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In order to improve the target recognition effect of small UAV (unmanned aerial vehicle) remote sensing image, this paper proposes a new super-resolution reconstruction method based on the recurrent convolutional network, which can achieve different degrees of super-resolution effect by controlling the number of cycles. Moreover, it can control the number of iterations of small UAVs with different degrees of blur and can be better adapted to the recognition scenarios of UAVs. In addition, this paper studies the target recognition method of small UAV remote sensing image, combines fuzzy clustering method to construct the intelligent remote sensing image target recognition model, combines it with the UAV structure, realizes remote sensing recognition by UAV, and designs experiments to analyze the effect of remote sensing recognition. Further, this paper improves the recognition algorithm and positioning algorithm of remote sensing image, so that recognition and positioning of UAV video remote sensing image can get better results. Finally, this paper verifies the performance of the system through simulation experiments. The research results show that the method proposed in this paper has certain reliability.

Universal challenge lies in torque feedback accuracy for steer-by-wire systems, especially on uneven and low-friction road. Therefore, this paper proposes a fusion method based on Kalman filter that combines a dynamics-reconstruction method and disturbance observer-based method. The dynamics- reconstruction method is designed according to the vehicle dynamics and used as the prediction model of the Kalman filter. While the disturbance observer-based method is performed as an observer model of the Kalman filter. The performance of all three methods is comprehensively evaluated in a hardware-in-the-loop system. Experimental results show that the proposed fusion method outperforms dynamics reconstruction method and disturbance observer-based method. Specifically, compared with the dynamics-reconstruction method, the root mean square error is reduced by 36.58% at the maximum on the flat road. Compared with the disturbance observer-based method, the root mean square error is reduced by 39.11% at the maximum on different-friction and uneven road.

This paper analyzes how motherhood and labor market status interact in their relationship to women’s affective well-being using Day Reconstruction Method data from the United Kingdom Time Use Survey 2014–2015. The dataset contains information on how much time respondents spent on their various daily activities as well as how much they enjoyed each episode. This information is aggregated into a measure of an individual’s affective well-being on a given day by calculating the mean of the reported enjoyments weighted by the duration of the respective episode. Results indicate a positive and significant correlation between motherhood and affective well-being. However, this relationship decreases in magnitude and even becomes insignificant in some specifications when controlling for labor market status. This could indicate that an increase in the likelihood of being in a labor market status associated with higher affective well-being mediates the positive relationship between motherhood and affective well-being. Women working part-time, self-employed women, homemakers and women on maternity leave are shown to have higher affective well-being than full-time employees. Furthermore, the time spent on and enjoyment experienced in employment related activities appears to be a key driver of many results in this study. Contrasting results in specifications using a measure of life satisfaction highlight the importance of considering affective as well as cognitive well-being.

Objectives Multiple b-value gas diffusion-weighted MRI (DW-MRI) enables non-invasive and quantitative assessment of lung morphometry, but its long acquisition time is not well-tolerated by patients. We aimed to accelerate multiple b-value gas DW-MRI for lung morphometry using deep learning. Methods A deep cascade of residual dense network (DC-RDN) was developed to reconstruct high-quality DW images from highly undersampled k-space data. Hyperpolarized 129Xe lung ventilation images were acquired from 101 participants and were retrospectively collected to generate synthetic DW-MRI data to train the DC-RDN. Afterwards, the performance of the DC-RDN was evaluated on retrospectively and prospectively undersampled multiple b-value 129Xe MRI datasets. Results Each slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms. For the retrospective test data, the DC-RDN showed significant improvement on all quantitative metrics compared with the conventional reconstruction methods (p < 0.05). The apparent diffusion coefficient (ADC) and morphometry parameters were not significantly different between the fully sampled and DC-RDN reconstructed images (p > 0.05). For the prospectively accelerated acquisition, the required breath-holding time was reduced from 17.8 to 4.7 s with an acceleration factor of 4. Meanwhile, the prospectively reconstructed results showed good agreement with the fully sampled images, with a mean difference of −0.72% and −0.74% regarding global mean ADC and mean linear intercept (Lm) values. Conclusions DC-RDN is effective in accelerating multiple b-value gas DW-MRI while maintaining accurate estimation of lung microstructural morphometry, facilitating the clinical potential of studying lung diseases with hyperpolarized DW-MRI. Key Points • The deep cascade of residual dense network allowed fast and high-quality reconstruction of multiple b-value gas diffusion-weighted MRI at an acceleration factor of 4. • The apparent diffusion coefficient and morphometry parameters were not significantly different between the fully sampled images and the reconstructed results (p > 0.05). • The required breath-holding time was reduced from 17.8 to 4.7 s and each slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08126-y.

Flow is a mental state of thorough absorption and concentration in an activity, in which intrinsic motivation and enjoyment are maximized, and the individual achieves optimal performance. This study investigated how daily flow experiences during parents' interactions with their children contribute to parental well-being. The Day Reconstruction Method was completed by 832 employed Israeli parents of children aged 2 to 12, who reconstructed their experiences on three different working days over three weeks, and rated their levels of flow, as well as positive and negative affect for each experience. Participants also completed measures of subjective well-being and parental satisfaction and efficacy. The results indicated that parents' flow experiences during interactions with their children were positively related to parents' positive affect, self-efficacy and satisfaction, and greater subjective well-being. The findings underscore the importance for parental well-being of daily optimal experiences with their children.

Based on the parent austenite orientation reconstruction method, it is aimed to reveal the origination of high angle grain boundaries (HAGBs) and its relationship with ductility of H13 steel. The orientation relationship between martensite and parent austenite of quenched H13 samples was (123.5°, 9.3°, 192.5°), which agreed with the Kurdumov–Sachs relationship. The variant distribution of quenched samples was dominated by close-packed plane group, and its high length fraction of V1/V2 inter-variant boundaries of calculated 62.6% was mainly contributed to HAGBs (> 45°). When the quenched H13 samples underwent the pre-tempering treatment, their density of HAGBs (> 45°) notably increased from 1.33 to 2.39 μm−1, which improved its total elongation from 8.3% to 11.5%. Compared with the quenched H13 samples, the length fraction of V1/V2 inter-variant boundaries of H13 samples with pre-tempering for 5, 10 and 60 min was reduced by 6.7%, 7.0% and 7.5%, respectively. During pre-tempering treatment, V1/V3&V5 variant pairs, etc., merged V1/V2 variant pair by strain-induced grain boundary migration, which decreased the length fraction of V1/V2 inter-variant boundaries by 7.0%. The pre-tempering treatment significantly increased HAGBs (> 45°) of H13 samples by sub-grains coarsening and strain-induced grain boundary migration mechanism.

AIM: Analysing Level of Evidence (LOE) provides an insight to evidence-based medicine (EBM). The aim of our study was to evaluate and analyse trends in Levels of Evidence (LOE) in Journal of Maxillofacial and Oral Surgery (MAOS) since inception, i.e. December 2009 along with categorization into subtopics. METHODOLOGY: LOE for each article was determined according to modified American Society of Plastic Surgeons (ASPS) scale and National Health and Medical Research Council (NHMRC) Evidence Hierarchy. RESULTS: A total of 1264 articles were included in the final analysis, out of which high-quality evidence (Level A) accounted for 7% of the journal. The percentage of Level I/II (Level A) has increased from 2.09% in 2009/2010 to 12.74% in 2019/2020, representing a promising trend toward higher-quality research in just 10 years. Case reports and narrative reviews with Level of Evidence value “D” account the highest number (36%) of all the published articles. The majority of articles fell under Class 2 (Maxillofacial pathology) classification (35%) highlighting myriad of articles covering pathologies and various reconstruction methods, followed by trauma (16%). CONCLUSION: The status of LOE and categorizing of published articles are the first step to audit and quantify the nature of literature published by JMOS and may further help in refining the quality of research jointly by the researchers and the editorial board.

Based on C-LSAT2.0, using high- and low-frequency components reconstruction methods, combined with observation constraint masking, a reconstructed C-LSAT2.0 with 756 ensemble members from the 1850s to 2018 has been developed. These ensemble versions have been merged with the ERSSTv5 ensemble dataset, and an upgraded version of the CMST-Interim dataset with 5° × 5° resolution has been developed. The CMST-Interim dataset has significantly improved the coverage rate of global surface temperature data. After reconstruction, the data coverage before 1950 increased from 78%–81% of the original CMST to 81%–89%. The total coverage after 1955 reached about 93%, including more than 98% in the Northern Hemisphere and 81%–89% in the Southern Hemisphere. Through the reconstruction ensemble experiments with different parameters, a good basis is provided for more systematic uncertainty assessment of C-LSAT2.0 and CMST-Interim. In comparison with the original CMST, the global mean surface temperatures are estimated to be cooler in the second half of 19th century and warmer during the 21st century, which shows that the global warming trend is further amplified. The global warming trends are updated from 0.085 ± 0.004°C (10 yr)−1 and 0.128 ± 0.006°C (10 yr)−1 to 0.089 ± 0.004°C (10 yr)−1 and 0.137 ± 0.007°C (10 yr)−1, respectively, since the start and the second half of 20th century.

How to effectively utilize inter-frame redundancies is the key to improve the accuracy and speed of video super-resolution reconstruction methods. Previous methods usually process every frame in the whole video in the same way, and do not make full use of redundant information between frames, resulting in low accuracy or long reconstruction time. In this paper, we propose the idea of reconstructing key frames and non-key frames respectively, and give a video super-resolution reconstruction method based on deep back projection and motion feature fusion. Key-frame reconstruction subnet can obtain key frame features and reconstruction results with high accuracy. For non-key frames, key frame features can be reused by fusing them and motion features, so as to obtain accurate non-key frame features and reconstruction results quickly. Experiments on several public datasets show that the proposed method performs better than the state-of-the-art methods, and has good robustness.

BACKGROUND: Management of complex wounds of the lower extremity with concomitant Achilles tendon injury is a challenging situation for orthopaedic surgeons and plastic surgeons. The objective of the current study was to evaluate the clinical outcome of chronic open defects of the Achilles tendon with composite tissue loss. We have performed single stage reconstruction using the central segment of the proximal part of the Achilles tendon as turn-down flap and was covered immediately with reverse sural flap. MATERIALS AND METHODS: Between March 2017 and February 2020, five cases of chronic open composite Achilles tendon defects which were treated by a single stage reconstruction method of “Repair and Flap technique” were included in this study. The patient with the defect for more than 4 weeks duration and had substance loss of Achilles tendon together with loss of overlying skin and soft tissue was included the current study. RESULTS: All the flaps survived and healed well, providing stable coverage of the wound. The mean operative duration, including flap elevation, definitive flap inset and donor-site coverage was 98 min (range 90–120 min). Focal areas of skin graft loss were seen in two patients which healed with conservative management. The functional results evaluation was performed with The Achilles Tendon Total Rupture Score (ATRS). The mean Achilles Tendon Total Rupture Score (ATRS) was 70 (range 65–76) CONCLUSION: To conclude, use of proximal turned down flap and coverage with reverse sural flap can be opted as a first option for the management of chronic open wounds with composite defects of the Achilles tendon. “Repair and Flap technique” will be a useful method of reconstruction in centres with limited resources for microsurgical flap. However, a multicenter study with more number of patients are required to further analyse this method.

A complete digital tooth model with both the dental crown and root is of great importance for computer-aided orthodontic treatment. This paper first proposes an automatic segmentation method for complete tooth models with both the crown and reconstructed root based on multimodal data. With the laser-scanned crown mesh and cone-beam computed tomography (CBCT) data of a patient, we propose an improved iterative closest point algorithm and convex hull selection method to obtain the initial contour and slice for segmentation. Based on the initialization, we propose an improved level set method with the shape prior, named LSS, to segment the root of the tooth slice by slice. After segmentation, we reconstruct the root model and replace the crown part with the scanned crown model to solve the occlusal problem. The experiments demonstrate that our method can obtain tooth models from CBCT automatically and accurately.

The majority of cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction approaches based on machine learning aim to separate those subpopulations in a sample and reconstruct their evolutionary history. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated by multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction (MOBSTER) that combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show this method is more robust and accurate than current techniques in single sample, multi-region and longitudinal data. This approach minimizes the confounding factors of non-evolutionary methods, leading to more accurate recovery of the evolutionary history of human cancers.

Craniofacial reconstruction (CFR) has been widely used to produce the facial appearance of an unidentified skull in the realm of forensic science. Many studies have indicated that the computerized CFR approach is fast, flexible, consistent and objective in comparison to the traditional manual CFR approach. This paper presents a computerized CFR system called CFRTools, which features a CFR method based on a statistical shape model (SSM) of living human head models. Given an unidentified skull, a geometrically-similar template skull is chosen as a template, and a non-registration method is used to improve the accuracy of the construction of dense corresponding vertices through the alignment of the template and the unidentified skull. Generalized Procrustes analysis (GPA) and principal component analysis (PCA) are carried out to construct the skull and face SSMs. The sex of the unidentified skull is then predicted based on skull SSM and centroid size, rather than geometric measurements based on anatomical landmarks. Furthermore, a craniofacial morphological relationship which is learnt from the principal component (PC) scores of the skull and face dataset is used to produce a possible reconstructed face. Finally, multiple possible reconstructed faces for the same skull can further be recreated based on adjusting the PC coefficients. The experimental results show that the average rate of sex classification is 97.14% and the reconstructed face of the unidentified skull can be produced. In addition, experts’ understanding and experience can be harnessed in production of face variations for the same skull, which can further be used as a reference for portraiture creation.

Background Proximal gastrectomy (PG) has become an increasingly preferred procedure for early cancer in the upper third of the stomach, owing to reportedly superior quality of life (QOL) after PG when compared with total gastrectomy. However, various methods of reconstruction have currently been proposed. We compared the postoperative QOL among the three different reconstruction methods after PG using the Postgastrectomy Syndrome Assessment Scale-45 (PGSAS-45) questionnaire. Methods Post Gastrectomy Syndrome Assessment Study (PGSAS), a nationwide multi-institutional survey, was conducted to evaluate QOL using the PGSAS-45 among various types of gastrectomy. Of the 2,368 eligible data from the PGSAS survey, data from 193 patients who underwent PG were retrieved and used in the current study. The PGSAS-45 consists of 45 items including 22 original gastrectomy specific items in addition to the SF-8 and GSRS. These were consolidated into 19 main outcome measures pertaining postgastrectomy symptoms, amount of food ingested, quality of ingestion, work, and level of satisfaction for daily work, and the three reconstruction methods (n = 193; 115 esophago-gastrostomy [PGEG], 34 jejunal interposition [PGJI], and 44 jejunal pouch interposition [PGJPI]) were compared using PGSAS-45. Results Size of the remnant stomach was significantly larger in PGEG, and significantly smaller in PGJI and PGJPI (P P = 0.047–0.076). Conclusion PGJPI appears to be the most favorable of the three reconstruction methods after PG especially when the size of remnant stomach is rather small. Trial registration number UMIN-CTR #000002116 entitled as “A study to observe correlation between resection and reconstruction procedures employed for gastric neoplasms and development of postgastrectomy syndrome”

This study aimed to analyze the morphology of the anterior femoral condyle using a quantitative three-dimensional reconstruction method. The morphological data were compared between genders. Computed tomography scans of femurs were taken from 90 healthy subjects and then reconstructed in 3D modeling software. Coaxial cutting planes were created at 10° increments to measure the lateral and medial anterior condylar heights (LACH and MACH, respectively), lateral and medial trochlear groove widths (LTW and MTW, respectively), and for trochlear groove tracking. The absolute values and normalized data were compared between male and female subjects. The sulcus angle and deepest point of the trochlear groove at each cross-section were also analyzed to determine the differences in the depth of the trochlear groove. The absolute dimensions of LACH, MACH, LTW, and MTW were significantly smaller in the female subjects, by 10.5%, 36.9%, 10.3%, and 11.0%, respectively, than in the males (p

In this paper, we make an improvement on the conventional visual hull reconstruction method that runs on a single consumer graphics card. The target application is a high-precision diamond inclusion reconstruction system. One major contribution of this paper is an evaluation system of voxels for high-precision reconstruction. In contrast to existing approaches, it allows us to reconstruct the thin structure of the diamond inclusion. Based on this, we obtain a significant improvement in reconstruction precision, especially for thin inclusion.

In this paper, we consider the inverse acoustic scattering problem by an unbounded rough surface. A direct imaging method is proposed to reconstruct the rough surfaces from scattered-field data for incident plane waves and the performance analysis is also presented. The reconstruction method is very robust to noises of measured data and does’t need to know the type of the boundary conditions of the surfaces in advance. Finally, numerical examples are carried out to illustrate that our method is fast, accurate and stable even for the case of multiple-scale profiles.

To better understand the psychological and physiological basis of human emotion, increasing interest has been drawn towards ambulatory recordings of emotion-related data beyond the laboratories. By employing smartphones-based ambulatory assessment and wrist-worn physiological recording devices, the Daily Ambulatory Psychological and Physiological recording for Emotion Research (DAPPER) dataset provides momentary self-reports and physiological data of people’s emotional experiences in their daily life. The dataset consists of ambulatory psychological recordings from 142 participants and physiological recordings from 88 of them over five days. Both the experience sampling method (ESM) and the day reconstruction method (DRM) were employed to have a comprehensive description of the participants’ daily emotional experiences. Heart rate, galvanic skin response, and three-axis acceleration were recorded during the day time. By including multiple types of physiological and self-report data at a scale of five days with 100+ participants, the present dataset is expected to promote emotion researches in real-life, daily settings., Measurement(s) Heart Rate • Galvanic Skin Response • Emotion Technology Type(s) photoplethysmography • Surface Electrode • Day Reconstruction Method • Experience Sampling Method Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13803185

The ICT revolution has impacted the way diasporic groups and individuals communicate and interact with one another. Diasporas are now fueled by unlimited flows of digital contents generated by daily activities or sudden historical events. As a natural result, the science of migration has evolved just as much as its own subject of research. Thus, dedicated branches of research like digital diasporas emerge at the crossroad between fields of social and computational sciences. Thereupon, new types of multi-scale reconstruction methods are developed to investigate the collective shapes of digital diasporas. They allow the researchers to focus on individual interactions before visualizing their global structures and dynamics. In this paper, we present three different multi-scale reconstruction methods applied to reveal the scientific landscape of digital diasporas and to explore the history of an extinct online collective of Moroccan migrants.

To investigate how nodule size, nodule density, scan dose, slice thickness and reconstruction methods affect the performance of a deep learning (DL) model for detection of pulmonary nodules in phantom CT scans. Spherical lung nodule phantoms of two different densities (− 630 HU and + 100 HU) and five different sizes (3, 5, 8, 10, and 12 mm) were inserted into an anthropomorphic chest phantom. CT data were scanned and reconstructed using three different tube current (10, 50, 200 mAs), two different slice thickness of 1 and 2 mm, four reconstruction methods (FBP-standard (FBP-STD), FBP-Y sharp kernel (FBP-YA), iDose4-standard kernel (iDose4-STD) and iDose4-Y sharp kernel (iDose4-YA). Evaluation of deep learning model focused on detection sensitivity and precision. According to the statistical results from the study, we found that the sensitivity and precision performance depends on the nodule sizes, nodule type, tube current, reconstruction methods and image thickness. Comparing the solid (100 HU) and ground-glass (GGO, − 630 HU) nodule phantoms, solid nodule phantom predictions are rarely affected by tube current, reconstruction methods and nodule sizes. Both sensitivity and precision are close to 100% in all solid nodule phantom prediction cases. While the sensitivity and precision metrics of GGO nodule phantoms change in a wide range from 42.9 to 100%. Larger nodule size and higher tube current gives a better sensitivity and precision for GGO nodule phantoms in most cases. We also analyze the relationships between the image thickness and the reconstruction methods. For 1-mm thickness images, iDose4-STD and FBP-STD shows a better result in both sensitivity and precision metrics. As for 2-mm thickness images, iDose-YA and FBP-YA gain a better performance. The results of this phantom study demonstrated that high stability and flexibility of deep learning model can be used in daily clinical and screening practice.

Flow, transport, mechanical, and fracture properties of porous media depend on their morphology and are usually estimated by experimental and/or computational methods. The precision of the computational approaches depends on the accuracy of the model that represents the morphology. If high accuracy is required, the computations and even experiments can be quite time-consuming. At the same time, linking the morphology directly to the permeability, as well as other important flow and transport properties, has been a long-standing problem. In this paper, we develop a new network that utilizes a deep learning (DL) algorithm to link the morphology of porous media to their permeability. The network is neither a purely traditional artificial neural network (ANN), nor is it a purely DL algorithm, but, rather, it is a hybrid of both. The input data include three-dimensional images of sandstones, hundreds of their stochastic realizations generated by a reconstruction method, and synthetic unconsolidated porous media produced by a Boolean method. To develop the network, we first extract important features of the images using a DL algorithm and then feed them to an ANN to estimate the permeabilities. We demonstrate that the network is successfully trained, such that it can develop accurate correlations between the morphology of porous media and their effective permeability. The high accuracy of the network is demonstrated by its predictions for the permeability of a variety of porous media.

Cleft palate (CP) is one of the most common human birth defects. The routine clinical technique for prenatal diagnosis of CP is ultrasound (US). However, US has many limitations on the diagnosis of CP especially posterior CP. In this work, we employed a 3D super-resolution reconstruction method to reconstruct 3D isotropic volumetric images from a series of 2D images and evaluate its feasibility to the diagnosis of CP. Quantitative comparison between original 2D and the reconstructed 3D images shows that the reconstructed 3D images have better image quality than the original 2D images in terms of SNR (21.48 ± 0.58 vs. 14.80 ± 2.59, p = 0.021), CNR (73.13 ± 10.96 vs. 42.97 ± 6.75, p = 0.022), image quality score (3.4 ± 0.7 vs. 3.0 ± 0.8, p = 0.046), and diagnostic confidence score (3.8 ± 0.4 vs. 3.1 ± 0.9, p = 0.038). Using the findings at birth as standard reference, the sensitivity (SE), specificity (SP), positive and negative predictive values (PPV and NPV), and the accuracy (ACC) of conventional 2D method and the proposed 3D method were calculated, and all participants were correctly identified by the radiologists based on the reconstructed 3D images. There was one participant with fetal cleft lip and palate that were not correctly identified based the original 2D images. Therefore, the reconstructed 3D images can improve the accuracy and efficiency of the prenatal diagnosis of CP. It can serve as a complementary tool for the diagnosis of CP.

The gold standard for the treatment of periocular basal cell carcinoma is surgical resection followed by ophthalmoplastic reconstruction. The highest priority in most cases is the complete histopathologically controlled tumor excision. The histopathological preparation can be carried out in two stages by rapid overnight embedding or intraoperatively by a rapid frozen section procedure. A variety of reconstruction methods enable a customized and in most cases also a cosmetically and functionally attractive defect coverage. Postoperatively, a regularly performed tumor aftercare is essential.

Shape-from-Template (SfT) is the problem of using a shape template to infer the shape of a deformable object observed in an image. The usual case of SfT is ‘Surface’ SfT, where the shape is a 2D surface embedded in 3D, and the image is a 2D perspective projection. We introduce ‘Curve’ SfT, comprising two new cases of SfT where the shape is a 1D curve. The first new case is when the curve is embedded in 2D and the image a 1D perspective projection. The second new case is when the curve is embedded in 3D and the image a 2D perspective projection. We present a thorough theoretical study of these new cases for isometric deformations, which are a good approximation of ropes, cables and wires. Unlike Surface SfT, we show that Curve SfT is only ever solvable up to discrete ambiguities. We present the necessary and sufficient conditions for solvability with critical point analysis. We further show that unlike Surface SfT, Curve SfT cannot be solved locally using exact non-holonomic Partial Differential Equations. Our main technical contributions are two-fold. First, we give a stable, global reconstruction method that models the problem as a discrete Hidden Markov Model. This can generate all candidate solutions. Second, we give a non-convex refinement method using a novel angle-based deformation parameterization. We present quantitative and qualitative results showing that real curve shaped objects such as a necklace can be successfully reconstructed with Curve SfT.

How to reconstruct a dynamic displacement of slender flexible structures is the key technology to develop smart structures and structural health monitoring (SHM), which are beneficial for controlling the structural vibration and protecting the structural safety. In this paper, the displacement reconstruction method based on cubic spline fitting is put forward to reconstruct the dynamic displacement of slender flexible structures without the knowledge of mode-shapes and applied loading. The obtained strains and displacements are compared with the results calculated by ABAQUS to check the method’s validity. It can be found that the proposed method can accurately identify the strains and displacement of slender flexible structures undergoing linear vibrations, nonlinear vibrations, and parametric vibrations. Under the concentrated force, the strains of slender flexible structures will change suddenly along the axial direction. With locally densified measurement points, the present reconstruction method still works well for the strain concentration problem.

Image will be seriously distorted when camera captures the scene through water surface. This will cause the captured image to lose its readability. Although several previous methods have been proposed to resolve this problem, the methods still need improvement. In this paper, we propose a reconstruction method to restore the distorted images based on a twice registration algorithm. Several calculation methods will be applied to update reference image in different registration phase. Firstly, the method uses the mean image of the image sequence as the reference image for the first registration. Due to the reference image may be blurred, a deblurring step has been applied in this paper. To register the distorted image sequence, a non-rigid registration method has been employed based on B-spline method. In addition, a patch search method also is proposed to update the reference image in the second registration because reference image is the key to improve the accuracy of image registration. It is designed to obtain the most similar patches from the image sequence. Compared with the previous methods using the existing database and generated database, the results show that our method performs better than the state-of-the-art methods.

Dosimetry models using preclinical positron emission tomography (PET) data are commonly employed to predict the clinical radiological safety of novel radiotracers. However, unbiased clinical safety profiling remains difficult during the translational exercise from preclinical research to first-in-human studies for novel PET radiotracers. In this study, we assessed PET dosimetry data of six 18F-labelled radiotracers using preclinical dosimetry models, different reconstruction methods and quantified the biases of these predictions relative to measured clinical doses to ease translation of new PET radiotracers to first-in-human studies. Whole-body PET images were taken from rats over 240 min after intravenous radiotracer bolus injection. Four existing and two novel PET radiotracers were investigated: [18F]FDG, [18F]AlF-NOTA-RGDfK, [18F]AlF-NOTA-octreotide ([18F]AlF-NOTA-OC), [18F]AlF-NOTA-NOC, [18F]ENC2015 and [18F]ENC2018. Filtered-back projection (FBP) and iterative methods were used for reconstruction of PET data. Predicted and true clinical absorbed doses for [18F]FDG and [18F]AlF-NOTA-OC were then used to quantify bias of preclinical model predictions versus clinical measurements. Our results show that most dosimetry models were biased in their predicted clinical dosimetry compared to empirical values. Therefore, normalization of rat:human organ sizes and correction for reconstruction method biases are required to achieve higher precision of dosimetry estimates.

Advances in the study of chromosome conformation capture technologies, such as Hi-C technique - capable of capturing chromosomal interactions in a genome-wide scale - have led to the development of three-dimensional chromosome and genome structure reconstruction methods from Hi-C data. The three dimensional genome structure is important because it plays a role in a variety of important biological activities such as DNA replication, gene regulation, genome interaction, and gene expression. In recent years, numerous Hi-C datasets have been generated, and likewise, a number of genome structure construction algorithms have been developed.In this work, we outline the construction of a novel Genome Structure Database (GSDB) to create a comprehensive repository that contains 3D structures for Hi-C datasets constructed by a variety of 3D structure reconstruction tools. The GSDB contains over 50,000 structures from 12 state-of-the-art Hi-C data structure prediction algorithms for 32 Hi-C datasets.GSDB functions as a centralized collection of genome structures which will enable the exploration of the dynamic architectures of chromosomes and genomes for biomedical research. GSDB is accessible at http://sysbio.rnet.missouri.edu/3dgenome/GSDB

With the presentation of super-resolution convolutional neural network, deep learning approach was applied to image super-resolution reconstruction for the first time. By using convolutional neural network, the deep learning approaches can directly learn the mapping relationship between the low-resolution image and high-resolution image, and have achieved better reconstruction effects than the traditional image super-resolution reconstruction methods. Subsequently, a series of improved deep learning approaches have been proposed, and the reconstruction effects have been improved continuously. This paper systematically summa rizes the image super-resolution reconstruction approaches based on deep learning, analyzes the characteristics of different models, and compares the main deep learning models based on the experiments. Furthermore, based on deep learning model, the future research directions of the image super-resolution reconstruction methods based on deep learning models are reasonably predicted.

The photothermal imaging technique is a nondestructive inspection technique that visualizes the inside of a metal by utilizing the photothermal effect. Although the concept of photothermal imaging techniques has been proposed, systematic research on the characteristics thereof has not been conducted. This study attempts to enhance the measurement and reconstruction process for a photothermal imaging method. To detect the edge of a subsurface pattern more accurate, low-pass FFT (fast Fourier transform) filter for noise reduction of measured data, and derivative detectors are adopted for the reconstruction of photothermal imaging. The adopted methods are applied to and visualize 20 mm × 25 mm × 1.5 mm copper block including radius 5 mm, height 1 mm cylindrical resin as a subsurface pattern. The results show that the developed method can detect the edge of the resin subsurface 50% more accurately than the previous reconstruction method for photothermal imaging.

Human pose reconstruction using depth images has received much attention for human-centric applications. Body-part labeling at pixel-level has shown to be efficient for human pose reconstruction. This paper presents an accurate human pose reconstruction method from a single depth image by combining body-part labeling and nearest pose-matching techniques. New pixel-level depth difference and local curvature-encoding features are introduced to provide more contextual depth information for pixel-level body-part labeling. To reduce the misclassification error, inspired by pose-matching techniques, a corrective step is also proposed. The method extracts depth region proposals from a reference pose and finds the best match using PCT coefficients to correct uncertain labels. Tests on a set of synthetic and natural depth poses showed improved accuracy of body-part labeling compared to the state-of-the-art methods. In addition, in comparison with the previous methods and the Kinect camera, an improved accuracy for human range of motion measurement was obtained .

In cardiac SPECT perfusion imaging, respiratory motion can cause non-uniform blurring in the reconstructed myocardium. We investigate the potential benefit of respiratory correction with respiratory-binned acquisitions, both at standard dose and at reduced dose, for defect detection and for left ventricular (LV) wall resolution. We applied two reconstruction methods for respiratory motion correction: post-reconstruction motion correction (PMC) and motion-compensated reconstruction (MCR), and compared with reconstruction without motion correction (Non-MC). We quantified the presence of perfusion defects in reconstructed images by using the total perfusion deficit (TPD) scores and conducted receiver-operating-characteristic (ROC) studies using TPD. We quantified the LV spatial resolution by using the FWHM of its cross-sectional intensity profile. The values in the area-under-the-ROC-curve (AUC) achieved by MCR, PMC, and Non-MC at standard dose were 0.835, 0.830, and 0.798, respectively. Similar AUC improvements were also obtained by MCR and PMC over Non-MC at 50%, 25%, and 12.5% of full dose. Improvements in LV resolution were also observed with motion correction. Respiratory-binned acquisitions can improve perfusion-defect detection accuracy over traditional reconstruction both at standard dose and at reduced dose. Motion correction may contribute to achieving further dose reduction while maintaining the diagnostic accuracy of traditional acquisitions.

The use of 3D data in the analysis of skeletal and fossil materials has conveyed numerous advantages in many fields; however, as the availability and use of 3D scanning equipment are rapidly increasing, it is important for researchers to consider whether these methods are suitable for the proposed study. The issue of suitability has been largely overlooked in previous research; for instance, casts and reconstruction methods are frequently used to increase sample sizes, without sufficient assessment of the effect, this may have on the accuracy and reliability of results. Furthermore, the reliability of geometric morphometric methods and the implications of virtual curation have not received sufficient consideration. This paper discusses the suitability of 3D research with regard to the accuracy, reliability, and accessibility of methods and materials, as well as the effects of the current learning environment. Areas where future work will progress 3D research are proposed.

PURPOSE OF REVIEW:To review the current literature on clinical outcomes following open tibial inlay posterior cruciate ligament (PCL) reconstruction and provide the reader with a detailed description of the author's preferred surgical technique. RECENT FINDINGS:Despite earlier biomechanical studies which demonstrated superiority of the PCL inlay technique when compared to transtibial techniques, recent longitudinal cohort studies have shown no significant differences in clinical or functional outcomes at 10-year follow-up. Furthermore, no significant clinical differences have been shown between graft types used and/or single- versus double-bundle reconstruction methods. The optimal treatment for the PCL-deficient knee remains unclear. Open tibial inlay PCL reconstruction is safe, reproducible, and avoids the "killer turn" that may potentially lead to graft weakening and failure seen in transtibial reconstruction methods. No significant differences in subjective outcomes or clinical laxity have been shown between single-bundle versus double-bundle reconstruction methods.

In order to improve depth extraction accuracy, a method using moving array lenslet technique (MALT) in pickup stage is proposed, which can decrease the depth interval caused by pixelation. In this method, the lenslet array is moved along the horizontal and vertical directions simultaneously for N times in a pitch to get N sets of elemental images. Computational integral imaging reconstruction method for MALT is taken to obtain the slice images of the 3D scene, and the sum modulus (SMD) blur metric is taken on these slice images to achieve the depth information of the 3D scene. Simulation and optical experiments are carried out to verify the feasibility of this method.

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In this paper, we present an adaptive reconstruction method for temporal compressive imaging with pixel-wise exposure. The motion of objects is first estimated from interpolated images with a designed coding mask. With the help of motion estimation, image blocks are classified according to the degree of motion and reconstructed with the corresponding dictionary, which was trained beforehand. Both the simulation and experiment results show that the proposed method can obtain accurate motion information before reconstruction and efficiently reconstruct compressive video.

Compressed sensing (CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation (NCSR), in terms of both visual results and quantitative measures.

There is an ongoing debate as to whether pursuing happiness is beneficial for people’s subjective well-being (SWB). To address this question, we tested whether attention to SWB – measured by participation in SWB surveys – is related to experienced SWB in two longitudinal studies. The initial study was conducted from November to December 2013 (N = 129), and the replication study, three years later from November to December 2016 (N = 120). The studies include two groups: one group (the control group) answered three SWB surveys over 4 weeks, and the other group (the experimental group) followed the same procedure but additionally tracked their SWB in detail using the experience sampling method four times a day and the day reconstruction method once a day using a smartphone application for two weeks to heighten their attention to their SWB. Both studies show higher SWB scores at later measurements compared to the first ones.

The quadrant method was described by Bernard et al. and it has been widely used for postoperative evaluation of anterior cruciate ligament (ACL) reconstruction. The purpose of this research is to further develop the quadrant method measuring four points, which we named four-point quadrant method, and to compare with the quadrant method. Three-dimensional computed tomography (3D-CT) analyses were performed in 25 patients who underwent double-bundle ACL reconstruction using the outside-in technique. The four points in this study’s quadrant method were defined as point1—highest, point2—deepest, point3—lowest, and point4—shallowest, in femoral tunnel position. Value of depth and height in each point was measured. Antero-medial (AM) tunnel is (depth1, height2) and postero-lateral (PL) tunnel is (depth3, height4) in this four-point quadrant method. The 3D-CT images were evaluated independently by 2 orthopaedic surgeons. A second measurement was performed by both observers after a 4-week interval. Intra- and inter-observer reliability was calculated by means of intra-class correlation coefficient (ICC). Also, the accuracy of the method was evaluated against the quadrant method. Intra-observer reliability was almost perfect for both AM and PL tunnel (ICC > 0.81). Inter-observer reliability of AM tunnel was substantial (ICC > 0.61) and that of PL tunnel was almost perfect (ICC > 0.81). The AM tunnel position was 0.13% deep, 0.58% high and PL tunnel position was 0.01% shallow, 0.13% low compared to quadrant method. The four-point quadrant method was found to have high intra- and inter-observer reliability and accuracy. This method can evaluate the tunnel position regardless of the shape and morphology of the bone tunnel aperture for use of comparison and can provide measurement that can be compared with various reconstruction methods. The four-point quadrant method of this study is considered to have clinical relevance in that it is a detailed and accurate tool for evaluating femoral tunnel position after ACL reconstruction. Case series, Level IV.

Raw coal is burned in furnaces or chain grate boilers. In this work, we studied the chamber combustion mechanism in a chain grate boiler (CGB) with a capacity of 20 t/hr. A mathematical model was built using Computational Fluid Dynamics (CFD). It was found that chamber combustion with swirl burners instead of grate firing has a positive effect on NO emissions. NO emissions dropped from 700 ppm in case 5 to 250 ppm in case 1. The calculated and measured NO emissions agreed well, with a discrepancy of less than 15%, except on plane 2 for four calculated cases. For the temperature field, the discrepancy between the measured and simulated values was below 12% for the four cases. These discrepancies between calculated and measured results are acceptable. This study provides a good idea for the design and reconstruction of CGBs. To reduce NO emissions, the reconstruction method can be used for CGBs instead of the grate firing system.

We present a novel reconstruction method for dynamic MR images from highly under-sampled k-space measurements. The reconstruction problem is posed as spectrally regularized matrix recovery problem, where kernel-based low rank constraint is employed to effectively utilize the non-linear correlations between the images in the dynamic sequence. Unlike other kernel-based methods, we use a single-step regularized reconstruction approach to simultaneously learn the kernel basis functions and the weights. The objective function is optimized using variable splitting and alternating direction method of multipliers. The framework can seamlessly handle additional sparsity constraints such as spatio-temporal total variation. The algorithm performance is evaluated on a numerical phantom and in vivo data sets and it shows significant improvement over the comparison methods.

In the online published article, electronic supplementary material has been published incorrectly. The correct supplementary material is uploaded.

Tibetan Plateau is the headwater region of many major Asian rivers and very susceptive to climate change. Therefore, knowledge about climate and its spatiotemporal variability in this area is very important for ecological conservation, water resource management and social development. The aim of this study was to reconstruct and analyze the hydroclimate variation on eastern Tibetan Plateau (ETP) over many centuries and explore possible forcing factors on regional hydroclimate variability. We used 118 tree-ring chronologies from ETP to reconstruct the gridded May–July Standardized Precipitation Evapotranspiration Index for the ETP over the last millennium. The reconstruction was developed using an ensemble point-by-point reconstruction method, and a searching region method was used to locate the candidate tree-ring chronologies. The reconstructions have nicely captured the spatial and temporal features of the regional drought variation. The drought variations in south and north of 32.5°N are notably different, which may be related to the divergence influence of North Atlantic Oscillation on the climate systems in the south and north, as well as differences in local climate. Spectral analysis and series comparison suggest that the drought variation in the northeastern Tibetan Plateau has been possibly influenced by solar activity on centurial and longer time scale.

A computed tomography radiation dose survey was performed within our enterprise using three age-based paediatric phantoms representing a 1, 5 and 10 years old. Twenty-seven scanners were surveyed with volume computed tomography dose index and dose length product data collected for head, chest and abdomen-pelvis protocols at each age. Reconstruction method e.g. filtered back projection (FBP) or iterative (IR) was also recorded. About two-thirds of the 1 year old FBP chest scans exceeded the national Baby diagnostic reference level (DRL). A small number of scanners also exceeded the national Child DRL for the 1 and 5 years old phantoms. Only about half of the phantom protocols showed a difference of statistical significance between FBP and IR scanners. The results suggested the need for optimisation work at a number of sites. It was determined that the proposed local (i.e. enterprise-wide) DRLs are presented best in terms of weight or girth rather than age.

The development of new reconstruction methods of acoustic sources presents a domain of great interest seen its applications in reducing the noise, realizing the acoustic comfort and diagnosing systems. The duct systems are used in several systems such as building, ventilation and aeronautic systems. In this work, a new reconstruction technique of the acoustic sources in duct systems is presented. This technique is based on the multimodal scattering matrix that provides an estimation of the acoustic power dissipation and attenuation in the case of a point acoustic source. These energetic parameters are then injected into an identification technique based on the independent component analysis technique allowing the reconstruction of original sources. The method was applied to a cylindrical rigid-lined-rigid duct element. A comparison between the estimated and imposed acoustic pressures emitted by point sources is presented. This comparison showed that the proposed method gives good results, and the original signals are well reconstructed.

Purpose To assess (1) the repeatability and (2) the impact of reconstruction methods and delineation on the repeatability of 105 radiomic features in non-small-cell lung cancer (NSCLC) 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomorgraphy/computed tomography (PET/CT) studies. Procedures Eleven NSCLC patients received two baseline whole-body PET/CT scans. Each scan was reconstructed twice, once using the point spread function (PSF) and once complying with the European Association for Nuclear Medicine (EANM) guidelines for tumor PET imaging. Volumes of interest (n = 19) were delineated twice, once on PET and once on CT images. Results Sixty-three features showed an intraclass correlation coefficient ≥ 0.90 independent of delineation or reconstruction. More features were sensitive to a change in delineation than to a change in reconstruction (25 and 3 features, respectively). Conclusions The majority of features in NSCLC [18F]FDG-PET/CT studies show a high level of repeatability that is similar or better compared to simple standardized uptake value measures. Electronic supplementary material The online version of this article (doi:10.1007/s11307-016-0940-2) contains supplementary material, which is available to authorized users.

The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commercially available and replace conventional filtered back projection. Since then, this technique has caused a true hype in the field of radiology. Within a few years, all major CT vendors introduced iterative reconstruction algorithms for clinical routine, which evolved rapidly into increasingly advanced reconstruction algorithms. The complexity of algorithms ranges from hybrid-, model-based to fully iterative algorithms. As a result, the number of scientific publications on this topic has skyrocketed over the last decade. But what exactly has this technology brought us so far? And what can we expect from future hardware as well as software developments, such as photon-counting CT and artificial intelligence? This paper will try answer those questions by taking a concise look at the overall evolution of CT image reconstruction and its clinical implementations. Subsequently, we will give a prospect towards future developments in this domain. Key Points • Advanced CT reconstruction methods are indispensable in the current clinical setting. • IR is essential for photon-counting CT, phase-contrast CT, and dark-field CT. • Artificial intelligence will potentially further increase the performance of reconstruction methods.

Zr-89 positron emission tomography (PET) is a valuable tool for understanding the biodistribution and pharmacokinetics of antibody-based therapeutics. We compared the image quality of Zr-89 PET and F-18 PET in the Siemens microPET Focus 220 preclinical scanner using different reconstruction methods.Image quality metrics were measured in various Zr-89 and F-18 PET phantoms, including the NEMA NU 4-2008 image quality phantom. Images were reconstructed using various algorithms.Zr-89 PET had greater image noise, inferior spatial resolution, and greater spillover than F-18 PET, but comparable recovery coefficients for cylinders of various diameters. Of the reconstruction methods, OSEM3D resulted in the lowest noise, highest recovery coefficients, best spatial resolution, but also the greatest spillover. Scatter correction results were found to be sensitive to varying object sizes.Zr-89 PET image quality was inferior to that of F-18, and no single reconstruction method was superior in all aspects of image quality.