To explore diagnostic acuity of helical CT colonscopic examination, simulated polyps were reproduced in 10 pig colons and 6 human colons obtained from colon resection. Helical CT colonoscopic scanning with different parameters was performed. The images were evaluated with Nav, Axial+MPR, SSD+Raysum. The diagnostic sensitivity of each method for the detection of simulated colonic polyps were assessed. The results indicated that the image quality of CT colonoscopy was improved with the decrease in collimation and pitch. The optimal angle of the colon lumen to the gantry was 90?. CT colonoscopy was superior to other imaging methods. Therefore, the performance of CTVC has a close relationship with the scanning parameters. The optimal scanning parameters were 5mm collimation, 1 5 pitch, and filling the colon with air. The combination of CTVC with other imaging methods would contribute to improving the diagnostic accuracy for the detection of colonic polyps.

Objective To explore the effect of D-galactose(D-gal) on murine pancreatic injury and its pathogenesis.Methods C57BL/6J mice were randomly divided into control group and D-gal model group [D-gal 120 mg/(kg · d) for 42 days].On the 2nd day after drug injection completed,the peripheral blood was taken for measuring the level of fasting blood glucose(FBG) and fasting insulin(FINS);and then the organ index of pancreas was calculated by the ratio of pancreatic wet weight(mg) and mouse body weight(g);HE stain was routinely prepared to observe the histologic structure of pancreatic tissue;the TEM was used to analyze ultrastructural changes of pancreatic cells;the pancreatic frozen sections were prepared to test senescence-associated β-galactosidase (SA-β-gal) and its relative absorbance(RA) of positively stained cells in the pancreatic islets;immunohistochemistry assays to study advanced glycation end products (AGEs) and its RA;pancreas tissue homogenate was made to detect the content of superoxide dismutase(SOD),malonaldehyde(MDA) and total antioxidant capacity(T-AOC).Results In D-gal group mice,the FBG increased(P＜0.05) and FINS reduced;pancreas wet weight and organ increased obviously (P＜0.01);light microscopic structure of the pancreas presented without typical pathologic change,however the single nucleated cell's area within the islet was increased significantly(P＜0.05);the pancreas endocrine and exocrine cells were showed the ultrastructure damaged and lipofuscin formation increased;the RA of positive pancreas cells in SA-β-gal staining increased(P＜0.05);the RA of AGEs positive regional expression markedly increased (P＜0.01);the content of SOD and T-AOC decreased (P ＜ 0.05),the content of MDA increased (P ＜ 0.01).Conclusions Aging mice model replicated by D-gal can cause the pancreatic injury,its mechanisms may be closely related to oxidative injury of pancreatic cells caused by D-gal.

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.

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 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

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