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Background: Hypertension is the most common chronic disease and the leading risk factor for disability and premature deaths worldwide. Approximately 10–20% of all patients with hypertension and 15–18% of the general population who are treated for hypertension have resistant hypertension (RH). Patients with RH have a higher risk of end-organ damage, such as carotid intima–media thickening, retinopathy, left ventricular hypertrophy and heart failure, myocardial infarction, stroke, impaired renal function, and death than those with controlled blood pressure. In the present study, we applied echocardiography to patients with RH to evaluate myocardial work (MW) and determine whether it is predictive for the occurrence of adverse events within 3 years. Methods: We included 283 outpatients and inpatients aged ≥ 18 years who met the clinical criteria for RH, without arrhythmia and severe aortic valve stenosis, between July 2018 and June 2019. The patients were followed up for 3 years from starting enrollment, and any adverse event that occurred during the period was used as the observation end point. Each enrolled patient underwent a complete transthoracic echocardiogram examination, blood pressure was measured and recorded, and MW was then analyzed. Results: Eighty-two (28.98%) patients with RH had adverse events, such as myocardial infarction (n = 29, 35.36%), heart failure (n = 4, 0.05%), renal insufficiency (n = 40, 48.78%), renal failure (n = 2, 0.02%), cerebral infarction (n = 5, 0.06%), and cerebral hemorrhage (n = 2, 0.02%), and no death events occurred. In patients with RH and adverse events, global longitudinal strain (GLS) (− 16% vs. − 18%), the global work index (2079 mmHg% vs. 2327 mmHg%), global constructive work (2321 mmHg% vs. 2610 mmHg%), and global work efficiency (93% vs. 94%) were lower than those in patients without adverse events. However, global wasted work (GWW) was higher in patients with RH and adverse events than in those without adverse events (161 mmHg% vs. 127 mmHg%). GLS and GWW were the most significant in predicting adverse events. Conclusions: MW, especially GLS and GWW, is a good method to predict 3-year adverse events in patients with RH. [ABSTRACT FROM AUTHOR]

Primary cardiac tumors are extremely rare, among which malignancies comprise about 15–25%. As the most common type of primary cardiac malignancies, angiosarcomas tend to arise in the right heart, especially right atrium. In this case report, we presented a 32‐year‐old female with primary cardiac angiosarcoma in the right atrial appendage detected by transesophageal echocardiography, as it is difficult to display on conventional transthoracic echocardiography. [ABSTRACT FROM AUTHOR]

Skin cancers, including those of both both melanoma and non-melanoma subtypes, remain among the most common forms of human cancer. Non-melanoma skin cancers are typically further differentiated into the basal cell carcinoma and cutaneous squamous cell carcinoma (cSCC) categories. Current approaches to diagnosing and treating cSCC remain unsatisfactory, and the prognosis for patients with this disease is relatively poor. Recent advances in high-throughput sequencing have led to an increasingly robust understanding of the diversity of non-coding RNAs (ncRNAs) expressed in both physiological and pathological contexts. These ncRNAs include microRNAs, long ncRNAs, and circular RNAs, all of which have been found to play key functional roles and/or to have value as diagnostic biomarkers or therapeutic targets in a range of different disease contexts. The number of ncRNAs associated with cSCC continues to rise, and as such, there is clear value in comprehensively reviewing the functional roles of these molecules in this form of cancer in order to highlight future avenues for research and clinical development. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Morphine tolerance leads to increased mGluR5 levels and phosphorylation of NR2B. • Loss of mGluR5 prevents the development of tolerance and thermal hyperalgesia. • Disruption of NMDA receptor and mGluR5 connection by mutation of shank3 doesn't prevent the development of morphine-induced tolerance. Abstract Background and purpose The aim of this study was to investigate the interactions between mGluR5 and NMDAR during the development of tolerance to the analgesic effect of chronic morphine following disruption of the link between mGluR5 and NMDAR. Methods For these experiments, mGluR5 knockout (KO) mice, Shank3 ΔC mutant mice and their littermate controls were used. Repeated morphine treatment through intrathecal injections (i.t. 10 μg/10 μl twice daily for 5 days) was used to induce antinociceptive tolerance and thermal hyperalgesia, which was determined by tail-flick latency and calculated as a percentage of the maximum possible effect (%MPE). Expression of NMDAR subunits (NR1, NR2A and NR2B) and phosphorylation of NR2B in cortex were evaluated by Western blot. Results Chronic morphine treatment increased the total expression of mGluR5 and NR2A but not NR1 and NR2B in the cortex and enhanced the phosphorylation of NR2B in wild-type mice after tolerance developed. mGluR5 KOs had attenuated morphine-induced tolerance and thermal hyperalgesia and reduced chronic morphine-induced phosphorylation of NR2B but not total NR2A expression. Shank3 ΔC mice were used to imitate the loss of the physiological connection between mGluR5 and NMDAR, and the mice showed a similar analgesic effect to morphine as their littermate controls, and there were no differential effects on NMDAR subunits compared with controls during the development of morphine-induced tolerance. Conclusions It may be mGluR5-mediated PKC signalling that plays the most significant role in the mechanisms underlying morphine-induced antinociceptive tolerance rather than the physiological connection between mGluR5 and NMDAR. [ABSTRACT FROM AUTHOR]

• Patients who are infected by SARS-Cov-2 and HIV tend to have a longer disease course and slower generation of specific antibodies • For suspected cases of COVID-19, nucleic acid detection, gene sequencing and antibody detection can confirm the diagnosis, respectively • SARS-Cov-2 may damage lymphocytes, especially T-lymphocytes, and the immune system is impaired during the period of disease The ongoing outbreak of COVID-19 that began in Wuhan, China, become an emergency of international concern when thousands of people were infected around the world. This study reports a case simultaneously infected by SARS-Cov-2 and HIV, which showed a longer disease course and slower generation of specific antibodies. This case highlights that a co-infection of SARS-Cov-2 and HIV may severely impair the immune system. [ABSTRACT FROM AUTHOR]

Chronic morphine exposure persistently activates Gαi/o protein-coupled receptors and enhances adenylyl cyclase (AC) activity, which can increase cyclic adenosine monophosphate (cAMP) production. Direct binding of cAMP to the cytoplasmic site on hyperpolarization-activated cyclic nucleotide-gated (HCN) channels increases the probability of channel opening. HCN channels play a prominent role in chronic pain the disease that shares some common mechanisms with opioid tolerance. This compensatory AC activation may be responsible for the induction of morphine-induced analgesic tolerance. We investigated spinal cAMP formation and expression of HCN2 in the spinal cord, and observed the effect of AC inhibition on the induction of morphine analgesic tolerance. We found that chronic morphine-induced antinociceptive tolerance increased spinal cAMP formation and the expression of spinal HCN2. Inhibition of spinal AC partially blocked chronic morphine-induced cAMP formation and prevented the induction of morphine-induced analgesic tolerance. Inhibition of HCN2 also showed a partial preventive effect on morphine-induced tolerance, hypothermia tolerance and also the right-shift of the dose-response curve. We conclude that repeated morphine treatment increases AC activity and cAMP formation, and also spinal HCN2 expression, blockade of AC or HCN2 can prevent the development of morphine-induced analgesic tolerance. Image 1 • Chronic morphine exposure increases spinal AC activity and cAMP formation. • Morphine tolerance increases the expression of spinal HCN2. • Blockade of spinal AC decreases morphine tolerance, but not spinal HCN2 upregulation. • Inhibition of HCN2 partial prevents morphine-induced tolerance, hypothermia tolerance, and right-shift of dose-response curve. [ABSTRACT FROM AUTHOR]

Metabotropic glutamate receptor 5 (mGluR5) has been reported to contribute to inflammatory pain. The intracellular C-terminal domain has a Homer-binding motif that can form an mGluR5/Homer complex. Phosphorylation of mGluR5 at the Homer binding domain enhances the mGluR5/Homer interaction and modulates intracellular signal transduction. However, the characteristics of this interaction have not been fully elucidated in inflammatory pain. We aimed to evaluate the effects of CFA-induced phosphorylation of mGluR5 at the Homer binding domain on the mGluR5/Homer interaction. Von-frey filaments and thermal latency were used to monitor the development of inflammatory pain. Spinal mGluR5 phosphorylation at Ser1126 and mGluR5/Homer crosslinking were detected. Mutant mGluR5 that could not be phosphorylated at Thr1123 or Ser1126 was evaluated in inflammatory pain. CFA-induced inflammatory pain resulted in obvious phosphorylation at Ser1126 of mGluR5. Moreover, increased phosphorylation at the Homer-binding motif enhanced crosslinking between mGluR5 and Homer. Mutations at Thr1123 and Ser1126 of mGluR5 blocked the development of CFA-induced inflammatory pain. Overall, our findings showed that disruption of the phosphorylation of mGluR5 Thr1123 and Ser1126 alleviated CFA-induced inflammatory pain. Image 1 • CFA-induced inflammatory pain caused phosphorylation at Ser1126 of mGluR5. • Phosphorylation at Ser1126 of mGluR5 increases mGluR5-Homer interaction in CFA pain. • Mutation at Thr1123 and Ser1126 of mGluR5 alleviated CFA-induced inflammatory pain. [ABSTRACT FROM AUTHOR]

Background and Purpose: The P2X3 receptor is a major receptor in the processing of nociceptive information in dorsal root ganglia. We investigated the role of the P2X3 receptor and the detailed mechanisms underlying chronic morphine-induced analgesic tolerance in rats.Experimental Approach: Repeated i.t. morphine treatment was used to induce anti-nociceptive tolerance. The expression of spinal P2X3 receptor, phosphorylated PKCε and exchange factor directly activated by cAMP (Epac) were evaluated. Effects of A-317491 (P2X3 antagonist), ε-V1-2 (PKCε inhibitor) and ESI-09 (Epac inhibitor) on mechanical pain thresholds and tail-flick latency after chronic morphine treatment were determined. Co-localization of P2X3 receptor with NeuNs (marker of neuron), IB4 (marker of small DRG neurons), peripherin, PKCε and Epac were performed by double immunofluorescence staining.Key Results: Chronic morphine time-dependently increased the expression of P2X3 receptor, phosphorylated PKCε and Epac in DRGs. ε-V1-2 prevented chronic morphine-induced expression of P2X3 receptor. ESI-09 decreased the phosphorylation of PKCε and up-regulated expression of Epac after chronic morphine exposure. Mechanical pain thresholds and tail-flick latency showed that A317491, ε-V1-2 and ESI-09 significantly attenuated the loss of morphine's analgesic potency. Morphine-induced P2X3 receptor expression mainly occurred in neurons staining for IB4 and peripherin. Co-localization of P2X3 receptor with PKCε and Epac was demonstrated in the same neurons.Conclusions and Implications: Chronic morphine exposure increased the expression of P2X3 receptor, and i.t. P2X3 receptor antagonists attenuated the loss of morphine's analgesic effect. Inhibiting Epac/PKCε signalling was shown to play a significant inhibitory role in chronic morphine-induced P2X3 receptor expression and attenuate morphine-induced tolerance. [ABSTRACT FROM AUTHOR]

To search the global optimum across the entire search space with a very fast convergence speed, we propose a multi-strategy adaptive particle swarm optimization (MAPSO). MAPSO develops an innovative strategy of diversity-measurement to evaluate the population distribution, and performs a real-time alternating strategy to determine one of two predefined evolutionary states, exploration and exploitation, in each iteration. During iterative optimization, MAPSO can dynamically control the inertia weight according to the diversity of particles. Moreover, MAPSO introduces an elitist learning strategy to enhance population diversity and to prevent the population from possibly falling into local optimal solutions. The elitist learning strategy not only acts on the globally best particle, but also on some special particles that are very near to the globally best particle. The aforementioned features of MAPSO have been comprehensively analyzed and tested on eight benchmark problems and a standard test image. Experimental results show that MAPSO can substantially enhance the ability of PSOs to jump out of the local optimal solutions and significantly improve the search efficiency and convergence speed. [ABSTRACT FROM AUTHOR]

Introduction: Metal artefacts seriously degrade the quality of the CT images. Blurring around the junctions between metal and non-metal regions in CT images, metal artefacts often prevent right diagnoses, and even lead to misdiagnoses of patients. The aim of the study was to devise a fast and robust method to improve the quality of the artefact-contaminated CT images. Methods: The proposed artefact correction includes the following five steps: metal object segmentation, forward projection, region-based filling, adaptive scaling and final image reconstruction. Results: The feasibility of the proposed method in correcting metal artefacts was validated by experiments on both simulated and clinical images. Experiments showed the proposed correction could lead to fast and effective reduction of metal artefacts in CT images. Conclusions: Compared with other methods, the proposed method has less computational cost and allows a feasible and easy implantation into current CT imaging systems. [ABSTRACT FROM AUTHOR]

Abstract: A genetic algorithm for solving min− ε polygonal approximation and min−# polygonal approximation is proposed in this paper. It combines traditional split-and-merge techniques with a novel chromosome-repairing scheme to cope with constraints. Due to this combination of techniques we call our new method SMCR. In this new scheme an infeasible solution cannot only be easily transformed into a feasible one, but also be optimized. The experimental results show that the proposed SMCR has higher performance than the other GA-based methods and some non-GA-based methods. [Copyright &y& Elsevier]

Abstract: This paper proposes a new sequential weighted least squares (SWLS) method for positron emission tomography (PET) reconstruction. The SWLS algorithm is noniterative and can be considered as equivalent to the penalized WLS (PWLS) method under certain initial conditions. However, a full implementation of SWLS is computationally intensive. To overcome this problem, we propose a simplified SWLS as a reasonable alternative to the SWLS. The performance of this SWLS method is evaluated in experiments using both simulated and clinical data. The results show that the method can be advantageously compared with the original SWLS both in computation time and reconstruction quality. [Copyright &y& Elsevier]

For the purpose of ensuring public security, automatic inspection of X-ray scanners has been deployed at the entry points of many public places to detect dangerous objects. However, current surveillance systems cannot function without human supervision and intervention. In this article, we propose an effective method using deep convolutional neural networks to detect objects during X-ray baggage inspection. As a first step, a large amount of training data is generated by a specific data augmentation technique. Second, a feature enhancement module is used to improve feature extraction capabilities. Then, in order to address the foreground–background imbalance in the region proposal network, focal loss is adopted. Third, the multiscale fused region of interest is utilized to obtain more robust proposals. Finally, soft nonmaximum suppression is adopted to alleviate overlaps in baggage detection. As compared with existing algorithms, the proposed method proves that it is more accurate and robust when dealing with densely cluttered backgrounds during X-ray baggage inspection. [ABSTRACT FROM AUTHOR]

Pulmonary nodule false-positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computed tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities between true nodules and false positives as soft tissues in LDCT images, the current clinical practices remain subject to shortcomings of potential high-risk and time-consumption issues. In this paper, we propose a multi-dimensional nodule detection network (MD-NDNet) for automatic nodule false-positive reduction using deep convolutional neural network (DCNNs). The underlying method collaboratively integrates multi-dimensional nodule information to complementarily and comprehensively extract nodule inter-plane volumetric correlation features using three-dimensional CNNs (3D CNNs) and spatial nodule correlation features from sagittal, coronal, and axial planes using two-dimensional CNNs (2D CNNs) with attention module. To incorporate different sizes and shapes of nodule candidates, a multi-scale ensemble strategy is employed for probability aggregation with weights. The proposed method is evaluated on the LUNA16 challenge dataset in ISBI 2016 with ten-fold cross-validation. Experiment results show that the proposed framework achieves classification performance with a CPM score of 0.9008. All of these indicate that our method enables an efficient, accurate and reliable pulmonary nodule detection for clinical diagnosis. [ABSTRACT FROM AUTHOR]

The integration of segmentation and direct quantification on the left ventricle (LV) from the paired apical views(i.e., apical 4-chamber and 2-chamber together) echo sequence clinically achieves the comprehensive cardiac assessment: multiview segmentation for anatomical morphology, and multidimensional quantification for contractile function. Direct quantification of LV, i.e., to automatically quantify multiple LV indices directly from the image via task-aware feature representation and regression, avoids accumulative error from the inter-step target. This integration sequentially makes a stereoscopical reflection of cardiac activity jointly from the paired orthogonal cross views sequences, overcoming limited observation with a single plane. We propose a K-shaped Unified Network (K-Net), the first end-to-end framework to simultaneously segment LV from apical 4-chamber and 2-chamber views, and directly quantify LV from major- and minor-axis dimensions (1D), area (2D), and volume (3D), in sequence. It works via four components: 1) the K-Net architecture with the Attention Junction enables heterogeneous tasks learning of segmentation task of pixel-wise classification, and direct quantification task of image-wise regression, by interactively introducing the information from segmentation to jointly promote spatial attention map to guide quantification focusing on LV-related region, and transferring quantification feedback to make global constraint on segmentation; 2) the Bi-ResLSTMs distributed in K-Net layer-by-layer hierarchically extract spatial-temporal information in echo sequence, with bidirectional recurrent and short-cut connection to model spatial-temporal information among all frames; 3) the Information Valve tailing the Bi-ResLSTMs selectively exchanges information among multiple views, by stimulating complementary information and suppressing redundant information to make the efficient cross-flow for each view; 4) the Evolution Loss comprehensively guides sequential data learning, with static constraint for frame values, and dynamic constraint for inter-frame value changes. The experiments show that our K-Net gains high performance with a Dice coefficient up to 91.44% and a mean absolute error of the major-axis dimension down to 2.74mm, which reveal its clinical potential. [ABSTRACT FROM AUTHOR]

The wide applications of X-ray computed tomography (CT) bring low-dose CT (LDCT) into a clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly increased noise and artifacts, which might lower the judgment accuracy of radiologists. In this paper, we put forward a domain progressive 3D residual convolution network (DP-ResNet) for the LDCT imaging procedure that contains three stages: sinogram domain network (SD-net), filtered back projection (FBP), and image domain network (ID-net). Though both are based on the residual network structure, the SD-net and ID-net provide complementary effect on improving the final LDCT quality. The experimental results with both simulated and real projection data show that this domain progressive deep-learning network achieves significantly improved performance by combing the network processing in the two domains. [ABSTRACT FROM AUTHOR]

Low-dose computed tomography (LDCT) images are often highly degraded by amplified mottle noise and streak artifacts. Maintaining image quality under low-dose scan protocols is a well-known challenge. Recently, sparse representation-based techniques have been shown to be efficient in improving such CT images. In this paper, we propose a 3D feature constrained reconstruction (3D-FCR) algorithm for LDCT image reconstruction. The feature information used in the 3D-FCR algorithm relies on a 3D feature dictionary constructed from available high quality standard-dose CT sample. The CT voxels and the sparse coefficients are sequentially updated using an alternating minimization scheme. The performance of the 3D-FCR algorithm was assessed through experiments conducted on phantom simulation data and clinical data. A comparison with previously reported solutions was also performed. Qualitative and quantitative results show that the proposed method can lead to a promising improvement of LDCT image quality. [ABSTRACT FROM PUBLISHER]

Noise detection accuracy is crucial in suppressing random-valued impulse noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued impulse noise points. This paper proposes an iterative structure-adaptive fuzzy estimation (SAFE) for random-valued impulse noise suppression. This SAFE method is developed in the framework of Gaussian maximum likelihood estimation. The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as noise free) is estimated via a novel-minimal-path-based structure propagation to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the reestimation error of the uncorrupted intensity information. The comparative experimental results show that the proposed structure-adaptive fuzziness can lead to effective restoration. An efficient implementation of this SAFE method is also realized via graphics-processing-unit-based parallelization. [ABSTRACT FROM AUTHOR]

In low dose computed tomography (LDCT) imaging, the data inconsistency of measured noisy projections can significantly deteriorate reconstruction images. To deal with this problem, we propose here a new sinogram restoration approach, the sinogram- discriminative feature representation (S-DFR) method. Different from other sinogram restoration methods, the proposed method works through a 3-D representation-based feature decomposition of the projected attenuation component and the noise component using a well-designed composite dictionary containing atoms with discriminative features. This method can be easily implemented with good robustness in parameter setting. Its comparison to other competing methods through experiments on simulated and real data demonstrated that the S-DFR method offers a sound alternative in LDCT. [ABSTRACT FROM PUBLISHER]

By providing fast scanning with low radiation doses, sparse-view (or sparse-projection) reconstruction has attracted much research attention in X-ray computerized tomography (CT) imaging. Recent contributions have demonstrated that the total variation (TV) constraint can lead to improved solution by regularizing the underdetermined ill-posed problem of sparse-view reconstruction. However, when the projection views are reduced below certain numbers, the performance of TV regularization tends to deteriorate with severe artifacts. In this paper, we explore the applicability of Gamma regularization for the sparse-view CT reconstruction. Experiments on simulated data and clinical data demonstrate that the Gamma regularization can lead to good performance in sparse-view reconstruction. [ABSTRACT FROM AUTHOR]

Blood vessel images can provide considerable information of many diseases, which are widely used by ophthalmologists for disease diagnosis and surgical planning. In this paper, we propose a novel method for the blood Vessel Enhancement via Multi-dictionary and Sparse Coding (VE-MSC). In the proposed method, two dictionaries are utilized to gain the vascular structures and details, including the Representation Dictionary (RD) generated from the original vascular images and the Enhancement Dictionary (ED) extracted from the corresponding label images. The sparse coding technology is utilized to represent the original target vessel image with RD. After that, the enhanced target vessel image can be reconstructed using the obtained sparse coefficients and ED. The proposed method has been evaluated for the retinal vessel enhancement on the DRIVE and STARE databases. Experimental results indicate that the proposed method can not only effectively improve the image contrast but also enhance the retinal vascular structures and details. [ABSTRACT FROM AUTHOR]

Although the nonlocal means (NLM) algorithm takes a significant step forward in image filtering field, it suffers from a high computational complexity. To deal with this drawback, this paper proposes an acceleration strategy based on a correlation operation. Instead of per-pixel processing, this approach performs a simultaneous calculation of all the image pixels with the help of correlation operators. Complexity analysis and experimental results are reported and show the advantage of the proposed algorithm in terms of computation and time cost. [ABSTRACT FROM AUTHOR]

Projection and back-projection are the most computationally intensive parts in Computed Tomography (CT) reconstruction, and are essential to acceleration of CT reconstruction algorithms. Compared to back-projection, parallelization efficiency in projection is highly limited by racing condition and thread unsynchronization. In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU). Texture fetching is also used utilized to further accelerate the interpolations in both projection and back-projection. We validate the performance of this FSNP approach using both simulated and real cone-beam CT data. Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction. [ABSTRACT FROM AUTHOR]

The reliable estimation of left ventricle (LV) volume from 3D medical data enables both visible shape exhibition and accurate volume report, for efficiently clinical cardiac diagnosis. However, it is still open challenging as: 1) intermediate volume error caused by fully depending on segmentation, 2) non-visualized result via solo using direct regression, 3) ambiguous conclusion from the unmatching multi-type outputs, 4) large spatial scene of stereo domain, 5) unbalanced information in 3D medical data between depth and slice plane, 6) large regression output space. We proposed RE-3DLVNet the first powerful work for the refined volume estimation of 3D LV in a novel way segmentation volume ± reinforced volume. It uses segmentation with advantage of intuitive voxel-to-voxel optimization to fast stabilize a rough volume. Reciprocally, it makes reinforced quantification (ReinQuan) with advantage of global inference to focus on checking scene misunderstanding and precisely remedy accumulative error caused by massive local optimization, for refining the volume. 1) In segmentation, a probe convolution creatively perceives global scene distribution, and a low-to-high depth segmentation net with high depth connection releases latent structure in low-resolution depth. 2) In ReinQuan, a regression net is conducted to refine estimating the reinforced volume of the segmentation, with focused contracted output space. 3) Interactively, a consistent constraint transfers the ReinQuan deviation between segmented result and its ground truth for penetratively penalizing. Extensive experiments show RE-3DLVNet clinical potential in assessing cardiac function and anatomy. It 1) enables exact 3D LV volume estimation with mean absolute error down to 1.03ml , increasing measurement precise by 30.41% compared to segmentation volume, and 2) achieves accurate visualization segmentation with Dice coefficient up to 95.13%. [ABSTRACT FROM AUTHOR]

Purpose: This study was aimed to identify the expression pattern of vascular endothelial growth factor (VEGF) in non-small cell lung cancer (NSCLC) and to explore its potential correlation with the progression of NSCLC. Methods: Gene expression profile GSE39345 was downloaded from the Gene Expression Omnibus database. Twenty healthy controls and 32 NSCLC samples before chemotherapy were analyzed to identify the differentially expressed genes (DEGs). Then pathway enrichment analysis of the DEGs was performed and protein-protein interaction networks were constructed. Particularly, VEGF genes and the VEGF signaling pathway were analyzed. The sub-network was constructed followed by functional enrichment analysis. Results: Total 1666 up-regulated and 1542 down-regulated DEGs were identified. The down-regulated DEGs were mainly enriched in the pathways associated with cancer. VEGFA and VEGFB were found to be the initiating factor of VEGF signaling pathway. In addition, in the epidermal growth factor receptor (EGFR), VEGFA and VEGFB associated sub-network, kinase insert domain receptor (KDR), fibronectin 1 (FN1), transforming growth factor beta induced (TGFBI) and proliferating cell nuclear antigen (PCNA) were found to interact with at least two of the three hub genes. The DEGs in this sub-network were mainly enriched in Gene Ontology terms related to cell proliferation. Conclusion: EGFR, KDR, FN1, TGFBI and PCNA may interact with VEGFA to play important roles in NSCLC tumorigenesis. These genes and corresponding proteins may have the potential to be used as the targets for either diagnosis or treatment of patients with NSCLC. [ABSTRACT FROM AUTHOR]

Low dose CT (LDCT) images are often significantly degraded by severely increased mottled noise/artifacts, which can lead to lowered diagnostic accuracy in clinic. The nonlocal means (NLM) filtering can effectively remove mottled noise/artifacts by utilizing large-scale patch similarity information in LDCT images. But the NLM filtering application in LDCT imaging also requires high computation cost because intensive patch similarity calculation within a large searching window is often required to be used to include enough structure-similarity information for noise/artifact suppression. To improve its clinical feasibility, in this study we further optimize the parallelization of NLM filtering by avoiding the repeated computation with the row-wise intensity calculation and the symmetry weight calculation. The shared memory with fast I/O speed is also used in row-wise intensity calculation for the proposed method. Quantitative experiment demonstrates that significant acceleration can be achieved with respect to the traditional straight pixel-wise parallelization. [ABSTRACT FROM AUTHOR]

Median type filters coupled with the Laplacian distribution assumption have shown a high efficiency in suppressing impulse noise. We however demonstrate in this paper that the Gaussian distribution assumption is more preferable than Laplacian distribution assumption in suppressing impulse noise, especially for high noise densities. This conclusion is supported by numerical experiments with different noise densities and filter models. [ABSTRACT FROM AUTHOR]

Low-dose computed tomography (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts. These artifacts are often hard to suppress without introducing tissue blurring effects. In this paper, we propose to process LDCT images using a novel image-domain algorithm called “artifact suppressed dictionary learning (ASDL).” In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries. The streak artifacts are cancelled via a discriminative sparse representation operation based on these dictionaries. Then, a general dictionary learning processing is applied to further reduce the noise and residual artifacts. Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems. [ABSTRACT FROM AUTHOR]

The moment invariants have been widely used as feature descriptors against both blur degradation and similarity transformation in pattern recognition. Since Pseudo-Zernike moments outperform other moments in feature matching and object recognition, we propose a new set of Pseudo-Zernike moment invariants holding for blur and similarity transformation simultaneously in this paper to improve the match results. In order to construct the proposed invariants, we also establish the relationship between the Pseudo-Zernike moments of the distorted images and those of the original images with respect to circularly symmetric point spread function. The experimental results are presented to confirm that compared to the other moment invariants, the proposed combined invariants have better invariance and more powerful discriminating ability in terms of pattern recognition accuracy, especially when the images contain noises. [ABSTRACT FROM PUBLISHER]

Abstract: In CT (computed tomography), reconstruction from undersampling projection data is often ill-posed and suffers from severe artifact in the reconstructed images. To overcome this problem, this paper proposes a sinogram inpainting method based on recently rising sparse representation technology. In this approach, a dictionary learning based inpainting is used to estimate the missing projection data. The final image can be reconstructed by the analytic filtered back projection (FBP) reconstruction. We conduct experiments using both simulated and real phantom data. Compared to the comparative interpolation method, visual and numerical results validate the clinical potential of the proposed method. [Copyright &y& Elsevier]

The processing of the images simultaneously degraded by blur and affine transformation has become a key task in many applications and many novel methods are designed specifically for it in which the moment-based methods play an important role. However, the existing moment-based methods all resort to non-orthogonal moments invariants which have problem of information redundancy and are sensitive to noise. In this paper, we construct a new set of combined invariants of orthogonal Legendre moments which hold for blur and affine transformation together. The experimental results show that the proposed invariants have better discriminative power and robustness to noise with the comparison to other invariants. [ABSTRACT FROM AUTHOR]

An effective approach termed Recursive Gaussian Maximum Likelihood Estimation (RGMLE) is developed in this paper to suppress 2-D impulse noise. And two algorithms termed RGMLE-C and RGMLE-CS are derived by using spatially-adaptive variances, which are respectively estimated based on certainty and joint certainty & similarity information. To give reliable implementation of RGMLE-C and RGMLE-CS algorithms, a novel recursion stopping strategy is proposed by evaluating the estimation error of uncorrupted pixels. Numerical experiments on different noise densities show that the proposed two algorithms can lead to significantly better results than some typical median type filters. Efficient implementation is also realized via GPU (Graphic Processing Unit)-based parallelization techniques. [ABSTRACT FROM AUTHOR]

• An efficient early fusion structure is designed to fuse the features of native CTP, CBF, CBV, MTT and Tmax for accurate ischemic stroke prediction. • The attention mechanism is firstly employed into multi-scale convolutional extractor to capture the context information. • Model was evaluated on a real clinical dataset which was collected from multiple stroke centers. • The dataset uses two kinds of follow-up images: Non-contrast CT and MRI DWI. [Display omitted] Background: Acute ischemic stroke is one of the leading death causes. Delineating stoke infarct core in medical images plays a critical role in optimal stroke treatment selection. However, accurate estimation of infarct core still remains challenging because of 1) the large shape and location variation of infarct cores; 2) the complex relationships between perfusion parameters and final tissue outcome. Methods: We develop an encoder-decoder based semantic model, i.e., Ischemic Stroke Prediction Network (ISP-Net), to predict infarct core after thrombolysis treatment on CT perfusion (CTP) maps. Features of native CTP, CBF (Cerebral Blood Flow), CBV (Cerebral Blood Volume), MTT (Mean Transit Time), Tmax are generated and fused with five-path convolutions for comprehensive analysis. A multi-scale atrous convolution (MSAC) block is firstly put forward as the enriched high-level feature extractor in ISP-Net to improve prediction accuracy. A retrospective dataset which is collected from multiple stroke centers is used to evaluate the performance of ISP-Net. The gold standard infarct cores are delineated on the follow-up scans, i.e., non-contrast CT (NCCT) or MRI diffusion-weighted image (DWI). Results: In clinical dataset cross-validation, we achieve mean Dice Similarity Coefficient (DSC) of 0.801, precision of 81.3 % , sensitivity of 79.5 % , specificity of 99.5 % , Area Under Curve (AUC) of 0.721. Our approach yields better outcomes than several advanced deep learning methods, i.e., Deeplab V3, U-Net++, CE-Net, X-Net and Non-local U-Net, demonstrating the promising performance in infarct core prediction. No significant difference of the prediction error is shown for the patients with follow-up NCCT and follow-up DWI (P >0.05). Conclusion: This study provides an approach for fast and accurate stroke infarct core estimation. We anticipate the prediction results of ISP-Net could offer assistance to the physicians in the thrombolysis or thrombectomy therapy selection. [ABSTRACT FROM AUTHOR]

Orthogonal 2D cervical vertebra (C-vertebra) X-ray images have the advantages of high imaging efficiency, low radiation risk, easy operation and low cost for rapid primary clinical diagnoses. Especially in emergency departments, this technique is known to be significantly useful in triage for trauma patients. However, the technique can only provide overlapping anatomic information from limited projection views and is unable to visually exhibit full-view anatomy and precise stereo structures without further CT examination. To promote "once is enough" for visualizing 3D anatomy & structures and reducing repetitive radiation as much as possible, we proposed X-CTRSNet for 2D X-ray images. This is the first powerful work that simultaneously and accurately enables 3D C-vertebra CT reconstruction and segmentation directly from orthogonally anteroposterior- and lateral-view 2D X-ray images. X-CTRSNet combines the reciprocally coupled SpaDRNet for reconstruction & MulSISNet for segmentation, and a RSC Learning for tasks consistency. The experiment shows that X-CTRSNet successfully reconstructs and segments the 3D C-vertebra CT from the 2D X-ray images with a PSNR of 24.58 dB, an SSIM of 0.749, and an average Dice of 80.44%. All these findings reveal the great potential of X-CTRSNet in clinical imaging and diagnosis to facilitate emergency triage by enabling precise 3D reconstruction and segmentation on 2D X-ray images. [ABSTRACT FROM AUTHOR]

Abstract: As is known, low-dose computed tomography (CT) image can be severely degraded by the excessive quantum noise. In order to address this problem, we firstly present a novel anisotropic diffusion weighted prior applied in Bayesian-based statistical sinogram smoothing approach in this work. Then, the reconstructed image is obtained by the filtered back-projection (FBP) from the smoothed projection data. Compared with the traditional priors, the proposed novel prior can adaptively adjust the smoothing degree according to the sinogram characteristic. The effectiveness and feasibility of the proposed approach are validated by both digital phantom and clinical data experiments. The superiority of the presented method over other methods is also quantitatively studied by resolution–noise tradeoff curves and signal to noise ratio (SNR). The experimental results indicate that the developed approach has the excellent performance for low-dose CT imaging. [Copyright &y& Elsevier]

Purpose: In computed tomography, metallic objects in the scanning field create the so-called metal artifacts in the reconstructed images. Interpolation-based methods for metal artifact reduction (MAR) replace the metal-corrupted projection data with surrogate data obtained from interpolation using the surrounding uncorrupted sinogram information. Prior-based MAR methods further improve interpolation-based methods by better estimating the surrogate data using forward projections from a prior image. However, the prior images in most existing prior-based methods are obtained from segmented images and misclassification in segmentation often leads to residual artifacts and tissue structure loss in the final corrected images. To overcome these drawbacks, the authors propose a fusion scheme, named fusion prior-based MAR (FP-MAR). Methods: The FP-MAR method consists of (i) precorrect the image by means of an interpolation-based MAR method and an edge-preserving blur filter; (ii) generate a prior image from the fusion of this precorrected image and the originally reconstructed image with metal parts removed; (iii) forward project this prior image to guide the estimation of the surrogate data using well-developed replacement techniques. Results: Both simulations and clinical image tests are carried out to show that the proposed FP-MAR method can effectively reduce metal artifacts. A comparison with other MAR methods demonstrates that the FP-MAR method performs better in artifact suppression and tissue feature preservation. Conclusions: From a wide range of clinical cases to which FP-MAR has been tested (single or multiple pieces of metal, various shapes, and sizes), it can be concluded that the proposed fusion based prior image preserves more tissue information than other segmentation-based prior approaches and can provide better estimates of the surrogate data in prior-based MAR methods. [ABSTRACT FROM AUTHOR]

Abstract: Fatigue, creep, oxidation or their combinations have long been recognized as the principal mechanisms in many high-temperature failures in power plant components, turbine engines, and exhaust systems in vehicles. Depending on the specific materials and loading conditions and temperature, the role of each damage mechanism may change significantly, ranging from independent development to competing and combined creep–fatigue, fatigue–oxidation, and creep–fatigue–oxidation. In this paper a new linear superposition theory is proposed to model the cycle-dependent and time-dependent creep–fatigue–oxidation crack growth phenomena. The model can be reduced to creep–fatigue and fatigue–oxidation crack growth models previously developed by the authors as well as, under some assumptions, the current widely used linear superposition theory. The limits of the current superposition theory and the advantages of the new theory are clearly demonstrated with several worked examples. A general probabilistic analysis procedure is also proposed by introducing the uncertainties of parameters in fatigue, creep, and oxidation crack growth laws with the help of the Monte Carlo simulation. [Copyright &y& Elsevier]

Geometric distortions are generally simple and effective attacks for many watermarking methods. They can make detection and extraction of the embedded watermark difficult or even impossible by destroying the synchronization between the watermark reader and the embedded watermark. In this paper, we propose a new watermarking approach which allows watermark detection and extraction under affine transformation attacks. The novelty of our approach stands on a set of affine invariants we derived from Legendre moments. Watermark embedding and detection are directly performed on this set of invariants. We also show how these moments can be exploited for estimating the geometric distortion parameters in order to permit watermark extraction. Experimental results show that the proposed watermarking scheme is robust to a wide range of attacks: geometric distortion, filtering, compression, and additive noise. [ABSTRACT FROM PUBLISHER]

Abstract: Sequency-ordered complex Hadamard transform (SCHT) is a novel discrete orthogonal transform and has been applied to spectrum analysis and image watermarking. In this paper, a novel SCHT based shape descriptors, termed SCHDs, is proposed and applied to image retrieval. SCHDs first expresses the shape boundary as a 1-D complex signal, and SCHT is then applied to it. The resulting transform coefficients are used to characterize the shapes. This description scheme is applied to shape based image retrieval, and the experimental results show that the proposed SCHDs outperforms Fourier descriptors (FDs) and wavelet transform combined with Fourier transform based method (WFDs). [Copyright &y& Elsevier]

The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the overall better. [ABSTRACT FROM AUTHOR]

Discrete orthogonal moments have been recently introduced in the field of image analysis. It was shown that they have better image representation capability than the continuous orthogonal moments. One problem concerning the use of moments as feature descriptors is the high computational cost, which may limit their application to the problems where the online computation is required. In this paper, we present a new approach for fast computation of the 2-D Tchebichef moments. By deriving some properties of Tchebichef polynomials, and using the image block representation for binary images and intensity slice representation for grayscale images, a fast algorithm is proposed for computing the moments of binary and grayscale images. The theoretical analysis shows that the computational complexity of the proposed method depends upon the number of blocks of the image, thus, it can speed up the computational efficiency as far as the number of blocks is smaller than the image size. [ABSTRACT FROM AUTHOR]

Abstract: In positron emission tomography, transmission scans can be performed to estimate attenuation correction factors (ACFs) which are in turn used to correct the emission scans. And such an attenuation correction is crucial for quantitatively accurate PET reconstructions. The prior model used in this work was based on our assumption that the attenuation values vary smoothly, with occasional discontinuities at anatomical borders. And on the other hand, long acquisition or scan times, although alleviating the noise effect of the count-limited scans, are blamed for patient uncomfortableness and movements. So, transmission tomography often suffers from the noise effect because of the short scan time. Thus reconstruction which is capable of overcoming the noise effect is highly needed. In this article, we apply the nonlocal prior Bayesian reconstruction method in PET transmission tomography. Resulting experimentations validate that the reconstructions using the nonlocal prior can reconstruct better transmission images and overcome noise effect even when the scan time is relatively short. [Copyright &y& Elsevier]

Abstract: How to reduce the radiation dose delivered to the patients has always been a important concern since the introduction of computed tomography (CT). Though clinically desired, low-dose CT images can be severely degraded by the excessive quantum noise under extremely low X-ray dose circumstances. Bayesian statistical reconstructions outperform the traditional filtered back-projection (FBP) reconstructions by accurately expressing the system models of physical effects and the statistical character of the measurement data. This work aims to improve the image quality of low-dose CT images using a novel AW nonlocal (adaptive-weighting nonlocal) prior statistical reconstruction approach. Compared to traditional local priors, the proposed prior can adaptively and selectively exploit the global image information. It imposes an effective resolution-preserving and noise-removing regularization for reconstructions. Experimentation validates that the reconstructions using the proposed prior have excellent performance for X-ray CT with low-dose scans. [Copyright &y& Elsevier]

• An algorithm for dissected aorta segmentation based on 3-D and 2-D convolutional neural networks. • A 3-D CNN model identifies the proximal and distal regions of the dissected aorta data. • A 2-D CNN model for aorta segmentation which incorporates boundary information. Aortic dissection is a severe cardiovascular pathology in which an injury of the intimal layer of the aorta allows blood flowing into the aortic wall, forcing the wall layers apart. Such situation presents a high mortality rate and requires an in-depth understanding of the 3-D morphology of the dissected aorta to plan the right treatment. An accurate automatic segmentation algorithm is therefore needed. In this paper, we propose a deep-learning-based algorithm to segment dissected aorta on computed tomography angiography (CTA) images. The algorithm consists of two steps. Firstly, a 3-D convolutional neural network (CNN) is applied to divide the 3-D volume into two anatomical portions. Secondly, two 2-D CNNs based on pyramid scene parsing network (PSPnet) segment each specific portion separately. An edge extraction branch was added to the 2-D model to get higher segmentation accuracy on intimal flap area. The experiments conducted and the comparisons made show that the proposed solution performs well with an average dice index over 92%. The combination of 3-D and 2-D models improves the aorta segmentation accuracy compared to 3-D only models and the segmentation robustness compared to 2-D only models. The edge extraction branch improves the DICE index near aorta boundaries from 73.41% to 81.39%. The proposed algorithm has satisfying performance for capturing the aorta structure while avoiding false positives on the intimal flaps. [ABSTRACT FROM AUTHOR]

Abstract: A novel stochastic search method (NSSM) is proposed for the polygonal approximation problem. NSSM incorporates the ranking selection scheme, which is initially developed for solving the premature convergence of genetic algorithms (GAs), into the traditional split-and-merge technique. For avoiding getting trapped in a local optimum, NSSM randomly selects the splitting points and the merging points and determines the selection probability using the ranking selection scheme. Three groups of digital curves, including the synthesized benchmark curves and the real image curves, are used to test the performance of NSSM. The experimental results show the higher performance over the other methods including the GA-based methods and the local search methods. [Copyright &y& Elsevier]

Abstract: The method Hankel Total Least Squares (HTLS)-PK, which successfully incorporates prior knowledge of known signal poles into the method HTLS, has been proven to greatly improve the performance for parameter estimation of overlapping peaks of magnetic resonance spectroscopy (MRS) signal. In addition, decimation is also proposed as a way to increase the performance of subspace-based parameter estimation methods in the case of oversampling. Taking advantage of decimation in combination with prior knowledge to estimate the MRS signal parameters, two novel subspace-based parameter estimation methods, HTLSDSumPK and HTLSDStackPK, are presented in this paper. The experimental results and relevant analysis show that the methods HTLSDSumPK, HTLSDStackPK and HTLS-PK are slightly better than the method HTLS at low noise levels; however, the three prior-knowledge-incorporating methods, especially the method HTLSDSumPK, have much better performance than the method HTLS at high noise levels in the terms of robustness, estimated accuracy and computational complexity. Even if some inaccuracy of prior knowledge is considered, the method HTLSDSumPK also shows some advantages. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a set of discrete orthogonal functions known as dual Hahn polynomials. The Tchebichef and Krawtchouk polynomials are special cases of dual Hahn polynomials. The dual Hahn polynomials are scaled to ensure the numerical stability, thus creating a set of weighted orthonormal dual Hahn polynomials. They are allowed to define a new type of discrete orthogonal moments. The discrete orthogonality of the proposed dual Hahn moments not only ensures the minimal information redundancy, but also eliminates the need for numerical approximations. The paper also discusses the computational aspects of dual Hahn moments, including the recurrence relation and symmetry properties. Experimental results show that the dual Hahn moments perform better than the Legendre moments, Tchebichef moments, and Krawtchouk moments in terms of image reconstruction capability in both noise-free and noisy conditions. The dual Hahn moment invariants are derived using a linear combination of geometric moments. An example of using the dual Hahn moment invariants as pattern features for a pattern classification application is given. [Copyright &y& Elsevier]

Abstract: Discrete orthogonal moments such as Tchebichef moments have been successfully used in the field of image analysis. However, the invariance property of these moments has not been studied mainly due to the complexity of the problem. Conventionally, the translation and scale invariant functions of Tchebichef moments can be obtained either by normalizing the image or by expressing them as a linear combination of the corresponding invariants of geometric moments. In this paper, we present a new approach that is directly based on Tchebichef polynomials to derive the translation and scale invariants of Tchebichef moments. Both derived invariants are unchanged under image translation and scale transformation. The performance of the proposed descriptors is evaluated using a set of binary characters. Examples of using the Tchebichef moments invariants as pattern features for pattern classification are also provided. [Copyright &y& Elsevier]

Abstract: In this paper, a new inter-iteration filtering scheme based on diffusion Maximum a Posteriori (MAP) estimation for Positron emission tomography (PET) image reconstruction is proposed. This is achieved by gaining the insights into the classical MAP iteration (e.g. the ‘one-step-late’ algorithm, OSL) and the several well-established approximations to the diffusion process. We show that such a new technique allows additional insight and sufficient flexibility for further investigations on some nonlinear filters based reconstruction algorithms. In particular, we suggest the bilateral filter as a nice application in which image smoothing while edge preserving can be readily obtained by the combination of the range and domain filters. The feasibility and efficiency of the proposed method are verified in the substantiating experiments conducted on both the computer simulated and real clinical data. [Copyright &y& Elsevier]

Abstract: This work deals with the treatment planning optimization for intravascular brachytherapy (IVB) in peripheral arteries. The objective is both to quantitatively study the validity of different hypotheses required for a reliable application of the treatment with current techniques, and to contribute to the definition and the specification of a new optimized procedure taking into account the actual patient''s vessel geometry. The detection of vascular luminal surface was performed by an image analysis process, i.e., virtual active navigation, applied to standard CT data. Dose distribution was calculated according to the formalism proposed and recommended by the AAPM in TG43 and TG60. A method combining simulated annealing and BFGS algorithms was applied to optimize the parameters associated with the dwell points such as their number, positions, and dwell times. Dose-surface histogram (DSH) was used to evaluate the dose distribution results. Four levels of accuracy in target surface description were tested. The application of this optimization method to four different CT data sets including patient data, phantom and animal models showed that the treatment plan can be improved when the actual vessel geometry has been taken into account. [Copyright &y& Elsevier]