Adaptive scatter kernel deconvolution modeling for cone-beam CT scatter correction via deep reinforcement learning.

BACKGROUND: Scattering photons can seriously contaminate cone-beam CT (CBCT) image quality with severe artifacts and substantial degradation of CT value accuracy, which is a major concern limiting the widespread application of CBCT in the medical field. The scatter kernel deconvolution (SKD) method commonly used in clinic requires a Monte Carlo (MC) simulation to determine numerous quality-related kernel parameters, and it cannot realize intelligent scatter kernel parameter optimization, causing limited accuracy of scatter estimation. PURPOSE: Aiming at improving the scatter estimation accuracy of the SKD algorithm, an intelligent scatter correction framework integrating the SKD with deep reinforcement learning (DRL) scheme is proposed. METHODS: Our method firstly builds a scatter kernel model to iteratively convolve with raw projections, and then the deep Q-network of the DRL scheme is introduced to intelligently interact with the scatter kernel to achieve a projection adaptive parameter optimization. The potential of the proposed framework is demonstrated on CBCT head and pelvis simulation data and experimental CBCT measurement data. Furthermore, we have implemented the U-net based scatter estimation approach for comparison. RESULTS: The simulation study demonstrates that the mean absolute percentage error (MAPE) of the proposed method is less than 9.72% and the peak signal-to-noise ratio (PSNR) is higher than 23.90 dB, while for the conventional SKD algorithm, the minimum MAPE is 17.92% and the maximum PSNR is 19.32 dB. In the measurement study, we adopt a hardware-based beam stop array algorithm to obtain the scatter-free projections as a comparison baseline, and our method can achieve superior performance with MAPE < 17.79% and PSNR > 16.34 dB. CONCLUSIONS: In this paper, we propose an intelligent scatter correction framework that integrates the physical scatter kernel model with DRL algorithm, which has the potential to improve the accuracy of the clinical scatter correction method to obtain better CBCT imaging quality.

A transmission measurement-based spectrum estimation method incorporating X-ray tube voltage fluctuation.

Background: The energy spectrum is the property of the X-ray tube that describes the energy fluence per unit interval of photon energy. The existing indirect methods for estimating the spectrum ignore the influence caused by the voltage fluctuation of the X-ray tube. Methods: In this work, we propose a method for estimating the X-ray energy spectrum more accurately by including the voltage fluctuation of the X-ray tube. It expresses the spectrum as the weighted summation of a set of model spectra within a certain voltage fluctuation range. The difference between the raw projection and the estimated projection is considered as the objective function for obtaining the corresponding weight of each model spectrum. The equilibrium optimizer (EO) algorithm is used to find the weight combination that minimizes the objective function. Finally, the estimated spectrum is obtained. We refer to the proposed method as the poly-voltage method. The method is mainly aimed at the cone-beam computed tomography (CBCT) system. Results: The model spectra mixture evaluation and projection evaluation showed that the reference spectrum can be combined by multiple model spectra. They also showed that it is appropriate to choose about 10% of the preset voltage as the voltage range of the model spectra, which can match the reference spectrum and projection quite well. The phantom evaluation showed that the beam-hardening artifact can be corrected using the estimated spectrum via the poly-voltage method, and the poly-voltage method provides not only the accurate reprojection but also an accurate spectrum. The normalized root mean square error (NRMSE) index between the spectrum generated via the poly-voltage method and the reference spectrum could be kept within 3% according to above evaluations. There existed a 1.77% percentage error between the estimated scatter of polymethyl methacrylate (PMMA) phantom using the two spectra generated via the poly-voltage method and the single-voltage method, and it could be considered for scatter simulation. Conclusions: Our proposed poly-voltage method could estimate the spectrum more accurately for both ideal and more realistic voltage spectra, and it is robust to the different modes of voltage pulse.

New Function of Metal-Organic Framework: Structurally Ordered Metal Promoter.

The remarkable roles of metal promoters have been known for nearly a century, but it is still a challenge to find a suitable structure model to reveal the action mechanism behind metal promoters. Herein, a new function of metal-organic frameworks (MOFs) is developed as an ideal model to construct structurally ordered metal promoters by a targeted post-modification strategy. MOFs as model not only favor clearing the real action mechanism behind metal promoters, but also can anchor one or multiple kinds of metal promoters especially noble metal promoters. Typically, the as-prepared Pd/bpy-UiO-Cu catalysts show high selectivity (>99%) toward 4-nitrophenylethane in 4-nitrostyrene hydrogenation, mainly due to the enhanced interaction between Pd nanoparticles and MOF carriers induced by Cu promoters, thus inhibiting the hydrogenation of 4-nitrophenylethane. This strategy with flexibility and universality will open up a new route to synthesize efficient catalysts with structurally ordered metal promoters.

Deep learning based de-overlapping correction of projections from a flat-panel micro array X-ray source: Simulation study.

PURPOSE: Flat-panel X-ray source is an experimental X-ray emitter with target application of static computer tomography (CT), which can save imaging space and time. However, the X-ray cone beams emitted by the densely arranged micro-ray sources are overlapped, causing serious structural overlapping and visual blur in the projection results. Traditional deoverlapping methods can hardly solve this problem well. METHOD: We converted the overlapping cone beam projections to parallel beam projections through a U-like neural network and selected structural similarity (SSIM) loss as the loss function. In this study, we converted three kinds of overlapping cone beam projections of the Shepp-Logan, line-pairs, and abdominal data with two overlapping levels to corresponding parallel beam projections. Training completed, we tested the model using the test set data that was not used at the training phase, and evaluated the difference between the test set conversion results and their corresponding parallel beams through three indicators: mean squared error (MSE), peak signal-to-noise ratio (PSNR) and SSIM. In addition, projections from head phantoms were applied for generalization test. RESULT: In the Shepp-Logan low-overlapping task, we obtained a MSE of 1.624×10-5, a PSNR of 47.892 dB, and a SSIM of 0.998 which are the best results of the six experiments. For the most challenging abdominal task, the MSE, PSNR, and SSIM are 1.563×10-3, 28.0586 dB, and 0.983, respectively. In more generalized data, the model also achieved good results. CONCLUSION: This study proves the feasibility of utilizing the end-to-end U-net for deblurring and deoverlapping in the flat-panel X-ray source domain.

A correlated sampling-based Monte Carlo simulation for fast CBCT iterative scatter correction.

BACKGROUND: In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. PURPOSE: In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. METHODS: Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. RESULTS: Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half-fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2 HU in full-fan case and from 53 to 13 HU in half-fan case. Scatter-to-primary ratio is reduced to 0.02 for full-fan and 0.04 for half-fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half-fan and full-fan, respectively. The contrast after correction is increased by 19% for half-fan and 44% for full-fan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4 HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25 s for the whole iterative scatter correction process. CONCLUSIONS: The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.

Anisotropic MOF-on-MOF Growth of Isostructural Multilayer Metal-Organic Framework Heterostructures.

Isostructural MOFs with similar crystallographic parameter are easily available for MOF-on-MOF growth and possible to form core-shell structure by isotropic growth. However, due to well-matched cell lattice, selective growth in isostructural MOF heterostructures remains a great challenge for engineering atypical MOF heterostructures. Herein, an anisotropic MOF-on-MOF growth strategy was developed to structure a range of multilayer sandwich-like ZIF-L heterostructures via stacking isostructural ZIF-L-Zn and ZIF-L-Co alternately with three-, five-, seven-, and more layer structures. Moreover, these heterostructures with highly designable feature were fantastic precursors for fabricating derivatives with tunable magnetic and catalytic properties. Such strategy explores a novel way of achieving anisotropic MOF-on-MOF growth between isostructural MOFs and opens up new horizons for regulating the properties by MOF modular assembly in versatile functional nanocomposites.

Prediction Descriptor for Catalytic Activity of Platinum Nanoparticles/Metal-Organic Framework Composites.

Supported metal nanoparticles (MNPs) have exhibited superior catalytic performance in various heterogeneous catalysis applications, which is usually influenced or even determined by the physicochemical properties of their porous supports. It is well acknowledged that understanding the regulation mechanism of supports is an important prerequisite to predict the catalytic performance of supported MNPs as well as the development of advanced catalysts. Here, we demonstrated that different transition-metal clusters (from Group IIIB to Group IIB) within metal-organic frameworks (MOFs) could accurately regulate the surface electronic status of supported platinum nanoparticles (Pt NPs), and the Pt/MOF composites showed a periodic activity trend in hydrogenation of 1-hexene. A strong correlation was found between the catalytic activity of Pt/MOF composites and the number of electrons in their outmost d orbitals of the transition-metal species, suggesting that the latter could play the role of prediction descriptor. Furthermore, this descriptor can be extended to predict the hydrogenation activity of more Pt/MOF composites and provide an important guiding principle for the design of supported MNPs catalysts.

Exploring the Fundamental Roles of Functionalized Ligands in Platinum@Metal-Organic Framework Catalysts.

The metal nodes, functionalized ligands, and uniform channels of metal-organic frameworks (MOFs) are typically utilized to regulate the catalytic properties of metal nanoparticles (MNPs). However, though the ligand functionalization could impact the properties of the metal nodes and channels, which might further regulate the catalytic activity and selectivity of MNPs, related research in the design of MNP/MOF catalysts was usually neglected. Herein, we synthesized a series of Pt@UiO-66 composites (Pt@UiO-66-NH2, Pt@UiO-66-SO3H, and Pt@UiO-66) with slightly different organic ligands, which enhanced steric hindrance and contributed to multipathway electron transfer in selective hydrogenation of linear citronellal. The selectivity toward citronellol was gradually improved along with the increased size of functional groups (hydrogen, amino groups, and sulfo groups) on organic ligands, which enhanced steric hindrance provided by channels. In addition, the X-ray photoelectron spectroscopy measurements also revealed that the electronic state of Pt NPs was regulated through multipathway electron transfer from Pt NPs to metal nodes, between organic ligands and Pt NPs/metal nodes. Our research proved that the ligand functionalization altered physiochemical properties of the channels and metal nodes, further together managing the catalytic performance of Pt NPs through enhanced steric hindrance and multi-pathway electron transfer.