Multiple energy X-ray imaging of metal oxide particles inside gingival tissues.

BACKGROUND: Periodontal disease affects over 50% of the global population and is characterized by gingivitis as the initial sign. One dental health issue that may contribute to the development of periodontal disease is foreign body gingivitis (FBG), which can result from exposure to some kinds of foreign metal particles from dental products or food. OBJECTIVE: We design a novel, portable, affordable, multispectral X-ray and fluorescence optical microscopic imaging system dedicated to detecting and differentiating metal oxide particles in dental pathological tissues. A novel denoising algorithm is applied. We verify the feasibility and optimize the performance of the imaging system with numerical simulations. METHODS: The designed imaging system has a focused X-ray tube with tunable energy spectra and thin scintillator coupled with an optical microscope as detector. A simulated soft tissue phantom is embedded with 2-micron thick metal oxide discs as the imaged object. GATE software is used to optimize the systematic parameters such as energy bandwidth and X-ray photon number. We have also applied a novel denoising method, Noise2Sim with a two-layer UNet structure, to improve the simulated image quality. RESULTS: The use of an X-ray source operating with an energy bandwidth of 5 keV, X-ray photon number of 108, and an X-ray detector with a 0.5 micrometer pixel size in a 100 by 100-pixel array allowed for the detection of particles as small as 0.5 micrometer. With the Noise2Sim algorithm, the CNR has improved substantially. A typical example is that the Aluminum (Al) target's CNR is improved from 6.78 to 9.72 for the case of 108 X-ray photons with the Chromium (Cr) source of 5 keV bandwidth. CONCLUSIONS: Different metal oxide particles were differentiated using Contrast-to-Noise ratio (CNR) by utilizing four different X-ray spectra.

Simultaneous Ring Contraction and Expansion Reaction: Electrosynthesis of Heterocycle-Fused Fulleroids and Photovoltaic Application.

Simultaneous electrochemical ring contraction and expansion reactions remain unexplored to date. Herein, the reductive electrosynthesis of heterocycle-fused fulleroids from fullerotetrahydropyridazines and electrophiles in the presence of a trace amount of oxygen has been achieved with concurrent ring contraction and ring expansion. When trifluoroacetic acid and alkyl bromides are employed as electrophiles, heterocycle-fused fulleroids with a 1,1,2,6-configuration are regioselectively formed. In contrast, heterocycle-fused fulleroids with a 1,1,4,6-configuration are regioselectively produced as two separable stereoisomers if phthaloyl chloride is used as the electrophile. The reaction proceeds through multiple steps of electroreduction, heterocycle ring-opening, oxygen oxidation, heterocycle contraction, fullerene cage expansion, and nucleophilic addition. The structures of these fulleroids have been determined by spectroscopic data and single-crystal X-ray diffraction analyses. The observed high regioselectivities have been rationalized by theoretical calculations. Representative fulleroids have been applied in organic solar cells as the third component and exhibit good performance.

Palladium-Catalyzed C-H Activation/Cyclization for the Synthesis of [60]Fullerene-Fused Phosphinolactones.

A novel and efficient palladium-catalyzed C-H activation reaction of [60]fullerene with arylphosphinic acids has been developed for the synthesis of [60]fullerene-fused phosphinolactones. A possible reaction mechanism is proposed to explain the generation of the obtained products. A representative product can be further electrochemically transformed into bis-benzylated 1,2- and 1,4-adducts of [60]fullerene. In addition, a [60]fullerene-fused phosphinolactone with a 12-membered ring can also be synthesized from the electrochemical ring expansion of the employed phosphinolactone with a 6-memebered ring with 1,2-bis(bromomethyl)benzene.

Mechanochemical Dimerization of Aldoximes to Furoxans.

Solvent-free mechanical milling is a new, environmentally friendly and cost-effective technology that is now widely used in the field of organic synthesis. The mechanochemical solvent-free synthesis of furoxans from aldoximes was achieved through dimerization of the in situ generated nitrile oxides in the presence of sodium chloride, Oxone and a base. A variety of furoxans was obtained with up to a 92% yield. The present protocol has the advantages of high reaction efficiency and mild reaction conditions.

Anomalous Cis-Conformation Regioselectivity of Heterocycle-Fused Sc3 N@D3h -C78 Derivatives.

Endohedral clusterfullerenes exhibit unique chemical properties due to intramolecular electron transfer of the encaged metal cluster to the outer fullerene cages. We report the synthesis of two Sc3 N@D3h -C78 monoadducts 2 a and 2 b through the 1,3-dipolar reaction of Sc3 N@D3h -C78 with carbonyl ylide bearing anomalous cis-conformation regioselectivity. The molecular structures of these monoadducts are unambiguously confirmed by single-crystal X-ray crystallography, revealing that both 2 a and 2 b have cis-conformations with the furan moiety grafted via [6,6]-closed addition patterns. Under the same conditions, the control reaction of C60 with carbonyl ylide affords two monoadducts 3 a and 3 b, which exhibit cis- and trans-conformations, respectively, with [6,6]-closed addition patterns. According to theoretical calculations, the exclusive formation of the cis-only Sc3 N@D3h -C78 monoadducts is a consequence of conjunct effects of thermodynamic stability of adducts, the reactivity of the addition site, and the cis-dipole intermediate from trans 1.

Alternative Access to Cyclopentafullerenes through the Reaction of [60]Fullerene with Aldehydes and Secondary Amines.

A series of cyclopentafullerenes have been synthesized in high stereoselectivity by the thermal reaction of [60]fullerene with aldehydes and secondary amines. Both α,ß-unsaturated aldehydes and saturated aldehydes can be utilized to synthesize cyclopentafullerenes as the cis isomers. The possible reaction mechanisms for the formation of cyclopentafullerenes are proposed on the basis of the experimental results.

Electrochemical regioselective alkylations of a [60]fulleroindoline with bulky alkyl bromides.

Electrochemical alkylations of a [60]fulleroindoline with different bulky alkyl bromides exhibit different reaction behaviors. The hydroalkylation and dialkylation of the electrochemically generated dianionic [60]fulleroindoline with bulky 2,4,6-tris(bromomethyl)mesitylene give rise to 1,2,3,16-adducts. In comparison, the hydroalkylation of the dianionic [60]fulleroindoline with bulkier diphenylbromomethane still affords a 1,2,3,16-adduct, while the corresponding dialkylation provides a sterically favoured 1,4,9,12-adduct, which is scarcely investigated, as the major product along with the isomeric 1,2,3,16-adduct as the minor product. The structures of these products have been determined by spectroscopic data and single-crystal X-ray diffraction analysis. A plausible reaction mechanism has been proposed to explain the formation of the observed products.

Reaction of Aldoximes with Sodium Chloride and Oxone under Ball-Milling Conditions.

The solvent-free mechanochemical reaction has aroused increasing interest among scientists. Mechanical ball-milling can implement reactions under mild conditions, shorten the reaction time, and improve the reaction efficiency. Particularly, the most attractive characteristic of mechanochemistry is that it can alter the reaction pathway. However, few such examples have been reported so far. In this paper, we report the reaction of aldoximes with NaCl and Oxone under ball-milling conditions to afford N-acyloxyimidoyl chlorides, which are different from those of the liquid-phase counterpart.

The cyclopropanation of [60]fullerobenzofurans via electrosynthesis.

The electrochemical cyclopropanation of [60]fullerobenzofurans with diethyl dibromomalonate has been investigated. Controlled by the steric effect, the sterically favored e bisadducts are obtained as the major products along with two trans-3 bisadducts as minor products. The addition sites and patterns of this reaction are very different from those of our previously reported reaction with benzyl bromide, providing insights into the controlling factors for the electrophilic reactions of dianionic fullerene derivatives.

Flipover outperforms dropout in deep learning.

Flipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their connections, flipover randomly selects neurons and reverts their outputs using a negative multiplier during training. This approach offers stronger regularization than conventional dropout, refining model performance by (1) mitigating overfitting, matching or even exceeding the efficacy of dropout; (2) amplifying robustness to noise; and (3) enhancing resilience against adversarial attacks. Extensive experiments across various neural networks affirm the effectiveness of flipover in deep learning.

LIT-Former: Linking In-Plane and Through-Plane Transformers for Simultaneous CT Image Denoising and Deblurring.

This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perform denoising due to low-dose and deblurring for super-resolution separately. Up to date, little work was done for simultaneous in-plane denoising and through-plane deblurring, which is important to obtain high-quality 3D CT images with lower radiation and faster imaging speed. For this task, a straightforward method is to directly train an end-to-end 3D network. However, it demands much more training data and expensive computational costs. Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks. LIT-Former has two novel designs: efficient multi-head self-attention modules (eMSM) and efficient convolutional feed-forward networks (eCFN). First, eMSM integrates in-plane 2D self-attention and through-plane 1D self-attention to efficiently capture global interactions of 3D self-attention, the core unit of transformer networks. Second, eCFN integrates 2D convolution and 1D convolution to extract local information of 3D convolution in the same fashion. As a result, the proposed LIT-Former synergizes these two sub-tasks, significantly reducing the computational complexity as compared to 3D counterparts and enabling rapid convergence. Extensive experimental results on simulated and clinical datasets demonstrate superior performance over state-of-the-art models. The source code is made available at https://github.com/hao1635/LIT-Former.

IQAGPT: computed tomography image quality assessment with vision-language and ChatGPT models.

Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various tasks and attracted increasing interest as a natural language interface across many domains. Recently, large vision-language models (VLMs) that learn rich vision-language correlation from image-text pairs, like BLIP-2 and GPT-4, have been intensively investigated. However, despite these developments, the application of LLMs and VLMs in image quality assessment (IQA), particularly in medical imaging, remains unexplored. This is valuable for objective performance evaluation and potential supplement or even replacement of radiologists' opinions. To this end, this study introduces IQAGPT, an innovative computed tomography (CT) IQA system that integrates image-quality captioning VLM with ChatGPT to generate quality scores and textual reports. First, a CT-IQA dataset comprising 1,000 CT slices with diverse quality levels is professionally annotated and compiled for training and evaluation. To better leverage the capabilities of LLMs, the annotated quality scores are converted into semantically rich text descriptions using a prompt template. Second, the image-quality captioning VLM is fine-tuned on the CT-IQA dataset to generate quality descriptions. The captioning model fuses image and text features through cross-modal attention. Third, based on the quality descriptions, users verbally request ChatGPT to rate image-quality scores or produce radiological quality reports. Results demonstrate the feasibility of assessing image quality using LLMs. The proposed IQAGPT outperformed GPT-4 and CLIP-IQA, as well as multitask classification and regression models that solely rely on images.

Quad-Net: Quad-Domain Network for CT Metal Artifact Reduction.

Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various methods were developed for CT metal artifact reduction over the past decades, including the latest dual-domain deep networks, remaining metal artifacts are still clinically challenging in many cases. Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties. Our proposed quad-domain network for MAR, referred to as Quad-Net, takes little additional computational cost since the Fourier transform is highly efficient, and works across the four receptive fields to learn both global and local features as well as their relations. Specifically, we first design a Sinogram-Fourier Restoration Network (SFR-Net) in the sinogram domain and its Fourier space to faithfully inpaint metal-corrupted traces. Then, we couple SFR-Net with an Image-Fourier Refinement Network (IFR-Net) which takes both an image and its Fourier spectrum to improve a CT image reconstructed from the SFR-Net output using cross-domain contextual information. Quad-Net is trained on clinical datasets to minimize a composite loss function. Quad-Net does not require precise metal masks, which is of great importance in clinical practice. Our experimental results demonstrate the superiority of Quad-Net over the state-of-the-art MAR methods quantitatively, visually, and statistically. The Quad-Net code is publicly available at https://github.com/longzilicart/Quad-Net.

Synthesis of [60]Fullerene-Fused Lactones via Carboxylic Acid Group-Directed C-H Bond Activation and Further Retro Baeyer-Villiger Reaction.

An efficient palladium-catalyzed reaction of [60]fullerene with benzoic acids via carboxylic acid group-directed C-H bond activation is achieved. The obtained [60]fullerene-fused lactones can undergo a retro Baeyer-Villiger reaction to provide [60]fullerene-fused ketones via apparent reduction in the presence of triflic acid. A representative ketone product obtained by the reduction reaction can be employed as an overcoating layer for the electron-transporting layer in an n-type perovskite solar cell.

A denoising diffusion probabilistic model for metal artifact reduction in CT.

The presence of metal objects leads to corrupted CT projection measurements, resulting in metal artifacts in the reconstructed CT images. AI promises to offer improved solutions to estimate missing sinogram data for metal artifact reduction (MAR), as previously shown with convolutional neural networks (CNNs) and generative adversarial networks (GANs). Recently, denoising diffusion probabilistic models (DDPM) have shown great promise in image generation tasks, potentially outperforming GANs. In this study, a DDPM-based approach is proposed for inpainting of missing sinogram data for improved MAR. The proposed model is unconditionally trained, free from information on metal objects, which can potentially enhance its generalization capabilities across different types of metal implants compared to conditionally trained approaches. The performance of the proposed technique was evaluated and compared to the state-of-the-art normalized MAR (NMAR) approach as well as to CNN-based and GAN-based MAR approaches. The DDPM-based approach provided significantly higher SSIM and PSNR, as compared to NMAR (SSIM: p < 10-26; PSNR: p < 10-21), the CNN (SSIM: p < 10-25; PSNR: p < 10-9) and the GAN (SSIM: p < 10-6; PSNR: p < 0.05) methods. The DDPM-MAR technique was further evaluated based on clinically relevant image quality metrics on clinical CT images with virtually introduced metal objects and metal artifacts, demonstrating superior quality relative to the other three models. In general, the AI-based techniques showed improved MAR performance compared to the non-AI-based NMAR approach. The proposed methodology shows promise in enhancing the effectiveness of MAR, and therefore improving the diagnostic accuracy of CT.

Unexpected and divergent mechanosynthesis of furanoid-bridged fullerene dimers C120O and C120O2.

An unexpected, divergent and efficient approach toward furanoid-bridged fullerene dimers C120O and C120O2 was established under different solvent-free ball-milling conditions by simply using pristine C60 as the starting material, water as the oxygen source and FeCl3 as the mediator. The structures of C120O and C120O2 were unambiguously established by single-crystal X-ray crystallography. A plausible reaction mechanism is proposed on the basis of control experiments. Furthermore, C120O2 has been applied in organic solar cells as the third component and exhibits good performance.

Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial.

The latest X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging for tissue characterization and material decomposition. However, both radiation dose and imaging speed need improvement for contrast-enhanced and other studies. Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed in a New Zealand clinical trial. Particularly, we present a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and real-world data. The simulation and phantom experiments demonstrate consistently improved results under different acquisition conditions on both in- and off-domain structures using a fixed network. The image quality of 8 patients from the clinical trial are evaluated by three radiologists in comparison with the standard image reconstruction with a full-view dataset. It is shown that our proposed approach is essentially identical to or better than the clinical benchmark in terms of diagnostic image quality scores. Our approach has a great potential to improve the safety and efficiency of PCCT without compromising image quality.

Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT.

Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection views. However, either of the ways often compromises image quality. To address this challenge, here we introduce an iterative reconstruction algorithm regularized by a diffusion prior. Drawing on the exceptional imaging prowess of the denoising diffusion probabilistic model (DDPM), we merge it with a reconstruction procedure that prioritizes data fidelity. This fusion capitalizes on the merits of both techniques, delivering exceptional reconstruction results in an unsupervised framework. To further enhance the efficiency of the reconstruction process, we incorporate the Nesterov momentum acceleration technique. This enhancement facilitates superior diffusion sampling in fewer steps. As demonstrated in our experiments, our method offers a potential pathway to high-definition CT image reconstruction with minimized radiation.

NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search.

Neural architecture search (NAS) adopts a search strategy to explore the predefined search space to find superior architecture with the minimum searching costs. Bayesian optimization (BO) and evolutionary algorithms (EA) are two commonly used search strategies, but they suffer from being computationally expensive, challenging to implement, and exhibiting inefficient exploration ability. In this article, we propose a neural predictor guided EA to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is a BO acquisition function for which we design a graph-based uncertainty estimation network as the surrogate model. The second predictor is a graph-based neural network that directly predicts the performance of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP, respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Experimental results on five NAS search spaces indicate that NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-NP achieving state-of-the-art performance on four of the five search spaces.

QS-ADN: quasi-supervised artifact disentanglement network for low-dose CT image denoising by local similarity among unpaired data.

Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk. However, the widely reported supervised LDCT denoising networks require a training set of paired images, which is expensive to obtain and cannot be perfectly simulated. Unsupervised learning utilizes unpaired data and is highly desirable for LDCT denoising. As an example, an artifact disentanglement network (ADN) relies on unpaired images and obviates the need for supervision but the results of artifact reduction are not as good as those through supervised learning. An important observation is that there is often hidden similarity among unpaired data that can be utilized. This paper introduces a new learning mode, called quasi-supervised learning, to empower ADN for LDCT image denoising. For every LDCT image, the best matched image is first found from an unpaired normal-dose CT (NDCT) dataset. Then, the matched pairs and the corresponding matching degree as prior information are used to construct and train our ADN-type network for LDCT denoising. The proposed method is different from (but compatible with) supervised and semi-supervised learning modes and can be easily implemented by modifying existing networks. The experimental results show that the method is competitive with state-of-the-art methods in terms of noise suppression and contextual fidelity. The code and working dataset are publicly available athttps://github.com/ruanyuhui/ADN-QSDL.git.