Monte Carlo study of x-ray detection configurations for benchtop x-ray fluorescence computed tomography of gold nanoparticle-loaded objects.

Over the last decade, the performance of benchtop x-ray fluorescence computed tomography (XFCT) systems has been significantly enhanced through hardware and software optimizations. Recent studies have indicated the need of energy-resolving pixelated/array detectors in the x-ray detection component to further improve the sensitivity and image resolution of benchtop XFCT systems while meeting the realistic constraints of dose and scan time. Thus, it is of immediate interest in the research community to conduct the following investigations: (a) delineation of strengths/weaknesses of detection configurations that incorporate pixelated/array detectors in combination with two most frequently used (parallel-hole and pinhole) collimators; (b) one-to-one comparison of their performance under identical imaging conditions of benchtop XFCT. In this study, we developed a Geant4-based Monte Carlo model to investigate the effects of the aforementioned detection configurations on the sensitivity and image resolution of a benchtop XFCT system. Using this model, we simulated the detection of x-ray fluorescence and scattered photons from gold nanoparticle-containing phantoms using energy-resolving pixelated detectors coupled with parallel-hole and pinhole collimators. Simulation results demonstrated that the detector consisting of large pixels (1 mm × 1 mm) combined with a parallel-hole collimator had better sensitivity (i.e. lower detection limit) than the detector made of smaller pixels (0.25 mm × 0.25 mm) coupled with a pinhole collimator. In comparison, although slightly less sensitive, the latter detector configuration achieved better image resolution than did the former. Thus, a detection configuration consisting of a pixelated detector with submillimeter pixels and a pinhole collimator is preferable when image resolution is critical for benchtop XFCT applications. On the other hand, the detector with larger pixels coupled with a parallel-hole collimator is better suited for benchtop XFCT applications in which higher sensitivity and shorter scan time are essential.

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

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.

A detector's eye view (DEV)-based OSEM algorithm for benchtop x-ray fluorescence computed tomography (XFCT) image reconstruction.

In this study, we developed a detector's eye view (DEV)-based ordered subsets expectation maximization (OSEM) algorithm for more accurate reconstruction of benchtop x-ray fluorescence computed tomography (XFCT) images. The proposed approach was tested using two sets of benchtop XFCT imaging data derived from a newly performed gold nanoparticle (GNP)-containing phantom imaging study and a previously published postmortem benchtop XFCT imaging study of a tumor-bearing mouse injected with GNPs. DEV-based OSEM resulted in higher spatial resolution (up to ~20% decrease in the full width at half maximum values of the regions of interest), compared with filtered back-projection (FBP) and traditional OSEM. It also resulted in up to an order of magnitude smaller background noise in the reconstructed images than FBP, while producing consistently less background noise than traditional OSEM.

Investigation of transmission computed tomography (CT) image quality and x-ray dose achievable from an experimental dual-mode benchtop x-ray fluorescence CT and transmission CT system.

OBJECTIVE: To investigate the image quality and x-ray dose associated with a transmission computed tomography (CT) component implemented within the same platform of an experimental benchtop x-ray fluorescence CT (XFCT) system for multimodal preclinical imaging applications. METHODS: Cone-beam CT scans were performed using an experimental benchtop CT + XFCT system and a cylindrically-shaped 3D-printed polymethyl methacrylate phantom (3 cm in diameter, 7 cm in height) loaded with various concentrations (0.05-1 wt. %) of gold nanoparticles (GNPs). Two commercial CT quality assurance phantoms containing 3D line-pair (LP) targets and contrast targets were also scanned. The x-ray beams of 40 and 62 kVp, both filtered by 0.08 mm Cu and 0.4 mm Al, were used with 17 ms of exposure time per projection at three current settings (2.5, 5, and 10 mA). The ordered-subset simultaneous algebraic reconstruction and total variation-minimization methods were used to reconstruct images. Sparse projection and short scan were considered to reduce the x-ray dose. The contrast-to-noise ratio (CNR) and modulation transfer function (MTF) were calculated. RESULTS: The lowest detectable concentration of GNPs (CNR > 5) and the highest spatial resolution (per MTF50%) were 0.10 wt. % and 9.5 LP/CM, respectively, based on the images reconstructed from 360 projections of the 40 kVp beam (or x-ray dose of 3.44 cGy). The background noise for the image resulting in the lowest GNP detection limit was 25 Hounsfield units. CONCLUSION: The transmission CT component within the current experimental benchtop CT + XFCT system produced images deemed acceptable for multimodal (CT + XFCT) imaging purposes, with less than 4 cGy of x-ray dose.

A Geant4-based Monte Carlo study of a benchtop multi-pinhole X-ray fluorescence computed tomography imaging.

BACKGROUND: X-ray fluorescence (XRF) computed tomography (XFCT) has shown promise for molecular imaging of metal nanoparticles such as gold nanoparticles (GNPs) and benchtop XFCT is under active development due to its easy access, low-cost instrumentation and operation. PURPOSE: To validate the performance of a Geant4-based Monte Carlo (MC) model of a benchtop multi-pinhole XFCT system for quantitative imaging of GNPs. METHODS: The MC mode consisted of a fan-beam x-ray source (125 kVp), which was used to stimulate the emission of XRF from the GNPs, a phantom (3 cm in diameter) which included six or nine inserts (3 mm in diameter), each of which contained the same (1 wt. %) or various (0.08-1 wt. %) concentrations of GNPs, a multi-pinhole collimator which could acquire multiple projections simultaneously and a one-sided or two-sided two-dimensional (2D) detector. Various pinhole diameters (3.7, 2, 1, 0.5 and 0.25 mm) and various particle numbers (20, 40, 80 and 100 billion) were simulated and the results for single pinhole and multi-pinhole (9 pinholes) imaging were compared. RESULTS: The image resolution for a 1 mm multi-pinhole was between 0.88 and 1.38 mm. The detection limit for multi-pinhole operation was about 0.09 wt. %, while that for the single pinhole was about 0.13 wt. %. For a fixed number of pinholes, noise increased with decreasing number of photons. CONCLUSION: The MC mode could acquire 2D slice images of the object without rotation and demonstrated that a multi-pinhole XFCT imaging system could be a potential bioimaging modality for nanomedical applications.

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

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 (A and B) filled with PMMA are used to simulate imaging process through GEANT 4. Phantom A 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 B 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 A and B 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.

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.

A CT reconstruction approach from sparse projection with adaptive-weighted diagonal total-variation in biomedical application.

For lack of directivity in Total Variation (TV) which only uses x-coordinate and y-coordinate gradient transform as its sparse representation approach during the iteration process, this paper brought in Adaptive-weighted Diagonal Total Variation (AwDTV) that uses the diagonal direction gradient to constraint reconstructed image and adds associated weights which are expressed as an exponential function and can be adaptively adjusted by the local image-intensity diagonal gradient for the purpose of preserving the edge details, then using the steepest descent method to solve the optimization problem. Finally, we did two sets of numerical simulation and the results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection, which has lower Root Mean Square Error (RMSE) and higher Universal Quality Index (UQI) than Algebraic Reconstruction Technique (ART) and TV-based reconstruction method.

A CT reconstruction algorithm based on non-aliasing Contourlet transform and compressive sensing.

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.

A CT Reconstruction Algorithm Based on L1/2 Regularization.

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.