OBJECTIVES: We propose a sparse-view cone-beam CT reconstruction algorithm based on bidirectional flow field guided projection completion (BBC-Recon) to solve the ill-posed inverse problem in sparse-view cone-beam CT imaging. METHODS: The BBC-Recon method consists of two main modules: the projection completion module and the image restoration module. Based on flow field estimation, the projection completion module, through the designed bidirectional and multi-scale correlators, fully calculates the correlation information and redundant information among projections to precisely guide the generation of bidirectional flow fields and missing frames, thus achieving high-precision completion of missing projections and obtaining pseudo complete projections. The image restoration module reconstructs the obtained pseudo complete projections and then refines the image to remove the residual artifacts and further improve the image quality. RESULTS: The experimental results on the public datasets of Mayo Clinic and Guilin Medical University showed that in the case of a 4-fold sparse angle, compared with the suboptimal method, the BBC-Recon method increased the PSNR index by 1.80% and the SSIM index by 0.29%, and reduced the RMSE index by 4.12%; In the case of an 8-fold sparse angle, the BBC-Recon method increased the PSNR index by 1.43% and the SSIM index by 1.49%, and reduced the RMSE index by 0.77%. CONCLUSIONS The BBC-Recon algorithm fully exploits the correlation information between projections to allow effective removal of streak artifacts while preserving image structure information, and demonstrates significant advantages in maintaining inter-slice consistency.
Algorithms ; Cone-Beam Computed Tomography/methods* ; Image Processing, Computer-Assisted/methods* ; Humans

OBJECTIVES: We propose a segmented backprojection tensor degradation feature encoding (SBP-MAC) model for motion artifact correction in dental cone beam computed tomography (CBCT) to improve the quality of the reconstructed images. METHODS: The proposed motion artifact correction model consists of a generator and a degradation encoder. The segmented limited-angle reconstructed sub-images are stacked into the tensors and used as the model input. A degradation encoder is used to extract spatially varying motion information in the tensor, and the generator's skip connection features are adaptively modulated to guide the model for correcting artifacts caused by different motion waveforms. The artifact consistency loss function was designed to simplify the learning task of the generator. RESULTS: The proposed model could effectively remove motion artifacts and improve the quality of the reconstructed images. For simulated data, the proposed model increased the peak signal-to-noise ratio by 8.28%, increased the structural similarity index measurement by 2.29%, and decreased the root mean square error by 23.84%. For real clinical data, the proposed model achieved the highest expert score of 4.4221 (against a 5-point scale), which was significantly higher than those of all the other comparison methods. CONCLUSIONS The SBP-MAC model can effectively extract spatially varying motion information in the tensors and achieve adaptive artifact correction from the tensor domain to the image domain to improve the quality of reconstructed dental CBCT images.
Cone-Beam Computed Tomography/methods* ; Artifacts ; Humans ; Motion ; Image Processing, Computer-Assisted/methods* ; Signal-To-Noise Ratio ; Algorithms

OBJECTIVES: We propose a low-dose CT image restoration method based on central guidance and alternating optimization (FedGP). METHODS: The FedGP framework revolutionizes the traditional federated learning model by adopting a structure without a fixed central server, where each institution alternatively serves as the central server. This method uses an institution-modulated CT image restoration network as the core of client-side local training. Through a federated learning approach of central guidance and alternating optimization, the central server leverages local labeled data to guide client-side network training to enhance the generalization capability of the CT imaging model across multiple institutions. RESULTS: In the low-dose and sparse-view CT image restoration tasks, the FedGP method showed significant advantages in both visual and quantitative evaluation and achieved the highest PSNR (40.25 and 38.84), the highest SSIM (0.95 and 0.92), and the lowest RMSE (2.39 and 2.56). Ablation study of FedGP demonstrated that compared with FedGP(w/o GP) without central guidance, the FedGP method better adapted to data heterogeneity across institutions, thus ensuring robustness and generalization capability of the model in different imaging conditions. CONCLUSIONS FedGP provides a more flexible FL framework to solve the problem of CT imaging heterogeneity and well adapts to multi-institutional data characteristics to improve generalization ability of the model under diverse imaging geometric configurations.
Tomography, X-Ray Computed/methods* ; Humans ; Radiation Dosage ; Image Processing, Computer-Assisted/methods* ; Algorithms

Evaluating toxicity and decoding the underlying mechanisms of active compounds are crucial for drug development.In this study,we present an innovative,integrated approach that combines air flow-assisted desorption electrospray ionization mass spectrometry imaging(AFADESI-MSI),time-of-flight secondary ion mass spectrometry(ToF-SIMS),and spatial metabolomics to comprehensively investi-gate the nephrotoxicity and underlying mechanisms of nitidine chloride(NC),a promising anti-tumor drug candidate.Our quantitive AFADESI-MSI analysis unveiled the region specific of accumulation of NC in the kidney,particularly within the inner cortex(IC)region,following single and repeated dose of NC.High spatial resolution ToF-SIMS analysis further allowed us to precisely map the localization of NC within the renal tubule.Employing spatial metabolomics based on AFADESI-MSI,we identified over 70 discriminating endogenous metabolites associated with chronic NC exposure.These findings suggest the renal tubule as the primary target of NC toxicity and implicate renal transporters(organic cation transporters,multidrug and toxin extrusion,and organic cation transporter 2(OCT2)),metabolic en-zymes(protein arginine N-methyltransferase(PRMT)and nitric oxide synthase),mitochondria,oxidative stress,and inflammation in NC-induced nephrotoxicity.This study offers novel insights into NC-induced renal damage,representing a crucial step towards devising strategies to mitigate renal damage caused by this compound.

Polygala tenuifolia,commonly known as Yuanzhi(YZ)in Chinese,has been shown to possess anti-insomnia properties.However,the material basis and the mechanism underlying its sedative-hypnotic effects remain unclear.Herein,we investigated the active components and neurochemical mechanism of YZ extracts using liquid chromatography tandem mass spectrometry(LC-MS/MS)-based pharmaco-metabolomics and mass spectrometry imaging(MSI)-based spatial resolved metabolomics.According to the results,17 prototypes out of 101 ingredients in the YZ extract were detected in both the plasma and brain,which might be the major components contributing to the sedative-hypnotic effects.Network pharmacology analysis revealed that these prototypes may exert their effects through neuroactive ligand-receptor interaction,serotonergic synapse,dopaminergic synapse,and dopaminergic synapse,among other pathways.LC-MS/MS-based targeted metabolomics and Western blot(WB)revealed that tryptophan-serotonin-melatonin(Trp-5-HT-Mel)and tyrosine-norepinephrine-adrenaline(Tyr-Ne-Ad)are the key regulated pathways.Dopa decarboxylase(DDC)upregulation and phenylethanolamine N-methyltransferase(PNMT)downregulation further confirmed these pathways.Furthermore,MSI-based spatially resolved metabolomics revealed notable alterations in 5-HT in the pineal gland(PG),and Ad in the brainstem,including the middle brain(MB),pons(PN),and hypothalamus(HY).In summary,this study illustrates the efficacy of an integrated multidimensional metabolomics approach in unraveling the sedative-hypnotic effects and neurochemical mechanisms of a Chinese herbal medicine,YZ.

Objective To propose a CT truncated data reconstruction model(DDTrans)based on projection and image dual-domain Transformer coupled feature learning for reducing truncation artifacts and image structure distortion caused by insufficient field of view(FOV)in CT scanning.Methods Transformer was adopted to build projection domain and image domain restoration models,and the long-range dependency modeling capability of the Transformer attention module was used to capture global structural features to restore the projection data information and enhance the reconstructed images.We constructed a differentiable Radon back-projection operator layer between the projection domain and image domain networks to enable end-to-end training of DDTrans.Projection consistency loss was introduced to constrain the image forward-projection results to further improve the accuracy of image reconstruction.Results The experimental results with Mayo simulation data showed that for both partial truncation and interior scanning data,the proposed DDTrans method showed better performance than the comparison algorithms in removing truncation artifacts at the edges and restoring the external information of the FOV.Conclusion The DDTrans method can effectively remove CT truncation artifacts to ensure accurate reconstruction of the data within the FOV and achieve approximate reconstruction of data outside the FOV.

Objective We propose a dual-domain cone beam computed tomography(CBCT)reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction.Methods The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules:projection preprocessing,differentiable domain transform,and image post-processing.The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray.The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes,where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation,thus enabling precise learning of cone-beam region data.The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises.Results The results of validation experiments conducted on Mayo's public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation.Compared with the latest methods,the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074,respectively.Conclusion The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.

Objective We propose a motion artifact correction algorithm(DMBL)for reducing motion artifacts in reconstructed dental cone-beam computed tomography(CBCT)images based on deep blur learning.Methods A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion,and the obtained motion degradation features were imported in the artifact correction module for artifact removal.The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns.Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets.Results The experimental results with the simulated dataset showed that compared with the existing methods,the PSNR of the proposed method increased by 2.88%,the SSIM increased by 0.89%,and the RMSE decreased by 10.58%.The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417(in a 5-point scale),significantly higher than those of the comparison methods.Conclusion The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

Objective To propose a dual-domain CBCT reconstruction framework(DualSFR-Net)based on generative projection interpolation to reduce artifacts in sparse-view cone beam computed tomography(CBCT)reconstruction.Methods The proposed method DualSFR-Net consists of a generative projection interpolation module,a domain transformation module,and an image restoration module.The generative projection interpolation module includes a sparse projection interpolation network(SPINet)based on a generative adversarial network and a full-view projection restoration network(FPRNet).SPINet performs projection interpolation to synthesize full-view projection data from the sparse-view projection data,while FPRNet further restores the synthesized full-view projection data.The domain transformation module introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes.The image restoration module includes an image restoration network FIRNet that fine-tunes the domain-transformed images to eliminate residual artifacts and noise.Results Validation experiments conducted on a dental CT dataset demonstrated that DualSFR-Net was capable to reconstruct high-quality CBCT images under sparse-view sampling protocols.Quantitatively,compared to the current best methods,the DualSFR-Net method improved the PSNR by 0.6615 and 0.7658 and increased the SSIM by 0.0053 and 0.0134 under 2-fold and 4-fold sparse protocols,respectively.Conclusion The proposed generative projection interpolation-based dual-domain CBCT sparse-view reconstruction method can effectively reduce stripe artifacts to improve image quality and enables efficient joint training for dual-domain imaging networks in sparse-view CBCT reconstruction.

Objective To propose a CT truncated data reconstruction model(DDTrans)based on projection and image dual-domain Transformer coupled feature learning for reducing truncation artifacts and image structure distortion caused by insufficient field of view(FOV)in CT scanning.Methods Transformer was adopted to build projection domain and image domain restoration models,and the long-range dependency modeling capability of the Transformer attention module was used to capture global structural features to restore the projection data information and enhance the reconstructed images.We constructed a differentiable Radon back-projection operator layer between the projection domain and image domain networks to enable end-to-end training of DDTrans.Projection consistency loss was introduced to constrain the image forward-projection results to further improve the accuracy of image reconstruction.Results The experimental results with Mayo simulation data showed that for both partial truncation and interior scanning data,the proposed DDTrans method showed better performance than the comparison algorithms in removing truncation artifacts at the edges and restoring the external information of the FOV.Conclusion The DDTrans method can effectively remove CT truncation artifacts to ensure accurate reconstruction of the data within the FOV and achieve approximate reconstruction of data outside the FOV.