The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.

/pdf/photon-counting-x-ray-detectors-for-ct-4ma8oc2u3q.pdf

Photon-counting x-ray detectors for CT

This review describes advances in the XRF group of techniques published approximately between April 2015 and March 2016. Fundamental contributions in the instrumentation sections include the development of synchrotron radiation sources, semiconductor design technology and evaluating the quality of in situ hand-held XRF results. A bench top TXRF spectrometer for the measurement of the elements from carbon to uranium is described that offers achievable detection limits that range from ng g−1 to μg g−1. The applications sections aim to give a representative overview of the range of applications that use XRF techniques with an emphasis on papers that describe technical or application innovation. Sample size investigated ranged from the planet Mercury down to nano-gram quantities of cement secreted by settlement stage barnacle larvae. The archaeological and cultural heritage section this year includes an authentic report on the use of cannabis as a filler in ancient lime plaster and heartening news that the construction of an underground parking garage in the centre of Zurich (Switzerland) unearthed the remains of seven Neolithic settlements from the 4th and 3rd millennium BC. The X-ray excitation of iron present in ancient iron-gall inks was presented as a step toward the non-invasive reading of fragile and/or unopenable documents. Feedback from readers of this review is most welcome and the review coordinator may be contacted using the email address provided.

https://pubs.rsc.org/en/content/articlepdf/2021/ja/d1ja90033a

2016 Atomic Spectrometry Update – a review of advances in X-ray fluorescence spectrometry and its applications

Limited-angle problems encountered in computed tomography (CT) often necessitate image reconstruction using projection data from a particular angle range. To solve this severely ill-posed problem, prior information is utilized to constrain the problem. As a special case of compressed sensing, a total variation (TV) transform with an $l_{1}$ -norm image gradient is utilized in most cases, and manages to obtain very impressive reconstruction results. However, it is unfit for limited-angle problems owing to its isotropic property. This paper proposes a new iteratively reweighted anisotropic TV (ATV) method, in which a reweighted technique is incorporated into the idea of ATV. Our strategy successfully combines their merits and results in significantly improved performance. By using the reweighted technique, we are able to approximate the most direct measure of sparsity— $l_{0}$ -norm—better than $l_{1}$ -norm. As a result, the property of image sparsity can be utilized more efficiently. Because TV is isotropic, which prevents detection of blurred edges caused by missing angle ranges and may weaken edge-preserving ability along nonblurred directions, we consider the angle range of the data as additional prior information by assigning different weights to different directions; this allows the anisotropic property to be utilized. Therefore, the blurred directions can be prevented from affecting edge detection, and better reconstruction results can be achieved. To demonstrate the advantages of our method, we perform reconstruction using projection data from phantom CT scans and actual CT scans. We conducted comprehensive comparison between our method and many existing TV-based methods. Both qualitative and quantitative results are presented.

Reweighted Anisotropic Total Variation Minimization for Limited-Angle CT Reconstruction

Purpose: The large variation of x‐ray fluence at the detector across the imaged field‐of‐view in cone‐beam CT causes the loss of skin‐line and reduces CT number accuracy and image uniformity We report the performance of a bowtie filter (BTF) as a compensator for improved uniformity, skin‐line and CT number accuracy Method:Image enhancement is performed using a BT filter with a 20cm collimator The BTF was inserted 30cm from the x‐ray source on an Elekta Synergy XVI This filter modulates the 2D x‐ray fluence making it non‐uniform across the field‐of‐view This compensates for the limited attenuation near the skin, resulting in enhancement of skin‐line and uniformity Two phantoms (1) CatPhan and 2) CatPhan with irregular acrylic annulus (Cat‐Irreg) were scanned on an Elekta CBCT system The reconstructed images with and without a BTF were analyzed The images were transformed into polar coordinates to allow quanitfication of radial lag artifacts (radar artifact) for the Cat‐Irreg phantom Results: The use of the bowtie filter demonstrated a considerable improvement in CT accuracy in the skin‐line region The uniformity increases 30% for Cat‐Irreg phantom, and skin‐line edges for both phantoms are visible CT number accuracy with the BT filter improved by 2% for the CatPhan phantom while no improvement was evident for the Cat‐Irreg phantom The CT♯ linearity for the catphan phantom with the BT filter improved by 8% In addition, a 45% reduction in the “radar artifact” was observed in the Cat‐Irreg phantom images acquired with the BT filter Conclusion: The implemented BTF shows improvement in image quality including uniformity and skin‐line reconstruction This compensator is static and makes many compromises for anatomical imaging site, patient size, and imaged field‐of‐view The ideal compensator would optimize the fluence profile to account for numerous properties of the patient and imaging system Research sponsored by Elekta

SU‐FF‐I‐24: The Influence of Bowtie Filtration On Cone‐Beam CT Image Quality

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.

https://www.mdpi.com/1422-0067/20/9/2315/pdf

Quantitative Imaging of Gd Nanoparticles in Mice Using Benchtop Cone-Beam X-ray Fluorescence Computed Tomography System

Computed tomography (CT) reconstruction with low radiation dose is a significant research point in current medical CT field. Compressed sensing has shown great potential reconstruct high-quality CT images from few-view or sparse-view data. In this paper, we use the sparser L1/2 regularization operator to replace the traditional L1 regularization and combine the Split Bregman method to reconstruct CT images, which has good unbiasedness and can accelerate iterative convergence. In the reconstruction experiments with simulation and real projection data, we analyze the quality of reconstructed images using different reconstruction methods in different projection angles and iteration numbers. Compared with algebraic reconstruction technique (ART) and total variance (TV) based approaches, the proposed reconstruction algorithm can not only get better images with higher quality from few-view data but also need less iteration numbers.

/pdf/a-ct-reconstruction-algorithm-based-on-l1-2-regularization-2rtetvvd1y.pdf

A CT Reconstruction Algorithm Based on L1/2 Regularization.

Background Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which has the potential to distinguish different material compositions. Although material decomposition methods based on x-ray attenuation characteristics have good performance in dual-energy CT imaging, there are some limitations in terms of image contrast and noise levels. Objective This study focused on multi-material decomposition of spectral CT images based on a deep learning approach. Methods To classify and quantify different materials, we proposed a multi-material decomposition method via the improved Fully Convolutional DenseNets (FC-DenseNets). A mouse specimen was first scanned by spectral CT system based on a photon-counting detector with different energy ranges. We then constructed a training set from the reconstructed CT images for deep learning to decompose different materials. Results Experimental results demonstrated that the proposed multi-material decomposition method could more effectively identify bone, lung and soft tissue than the basis material decomposition based on post-reconstruction space in high noise levels. Conclusions The new proposed approach yielded good performance on spectral CT material decomposition, which could establish guidelines for multi-material decomposition approaches based on the deep learning algorithm.

Multi-material decomposition of spectral CT images via Fully Convolutional DenseNets.

X-ray fluorescence computed tomography (XFCT) based on sheet beam can save a huge amount of time to obtain a whole set of projections using synchrotron. However, it is clearly unpractical for most biomedical research laboratories. In this paper, polychromatic X-ray fluorescence computed tomography with sheet-beam geometry is tested by Monte Carlo simulation. First, two phantoms ( and ) filled with PMMA are used to simulate imaging process through GEANT 4. Phantom contains several GNP-loaded regions with the same size (10 mm) in height and diameter but different Au weight concentration ranging from 0.3% to 1.8%. Phantom contains twelve GNP-loaded regions with the same Au weight concentration (1.6%) but different diameter ranging from 1 mm to 9 mm. Second, discretized presentation of imaging model is established to reconstruct more accurate XFCT images. Third, XFCT images of phantoms and are reconstructed by filter back-projection (FBP) and maximum likelihood expectation maximization (MLEM) with and without correction, respectively. Contrast-to-noise ratio (CNR) is calculated to evaluate all the reconstructed images. Our results show that it is feasible for sheet-beam XFCT system based on polychromatic X-ray source and the discretized imaging model can be used to reconstruct more accurate images.

/pdf/monte-carlo-simulation-for-polychromatic-x-ray-fluorescence-nss19incul.pdf

Monte Carlo Simulation for Polychromatic X-Ray Fluorescence Computed Tomography with Sheet-Beam Geometry

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.

/pdf/an-improved-total-variation-minimization-method-using-prior-hn4l3mmqvh.pdf

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 CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method.

/pdf/a-ct-reconstruction-algorithm-based-on-non-aliasing-409l0rvaoj.pdf

Luzhen Deng

Papers

A CT Reconstruction Algorithm Based on L1/2 Regularization.

Multi-material decomposition of spectral CT images via Fully Convolutional DenseNets.

Monte Carlo Simulation for Polychromatic X-Ray Fluorescence Computed Tomography with Sheet-Beam Geometry

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

A CT Reconstruction Algorithm Based on Non-Aliasing Contourlet Transform and Compressive Sensing