An algorithm for computed tomography image reconstruction from limited-view projections

With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.

Optimization-based image reconstruction in x-ray computed tomography by sparsity exploitation of local continuity and nonlocal spatial self-similarity

The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.

Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images

Deep learning based analyses of computed tomography(CT)images contribute to automated diagnosis of COVID-19,and ensemble learning may commonly provide a better solution.Here,we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19.Two ensemble strategies are considered:the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation;voting strategy.A database containing 8347 CT slices of COVID-19,common pneumonia and normal subjects was used as training and testing sets.Results show that the novel method can reach a high accuracy of 99.37%(recall:0.9981;precision:0.9893),with an increase of about 7% in comparison to single-component models.And the average test accuracy is 95.62%(recall:0.9587;precision:0.9559),with a corresponding increase of 5.2%.Compared with several latest deep learning models on the identical test set,our method made an accuracy improvement up to 10.88%.The proposed method may be a promising solution for the diagnosis of COVID-19.

Computed tomography vs liver stiffness measurement and magnetic resonance imaging in evaluating esophageal varices in cirrhotic patients:A systematic review and meta-analysis

BACKGROUND Computed tomography(CT),liver stiffness measurement(LSM),and magnetic resonance imaging(MRI)are non-invasive diagnostic methods for esophageal varices(EV)and for the prediction of high-bleeding-risk EV(HREV)in cirrhotic patients.However,the clinical use of these methods is controversial.AIM To evaluate the accuracy of LSM,CT,and MRI in diagnosing EV and predicting HREV in cirrhotic patients.METHODS We performed literature searches in multiple databases,including Pub Med,Embase,Cochrane,CNKI,and Wanfang databases,for articles that evaluated the accuracy of LSM,CT,and MRI as candidates for the diagnosis of EV and prediction of HREV in cirrhotic patients.Summary sensitivity and specificity,positive likelihood ratio and negative likelihood ratio,diagnostic odds ratio,and the areas under the summary receiver operating characteristic curves were analyzed.The quality of the articles was assessed using the quality assessment of diagnostic accuracy studies-2 tool.Heterogeneity was examined by Q-statistic test and I2 index,and sources of heterogeneity were explored using metaregression and subgroup analysis.Publication bias was evaluated using Deek’s funnel plot.All statistical analyses were conducted using Stata12.0,Meta Disc1.4,and Rev Man5.3.RESULTS Overall,18,17,and 7 relevant articles on the accuracy of LSM,CT,and MRI in evaluating EV and HREV were retrieved.A significant heterogeneity was observed in all analyses(P<0.05).The areas under the summary receiver operating characteristic curves of LSM,CT,and MRI in diagnosing EV and predicting HREV were 0.86(95%confidence interval[CI]:0.83-0.89),0.91(95%CI:0.88-0.93),and 0.86(95%CI:0.83-0.89),and 0.85(95%CI:0.81-0.88),0.94(95%CI:0.91-0.96),and 0.83(95%CI:0.79-0.86),respectively,with sensitivities of 0.84(95%CI:0.78-0.89),0.91(95%CI:0.87-0.94),and 0.81(95%CI:0.76-0.86),and 0.81(95%CI:0.75-0.86),0.88(95%CI:0.82-0.92),and 0.80(95%CI:0.72-0.86),and specificities of 0.71(95%CI:0.60-0.80),0.75(95%CI:0.68-0.82),and 0.82(95%CI:0.70-0.89),and 0.73(95%CI:0.66-0.80),0.87(95%CI:0.81-0.92),and 0.72(95%CI:0.62-0.80),respectively.The corresponding positive likelihood ratios were 2.91,3.67,and 4.44,and 3.04,6.90,and2.83;the negative likelihood ratios were 0.22,0.12,and 0.23,and 0.26,0.14,and 0.28;the diagnostic odds ratios were 13.01,30.98,and 19.58,and 11.93,49.99,and 10.00.CT scanner is the source of heterogeneity.There was no significant difference in diagnostic threshold effects(P>0.05)or publication bias(P>0.05).CONCLUSION Based on the meta-analysis of observational studies,it is suggested that CT imaging,a non-invasive diagnostic method,is the best choice for the diagnosis of EV and prediction of HREV in cirrhotic patients compared with LSM and MRI.

Dynamic enhanced computed tomography imaging findings of an inflammatory fibroid polyp with massive fibrosis in the stomach

Inflammatory fibroid polyp(IFP) is a rare benign lesion of the gastrointestinal tract. We report a case of computed tomography(CT) imaging finding of a gastric IFP with massive fibrosis. CT scans showed thickening of submucosal layer with overlying mucosal hyperenhancement in the gastric antrum. The submucosal layer showed increased enhancement on delayed phase imaging. An antrectomy with gastroduodenostomy was performed because gastric cancer was suspected, particularly signet ring cell carcinoma. The histopathological diagnosis was an IFP with massive fibrosis. The authors suggest that when the submucosal layer of the gastric wall is markedly thickened with delayed enhancement and preservation of the mucosal layer, an IFP with massive fibrosis should be considered in the differential diagnosis.

Terahertz Three-Dimensional Imaging Based on Computed Tomography with Photonics-Based Noise Source

Computed tomography has been proven to be useful for non-destructive inspection of structures and materials. We build a three-dimensional imaging system with the photonically generated incoherent noise source and the Schottky barrier diode detector in the terahertz frequency band （90–140GHz）. Based on the computed tomography technique, the three-dimensional image of a ceramic sample is reconstructed successfully by stacking the slices at different heights. The imaging results not only indicate the ability of terahertz wave in the non-invasive sensing and non-destructive inspection applications, but also prove the effectiveness and superiority of the uni-traveling-carrier photodiode as a terahertz source in the imaging applications.

Coronary arterial bypass graft patency evaluated by multi-detector computed tomography and magnetic resonance imaging

The progression of atherosclerosis of the coronary artery does not stop after coronary arterial bypass grafting (CABG) surgery.……

Effect of Data Augmentation of Renal Lesion Image by Nine-layer Convolutional Neural Network in Kidney CT

Artificial Intelligence(AI)becomes one hotspot in the field of the medical images analysis and provides rather promising solution.Although some research has been explored in smart diagnosis for the common diseases of urinary system,some problems remain unsolved completely A nine-layer Convolutional Neural Network(CNN)is proposed in this paper to classify the renal Computed Tomography(CT)images.Four group of comparative experiments prove the structure of this CNN is optimal and can achieve good performance with average accuracy about 92.07±1.67%.Although our renal CT data is not very large,we do augment the training data by affine,translating,rotating and scaling geometric transformation and gamma,noise transformation in color space.Experimental results validate the Data Augmentation(DA)on training data can improve the performance of our proposed CNN compared to without DA with the average accuracy about 0.85%.This proposed algorithm gives a promising solution to help clinical doctors automatically recognize the abnormal images faster than manual judgment and more accurately than previous methods.

Novel Approach for Automatic Region of Interest and Seed Point Detection in CT Images Based on Temporal and Spatial Data

Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing.We propose a novel approach of automatically identifying region of interest in Computed Tomography Image(CT)images based on temporal and spatial data.Our method is a 3 stages approach,1)We extract organ features from the CT images by adopting the Hounsfield filter.2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’area and automatically detect a seed point.3)We use a novel approach to track the growing region changes across the CT image sequence in detecting region of interest,given a seed point as our input.We used quantitative and qualitative analysis to measure the accuracy against the given ground truth and our results presented a better performance than other generic approaches for automatic region of interest detection of organs in abdominal CT images.With the results presented in this research work,our proposed novel sequence approach method has been proven to be superior in terms of accuracy,automation and robustness.

An Optimal Weight Method for CT Image Denoising

This paper proposes a novel exemplar- based method for reducing noise in computed tomography （CT） images. In the proposed method, denoising is performed on each block with the help of a given database of standard image blocks. For each noisy block, its denoised version is the best sparse positive linear combination of the blocks in the database. We formulate the problem as a constrained optimization problem such that the solution is the denoised block. Experimental results demonstrate the good performance of the proposed method over current state-of-the-art denoising methods, in terms of both objective and subjective evaluations.

Radiation Dose from CT-Scan of Childhood’s Head: Results of the First Ivorian Survey in a Single Study Site

Objectives: This study aims to evaluate the level of X-ray doses used in childhood’s head as Local Diagnostic Reference Levels (LDRLs) in computed tomography (CT) at a university hospital in Côte d’Ivoire. The Diagnostic Reference Level (DRL) have been set up and used to prevent unusually high radiation doses used in radiology departments and is therefore an optimization tool for practices and procedures in medical X-ray imaging for the radiation protection of patients. Methods: A prospective study of volume CT dose index (CTDIvol) and dose length product (DLP) was performed on images of childhood’s head obtained from a CT-scanner of 64 bars equipped with the tube current modulation capability and manufactured by Hitachi Medical System. 122 CT-scan data from 55 childhood’s head were analyzed. The scan data were stratified in four age groups: Results: The 75th percentile of CTDIvol and DLP (set as LDRL) obtained with respect to the stratified age groups are: 22.5 mGy and 452.5 mGy∙cm, 27.7 mGy and 690.6 mGy∙cm, 28 mGy and 722.4 mGy∙cm, 33.6 mGy and 736.8 mGy∙cm respectively. These outcome values increase with respect to the age of pediatric patients and are comparable to DRLs values obtained internationally. Conclusions: Obtaining good image quality while using low dose in children’s head computed tomography for radiation protection require to setup more surveys in Côte d’Ivoire for regional and national DRL. We proposed through this survey LDRLs in terms of CTDIvol and DLP, comparable to international DRLs values. This survey will be strengthened by additional surveys in order to obtain national DRLs for the radiation protection of the child patient in Côte d’Ivoire.

Establishment of FAP-overexpressing Cells for FAP-targeted Theranostics

Objective Fibroblast activation protein(FAP)has been widely studied and exploited for its clinical applications.One of the difficulties in interpreting reports of FAP-targeted theranostics is due to the lack of accurate controls,making the results less specific and less confirmative.This study aimed to establish a pair of cell lines,in which one highly expresses FAP(HT1080-hFAP)and the other has no detectable FAP(HT1080-vec)as control,to accurately evaluate the specificity of the FAP-targeted theranostics in vitro and in vivo.Methods The cell lines of the experimental group(HT1080-hFAP)and no-load group(HT1080-vec)were obtained by molecular construction of the recombinant plasmid pIRES-hFAP.The expression of hFAP in HT1080 cells was detected by PCR,Western blotting and flow cytometry.CCK-8,Matrigel transwell invasion assay,scratch test,flow cytometry and immunofluorescence were used to verify the physiological function of FAP.The activities of human dipeptidyl peptidase(DPP)and human endopeptidase(EP)were detected by ELISA in HT1080-hFAP cells.PET imaging was performed in bilateral tumor-bearing nude mice models to evaluate the specificity of FAP.Results RT-PCR and Western blotting demonstrated the mRNA and protein expression of hFAP in HT1080-hFAP cells but not in HT1080-vec cells.Flow cytometry confirmed that nearly 95%of the HT1080-hFAP cells were FAP positive.The engineered hFAP on HT1080 cells had its ability to retain enzymatic activities and a variety of biological functions,including internalization,proliferation-,migration-,and invasion-promoting activities.The HT1080-hFAP xenografted tumors in nude mice bound and took up^(68)GA-FAPI-04 with superior selectivity.High image contrast and tumor-organ ratio were obtained by PET imaging.The HT1080-hFAP tumor retained the radiotracer for at least 60 min.Conclusion This pair of HT1080 cell lines was successfully established,making it feasible for accurate evaluation and visualization of therapeutic and diagnostic agents targeting the hFAP.

A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

The key to preventing the COVID-19 is to diagnose patients quickly and accurately.Studies have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 diagnosis.However,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 diagnosis.Also,many CNNs have complex structures and massive parameters.Even if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread application.To solve above problems,this paper proposes a lightweight CNN classification model based on transfer learning.Use the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing power.In order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the model.The study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by Kaggle.Experimental results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT image.Compared to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU acceleration.Code:github.com/ZhouJie-520/paper-codes.

Sinogram fusion-based metal artifact correction method

To solve the problem that metal artifacts severely damage the clarity of the organization structure in computed tomography（CT） images, a sinogram fusion-based metal artifact correction method is proposed. First, the metal image is segmented from the original CT image by the pre-set threshold. The original CT image and metal image are forward projected into the original projection sinogram and metal projection sinogram, respectively. The interpolation-based correction method and mean filter are used to correct the original CT image and preserve the edge of the corrected CT image, respectively. The filtered CT image is forward projected into the filtered image sinogram. According to the position of the metal sinogram in the original sinogram and filtered image sinogram, the corresponding sinograms PM^D （ in the original sinogram） and PM^C （ in the filtered image sinogram）can be acquired from the original sinogram and filtered image sinogram, respectively. Then, PM^D and PM^C are fused into the fused metal sinogram PM^F according to a certain proportion.The final sinogram can be acquired by fusing PM^F , PM^D and the original sinogram P^O. Finally, the final sinogram is reconstructed into the corrected CT image and metal information is compensated into the corrected CT image.Experiments on clinical images demonstrate that the proposed method can effectively reduce metal artifacts. A comparison with classical metal artifacts correction methods shows that the proposed metal artifacts correction method performs better in metal artifacts suppression and tissue feature preservation.

Comparison of Computed Tomography Imaging with Traditional Autopsy Results

The role of computed tomography(CT)images in forensic identification has been widely recognized.Such images can provide an important basis for identification of the cause of death in complicated and difficult cases,including falls from a height,drowning,explosion and gunshot cases,traffic accidents,and sudden death.However,few reports have focused on the application of CT images in cases of death caused by sharp object injuries.Therefore,the CT images and autopsy findings were compared in a case of death caused by sharp object injuries to the chest and abdomen,and the importance of CT images in cases of death from common sharp object injuries is herein discussed.

Advanced imaging techniques in the therapeutic response of transarterial chemoembolization for hepatocellular carcinoma

Hepatocellular carcinoma(HCC) is one of the major causes of morbidity and mortality in patients with chronic liver disease. Transarterial chemoembolization(TACE) can significantly improve the survival rate of patients with HCC and is the first treatment choice for patients who are not suitable for surgical resections. The evaluation of the response to TACE treatment affects not only the assessment of the therapy efficacy but also the development of the next step in the treatment plan. The use of imaging to examine changes in tumor volume to assess the response of solid tumors to treatment has been controversial. In recent years, the emergence of new imaging technology has made it possible to observe the response of tumors to treatment prior to any morphological changes. In this article, the advances in studies reporting the use of computed tomography perfusion imaging, diffusionweighted magnetic resonance imaging(MRI), intravoxel incoherent motion, diffusion kurtosis imaging, magnetic resonance spectroscopy, magnetic resonance perfusionweighted imaging, blood oxygen level-dependent MRI, positron emission tomography(PET)/computed tomography and PET/MRI to assess the TACE treatment response are reviewed.

Role of 3DCT in laparoscopic total gastrectomy with spleen-preserving splenic lymph node dissection

AIM: To investigate whether computed tomography with 3D imaging (3DCT) can reduce the risks associated with laparoscopic surgery.

Acoustic emission characterization of microcracking in laboratory-scale hydraulic fracturing tests

Understanding microcracking near coalesced fracture generation is critically important for hydrocarbon and geothermal reservoir characterization as well as damage evaluation in civil engineering structures. Dense and sometimes random microcracking near coalesced fracture formation alters the mechanical properties of the nearby virgin material. Individual microcrack characterization is also significant in quantifying the material changes near the fracture faces （i.e. damage）. Acoustic emission （AE） monitoring and analysis provide unique information regarding the microcracking process temporally, and infor- mation concerning the source characterization of individual microcracks can be extracted. In this context, laboratory hydraulic fracture tests were carried out while monitoring the AEs from several piezoelectric transducers. In-depth post-processing of the AE event data was performed for the purpose of under- standing the individual source mechanisms. Several source characterization techniques including moment tensor inversion, event parametric analysis, and volumetric deformation analysis were adopted. Post-test fracture characterization through coring, slicing and micro-computed tomographic imaging was performed to determine the coalesced fracture location and structure. Distinct differences in fracture characteristics were found spatially in relation to the openhole injection interval. Individual microcrack AE analysis showed substantial energy reduction emanating spatially from the injection interval. It was quantitatively observed that the recorded AE signals provided sufficient information to generalize the damage radiating spatially away from the injection wellbore.

Computed tomography and photoacoustic imaging guided photodynamic therapy against breast cancer based on mesoporous platinum with insitu oxygen generation ability

Photodynamic therapy(PDT)has been widely used in cancer treatment.However,hypoxia in most solid tumors seriously restricts the efficacy of PDT.To improve the hypoxic microenvironment,we designed a novel mesoporous platinum(mPt)nanoplatform to catalyze hydrogen peroxide(H2 O2)within the tumor cells in situ without an extra enzyme.During the fabrication,the carboxy terminus of the photosensitizer chlorin e6(Ce6)was connected to the amino terminus of the bifunctional mercaptoaminopolyglycol(SH-PEG-NH2)by a condensation reaction,and then PEG-Ce6 was modified onto the mPt moiety via the mercapto terminal of SH-PEG-NH2.Material,cellular and animal experiments demonstrated that Pt@PEG-Ce6 catalyzed H2 O2 to produce oxygen(O2)and that Ce6 transformed O2 to generate reactive oxygen species(ROS)upon laser irradiation.The Pt@PEG-Ce6 nanoplatform with uniform diameter presented good biocompatibility and efficient tumor accumulation.Due to the high atomic number and good near-infrared absorption for Pt,this Pt@PEG-Ce6 nanoplatform showed computed tomography(CT)and photoacoustic(PA)dual-mode imaging ability,thus providing an important tool for monitoring the tumor hypoxic microenvironment.Moreover,the Pt@PEG-Ce6 nanoplatform reduced the expression of hypoxia-inducible factor-la(HIF-1α)and programmed death-1(PD-1)in tumors,discussing the relationship between hypoxia,PD-1,and PDT for the first time.

^(18)F-FDG PET/CT in differentiating malignant from benign origins of obstructive jaundice

BACKGROUND： The various origins of obstructive jaundice make the diagnosis of the disease difficult. This study was undertaken to evaluate the role of 18F-FDG PET/CT in differentiating malignant from benign origins of obstructive jaundice and to quantify the added value of 18F-FDG PET/CT over conventional imaging（enhanced CT and/or MRI）.METHODS： Eighty-five patients with obstructive jaundice who underwent 18F-FDG PET/CT within 2 weeks after enhanced CT and/or MRI were reviewed retrospectively. All 18F-FDG PET/CT images were independently evaluated by 2 nuclear medicine physicians who were unaware of other imaging data; differences were resolved by consensus of the physicians. All conventional imaging interpretations, according to the medical records, were reviewed by 2 radiologists to determine the potential value. Final diagnoses were based on histological or surgical findings.RESULTS： Sixty-six patients were diagnosed with malignancies, and 19 patients with benign lesions. The maximum standardized uptake values for malignant and benign lesions causing biliary obstruction were 8.2±4.4 and 4.0±5.0, respectively（P〈0.05）. The sensitivity, specificity, and overall accuracy for differentiating malignant from benign origins with 18F-FDG PET/CT were 86.4%（57/66）, 73.7%（14/19）, and 83.5%（71/85）, respectively. 18F-FDG PET/CT in conjunction with conventional imaging changed the sensitivity, specificity, and overall accuracy of conventional imaging alone from 75.8%（50/66） to 95.5%（63/66）（P〈0.05）, 68.4%（13/19） to 57.9%（11/19）（P〉0.05）, and 74.1%（63/85） to 87.1%（74/85）（P〈0.05）, respectively.CONCLUSIONS： 18F-FDG PET/CT is of great value in differentiating malignant from benign origins of obstructive jaundice and is a useful adjuvant to conventional imaging. 18F-FDG PET/CT should be recommended for further etiological clarification.