Objective To understand the prevalence and sub‐genotypes distribution situation of high risk human papillomavirus (HPV) infection in the gynecological outpatient department in Beijing area in order to provide the reference basis for the prevention and treatment of HPV infection and cervical cancer .Methods The detection results of 13 kinds of high risk HPV genotypes among 1 294 women in the gynecological outpatient department of this hospital from January 2013 to May 2014 were performed the retro‐spective analysis for comparing the epidemiological characteristics of different HPV genotypes .The SPSS17 .0 software was adopted to perform the statistical analysis .Results Among 1 294 detected women ,HPV‐58 ,HPV‐16 and HPV‐52 were most common ,the detection rates were 10 .5% ,9 .2% and 8 .2% respectively .Among various age groups ,the 30 - < 40 years group had the highest HPV detection rate(39 .9% ) ,followed by the 40 - < 50 years group and the ≥ 60 years group ,but the difference among them had no statistically significance (P> 0 .05) .Conclusion The women going to the local outpatient department have the higher prevalence of high risk HPV infection .The intensity of HPV screening should be strengthened in order to provide the fundamental basis for the prevention and treatment of HPV related diseases .

OBJECTIVETo compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging.

METHODSHuber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators.

RESULTSThe experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal.

CONCLUSIONBoth of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.

OBJECTIVETo develop a computed tomography myocardial perfusion (CT-MP) deconvolution algorithm by incorporating high-dimension total variation (HDTV) regularization.

METHODSA perfusion deconvolution model was formulated for the low-dose CT-MPI data, followed by HDTV regularization to regularize the consistency of the solution by fusing the spatial correlation of the vascular structure and the temporal continuation of the blood flow signal.

RESULTSBoth qualitative and quantitative studies were conducted using XCAT and pig myocardial perfusion data to evaluate the present algorithm. The experimental results showed that this algorithm achieved hemodynamic parameter maps with better performances than the existing methods in terms of streak-artifacts suppression, noise-resolution tradeoff, and diagnosis structure preservation.

CONCLUSIONThe proposed algorithm can achieve high-quality hemodynamic parameter maps in low-dose CT-MPI.

OBJECTIVETo minimize of the radiation dose of cardiovascular CT angiography (CTA) imaging while preserving the image quality.

METHODSTo reduce the radiation dose in CTA imaging, the normal-dose scan induced non-local means (ndiNLM) algorithm was adapted for low-mAs scanned CTA image restoration by using the previous scanned high-quality image.

RESULTSQualitative and quantitative evaluations were carried out on both simulated phantom and clinical CTA scans in terms of accuracy and resolution properties. Compared to the original NLM algorithm, the ndiNLM method could achieve noticeable gains in terms of noise-induced artifacts suppression and enhanced structure preservation.

CONCLUSIONThe ndiNLM algorithm is a potential useful technique to reduce the radiation dose in CTA imaging.

Objective:To analyze the efficacy and safety of enzyme therapy in late-onset Pompe disease (LOPD) patients, so as to provide reference for the treatment and prognosis of LOPD.Methods:The effect of α-glucosidase (GAA) on a patient diagnosed with LOPD in the Affiliated Hospital of Jining Medical University was observed and analyzed. Besides, literature related to enzyme therapy in LOPD patients were searched in PubMed, Web of Science, Medline databases. Twenty-one studies containing clinical data from 910 LOPD patients related to enzyme therapy were finally included for analysis.Results:The patient developed muscle weakness since he was 16 years old. The GAA activity in peripheral blood was 0. Electromyography suggested myogenic lesions in both lower extremities. Compound heterozygous mutations of GAA gene were found by next- generation sequencing. Muscle biopsy revealed characteristic vacuolar changes. After eight years of diagnosis, the patient was given enzyme therapy for 18.5 months, 20 times in total. The symptoms of muscle weakness were slightly improved in the early stages of treatment without obvious adverse reactions. Most of the 910 LOPD patients were stabilized or had improved muscular and (or) respiratory function following treatment with GAA.Conclusion:GAA treatment is effective and well tolerated. In patients with advanced severe LOPD, enzyme replacement therapy remains effective even years after onset.

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

OBJECTIVE: To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography. METHODS: The data of full-filed digital mammography in both craniocaudal view and mediolateral oblique view from 106 patients with breast neoplasms were analyzed. Twenty-three handcrafted features (HCF) were extracted from the images of the breast tumors and a suitable feature set of HCF was selected using -test. The deep features (DF) were extracted from the 3 pre-trained deep learning models, namely AlexNet, VGG16 and GoogLeNet. With abundant breast tumor information from the craniocaudal view and mediolateral oblique view, we combined the two extracted features (DF and HCF) as the two-view features. A multi-classifier model was finally constructed based on the combined HCF and DF sets. The classification ability of different deep learning networks was evaluated. RESULTS: Quantitative evaluation results showed that the proposed HCF+DF model outperformed HCF model, and AlexNet produced the best performances among the 3 deep learning models. CONCLUSIONS The proposed model that combines DF and HCF sets of breast tumors can effectively distinguish benign and malignant breast lesions on full-filed digital mammography.
Breast Neoplasms ; classification ; diagnostic imaging ; Deep Learning ; Diagnosis, Computer-Assisted ; methods ; Female ; Humans ; Mammography ; methods

OBJECTIVE: To establish a cone beam computed tomography (ECBCT) system for high-resolution imaging of the extremities. METHODS: Based on three-dimensional X-Ray CT imaging and high-resolution flat plate detector technique, we constructed a physical model and a geometric model for ECBCT imaging, optimized the geometric calibration and image reconstruction methods, and established the scanner system. In the experiments, the pencil vase phantom, image quality (IQ) phantom and a swine feet were scanned using this imaging system to evaluate its effectiveness and stability. RESULTS: On the reconstructed image of the pencil vase phantom, the edges were well preserved with geometric calibrated parameters and no aliasing artifacts were observed. The reconstructed images of the IQ phantom showed a uniform distribution of the CT number, and the noise power spectra were stable in multiple scanning under the same condition. The reconstructed images of the swine feet had clearly displayed the bones with a good resolution. CONCLUSIONS The ECBCT system can be used for highresolution imaging of the extremities to provide important imaging information to assist in the diagnosis of bone diseases.
Algorithms ; Animals ; Artifacts ; Calibration ; Cone-Beam Computed Tomography ; instrumentation ; methods ; Equipment Design ; Extremities ; diagnostic imaging ; Image Processing, Computer-Assisted ; methods ; Phantoms, Imaging ; Radiographic Image Enhancement ; instrumentation ; methods ; Swine

OBJECTIVE: To develop a digital breast tomosynthesis (DBT) imaging system with optimizes imaging chain. METHODS: Based on 3D tomography and DBT imaging scanning, we analyzed the methods for projection data correction, geometric correction, projection enhancement, filter modulation, and image reconstruction, and established a hardware testing platform. In the experiment, the standard ACR phantom and high-resolution phantom were used to evaluate the system stability and noise level. The patient projection data of commercial equipment was used to test the effect of the imaging algorithm. RESULTS: In the high-resolution phantom study, the line pairs were clear without confusing artifacts in the images reconstructed with the geometric correction parameters. In ACR phantom study, the calcified foci, cysts, and fibrous structures were more clearly defined in the reconstructed images after filtering and modulation. The patient data study showed a high contrast between tissues, and the lesions were more clearly displayed in the reconstructed image. CONCLUSIONS This DBT imaging system can be used for mammary tomography with an image quality comparable to that of commercial DBT systems to facilitate imaging diagnosis of breast diseases.
Algorithms ; Artifacts ; Breast ; diagnostic imaging ; Female ; Humans ; Mammography ; methods ; Phantoms, Imaging ; Radiographic Image Enhancement ; methods