ALDOB/KAT2A interactions epigenetically modulate TGF-ß expression and T cell functions in hepatocellular carcinogenesis.

BACKGROUND AND AIMS: Cross talk between tumor cells and immune cells enables tumor cells to escape immune surveillance and dictate responses to immunotherapy. Previous studies have identified that downregulation of the glycolytic enzyme fructose-1,6-bisphosphate aldolase B (ALDOB) in tumor cells orchestrated metabolic programming to favor HCC. However, it remains elusive whether and how ALDOB expression in tumor cells affects the tumor microenvironment in HCC. APPROACH AND RESULTS: We found that ALDOB downregulation was negatively correlated with CD8 + T cell infiltration in human HCC tumor tissues but in a state of exhaustion. Similar observations were made in mice with liver-specific ALDOB knockout or in subcutaneous tumor models with ALDOB knockdown. Moreover, ALDOB deficiency in tumor cells upregulates TGF-ß expression, thereby increasing the number of Treg cells and impairing the activity of CD8 + T cells. Consistently, a combination of low ALDOB and high TGF-ß expression exhibited the worst overall survival for patients with HCC. More importantly, the simultaneous blocking of TGF-ß and programmed cell death (PD) 1 with antibodies additively inhibited tumorigenesis induced by ALDOB deficiency in mice. Further mechanistic experiments demonstrated that ALDOB enters the nucleus and interacts with lysine acetyltransferase 2A, leading to inhibition of H3K9 acetylation and thereby suppressing TGFB1 transcription. Consistently, inhibition of lysine acetyltransferase 2A activity by small molecule inhibitors suppressed TGF-ß and HCC. CONCLUSIONS: Our study has revealed a novel mechanism by which a metabolic enzyme in tumor cells epigenetically modulates TGF-ß signaling, thereby enabling cancer cells to evade immune surveillance and affect their response to immunotherapy.

Broad-band-enhanced plasmonic random laser in silver nanostar arrays.

As a novel optical device, the plasmonic random laser has unique working principle and emission characteristics. However, the simultaneous enhancement of absorption and emission by plasmons is still a problem. In this paper, we propose a broad-band-enhanced plasmonic random laser. Two-dimensional silver (Ag) nanostar arrays were prepared using a bottom-up method with the assistance of self-assembled nanosphere templates. The plasmon resonance of Ag nanostars contributes to the pump light absorption and photoluminescence (PL) of RhB. Coherent random lasing was achieved in RhB@PVA film based on localized surface plasmon resonance (SPR) dual enhancement and scattering feedback of Ag nanostars. Ag nanostars prepared with different nanosphere diameters affect the laser emission wavelength. In addition, the random laser device achieves wavelength tunability on a flexible substrate under mechanical external force.

A dual-energy CT reconstruction method based on anchor network from dual quarter scans.

Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses.

Deep unsupervised adversarial domain adaptation for underwater source range estimation.

In this study, an underwater source range estimation method based on unsupervised domain adaptation (UDA) is proposed. In contrast to traditional deep-learning frameworks using real-world data, UDA does not require labeling of the measured data, making it more practical. First, a classifier based on a deep neural network is trained with labeled simulated data generated using acoustic propagation models and, then, the adaptive procedure is applied, wherein unlabeled measured data are employed to adjust an adaptation module using the adversarial learning algorithm. Adversarial learning is employed to alleviate the marginal distribution divergence, which reflects the difference between the measured and theoretically computed sound field, in the latent space. This divergence, caused by environmental parameter mismatch or other unknown corruption, can be detrimental to accurate source localization. After the completion of the adaptive procedure, the measured and simulated data are projected to the same space, eliminating distribution discrepancy, which is beneficial for source localization tasks. Experimental results show that range estimation based on UDA outperforms the match-field-processing method under four scenarios of few snapshots, few array elements, low signal-to-noise ratio, and environmental parameter mismatch, verifying the robustness of the method.

Super-resolution image reconstruction from sparsity regularization and deep residual-learned priors.

BACKGROUND: Computed tomography (CT) plays an important role in the field of non-destructive testing. However, conventional CT images often have blurred edge and unclear texture, which is not conducive to the follow-up medical diagnosis and industrial testing work. OBJECTIVE: This study aims to generate high-resolution CT images using a new CT super-resolution reconstruction method combining with the sparsity regularization and deep learning prior. METHODS: The new method reconstructs CT images through a reconstruction model incorporating image gradient L0-norm minimization and deep image priors using a plug-and-play super-resolution framework. The deep learning priors are learned from a deep residual network and then plugged into the proposed new framework, and alternating direction method of multipliers is utilized to optimize the iterative solution of the model. RESULTS: The simulation data analysis results show that the new method improves the signal-to-noise ratio (PSNR) by 7% and the modulation transfer function (MTF) curves show that the value of MTF50 increases by 0.02 factors compared with the result of deep plug-and-play super-resolution. Additionally, the real CT image data analysis results show that the new method improves the PSNR by 5.1% and MTF50 by 0.11 factors. CONCLUSION: Both simulation and real data experiments prove that the proposed new CT super-resolution method using deep learning priors can reconstruct CT images with lower noise and better detail recovery. This method is flexible, effective and extensive for low-resolution CT image super-resolution.

Projection domain processing for low-dose CT reconstruction based on subspace identification.

PURPOSE: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT. METHODS: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom. RESULTS: In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04. CONCLUSION: This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.

Rylene-Fullerene Hybrid an Emerging Electron Acceptor for High-Performing and Photothermal-Stable Ternary Solar Cells.

Molecular carbon imides, especially extended perylene diimides (PDIs) have been the best wide-band-gap nonfullerene acceptors. Despite their excellent photothermal/chemical stability, flexible reaction sites, and unique photoelectronic properties, there is still a lack of fundamental understanding of their molecular characteristics as a third component. Here, generations of PDIs with distinctive molecular architecture, are deliberately screened out as the third component to PM6:Y6. Only a rylene-fullerene hybrid, S-Fuller-PMI, surprisingly boosts the fill factor (FF) of ternary organic solar cells (OSCs) to 0.77 from 0.72 for PM6:Y6 binary ones, and therefore the power conversion efficiency (PCE) of ternary cells is enhanced from 15.3% to 16.2%. Compared with highly-flexible rylene dimer and rigid multimer, S-Fuller-PMI exhibits higher electron mobility, favorable surface tension, and, therefore tailored compatibility with Y6. These formed Y6:S-Fuller-PMI alloys play as a morphological controller to improve charge separation and transport process. Simultaneously, the suppressed photothermal-induced traps, along with inherent enlarged entropy effect, endow the ternary OSCs still with ≈70% of initial PCE even after 500 h continuous illumination, whereas only 53% is left in their binary counterparts. These results provide new insight into the molecular design principle for distinctive molecular carbon imides as the third component for efficient and durable PM6:Y6-based OSCs.

Giant Rylene Imide-Based Electron Acceptors for Organic Photovoltaics.

ConspectusRylene imides are oligo(peri-naphthalene)s bearing one or two six-membered carboxylic imide rings. Their flexible reaction sites and unique photoelectronic properties have afforded active research for applications in photovoltaic devices, light-emitting diodes, and fluorescent sensors. Over the past few decades, synthetic flexibility along with the evolution of molecular design principles for novel aromatic imides has rendered these intriguing dyes considerably valuable, especially for organic photovoltaics (OPVs).During the course of molecular evolution, the most difficult criterion to meet is how to modulate the intra- and intermolecular interactions to alter the aggregation behavior of rylene imides as well as their compatibility with donor materials, with the prerequisite that the appropriate molecular energy level is maintained. In the meantime, our group has focused on the precise synthesis of π-extended rylene imide electron acceptors (RIAs) to rationally alter the molecular chemical and electronic structure, packing arrangement, and photoelectronic properties. These powerful molecular design strategies include the construction of a fully conjugated rigid multichromophoric architecture and successful integration of heteroatoms. Herein, these multichromophoric oligomers are precisely defined as giant rylene imides. Importantly, these strategies provide a vast space for progress in RIAs and present a more comprehensive structure-performance relationship network that can be distinguished from other electron acceptor systems. In particular, the successful acquisition of these fused superhelical architectures provides a meaningful reference for the pluralistic development of OPVs, such as triplet organic solar cells and polarized-light photovoltaic detectors. Meanwhile, the introduction of heteroatoms into the rylene conjugated skeleton provides donor/acceptor interfaces with enhanced electronic interactions and thereby suppresses the polaron-pair binding energy. Nonetheless, much remains to be implemented to broaden the absorption capability of rylene imides as well as to realize full utilization of these meaningful chiral isomers with a wide and strong UV-vis spectroscopic response.In this Account, we provide an overview of our novel approaches toward a supermolecular framework and of the reformed molecular design principle for rylene imide-based electron acceptors since 2012. We begin with a discussion of the rapidly emerging synthesis strategies for giant rylene imides. Then several typical examples with remarkable photovoltaic properties and unique working mechanisms are selected, aimed at providing an in-depth discussion of structure-property-performance relationships. The remaining challenges and newly emerging research information for giant rylene imide-based electron acceptors are further put forward. It is our aspiration that this Account will trigger intensive research interest in these pluralist rylene-based electron acceptors, thereby further accelerating the profound sustainable development of organic solar cells.

An effective sinogram inpainting for complementary limited-angle dual-energy computed tomography imaging using generative adversarial networks.

Dual-energy computed tomography (DECT) provides more anatomical and functional information for image diagnosis. Presently, the popular DECT imaging systems need to scan at least full angle (i.e., 360°). In this study, we propose a DECT using complementary limited-angle scan (DECT-CL) technology to reduce the radiation dose and compress the spatial distribution of the imaging system. The dual-energy total scan is 180°, where the low- and high-energy scan range is the first 90° and last 90°, respectively. We describe this dual limited-angle problem as a complementary limited-angle problem, which is challenging to obtain high-quality images using traditional reconstruction algorithms. Furthermore, a complementary-sinogram-inpainting generative adversarial networks (CSI-GAN) with a sinogram loss is proposed to inpainting sinogram to suppress the singularity of truncated sinogram. The sinogram loss focuses on the data distribution of the generated sinogram while approaching the target sinogram. We use the simultaneous algebraic reconstruction technique namely, a total variable (SART-TV) algorithms for image reconstruction. Then, taking reconstructed CT images of pleural and cranial cavity slices as examples, we evaluate the performance of our method and numerically compare different methods based on root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Compared with traditional algorithms, the proposed network shows advantages in numerical terms. Compared with Patch-GAN, the proposed network can also reduce the RMSE of the reconstruction results by an average of 40% and increase the PSNR by an average of 26%. In conclusion, both qualitative and quantitative comparison and analysis demonstrate that our proposed method achieves a good artifact suppression effect and can suitably solve the complementary limited-angle problem.

Effective Surface Nano-Crystallization of Ni2FeCoMo0.5V0.2 Medium Entropy Alloy by Rotationally Accelerated Shot Peening (RASP).

The surface nano-crystallization of Ni2FeCoMo0.5V0.2 medium-entropy alloy was realized by rotationally accelerated shot peening (RASP). The average grain size at the surface layer is ~37 nm, and the nano-grained layer is as thin as ~20 µm. Transmission electron microscopy analysis revealed that deformation twinning and dislocation activities are responsible for the effective grain refinement of the high-entropy alloy. In order to reveal the effectiveness of surface nano-crystallization on the Ni2FeCoMo0.5V0.2 medium-entropy alloy, a common model material, Ni, is used as a reference. Under the same shot peening condition, the surface layer of Ni could only be refined to an average grain size of ~234 nm. An ultrafine grained surface layer is less effective in absorbing strain energy than a nano-grain layer. Thus, grain refinement could be realized at a depth up to 70 µm in the Ni sample.

Doppler chirplet transform for the velocity estimation of a fast moving acoustic source of discrete tones.

An extended chirplet transform method termed as Doppler chirplet transform is proposed to estimate the velocity of a discrete tone source in uniform linear motion. This method directly uses the relation of the observed instantaneous frequency to the source velocity as the kernel of the chirplet transform. It is tested on a set of 30-s truck noise recordings and also on simulated data from a statistical perspective. The results show that the Doppler chirplet transform significantly reduces the run time that the polynomial chirplet transform [Xu, Yang, and Yu, J. Acoust. Soc. Am. 137(4), EL320-EL326 (2015)] costs to produce similarly accurate estimates of the source velocity.

Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging.

Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90°. We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms. With the development of deep learning, the generative adversarial network (GAN) performs well in image inpainting tasks and can add effective image information to restore missing parts of an image. In this study, given the characteristic of GAN to generate missing information, the sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction. We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images. Furthermore, we propose a joint projection domain and image domain loss function, in which the weighted image domain loss can be added by the back-projection operation. Then, by inputting a paired limited-angle/180° sinogram into the network for training, we can obtain the trained model, which has extracted the continuity feature of sinogram data. Finally, the classic CT reconstruction method is used to reconstruct the images after obtaining the estimated sinograms. The simulation studies and actual data experiments indicate that the proposed method performed well to reduce the serious artifacts caused by ultra-limited-angle scanning.

Efficient solving algorithm for determining the exact sampling condition of limited-angle computed tomography reconstruction.

Total variation (TV) regularization-based iterative reconstruction algorithms have an impressive potential to solve limited-angle computed tomography with insufficient sampling projections. The analysis of exact reconstruction sampling conditions for a TV-minimization reconstruction model can determine the minimum number of scanning angle and minimize the scanning range. However, the large-scale matrix operations caused by increased testing phantom size are the computation bottleneck in determining the exact reconstruction sampling conditions in practice. When the size of the testing phantom increases to a certain scale, it is very difficult to analyze quantitatively the exact reconstruction sampling condition using existing methods. In this paper, we propose a fast and efficient algorithm to determine the exact reconstruction sampling condition for large phantoms. Specifically, the sampling condition of a TV minimization model is modeled as a convex optimization problem, which is derived from the sufficient and necessary condition of solution uniqueness for the L1 minimization model. An effective alternating direction minimization algorithm is developed to optimize the objective function by alternatively solving two sub-problems split from the convex problem. The Cholesky decomposition method is used in solving the first sub-problem to reduce computational complexity. Experimental results show that the proposed method can efficiently solve the verification problem of the accurate reconstruction sampling condition. Furthermore, we obtain the lower bounds of scanning angle range for the exact reconstruction of a specific phantom with the larger size.

Joint regularization-based image reconstruction by combining data-driven tight frame and total variation for low-dose computed tomography.

Since the excessive radiation dose may induce potential body lesion, the low-dose computed tomography (LDCT) is widely applied for clinical diagnosis and treatment. However, the dose reduction will inevitably cause severe noise and degrade image quality. Most state-of-the-art methods utilize a pre-determined regularizer to account for the prior images, which may be insufficient for the most images acquired in the clinical practice. This study proposed and investigated a joint regularization method combining a data-driven tight frame and total variation (DDTF-TV) to solve this problem. Unlike the existing methods that designed pre-determined sparse transform for image domain, data-driven regularizer introduced a learning strategy to adaptively and iteratively update the framelets of DDTF, which can preferably recover the detailed image structures. The other regularizer, TV term can reconstruct strong edges and suppress noise. The joint term, DDTF-TV, collaboratively affect detail preservation and noise suppression. The proposed new model was efficiently solved by alternating the direction method of the multipliers. Qualitative and quantitative evaluations were carried out in simulation and real data experiments to demonstrate superiority of the proposed DDTF-TV method. Both visual inspection and numerical accuracy analysis show the potential of the proposed method for improving image quality of the LDCT.

The Crucial Role of Chlorinated Thiophene Orientation in Conjugated Polymers for Photovoltaic Devices.

Chlorinated conjugated polymers not only show great potential for the realization of highly efficient polymer solar cells (PSCs) but also have simple and high-yield synthetic routes and low-cost raw materials available for their preparation. However, the study of the structure-property relationship of chlorinated polymers is lagging. Now two chlorinated conjugated polymers, PCl(3)BDB-T and PCl(4)BDB-T are investigated. When the polymers were used to fabricate PSCs with the nonfullerene acceptor (IT-4F), surprisingly, the PCl(3)BDB-T:IT-4F-based device exhibited a negligible power conversion efficiency (PCE) of 0.18 %, while the PCl(4)BDB-T:IT-4F-based device showed an outstanding PCE of 12.33 %. These results provide new insight for the rational design and synthesis of novel chlorinated polymer donors for further improving the photovoltaic efficiencies of PSCs.

Spiro-Fused Perylene Diimide Arrays.

The straightforward palladium-catalyzed synthesis protocol toward spiro-fused perylene diimides is developed. The reaction involves two palladium-catalyzed C-H activations and 4-fold C-C bond formation sequence from readily available precursors. This facile and step-economic approach also provides another convenient access to ethylene-bridged dimer (NDP) and further π-extended spiro system (SNTP). In addition, the molecular structure of spirodiperylenetetraimide (SDP) is illustrated to show a three-dimensional (3D) cruciform configuration, and its absorbance is distinctly red-shifted due to the significant spiroconjugation effect. With combined properties of broadened and intensive absorption, aligned LUMO levels, and unique molecular geometry, the spiro-fused PDI system represents a new kind of high-performance semiconducting framework as the electron acceptor in high-efficiency organic solar cells.

Robust speed estimation for a moving harmonic acoustic source with a single stationary sensor.

A noise-insensitive cost function was developed for estimating the speed of harmonic acoustic sources in uniform linear motion. This function weighs and integrates the energy distribution of received tones in the time-frequency plane to enhance the robustness of parameter estimation under low signal-to-noise ratio conditions, where weight values are intentionally combined with the law of observed instantaneous frequency. As the cost function is differentiable, the procedure of parameter estimations also has high computing efficiency. Processing data of SWellEx-96 experiments with real ocean noise confirmed the anti-noise capabilities of this cost function to conventional processing methods.

Dual-color emissive OLED with orthogonal polarization modes.

Linearly polarized organic light-emitting diodes have become appealing functional expansions of polarization optics and optoelectronic applications. However, the current linearly polarized diodes exhibit low polarization performance, cost-prohibitive process, and monochromatic modulation limit. Herein, we develop a switchable dual-color orthogonal linear polarization mode in organic light-emitting diode, based on a dielectric/metal nanograting-waveguide hybrid-microcavity using cost-efficient laser interference lithography and vacuum thermal evaporation. This acquired diode presents a transverse-electric/transverse-magnetic polarization extinction ratio of 15.8 dB with a divergence angle of ±30°, an external quantum efficiency of 2.25%, and orthogonal polarized colors from green to sky-blue. This rasterization of dielectric/metal-cathode further satisfies momentum matching between waveguide and air mode, diffracting both the targeted sky-blue transverse-electric mode and the off-confined green transverse-magnetic mode. Therefore, a polarization-encrypted colorful optical image is proposed, representing a significant step toward the low-cost high-performance linearly polarized light-emitting diodes and electrically-inspired polarization encryption for color images.

One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging.

Objective.Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise.Approach.To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials.Main results.Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN.Significance.These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis.

MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography.

Background: Dual-energy computed tomography (DECT) is a promising technique, which can provide unique capability for material quantification. The iterative reconstruction of material maps requires spectral information and its accuracy is affected by spectral mismatch. Simultaneously estimating the spectra and reconstructing material maps avoids extra workload on spectrum estimation and the negative impact of spectral mismatch. However, existing methods are not satisfactory in image detail preservation, edge retention, and convergence rate. The purpose of this paper was to mine the similarity between the reconstructed images and the material images to improve the imaging quality, and to design an effective iteration strategy to improve the convergence efficiency. Methods: The material-image subspace decomposition-based iterative reconstruction (MISD-IR) with spectrum estimation was proposed for DECT. MISD-IR is an optimized model combining spectral estimation and material reconstruction with fast convergence speed and promising noise suppression capability. We proposed to reconstruct the material maps based on extended simultaneous algebraic reconstruction techniques and estimation of the spectrum with model spectral. To stabilize the iteration and alleviate the influence of errors, we introduced a weighted proximal operator based on the block coordinate descending algorithm (WP-BCD). Furthermore, the reconstructed computed tomography (CT) images were introduced to suppress the noise based on subspace decomposition, which relies on non-local regularization to prevent noise accumulation. Results: In numerical experiments, the results of MISD-IR were closer to the ground truth compared with other methods. In real scanning data experiments, the results of MISD-IR showed sharper edges and details. Compared with other one-step iterative methods in the experiment, the running time of MISD-IR was reduced by 75%. Conclusions: The proposed MISD-IR can achieve accurate material decomposition (MD) without known energy spectrum in advance, and has good retention of image edges and details. Compared with other one-step iterative methods, it has high convergence efficiency.