X-ray source produced by a conventional X-ray tube and a linear-array photon counting detector with a single pinhole collimator was placed on one side of the beamline to record the intensity and spatial information of the X-ray fluorescent photons. The maximum likelihood iterative algorithm with scatter correction and attenuation correction method was applied for quantitative reconstruction of the XFCT images. The results show that the distribution of GdNPs in each target slice (containing liver, kidney or tumor) was well reconstructed and the concentration of GdNPs deposited in each organ was quantitatively estimated, which indicates that this benchtop XFCT system provides convenient tools for obtaining accurate concentration distribution of NPs injected into animals and has potential for imaging of nanoparticles in vivo.

In this paper, we propose a sinogram inpainting network (SIN) to solve limited-angle CT reconstruction problem, which is a very challenging ill-posed issue and of great interest for several clinical applications. A common approach to the problem is an iterative reconstruction algorithm with regularization term, which can suppress artifacts and improve image quality, but requires high computational cost. The starting point of this paper is the proof of inpainting function for limited-angle sinogram is continuous, which can be approached by neural networks in a data-driven method, granted by the universal approximation theorem. Based on this, we propose SIN as the fitting function -- a convolutional neural network trained to generate missing sinogram data conditioned on scanned data. Besides CNN module, we design two differentiable and rapid modules, Radon and Inverse Radon Transformer network, to encapsulate the physical model in the training procedure. They enable new joint loss functions to optimize both sinogram and reconstructed image in sync, which improved the image quality significantly. To tackle the labeled data bottleneck in clinical research, we form a sinogram-image-sinogram closed loop, and the difference between sinograms can be used as training loss. In this way, the proposed network can be self-trained, with only limited-angle data for unsupervised domain adaptation. We demonstrate the performance of our proposed network on parallel beam X-ray CT in lung CT datasets from Data Science Bowl 2017 and the ability of unsupervised transfer learning in Zubal's phantom. The proposed method performs better than state-of-art method SART-TV in PSNR and SSIM metrics, with noticeable visual improvements in reconstructions.