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 diffusion tensor field estimation network based on 3D U-Net and diffusion tensor imaging (DTI) model constraint (3D DTI-Unet) to accurately estimate DTI quantification parameters from a small number of diffusion-weighted (DW) images with a low signal-to-noise ratio. METHODS: The input of 3D DTI-Unet was noisy diffusion magnetic resonance imaging (dMRI) data containing one non-DW image and 6 DW images with different diffusion coding directions. The noise-reduced non-DW image and accurate diffusion tensor field were predicted through 3D U-Net. The dMRI data were reconstructed using the DTI model and compared with the true value of dMRI data to optimize the network and ensure the consistency of the dMRI data with the physical model of the diffusion tensor field. We compared 3D DTI-Unet with two DW image denoising algorithms (MP-PCA and GL-HOSVD) to verify the effect of the proposed method. RESULTS: The proposed method was better than MP-PCA and GL-HOSVD in terms of quantitative results and visual evaluation of DW images, diffusion tensor field and DTI quantification parameters. CONCLUSION The proposed method can obtain accurate DTI quantification parameters from one non-DW image and 6 DW images to reduce image acquisition time and improve the reliability of quantitative diagnosis.
Diffusion Tensor Imaging ; Reproducibility of Results ; Diffusion Magnetic Resonance Imaging ; Algorithms ; Signal-To-Noise Ratio

Objective: To summarize and analyse of literature on the susceptibility genes of noise induced hearing loss (NIHL) , and the key genes were screened and obtained by bioinformatics method, so as to provide reference for the prevention research of NIHL. Methods: In September 2021, Based on CNKI, NCBI Pubmed database and Web of Science database, this paper conducted bibliometric analysis and bioinformatics analysis on the genetic literature related to the susceptibility to noise-induced hearing loss from 1999 to 2020. Endnote X9 software and the WPS office software were used for bibliometric analysis, and online software STRING and Cytoscape software were used for bioinformatics analysis. Results: A total of 131 literatures were included in the study, involving 40 genes in total. Bibliometric analysis shows that 131 papers which included 36 Chinese articles and 95 English articles were published in 63 biomedical journals; the highest number of published articles was 19 in 2020. Bioinformatics analysis suggests that GAPDH、SOD2、SOD1、CAT、CASP3、IL6 and other genes play a key role in the interaction network. The involved pathways mainly include MAP2K and MAPK activations, PTEN regulation, P53-depardent G1 DNA damage response, signaoling by BRAF and RAF fusions and soon. Conclusion: The study of noise induced hearing loss involves multi gene biological information, and bioinformatics analysis is helpful to predict the occurrence and development of noise induced hearing loss.
Humans ; Hearing Loss, Noise-Induced/epidemiology* ; Genetic Predisposition to Disease ; Polymorphism, Single Nucleotide ; Computational Biology ; Bibliometrics ; Noise, Occupational

Objective: To explore the change of hearing threshold of workers exposed to noise, establish an individual-based hearing loss early warning model, accurately and differentiated the health of workers exposed to noise. Methods: In September 2019, all physical examination data of 561 workers exposed to noise from an enterprise were collected since their employment. Three indicators of average hearing threshold of the better ear, namely, at high frequency, 4000 Hz and speech frequency, were constructed. The generalized estimating equation (GEE) was used to adjust gender and age and establish the warning model of each indicator. Finally, sensitive indicators and warning models were screened according to AUC and Yoden index. Results: Among the 561 workers exposed to noise, 26 (4.6%) workers had hearing loss. The sensitivity indicators were the average hearing threshold at speech frequency ≥20 dB, high frequency ≥30 dB and 4000 Hz ≥25 dB. The AUC of each index was 0.602, 0.794 and 0.804, and the Youden indexes were 0.204, 0.588 and 0.608, respectively. In GEE of hearing loss warning models, high-frequency hearing threshold ≥20 dB and 4000 Hz hearing threshold ≥25 dB were the optimal models, with AUC of 0.862. Conclusion: Combined with the changes of individual hearing threshold over the years, can accurately assess the risk of individual hearing loss of workers exposed to noise.
Humans ; Hearing Loss, Noise-Induced/diagnosis* ; Noise, Occupational/adverse effects* ; Audiometry ; Deafness ; Employment ; Occupational Exposure/adverse effects* ; Occupational Diseases/diagnosis*

OBJECTIVES: Magnetic resonance diffusion-weighted imaging (DWI) has important clinical value in diagnosis and curative effect evaluation on endometrial carcinoma. How to improve the detection rate of endometrial small lesions by DWI is the research focus of MRI technology. This study aims to analyze the image quality of small field MRI ZOOMit-DWI sequence and conventional single-shot echo-planar imaging (SS-EPI) DWI sequence in the scanning of endometrial carcinoma, and to explore the clinical value of ZOOMit-DWI sequence. METHODS: A total of 37 patients with endometrial carcinoma diagnosed by operation and pathology in the Second Xiangya Hospital of Central South University from July 2019 to May 2021 were collected. All patients were scanned with MRI ZOOMit-DWI sequence and SS-EPI DWI sequence before operation. Two radiologists subjectively evaluated the anatomical details, artifacts, geometric deformation and focus definition of the 2 groups of DWI images. At the same time, the signal intensity were measured and the signal-to-noise ratio (SNR), contrast to noise ratio (CNR), and apparent diffusion coefficient (ADC) of the 2 DWI sequences were calculated for objective evaluation. The differences of subjective score, objective score and ADC value of the 2 DWI sequences were analyzed. RESULTS: The SNR of the ZOOMit-DWI group was significantly higher than that of the SS-EPI DWI group (301.96±141.85 vs 94.66±41.26), and the CNR of the ZOOMit-DWI group was significantly higher than that of the SS-EPI DWI group (185.05±105.45 vs 57.91±31.54, P<0.05). There was no significant difference in noise standard deviation between the ZOOMit-DWI group and the SS-EPI DWI group (P>0.05). The subjective score of anatomical detail and focus definition in the ZOOMit-DWI group was significantly higher than that of the SS-EPI DWI group (both P<0.05). The subjective score of artifacts and geometric deformation of ZOOMit-DWI group was significantly lower than that of the SS-EPI DWI group (both P<0.05). ADC had no significant difference between the ZOOMit-DWI group and the SS-EPI DWI group (P>0.05). CONCLUSIONS The image quality of ZOOMit-DWI is significantly higher than that of conventional SS-EPI DWI. In the MRI DWI examination of endometrial carcinoma, ZOOMit-DWI can effectively reduce the geometric deformation and artifacts of the image, which is more conducive to clinical diagnosis and treatment.
Female ; Humans ; Signal-To-Noise Ratio ; Endometrial Neoplasms/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging/methods* ; Endometrium ; Echo-Planar Imaging/methods* ; Reproducibility of Results

Humans ; Presbycusis ; Speech Perception ; Hearing Aids ; Cognition ; Noise

Objective To explore the optimal parameters for virtual mono-energetic imaging of liver solid lesions. Methods A retrospective analysis was performed on 60 patients undergoing contrast-enhanced spectral CT of the abdomen.The iodine concentration values of hepatic arterial phase images and the CT values of different mono-energetic images were measured.The correlation coefficient and coefficient of variation were calculated. Results The average correlation coefficients between iodine concentrations and CT values of hepatic solid lesion images at 40,45,50,55,60,65,and 70 keV were 0.996,0.995,0.993,0.989,0.978,0.970,and 0.961,respectively.The correlation coefficients at 40(P=0.007),45(P=0.022),50 keV (P=0.035)were higher than that at 55 keV,and the correlation coefficients at 40 keV(P=0.134) and 45 keV(P=0.368) had no significant differences from that at 50 keV.The coefficients of variation of the CT values at 40,45,and 50 keV were 0.146,0.154,and 0.163,respectively. Conclusion The energy of 40 keV is optimal for virtual mono-energetic imaging of liver solid lesions in the late arterial phase,which is helpful for the diagnosis of liver diseases.
Humans ; Tomography, X-Ray Computed ; Retrospective Studies ; Abdomen ; Iodine ; Liver/diagnostic imaging* ; Signal-To-Noise Ratio ; Radiographic Image Interpretation, Computer-Assisted/methods*

Objective To evaluate the impact of deep learning reconstruction algorithm on the image quality of head and neck CT angiography (CTA) at 100 kVp. Methods CT scanning was performed at 100 kVp for the 37 patients who underwent head and neck CTA in PUMC Hospital from March to April in 2021.Four sets of images were reconstructed by three-dimensional adaptive iterative dose reduction (AIDR 3D) and advanced intelligent Clear-IQ engine (AiCE) (low,medium,and high intensity algorithms),respectively.The average CT value,standard deviation (SD),signal-to-noise ratio (SNR),and contrast-to-noise ratio (CNR) of the region of interest in the transverse section image were calculated.Furthermore,the four sets of sagittal maximum intensity projection images of the anterior cerebral artery were scored (1 point:poor,5 points:excellent). Results The SNR and CNR showed differences in the images reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D (all P<0.01).The quality scores of the image reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D were 4.78±0.41,4.92±0.27,4.97±0.16,and 3.92±0.27,respectively,which showed statistically significant differences (all P<0.001). Conclusion AiCE outperformed AIDR 3D in reconstructing the images of head and neck CTA at 100 kVp,being capable of improving image quality and applicable in clinical examinations.
Humans ; Computed Tomography Angiography/methods* ; Radiation Dosage ; Deep Learning ; Radiographic Image Interpretation, Computer-Assisted/methods* ; Signal-To-Noise Ratio ; Algorithms

Noise is one of the most common environmental hazards to which people are exposed,and the exposure to noise can cause not only hearing but also non-hearing damage.Although noise under safety limits may not affect the auditory system,it can cause changes in stress hormone levels,which is harmful to health.However,the current studies about the impact of noise on health mainly focus on the auditory system,and little is known about the relationship between noise and stress hormone levels.Therefore,this paper reviews the studies involving noise exposure and stress hormone levels,aiming to provide ideas for strengthening the prevention and control of noise hazards.
Humans ; Hearing ; Noise/adverse effects* ; Hormones

High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.
Humans ; Evoked Potentials, Visual ; Brain-Computer Interfaces ; Healthy Volunteers ; Signal-To-Noise Ratio