Objective To propose a low-dose CT reconstruction algorithm across different scanners based on federated feature learning(FedCT)to improve the generalization of deep learning models for multiple CT scanners and protect data privacy.Methods In the proposed FedCT framework,each client is assigned an inverse Radon transform-based reconstruction model to serve as a local network model that participates in federated learning.A projection-domain specific learning strategy is adopted to preserve the geometry specificity in the local projection domain.Federated feature learning is introduced in the model,which utilizes conditional parameters to mark the local data and feed the conditional parameters into the network for encoding to enhance the generalization of the model in the image domain.Results In the cross-client,multi-scanner,and multi-protocol low-dose CT reconstruction experiments,FedCT achieved the highest PSNR(+2.8048,+2.7301,and +2.7263 compared to the second best federated learning method),the highest SSIM(+0.0009,+0.0165,and +0.0131 in the same comparison),and the lowest RMSE(-0.6687,-1.5956,and-0.9962).In the ablation experiment,compared with the general federated learning strategy,the model with projection-specific learning strategy showed an average improvement by 1.18 on Q1 of the PSNR and an average decrease by 1.36 on Q3 of the RMSE on the test set.The introduction of federated feature learning in FedCT further improved the Q1 of the PSNR on the test set by 3.56 and reduced the Q3 of the RMSE by 1.80.Conclusion FedCT provides an effective solution for collaborative construction of CT reconstruction models,which can enhance model generalization and further improve the reconstruction performance on global data while protecting data privacy.

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 To propose a low-dose CT reconstruction algorithm across different scanners based on federated feature learning(FedCT)to improve the generalization of deep learning models for multiple CT scanners and protect data privacy.Methods In the proposed FedCT framework,each client is assigned an inverse Radon transform-based reconstruction model to serve as a local network model that participates in federated learning.A projection-domain specific learning strategy is adopted to preserve the geometry specificity in the local projection domain.Federated feature learning is introduced in the model,which utilizes conditional parameters to mark the local data and feed the conditional parameters into the network for encoding to enhance the generalization of the model in the image domain.Results In the cross-client,multi-scanner,and multi-protocol low-dose CT reconstruction experiments,FedCT achieved the highest PSNR(+2.8048,+2.7301,and +2.7263 compared to the second best federated learning method),the highest SSIM(+0.0009,+0.0165,and +0.0131 in the same comparison),and the lowest RMSE(-0.6687,-1.5956,and-0.9962).In the ablation experiment,compared with the general federated learning strategy,the model with projection-specific learning strategy showed an average improvement by 1.18 on Q1 of the PSNR and an average decrease by 1.36 on Q3 of the RMSE on the test set.The introduction of federated feature learning in FedCT further improved the Q1 of the PSNR on the test set by 3.56 and reduced the Q3 of the RMSE by 1.80.Conclusion FedCT provides an effective solution for collaborative construction of CT reconstruction models,which can enhance model generalization and further improve the reconstruction performance on global data while protecting data privacy.

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.

Gelsemium elegans is a traditional Chinese herb of medicinal importance, with indole terpene alkaloids as its main active components. To study the expression of the most suitable housekeeping reference genes in G. elegans, the root bark, stem segments, leaves and inflorescences of four different parts of G. elegans were used as materials in this study. The expression stability of 10 candidate housekeeping reference genes (18S, GAPDH, Actin, TUA, TUB, SAND, EF-1α, UBC, UBQ, and cdc25) was assessed through real-time fluorescence quantitative PCR, GeNorm, NormFinder, BestKeeper, ΔCT, and RefFinder. The results showed that EF-1α was stably expressed in all four parts of G. elegans and was the most suitable housekeeping gene. Based on the coexpression pattern of genome, full-length transcriptome and metabolome, the key candidate targets of 18 related genes (AS, AnPRT, PRAI, IGPS, TSA, TSB, TDC, GES, G8H, 8-HGO, IS, 7-DLS, 7-DLGT, 7-DLH, LAMT, SLS, STR, and SGD) involved in the Gelsemium alkaloid biosynthesis were obtained. The expression of 18 related enzyme genes were analyzed by qRT-PCR using the housekeeping gene EF-1α as a reference. The results showed that these genes' expression and gelsenicine content trends were correlated and were likely to be involved in the biosynthesis of the Gelsemium alkaloid, gelsenicine.
Genes, Essential ; Gelsemium/genetics* ; Peptide Elongation Factor 1/genetics* ; Transcriptome ; Gene Expression Profiling/methods* ; Alkaloids ; Real-Time Polymerase Chain Reaction/methods* ; Reference Standards

Auditory neuropathy spectrum disorder (ANSD) represents a variety of sensorineural deafness conditions characterized by abnormal inner hair cells and/or auditory nerve function, but with the preservation of outer hair cell function. ANSD represents up to 15% of individuals with hearing impairments. Through mutation screening, bioinformatic analysis and expression studies, we have previously identified several apoptosis-inducing factor (AIF) mitochondria-associated 1 (AIFM1) variants in ANSD families and in some other sporadic cases. Here, to elucidate the pathogenic mechanisms underlying each AIFM1 variant, we generated AIF-null cells using the clustered regularly interspersed short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system and constructed AIF-wild type (WT) and AIF-mutant (mut) (p.‍T260A, p.‍R422W, and p.‍R451Q) stable transfection cell lines. We then analyzed AIF structure, coenzyme-binding affinity, apoptosis, and other aspects. Results revealed that these variants resulted in impaired dimerization, compromising AIF function. The reduction reaction of AIF variants had proceeded slower than that of AIF-WT. The average levels of AIF dimerization in AIF variant cells were only 34.5%‍‒‍49.7% of that of AIF-WT cells, resulting in caspase-independent apoptosis. The average percentage of apoptotic cells in the variants was 12.3%‍‒‍17.9%, which was significantly higher than that (6.9%‍‒‍7.4%) in controls. However, nicotinamide adenine dinucleotide (NADH) treatment promoted the reduction of apoptosis by rescuing AIF dimerization in AIF variant cells. Our findings show that the impairment of AIF dimerization by AIFM1 variants causes apoptosis contributing to ANSD, and introduce NADH as a potential drug for ANSD treatment. Our results help elucidate the mechanisms of ANSD and may lead to the provision of novel therapies.
Humans ; Apoptosis Inducing Factor/metabolism* ; NAD/metabolism* ; Dimerization ; Apoptosis

OBJECTIVE: To propose a semi-supervised material quantitative intelligent imaging algorithm based on prior information perception learning (SLMD-Net) to improve the quality and precision of spectral CT imaging. METHODS: The algorithm includes a supervised and a self- supervised submodule. In the supervised submodule, the mapping relationship between low and high signal-to-noise ratio (SNR) data was constructed through mean square error loss function learning based on a small labeled dataset. In the self- supervised sub-module, an image recovery model was utilized to construct the loss function incorporating the prior information from a large unlabeled low SNR basic material image dataset, and the total variation (TV) model was used to to characterize the prior information of the images. The two submodules were combined to form the SLMD-Net method, and pre-clinical simulation data were used to validate the feasibility and effectiveness of the algorithm. RESULTS: Compared with the traditional model-driven quantitative imaging methods (FBP-DI, PWLS-PCG, and E3DTV), data-driven supervised-learning-based quantitative imaging methods (SUMD-Net and BFCNN), a material quantitative imaging method based on unsupervised learning (UNTV-Net) and semi-supervised learning-based cycle consistent generative adversarial network (Semi-CycleGAN), the proposed SLMD-Net method had better performance in both visual and quantitative assessments. For quantitative imaging of water and bone materials, the SLMD-Net method had the highest PSNR index (31.82 and 29.06), the highest FSIM index (0.95 and 0.90), and the lowest RMSE index (0.03 and 0.02), respectively) and achieved significantly higher image quality scores than the other 7 material decomposition methods (P < 0.05). The material quantitative imaging performance of SLMD-Net was close to that of the supervised network SUMD-Net trained with labeled data with a doubled size. CONCLUSIONS A small labeled dataset and a large unlabeled low SNR material image dataset can be fully used to suppress noise amplification and artifacts in basic material decomposition in spectral CT and reduce the dependence on labeled data-driven network, which considers more realistic scenario in clinics.
Tomography, X-Ray Computed/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Signal-To-Noise Ratio ; Perception

OBJECTIVE: To propose a tissue- aware contrast enhancement network (T- ACEnet) for CT image enhancement and validate its accuracy in CT image organ segmentation tasks. METHODS: The original CT images were mapped to generate low dynamic grayscale images with lung and soft tissue window contrasts, and the supervised sub-network learned to recognize the optimal window width and level setting of the lung and abdominal soft tissues via the lung mask. The self-supervised sub-network then used the extreme value suppression loss function to preserve more organ edge structure information. The images generated by the T-ACEnet were fed into the segmentation network to segment multiple abdominal organs. RESULTS: The images obtained by T-ACEnet were capable of providing more window setting information in a single image, which allowed the physicians to conduct preliminary screening of the lesions. Compared with the suboptimal methods, T-ACE images achieved improvements by 0.51, 0.26, 0.10, and 14.14 in SSIM, QABF, VIFF, and PSNR metrics, respectively, with a reduced MSE by an order of magnitude. When T-ACE images were used as input for segmentation networks, the organ segmentation accuracy could be effectively improved without changing the model as compared with the original CT images. All the 5 segmentation quantitative indices were improved, with the maximum improvement of 4.16%. CONCLUSION The T-ACEnet can perceptually improve the contrast of organ tissues and provide more comprehensive and continuous diagnostic information, and the T-ACE images generated using this method can significantly improve the performance of organ segmentation tasks.
Learning ; Image Enhancement ; Tomography, X-Ray Computed

Auditory neuropathy spectrum disorder (ANSD) represents a variety of sensorineural deafness conditions characterized by abnormal inner hair cells and/or auditory nerve function, but with the preservation of outer hair cell function. ANSD represents up to 15％ of individuals with hearing impairments. Through mutation screening, bioinformatic analysis and expression studies, we have previously identified several apoptosis-inducing factor (AIF) mitochondria-associated 1 (AIFM1) variants in ANSD families and in some other sporadic cases. Here, to elucidate the pathogenic mechanisms underlying each AIFM1 variant, we generated AIF-null cells using the clustered regularly interspersed short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system and constructed AIF-wild type (WT) and AIF-mutant (mut) (p.T260A, p.R422W, and p.R451Q) stable transfection cell lines. We then analyzed AIF structure, coenzyme-binding affinity, apoptosis, and other aspects. Results revealed that these variants resulted in impaired dimerization, compromising AIF function. The reduction reaction of AIF variants had proceeded slower than that of AIF-WT. The average levels of AIF dimerization in AIF variant cells were only 34.5％?49.7％ of that of AIF-WT cells, resulting in caspase-independent apoptosis. The average percentage of apoptotic cells in the variants was 12.3％?17.9％, which was significantly higher than that (6.9％?7.4％) in controls. However, nicotinamide adenine dinucleotide (NADH) treatment promoted the reduction of apoptosis by rescuing AIF dimerization in AIF variant cells. Our findings show that the impairment of AIF dimerization by AIFM1 variants causes apoptosis contributing to ANSD, and introduce NADH as a potential drug for ANSD treatment. Our results help elucidate the mechanisms of ANSD and may lead to the provision of novel therapies.

Lymphatic system is the transportation way of cerebrospinal fluid and brain interstitial fluid exchange. And this system is a central nervous drainage system which plays an important role in drainaging and discharging of metabolic waste in the brain. The function of this system can be evaluated indirectly by the perivascular space on magnetic resonance imaging. Parkinson′s disease is a common neurodegenerative disorder. It may be helpful to control the progression of the disease if the changes of perivascular space can be dynamically observed in the early or even prodromal stage of the disease. This article reviews the relationship between lymphatic system disfunction and early stage of Parkinson′s disease.