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To reduce metal artifacts, several sinogram inpainting-based metal artifact reduction (MAR) methods have been proposed where projection data within the metal trace region of the sinogram are treated as missing and subsequently estimated. However, these methods generally assume data truncation does not occur and all metal objects reside inside the field-of-view (FOV). For small FOV imaging, these assumptions are violated, and thus existing inpainting-based MAR methods would not be effective. In this paper, we propose a new MAR method to reduce metal artifacts effectively in the presence of data truncation. The main idea behind the proposed method is the synthesis of a sinogram, which is treated as the originally measured sinogram. First, an initial reconstruction step is performed to remove truncation artifacts. The next step consists of a forward projection of the small FOV image, replacing the original sinogram with the synthesized sinogram. The final step is the application of sinogram inpainting based MAR methods using the synthesized sinogram. Verification of the proposed method was performed for three situations: extended cardiac-torso (XCAT) simulation, clinical data, and experimental data. The proposed method was applied with linear MAR (LMAR) and normalized MAR (NMAR), and the performance of the proposed method was compared with that of the previous method. For quantitative evaluation, normalized mean squared error (NMSE) and structure similarity (SSIM) were used. The results show the effectiveness of the proposed method to reduce residual metal artifacts, which are present in the results obtained with the previous method. The evaluation results using NMSE and SSIM also indicate that the proposed method is more effective than the previous method.

Advances in intravital microscopy (IVM) have enabled the studies of cellular organization and dynamics in the native microenvironment of intact organisms with minimal perturbation. The abilities to track specific cell populations and monitor their interactions have opened up new horizons for visualizing cell biology in vivo, yet the success of standard fluorescence cell labeling approaches for IVM comes with a “dark side” in that unlabeled cells are invisible, leaving labeled cells or structures to appear isolated in space, devoid of their surroundings and lacking proper biological context. Here we describe a novel method for “filling in the void” by harnessing the ubiquity of extracellular (interstitial) fluid and its ease of fluorescence labelling by commonly used vascular and lymphatic tracers. We show that during routine labeling of the vasculature and lymphatics for IVM, commonly used fluorescent tracers readily perfuse the interstitial spaces of the bone marrow (BM) and the lymph node (LN), outlining the unlabeled cells and forming negative contrast images that complement standard (positive) cell labeling approaches. The method is simple yet powerful, offering a comprehensive view of the cellular landscape such as cell density and spatial distribution, as well as dynamic processes such as cell motility and transmigration across the vascular endothelium. The extracellular localization of the dye and the interstitial flow provide favorable conditions for prolonged Intravital time lapse imaging with minimal toxicity and photobleaching.

The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.

Wormian (intrasutural) bones are small, irregular bones, that are found in the cranial sutures of the skull. The occurrence of Wormian bones in human skulls has been well documented but few studies have detected the presence of such bones in domestic animals. Although some research has linked the presence of Wormian bones to bone pathology, its anatomical significance in healthy individuals is not known. To the best of our knowledge, no previous study has examined the biomechanical features of Wormian bone. This study uses microCT imaging of the parietal bone region to determine the frequency of occurrence of Wormian bones in horse skulls and, through 3-point bending tests, to calculate the mechanical differences that result from the presence of such bones. In addition, bone properties such as bone mineral density (BMD) and stiffness were measured and analysed to determine the influence of Wormian bone. Our findings on 54 specimens taken from 10 horses (ages ranging from 4 to 29 years) showed that Wormian bone was present in 70% of subjects and that its occurrence was unrelated to age or sex. 3-point bend tests revealed that the stiffness normalised by cross section area (P = 0.038) was lower in samples where Wormian bone was present. An idealised Finite Element simulation confirmed that the presence of Wormian bone reduced the maximum stress and strain, as well as their distribution throughout the sample. We consequently conclude that the presence of Wormian bones, which are confined to the calvaria, increase the compliance of the bone and reduce the likelihood of skull fracture. As all skull samples were collected from a local abattoir, ethical approval was not required for this work.

Diagnostic radiology is a leading cause of man-made radiation exposure to the population. It is an important factor in many epidemiological studies as variable of interest or as potential confounder. The effective dose as a risk related quantity is the most often stated patient dose. Nevertheless, there exists no comprehensive quantification model for retrospective analysis for this quantity. This paper gives a catalog of effective dose values for common and rare examinations and demonstrates how to modify the dose values to adapt them to different calendar years using a quantification concept already used for retrospective analysis of the red bone marrow dose. It covers the time period of 1946 to 1995 and allows considering technical development and different practical standards over time. For an individual dose assessment, if the dose area product is known, factors are given for most examinations to convert the dose area product into the effective dose. Additionally factors are stated for converting the effective dose into the red bone marrow dose or vice versa.

Objective Waist circumference (WC) is a widely accepted anthropometric parameter of central obesity. We investigated a fully automated body segmentation algorithm for measuring WC on abdominal computed tomography (CT) in comparison to manual WC measurements (WC-manual) and evaluated the performance of CT-measured WC for identifying overweight/obesity. Materials and methods This retrospective study included consecutive adults who underwent both abdominal CT scans and manual WC measurements at a health check-up between January 2013 and November 2019. Mid-waist WCs were automatically measured on noncontrast axial CT images using a deep learning-based body segmentation algorithm. The associations between CT-measured WC and WC-manual was assessed by Pearson correlation analysis and their agreement was assessed through Bland-Altman analysis. The performance of these WC measurements for identifying overweight/obesity (i.e., body mass index [BMI] ≥25 kg/m2) was evaluated using receiver operating characteristics (ROC) curve analysis. Results Among 763 subjects whose abdominal CT scans were analyzed using a fully automated body segmentation algorithm, CT-measured WCs were successfully obtained in 757 adults (326 women; mean age, 54.3 years; 64 women and 182 men with overweight/obesity). CT-measured WC was strongly correlated with WC-manual (r = 0.919, p < 0.001), and showed a mean difference of 6.1 cm with limits of agreement between -1.8 cm and 14.0 cm in comparison to WC-manual. For identifying overweight/obesity, CT-measured WC showed excellent performance, with areas under the ROC curve (AUCs) of 0.960 (95% CI, 0.933–0.979) in women and 0.909 (95% CI, 0.878–0.935) in men, which were comparable to WC-manual (AUCs of 0.965 [95% CI, 0.938–0.982] and 0.916 [95% CI, 0.886–0.941]; p = 0.735 and 0.437, respectively). Conclusion CT-measured WC using a fully automated body segmentation algorithm was closely correlated with manually-measured WC. While radiation issue may limit its general use, it can serve as an adjunctive output of abdominal CT scans to identify overweight/obesity.

Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and designed an ensemble of three convolutional neural network models: GoogLeNet, ResNet-18, and DenseNet-121. A weighted average ensemble technique was adopted, wherein the weights assigned to the base learners were determined using a novel approach. The scores of four standard evaluation metrics, precision, recall, f1-score, and the area under the curve, are fused to form the weight vector, which in studies in the literature was frequently set experimentally, a method that is prone to error. The proposed approach was evaluated on two publicly available pneumonia X-ray datasets, provided by Kermany et al. and the Radiological Society of North America (RSNA), respectively, using a five-fold cross-validation scheme. The proposed method achieved accuracy rates of 98.81% and 86.85% and sensitivity rates of 98.80% and 87.02% on the Kermany and RSNA datasets, respectively. The results were superior to those of state-of-the-art methods and our method performed better than the widely used ensemble techniques. Statistical analyses on the datasets using McNemar’s and ANOVA tests showed the robustness of the approach. The codes for the proposed work are available at https://github.com/Rohit-Kundu/Ensemble-Pneumonia-Detection.

Dating the wood from historical art objects is a crucial step to ascertain their production time, and support or refute attribution to an artist or a workshop. Dendrochronology is commonly used for this purpose but requires access to the tree-ring pattern in the wood, which can be hindered by preparatory layers, polychromy, wax, or integrated frames. Here we implemented non-invasive dendrochronology based on X-ray computed tomography (CT) to examine a painting on panel attributed to Rubens’ studio and its presumed dating around 1636 CE. The CT images achieved a resolution of 37.3 micron and revealed a double panelling, which was concealed by oak strips covering all four edges. The back (visible) board is made of deciduous oak (Quercus subg. Quercus), the most common type of wood used in 17th-century Netherlandish workshops, and was dated terminus post quem after 1557 CE. However, the front (original) board used for the painting has been identified through examination of the wood anatomy as a tropical wood, probably Swietenia sp., a species seldom used in Netherlandish paintings, and remains undated. Its very presence attests the global character of 17th-century trade, and demonstrates the use of exotic species in Flemish studios. The date of the oak board refutes previous results and suggests that this board was trimmed to meet the size of the tropical one, having been glued to it for conservation purposes or with deceiving intentions to pretend that the painting was made on an oak panel. These revelations have opened new lines of art historical inquiry and highlight the potential of X-ray CT as a powerful tool for non-invasive study of historical art objects to retrieve their full history.

Objective To develop a whole-joint, unidimensional, irreversible, and fine-grained MRI knee osteoarthritis (OA) severity score, based on cartilage, osteophytes and meniscus (OA-COM), and to predict progression across different severity states using OA-COM as outcome and clinical variables as predictors. Methods Population-based knee pain cohort aged 40–79 was assessed at baseline and 7-year follow-up. OA-COM score was defined as the sum of MRI scores for cartilage, osteophytes and menisci, measured at 6, 8 and 6 sites, total score 0–54. To anchor severity levels, we fit cross-sectional logistic models using OA-COM to predict Kellgren-Lawrence (KL) grades in subsets at or one point below each grade. OA-COM threshold scores were selected on sensitivity, specificity, positive and negative predictive value. We developed longitudinal logistic models for OA-COM progression over each threshold over 7 years. Potential predictors included age, sex, BMI, malalignment, physical exam effusion, quadriceps weakness, and crepitus, selected on area under the receiver operating characteristic curve (AUC) and Akaike’s Information Criterion (AIC). Results Optimal OA-COM thresholds were 12, 18, 24 and 30, for KL grades 1 to 4. Significant predictors of progression (depending on threshold) included physical exam effusion, malalignment and female sex, with other selected predictors age, BMI and crepitus. Conclusion OA-COM (0–54 range) is a whole-joint, unidimensional, irreversible, and fine-grained MRI OA severity score reflecting cartilage, osteophytes and menisci. OA-COM scores 12, 18, 24 and 30 are equivalent to KL grades 1 to 4, while offering fine-grained differentiation of states between KL grades, and within pre-radiographic disease (KL = 0) or late-stage disease (KL = 4). In modeling, several clinical variables predicted progression across different states over 7 years.

Material decomposition (MD) is an important application of computer tomography (CT). For phase contrast imaging, conventional MD methods are categorized into two types with respect to different operation sequences, i.e., “before” or “after” image reconstruction. Both categories come down to two-step methods, which have the problem of noise amplification. In this study, we incorporate both phase and absorption (PA) information into MD process, and correspondingly develop a simultaneous algebraic reconstruction technique (SART). The proposed method is referred to as phase & absorption material decomposition-SART (PAMD-SART). By iteratively solving an optimization problem, material composition and substance quantification are reconstructed directly from absorption and differential phase projections. Comparing with two-step MD, the proposed one-step method is superior in noise suppression and accurate decomposition. Numerical simulations and synchrotron radiation based experiments show that PAMD-SART outperforms the classical MD method (image-based and dual-energy CT iterative method), especially for the quantitative accuracy of material equivalent atomic number.

Objective Quantitative assessment of pulmonary blood flow distribution is important when determining the clinical indications for treating pulmonary arterial branch stenosis. Lung perfusion scintigraphy is currently the gold standard for quantitative blood flow measurement. However, it is expensive, cannot provide a real-time assessment, requires additional sedation, and exposes the patient to ionizing radiation. The aim of this study was to investigate the feasibility of a novel technology for measuring pulmonary blood flow distribution in each lung by conventional X-ray pulmonary angiography and to compare its performance to that of lung perfusion scintigraphy. Methods Contrast-enhanced X-ray pulmonary angiography images were acquired at a frame rate of 30 frames per second. The baseline mask image, obtained before contrast agent injection, was subtracted from subsequent, consecutive images. The time-signal intensity curves of two regions of interest, established at each lung field, were obtained on a frame-to-frame basis. The net increase in signal intensity within each region at the torrent period during the second cardiac cycle before contrast agent enhancement over the total lung field was measured, and the right-to-left ratio of the signal intensity was calculated. The right-to-left ratio obtained with this approach was compared to that obtained with scintigraphy. Agreement of the right-to-left ratio between X-ray angiography and lung scintigraphy measurements was assessed using linear fitting with the Pearson correlation coefficient. Result The calculation of the right-to-left ratio of pulmonary blood flow by our kinetic model was feasible for seven children as a pilot study. The right-to-left ratio of pulmonary blood flow distribution calculated from pulmonary angiography was in good agreement with that of lung perfusion scintigraphy, with a Pearson correlation coefficient of 0.91 and a slope of linear fit of 1.2 (p Conclusion The novel diagnostic technology using X-ray pulmonary angiography from our kinetic model can feasibly quantify the right-to-left ratio of pulmonary blood flow distribution. This technology may serve as a substitute for lung perfusion scintigraphy, which is quite beneficial for small children susceptible to radiation exposure.

Osteocytes remodel the perilacunar matrix and canaliculi. X-linked hypophosphatemia (XLH) is characterized by elevated serum levels of fibroblast growth factor 23 (FGF23), leading to decreased 1,25 dihydroxyvitamin D3 (1,25D) production and hypophosphatemia. Bones from mice with XLH (Hyp) have enlarged osteocyte lacunae, enhanced osteocyte expression of genes of bone remodeling, and impaired canalicular structure. The altered lacuno-canalicular (LCN) phenotype is improved with 1,25D or anti-FGF23 antibody treatment, pointing to roles for 1,25D and/or phosphate in regulating this process. To address whether impaired 1,25D action results in LCN alterations, the LCN phenotype was characterized in mice lacking the vitamin D receptor (VDR) in osteocytes (VDRf/f;DMP1Cre+). Mice lacking the sodium phosphate transporter NPT2a (NPT2aKO) have hypophosphatemia and high serum 1,25D levels, therefore the LCN phenotype was characterized in these mice to determine if increased 1,25D compensates for hypophosphatemia in regulating LCN remodeling. Unlike Hyp mice, neither VDRf/f;DMP1Cre+ nor NPT2aKO mice have dramatic alterations in cortical microarchitecture, allowing for dissecting 1,25D and phosphate specific effects on LCN remodeling in tibial cortices. Histomorphometric analyses demonstrate that, like Hyp mice, tibiae and calvariae in VDRf/f;DMP1Cre+ and NPT2aKO mice have enlarged osteocyte lacunae (tibiae: 0.15±0.02μm2(VDRf/f;DMP1Cre-) vs 0.19±0.02μm2(VDRf/f;DMP1Cre+), 0.12±0.02μm2(WT) vs 0.18±0.0μm2(NPT2aKO), calvariae: 0.09±0.02μm2(VDRf/f;DMP1Cre-) vs 0.11±0.02μm2(VDRf/f;DMP1Cre+), 0.08±0.02μm2(WT) vs 0.13±0.02μm2(NPT2aKO), pf/f;DMP1Cre+ and NPT2aKO mice have enhanced expression of matrix resorption genes that are classically expressed by osteoclasts (Ctsk, Acp5, Atp6v0d2, Nhedc2). Treatment of Ocy454 osteocytes with 1,25D or phosphate inhibits the expression of these genes. Like Hyp mice, VDRf/f;DMP1Cre+ and NPT2aKO mice have impaired canalicular organization in tibia and calvaria. These studies demonstrate that hypophosphatemia and osteocyte-specific 1,25D actions regulate LCN remodeling. Impaired 1,25D action and low phosphate levels contribute to the abnormal LCN phenotype observed in XLH.

From infancy, humans have the ability to distinguish animate agents from inert objects, and preschoolers map biological and mechanical insides to their appropriate kinds. However, less is known about how identifying something as an animate agent shapes specific inferences about its internal properties. Here, we test whether preschool children (N = 92; North American population) have specifically biological expectations about animate agents, or if they have more general expectations that animate agents should have an internal source of motion. We presented preschoolers with videos of two puppets: a “self-propelled” fur-covered puppet, and a fur-covered puppet that is seen to be moved by a human actor. In addition, we presented preschoolers with images of a familiar artifact (motorcycle) and familiar animal (sheep). For each item, we asked them to choose what they thought was inside each of these entities: nothing, biological insides, or mechanical insides. Preschoolers were less likely to say that a self-propelled fur-covered object was empty, compared to a fur-covered object that was moved by a human actor, which converges with past work with infants. However, preschoolers showed no specifically biological expectations about these objects, despite being able to accurately match biological insides to familiar animals and mechanical insides to familiar artifacts on the follow-up measure. These results suggest that preschoolers do not have specifically biological expectations about animate agents as a category, but rather general expectations that such agents should not be empty inside.

Objectives A novel magnetic resonance imaging (MRI) scan protocol is presented on the basis of ultra-short time to echo (UTE). By this MRI cephalometric projections (MCPs) can be acquired without the need of post processing in one shot. Different technical parameterizations of the protocol are performed. Their impact on the performance of MCPs is evaluated in comparison to the gold standard–the lateral cephalometric radiography (LCR) for cephalometric analysis (CA) in orthodontics. Methods Seven MCPs with various scan parameters influencing the scan duration and one LCR are used from one subject. 40 expert assessors performed CA for 14 predefined cephalometric landmarks. Relative metric distances and absolute angular measurements were calculated. Statistical analysis is presented and the deviations are highlighted to demonstrate the potential of the method for further analysis. Results The MCPs are acquired in 5–154 seconds, depending on resolution and contrast. Mean relative distances were 2.4–2.7 mm in MCPs and 1.6 mm in LCR, which demonstrate the accuracy and level of agreement of the expert assessors in identifying anatomical landmarks. In comparison to other studies, the presented MCP performed similar in angular analysis and demonstrated on average deviation of 1.2° ±1.1° in comparison to LCR. Despite the point articulare (Ar) and the related gonial angle the calculate distances and angles show outcomes in the range of ±2°/2mm. Conclusions MCPs can be acquired much faster in comparison to other techniques known from literature for CA. This study demonstrated the potential of the new method and showed first feasible results. Further research is needed to analyze the performance on a broad range of patients.

Purpose Previous efforts at increasing spatial resolution have relied on decreasing focal spot and or detector element size. Many “super resolution” methods require physical movement of a component of the imaging system. This work describes a method for achieving spatial resolution on a scale smaller than the detector pixel without motion of the object or detector. Methods We introduce a weighting of the photon energy spectrum on a length scale smaller than a single pixel using a physical filter that can be placed between the focal spot and the object, between the object and the detector, or integrated into the x-ray source or detector. We refer to the method as sub pixel encoding (SPE). We show that if one acquires multiple measurements (i.e. x-ray projections), information can be synthesized at a spatial scale defined by the spectrum modulation, not the detector element size. Specifically, if one divides a detector pixel into n sub regions, and m photon-matter interactions are present, the number of x-ray measurements needed to solve for the detector response of each sub region is mxn. We discuss realizations of SPE using multiple x-ray spectra with an energy integrating detector, a single spectra with a photon counting detector, and the single photon-matter interaction case. We demonstrate the feasibility of the approach using a simulated energy integrating detector with a detector pitch of 2 mm for 80-140 kV medical and 200-600 kV industrial applications. Phantoms used for both example SPE realization had some features only a 1 mm detector could resolve. We calculate the covariance matrix of SPE output to characterize the and noise propagation and correlation of our test examples. Results The mathematical foundation of SPE is provided, with details worked out for several detector types and energy ranges. Two numerical simulations were provided to demonstrate feasibility. In both the medical and industrial simulations, some phantom features were only observable with the 1 mm and SPE synthesized 2 mm detector, while the 2 mm detector was not able to visualize them. Covariance matrix analysis demonstrated negative diagonal terms for both example cases. Conclusions The concept of encoding object information at a length scale smaller than a single pixel element, and then retrieving that information was introduced. SPE simultaneously allows for an increase in spatial resolution and provides “dual energy” like information about the underlying photon-matter interactions.

研究グループは、骨欠損部位に照射可能なペンシルタイプの「低温大気圧プラズマ照射装置」を共同で開発し、患部へプラズマ照射することにより骨再生が促進することを明らかにしました。本研究成果は、医療分野において骨折治癒期間の短縮や難治性骨折、巨大骨欠損の確実かつ効率的な治療の実現に貢献することが期待できます。近年、プラズマ発生に関する理論・技術の革新に伴い、幅広い分野でプラズマ照射が応用されるようになり、特に生体組織に直接プラズマを照射することにより皮膚疾患の治癒・再生が促進される現象が報告されるなど、革新的医療技術としての期待が高まってきています。本研究グループはこの現象を骨折部の治癒促進に応用することで骨再生の促進や骨癒合期間の短縮が可能ではないかと考えました。今回、本研究グループは、ウサギで尺骨欠損モデルを用い、骨欠損部へ低温大気圧プラズマ照射をした群としない群で新生骨の再生に違いが出るかどうか比較しました。その結果、プラズマ照射時間を5分、10分、15分と変えたプラズマ照射群では、どれも新生骨の再生が確認されました。また、10分照射した群で、コントロールのプラズマを含まないガスを照射した群に比べて8週間後に最も新生骨量が多くなる結果が得られました。, Medical treatment using non-thermal atmospheric pressure plasma (NTAPP) is rapidly gaining recognition. NTAPP is thought to be a new therapeutic method because it could generate highly reactive species in an ambient atmosphere which could be exposed to biological targets (e.g., cells and tissues). If plasma-generated reactive species could stimulate bone regeneration, NTAPP can provide a new treatment opportunity in regenerative medicine. Here, we investigated the impact of NTAPP on bone regeneration using a large bone defect in New Zealand White rabbits and a simple atmospheric pressure plasma (helium microplasma jet). We observed the recovery progress of the large bone defects by X-ray imaging over eight weeks after surgery. The X-ray results showed a clear difference in the occupancy of the new bone of the large bone defect among groups with different plasma treatment times, whereas the new bone occupancy was not substantial in the untreated control group. According to the results of micro-computed tomography analysis at eight weeks, the most successful bone regeneration was achieved using a plasma treatment time of 10 min, wherein the new bone volume was 1.51 times larger than that in the plasma untreated control group. Using H&E and Masson trichrome stains, nucleated cells were uniformly observed, and no inclusion was confirmed, respectively, in the groups of plasma treatment. We concluded the critical large bone defect were filled with new bone. Overall, these results suggest that NTAPP is promising for fracture treatment.

Murine bone marrow (BM) chimeras are a versatile and valuable research tool in stem cell and immunology research. Engraftment of donor BM requires myeloablative conditioning of recipients. The most common method used for mice is ionizing radiation, and Cesium-137 gamma irradiators have been preferred. However, radioactive sources are being out-phased worldwide due to safety concerns, and are most commonly replaced by X-ray sources, creating a need to compare these sources regarding efficiency and potential side effects. Prior research has proven both methods capable of efficiently ablating BM cells and splenocytes in mice, but with moderate differences in resultant donor chimerism across tissues. Here, we compared Cesium-137 to 350 keV X-ray irradiation with respect to immune reconstitution, assaying complete, syngeneic BM chimeras and a mixed chimera model of autoimmune disease. Based on dose titration, we find that both gamma and X-ray irradiation can facilitate a near-complete donor chimerism. Mice subjected to 13 Gy Cesium-137 irradiation and reconstituted with syngeneic donor marrow were viable and displayed high donor chimerism, whereas X-ray irradiated mice all succumbed at 13 Gy. However, a similar degree of chimerism as that obtained following 13 Gy gamma irradiation could be achieved by 11 Gy X-ray irradiation, about 85% relative to the gamma dose. In the mixed chimera model of autoimmune disease, we found that a similar autoimmune phenotype could be achieved irrespective of irradiation source used. It is thus possible to compare data generated, regardless of the irradiation source, but every setup and application likely needs individual optimization.

Background As of June 1, 2020, coronavirus disease 2019 (COVID-19) has caused more than 6,000,000 infected persons and 360,000 deaths globally. Previous studies revealed pregnant women with COVID-19 had similar clinical manifestations to nonpregnant women. However, little is known about the outcome of neonates born to infected women. Methods and findings In this retrospective study, we studied 29 pregnant women with COVID-19 infection delivered in 2 designated general hospitals in Wuhan, China between January 30 and March 10, 2020, and 30 neonates (1 set of twins). Maternal demographic characteristics, delivery course, symptoms, and laboratory tests from hospital records were extracted. Neonates were hospitalized if they had symptoms (5 cases) or their guardians agreed to a hospitalized quarantine (13 cases), whereas symptom-free neonates also could be discharged after birth and followed up through telephone (12 cases). For hospitalized neonates, laboratory test results and chest X-ray or computed tomography (CT) were extracted from hospital records. The presence of antibody of SARS-CoV-2 was assessed in the serum of 4 neonates. Among 29 pregnant COVID-19-infected women (13 confirmed and 16 clinical diagnosed), the majority had higher education (56.6%), half were employed (51.7%), and their mean age was 29 years. Fourteen women experienced mild symptoms including fever (8), cough (9), shortness of breath (3), diarrhea (2), vomiting (1), and 15 were symptom-free. Eleven of 29 women had pregnancy complications, and 27 elected to have a cesarean section delivery. Of 30 neonates, 18 were admitted to Wuhan Children’s Hospital for quarantine and care, whereas the other 12 neonates discharged after birth without any symptoms and had normal follow-up. Five hospitalized neonates were diagnosed as COVID-19 infection (2 confirmed and 3 suspected). In addition, 12 of 13 other hospitalized neonates presented with radiological features for pneumonia through X-ray or CT screening, 1 with occasional cough and the others without associated symptoms. SARS-CoV-2 specific serum immunoglobulin M (IgM) and immunoglobulin G (IgG) were measured in 4 neonates and 2 were positive. The limited sample size limited statistical comparison between groups. Conclusions In this study, we observed COVID-19 or radiological features of pneumonia in some, but not all, neonates born to women with COVID-19 infection. These findings suggest that intrauterine or intrapartum transmission is possible and warrants clinical caution and further investigation. Trial registration Chinese Clinical Trial Registry, ChiCTR2000031954 (Maternal and Perinatal Outcomes of Women with coronavirus disease 2019 (COVID-19): a multicenter retrospective cohort study)., He-Feng Huang and colleagues describe outcomes for infants born to women with COVID-19 early in the outbreak in China., Author summary Why was this study done? Previous studies suggest COVID-19 infections in pregnant women are not more severe than in women of similar age. However, little is known about the outcome of neonates born to COVID-19-infected women. What did the researchers do and find? We analyzed the clinical features of all pregnant women with COVID-19 infection delivered in 2 of the 5 designated general hospitals in Wuhan, China between January 30 and March 10, 2020, and their neonates. Among the 30 neonates born to 29 pregnant COVID-19-infected women (1 set of twins), 5 were diagnosed as having COVID-19 (2 confirmed and 3 suspected), and the other 12 neonates presented radiological features of pneumonia, but none were diagnosed with pneumonia. Three neonates developed necrotizing enterocolitis, although none of them were premature. What do these findings mean? A considerable number of, but not all, neonates born to COVID-19-infected women developed COVID-19 (5/30) or had pneumonia-like radiological features (17/30). Intrapartum precaution should not be underestimated, and intrauterine transmission should be considered based on current evidence. Term neonates born to COVID-19-infected women are vulnerable to develop necrotizing enterocolitis.

Background Imaging for osteoporosis has two major aims, first, to identify the presence of low bone mass (osteopenia), and second, to quantify bone mass using semiquantitative (conventional radiography) or quantitative (densitometry) methods. In young children, densitometry is hampered by the lack of reference values, and high-quality radiographs still play a role although the evaluation of osteopenia as a marker for osteoporosis is subjective and based on personal experience. Medical experts questioned in court over child abuse, often refer to the literature and state that 20–40% loss of bone mass is warranted before osteopenia becomes evident on radiographs. In our systematic review, we aimed at identifying evidence underpinning this statement. A secondary outcome was identifying normal references for cortical thickness of the skeleton in infants born term, < 2 years of age. Methods We undertook systematic searches in Medline, Embase and Svemed+, covering 1946–2020. Unpublished material was searched in Clinical trials and International Clinical Trials Registry Platform (ICTRP). Both relevant subject headings and free text words were used for the following concepts: osteoporosis or osteopenia, radiography, children up to 6 years. Results A total 5592 publications were identified, of which none met the inclusion criteria for the primary outcome; the degree of bone loss warranted before osteopenia becomes visible radiographically. As for the secondary outcome, 21 studies were identified. None of the studies was true population based and none covered the pre-defined age range from 0–2 years. However, four studies of which three having a crossectional and one a longitudinal design, included newborns while one study included children 0–2 years. Conclusions Despite an extensive literature search, we did not find any studies supporting the assumption that a 20–40% bone loss is required before osteopenia becomes visible on radiographs. Reference values for cortical thickness were sparse. Further studies addressing this important topic are warranted. © 2020 Rosendahl et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Tissue engineering represents a promising alternative for reconstructive surgical procedures especially for the repair of bone defects that do not regenerate spontaneously. The present study aimed to evaluate the effects of the elastin matrix (E24/50 and E96/37) incorporated with hydroxyapatite (HA) or morphogenetic protein (BMP) on the bone repair process in the distal metaphysis of rat femur. The groups were: control group (CG), hydrolyzed elastin matrix at 50°C/24h (E24/50), E24/50 + HA (E24/50/HA), E24/50 + BMP (E24/50/BMP), hydrolyzed elastin matrix at 37°C/96h (E96/37), E96/37 + HA (E96/37/HA), E96/37 + BMP (E96/37/BMP). Macroscopic and radiographic analyses showed longitudinal integrity of the femur in all groups without fractures or bone deformities. Microtomographically, all groups demonstrated partial closure by mineralized tissue except for the E96/37/HA group with hyperdense thin bridge formation interconnecting the edges of the ruptured cortical. Histologically, there was no complete cortical recovery in any group, but partial closure with trabecular bone. In defects filled with biomaterials, no chronic inflammatory response or foreign body type was observed. The mean volume of new bone formed was statistically significant higher in the E96/37/HA and E24/50 groups (71.28 ± 4.26 and 66.40 ± 3.69, respectively) than all the others. In the confocal analysis, it was observed that all groups presented new bone markings formed during the experimental period, being less evident in the CG group. Von Kossa staining revealed intense calcium deposits distributed in all groups. Qualitative analysis of collagen fibers under polarized light showed a predominance of red-orange birefringence in the newly regenerated bone with no difference between groups. It was concluded that the E24/50 and E96/37/HA groups promoted, with greater speed, the bone repair process in the distal metaphysis of rat femur.

Keel bone fractures in laying hens have been described with increasing prevalence from several countries over the last twenty years and are considered one of the greatest welfare problems to the layer industry. In Denmark we have observed fracture prevalence in the range of 53% to 100% in flocks from cage-free systems whereas flock prevalences in birds from enriched cages ranged between 50–98%. Previous research have speculated that the underlying reason for the development of keel bone fractures is trauma in relation to impact of the bird with furniture, other equipment etc. However, little evidence of this theory has been provided. Predisposing factors have also been suggested including genetics of the bird, lack of specific feedstuff components, high egg production, management factors and layer fatigue. This study has addressed the possible pathogenesis of these fractures by pathological characterization of fractures in birds from different production systems. More than 60 keel bones with fractures have been characterized histo-pathologically and by CT scan. This included an assessment of damage to muscles and soft tissues, the bone and the healing process including callus formation. This investigation has shown that high energy collisions cannot be responsible for the majority of fractures, located at the caudal tip of the keel bone, observed in laying birds as markers associated trauma were not observed in the majority of the cases just as few recognized healing processes were observed. These results suggest an alternative pathogenesis to trauma.

As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent demand in developing countries. Under some circumstances, in order to reduce the scan time, decrease the X-ray radiation or scan long objects, furthermore, to avoid the inconsistency of the detector for the large angle scanning, we use the limited-angle TCT scanning mode to scan an object within a limited angular range. However, this scanning mode introduces some additional noise and limited-angle artifacts that seriously degrade the imaging quality and affect the diagnosis accuracy. To reconstruct a high-quality image for the limited-angle TCT scanning mode, we develop a limited-angle TCT image reconstruction algorithm based on a U-net convolutional neural network (CNN). First, we use the SART method to the limited-angle TCT projection data, then we import the image reconstructed by SART method to a well-trained CNN which can suppress the artifacts and preserve the structures to obtain a better reconstructed image. Some simulation experiments are implemented to demonstrate the performance of the developed algorithm for the limited-angle TCT scanning mode. Compared with some state-of-the-art methods, the developed algorithm can effectively suppress the noise and the limited-angle artifacts while preserving the image structures.

Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding the application of machine learning with emphasis on modern efficient convolutional neural networks (CNNs) algorithms, for the diagnosis of prostate cancer metastasis from bone scintigraphy images. The advantageous and outstanding capabilities of deep learning, machine learning's groundbreaking technological advancement, have not yet been fully investigated regarding their application in computer-aided diagnosis systems in the field of medical image analysis, such as the problem of bone metastasis classification in whole-body scans. In particular, CNNs are gaining great attention due to their ability to recognize complex visual patterns, in the same way as human perception operates. Considering all these new enhancements in the field of deep learning, a set of simpler, faster and more accurate CNN architectures, designed for classification of metastatic prostate cancer in bones, is explored. This research study has a two-fold goal: to create and also demonstrate a set of simple but robust CNN models for automatic classification of whole-body scans in two categories, malignant (bone metastasis) or healthy, using solely the scans at the input level. Through a meticulous exploration of CNN hyper-parameter selection and fine-tuning, the best architecture is selected with respect to classification accuracy. Thus a CNN model with improved classification capabilities for bone metastasis diagnosis is produced, using bone scans from prostate cancer patients. The achieved classification testing accuracy is 97.38%, whereas the average sensitivity is approximately 95.8%. Finally, the best-performing CNN method is compared to other popular and well-known CNN architectures used for medical imaging, like VGG16, ResNet50, GoogleNet and MobileNet. The classification results show that the proposed CNN-based approach outperforms the popular CNN methods in nuclear medicine for metastatic prostate cancer diagnosis in bones.

We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm (DE–VM–VDSR) that uses projection data to improve the nodule contrast and reduce ripple artifacts during chest digital tomosynthesis (DT). For estimating the residual errors from high-resolution and multiscale VM images from the projection space, the DE–VM–VDSR algorithm employs a training network (mini-batch stochastic gradient-descent algorithm with momentum) and a hybrid super-resolution (SR) image [simultaneous algebraic reconstruction technique (SART) total-variation (TV) first-iterative shrinkage–thresholding algorithm (FISTA); SART–TV–FISTA] that involves subjective reconstruction with bilateral filtering (BF) [DE–VM–VDSR with BF]. DE-DT imaging was accomplished by pulsed X-ray exposures rapidly switched between low (60 kV, 37 projection) and high (120 kV, 37 projection) tube-potential kVp by employing a 40° swing angle. This was followed by comparison of images obtained employing the conventional polychromatic filtered backprojection (FBP), SART, SART–TV–FISTA, and DE–VM–SART–TV–FISTA algorithms. The improvements in contrast, ripple artifacts, and resolution were compared using the signal-difference-to-noise ratio (SDNR), Gumbel distribution of the largest variations, radial modulation transfer function (radial MTF) for a chest phantom with simulated ground-glass opacity (GGO) nodules, and noise power spectrum (NPS) for uniform water phantom. The novel DE–VM–VDSR with BF improved the overall performance in terms of SDNR (DE–VM–VDSR with BF: 0.1603, without BF: 0.1517; FBP: 0.0521; SART: 0.0645; SART–TV–FISTA: 0.0984; and DE–VM–SART–TV–FISTA: 0.1004), obtained a Gumbel distribution that yielded good images showing the type of simulated GGO nodules used in the chest phantom, and reduced the ripple artifacts. The NPS of DE–VM–VDSR with BF showed the lowest noise characteristics in the high-frequency region (~0.8 cycles/mm). The DE–VM–VDSR without BF yielded an improved resolution relative to that of the conventional reconstruction algorithms for radial MTF analysis (0.2–0.3 cycles/mm). Finally, based on the overall image quality, DE–VM–VDSR with BF improved the contrast and reduced the high-frequency ripple artifacts and noise.

Accurate determination of physical/mass and electron densities are critical to accurate spatial and dosimetric delivery of radiotherapy for photon and charged particles. In this manuscript, the biology, chemistry, and physics that underly the relationship between computed tomography (CT) Hounsfield Unit (HU), mass density, and electron density was explored. In standard radiation physics practice, quantities such as mass and electron density are typically calculated based off a single kilovoltage CT (kVCT) scan assuming a one-to-one relationship between HU and density. It is shown that, in absence of mass density assumptions on tissues, the relationship between HU and density is not one-to-one with uncertainties as large as 7%. To mitigate this uncertainty, a novel multi-dimensional theoretical approach is defined between molecular (water, lipid, protein, and mineral) composition, HU, mass density, and electron density. Empirical parameters defining this relationship are x-ray beam energy/spectrum dependent and, in this study, two methods are proposed to solve for them including through a tissue mimicking phantom calibration process. As a proof of concept, this methodology was implemented in a separate in-house created tissue mimicking phantom and it is shown that sub 1% accuracy is possible for both mass and electron density. As molecular composition is not always known, the sensitivity of this model to uncertainties in molecular composition was investigated and it was found that, for soft tissue, sub 1% accuracy is achievable assuming nominal organ/tissue compositions. For boney tissues, the uncertainty in mineral content may lead to larger errors in mass and electron density compared with soft tissue. In this manuscript, a novel methodology to directly determine mass and electron density based off CT HU and knowledge of molecular compositions is presented. If used in conjunction with a methodology to determine molecular compositions, mass and electron density can be accurately calculated from CT HU.

Brain aggregates of β amyloid (βA) protein plaques have been widely recognized as associated with many neurodegenerative diseases, and their identification can assist in the early diagnosis of Alzheimer's disease. We investigate the feasibility of using a spectral x-ray coherent scatter system with a silicon strip photon-counting detector for identifying brain βA protein plaques. This approach is based on differences in the structure of amyloid, white and grey matter in the brain. We simulated an energy- and angular-dispersive X-ray diffraction system with an x-ray pencil beam and Silicon strip sensor, energy-resolving detectors. The polychromatic beam is geometrically focused toward a region of interest in the brain. First, the open-source MC-GPU code for Monte Carlo transport was modified to accommodate the detector model. Second, brain phantoms with and without βA were simulated to assess the method and determine the radiation dose required to obtain acceptable statistical power. For βA targets of 3, 4 and 5 mm sizes in a 15-cm brain model, the required incident exposure was about 0.44 mR from a 60 kVp tungsten spectrum and 3.5 mm of added aluminum filtration. The results suggest that the proposed x-ray coherent scatter technique enables the use of high energy x-ray spectra and therefore has the potential to be used for accurate in vivo detection and quantification of βA in the brain within acceptable radiation dose levels.

Background The presence of a radiopaque or digital anatomical side marker (ASM) is an important diagnostic feature on radiographs and should be a routine feature on every radiographic image. Despite its importance, research has indicated numerous instances where ASMs were absent which have the potential to lead to adverse events. To date, few studies have systematically examined the use of ASMs in clinical practice and explored medical imaging professionals’ perspectives on ASM use. This research aimed to address this knowledge gap. Methods This investigation was conducted in two stages. Stage 1 involved a retrospective clinical audit of 421 randomly selected radiographs within 12-months at a pediatric medical imaging department. The data were analyzed for overall presence and type of marker use. Stage 2 comprised of semi-structured interviews with 11 radiographers to garner their perspectives on ASM use, and barriers and enablers to their use in clinical practice. The interviews were transcribed verbatim and thematically analyzed. Results The overall presence of ASMs (radiopaque and digital) was observed on 99 per cent of radiographs. There was a noticeable shift towards the use of digital (78.8 per cent) compared to radiopaque ASMs (20.2 per cent), highlighting the growing trend towards using ASM in post-processing. A handful of images (N = 4) did not include any ASMs. Semi-structured interviews revealed multifaceted barriers (time, infection precautions, and patient factors) and few enablers (professionalism, legal requirement) for ASM use. Conclusion This investigation, informed by quantitative and qualitative research paradigms, has shed new light on an important area of radiography practice. While missing ASMs were a small feature, there continue to remain opportunities where best practice standards can be improved. The increasing use of digital ASMs potentially highlights a shift in clinical practice standards.

Dislocation in hindlimb tarsals are being observed at a low, but persistent frequency in adult male mice from C57BL/6N substrains. Clinical signs included a sudden onset of mild to severe unilateral or bilateral tarsal abduction, swelling, abnormal hindlimb morphology and lameness. Contraction of digits and gait abnormalities were noted in multiple cases. Radiographical and histological examination revealed caudal dislocation of the calcaneus and partial dislocation of the calcaneoquartal (calcaneous-tarsal bone IV) joint. The detection, frequency, and cause of this pathology in five large mouse production and phenotyping centres (MRC Harwell, UK; The Jackson Laboratory, USA; The Centre for Phenogenomics, Canada; German Mouse Clinic, Germany; Baylor College of Medicine, USA) are discussed.

This study evaluated the von Mises stress (MPa) and equivalent strain occurring around monolithic yttria-zirconia (Zir) implant using three clinically simulated finite element analysis (FEA) models for a missing maxillary central incisor. Two unidentified patients' cone-beam computed tomography (CBCT) datasets with and without right maxillary central incisor were used to create the FEA models. Three different FEA models were made with bone structures that represent a healed socket (HS), reduced bone width edentulous site (RB), and immediate extraction socket with graft (EG). A one-piece abutment-implant fixture mimicking Straumann Standard Plus tissue level RN 4.1 X 11.8mm, for titanium alloy (Ti) and Zir were modeled. 178 N oblique load and 200 N vertical load were used to simulate occlusal loading. Von Mises stress and equivalent strain values for around each implant model were measured. Within the HS and RB models the labial-cervical region in the cortical bone exhibited highest stress, with Zir having statistically significant lower stress-strain means than Ti in both labial and palatal aspects. For the EG model the labial-cervical area had no statistically significant difference between Ti and Zir; however, Zir performed better than Ti against the graft. FEA models suggest that Ti, a more elastic material than Zir, contributes to the transduction of more overall forces to the socket compared to Zir. Thus, compared to Ti implants, Zir implants may be less prone to peri-implant bone overloading and subsequent bone loss in high stress areas especially in the labial-cervical region of the cortical bone. Zir implants respond to occlusal loading differently than Ti implants. Zir implants may be more favorable in non-grafted edentulous or immediate extraction with grafting.

The largest dinosaurs were enormous animals whose body mass placed massive gravitational loads on their skeleton. Previous studies investigated dinosaurian bone strength and biomechanics, but the relationships between dinosaurian trabecular bone architecture and mechanical behavior has not been studied. In this study, trabecular bone samples from the distal femur and proximal tibia of dinosaurs ranging in body mass from 23-8,000 kg were investigated. The trabecular architecture was quantified from micro-computed tomography scans and allometric scaling relationships were used to determine how the trabecular bone architectural indices changed with body mass. Trabecular bone mechanical behavior was investigated by finite element modeling. It was found that dinosaurian trabecular bone volume fraction is positively correlated with body mass similar to what is observed for extant mammalian species, while trabecular spacing, number, and connectivity density in dinosaurs is negatively correlated with body mass, exhibiting opposite behavior from extant mammals. Furthermore, it was found that trabecular bone apparent modulus is positively correlated with body mass in dinosaurian species, while no correlation was observed for mammalian species. Additionally, trabecular bone tensile and compressive principal strains were not correlated with body mass in mammalian or dinosaurian species. Trabecular bone apparent modulus was positively correlated with trabecular spacing in mammals and positively correlated with connectivity density in dinosaurs, but these differential architectural effects on trabecular bone apparent modulus limit average trabecular bone tissue strains to below 3,000 microstrain for estimated high levels of physiological loading in both mammals and dinosaurs.

The COVID-19 epidemic appears to have a catastrophic impact on global well-being and public health. More than 10 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. To this end, in this paper, we aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a recently published public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. We observe, in the case of transfer learning, the classification accuracy drops with an increased number of classes. Our comprehensive ROC and confusion metric analysis with 10-fold cross-validation strongly underpin our findings.

Background and purpose The complex anatomy of the temporal bone is difficult to understand and constitutes a challenge in the daily diagnostic routine even for experienced neuroradiologists. In the context of otoneurological (oVEMP) and preoperative diagnostics, the diagnosis of superior semicircular canal dehiscence (SSCD) is of great importance for Ear, Nose, and Throat (ENT) specialists. The gold standard for this diagnosis is a high-resolution CT (HRCT) of the temporal bone. In order to correctly diagnose SSCD, special oblique reconstructions are necessary in addition to standard (axial, coronal, sagittal) reconstructions. We evaluated the frequency of diagnosis and its location in HRCT in correlation with otoneurological examination. From this analysis, we present a new SSCD classification. This classification yields the potential of a differentiated analysis of the patient’s clinical symptoms with correlation to the cross-sectional anatomy and may lead to a differentiated therapy approach. Study design and setting We evaluated 1370 temporal bone scans of patients with residual hearing and verified 343 superior semicircular canal dehiscence (SSCD). We conducted a subgroup analysis of these 343 HRCT scans displaying a SSCD and used them as a basis to create a classification. Results Three location types of SSCD were identified. These were anterior type 1, superior type 2 and posterior type 3. Type 2 were significantly more frequent in both sexes. SSCD at this location can be overlooked if diagnosis is performed only in the standard axial plane, since it can only be visualized by means of double oblique reconstruction. We present a standardized reconstruction algorithm. Conclusion In total, three types of SSCD with differing incidences can be extrapolated from the locations. Superior type 2 is the most frequent one. Both sexes are affected with roughly equal incidence. The use of standardized double oblique reconstruction algorithm ensures that all three types are diagnosed in the HRCT.

Background Forensic dentistry identifies deceased individuals by comparing postmortem dental charts, oral-cavity pictures and dental X-ray images with antemortem records. However, conventional forensic dentistry methods are time-consuming and thus unable to rapidly identify large numbers of victims following a large-scale disaster. Objective Our goal is to automate the dental filing process by using intraoral scanner images. In this study, we generated and evaluated an artificial intelligence-based algorithm that classified images of individual molar teeth into three categories: (1) full metallic crown (FMC); (2) partial metallic restoration (In); or (3) sound tooth, carious tooth or non-metallic restoration (CNMR). Methods A pre-trained model was created using oral-cavity pictures from patients. Then, the algorithm was generated through transfer learning and training with images acquired from cadavers by intraoral scanning. Cross-validation was performed to reduce bias. The ability of the model to classify molar teeth into the three categories (FMC, In or CNMR) was evaluated using four criteria: precision, recall, F-measure and overall accuracy. Results The average value (variance) was 0.952 (0.000140) for recall, 0.957 (0.0000614) for precision, 0.952 (0.000145) for F-measure, and 0.952 (0.000142) for overall accuracy when the algorithm was used to classify images of molar teeth acquired from cadavers by intraoral scanning. Conclusion We have created an artificial intelligence-based algorithm that analyzes images acquired with an intraoral scanner and classifies molar teeth into one of three types (FMC, In or CNMR) based on the presence/absence of metallic restorations. Furthermore, the accuracy of the algorithm reached about 95%. This algorithm was constructed as a first step toward the development of an automated system that generates dental charts from images acquired by an intraoral scanner. The availability of such a system would greatly increase the efficiency of personal identification in the event of a major disaster.

The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients. A new research direction is gaining increasing attention that involves the use of hybrid approaches based on natural language processing and computer vision techniques to create auto medical report generation systems. The auto report generator, producing radiology reports, will significantly reduce the burden on doctors and assist them in writing manual reports. Because the sensitivity of chest X-ray (CXR) findings provided by existing techniques not adequately accurate, producing comprehensive explanations for medical photographs remains a difficult task. A novel approach to address this issue was proposed, based on the continuous integration of convolutional neural networks and long short-term memory for detecting diseases, followed by the attention mechanism for sequence generation based on these diseases. Experimental results obtained by using the Indiana University CXR and MIMIC-CXR datasets showed that the proposed model attained the current state-of-the-art efficiency as opposed to other solutions of the baseline. BLEU-1, BLEU-2, BLEU-3, and BLEU-4 were used as the evaluation metrics.

Objectives To compare the osteoporosis-predicting ability of computed tomography (CT) indexes in abdomen-pelvic CT using the proximal femur and the reliability of measurements in two- and three-dimensional analyses. Methods Four hundred thirty female patients (age range, 50–96 years) who underwent dual-energy X-ray absorptiometry and abdominal-pelvic CT within 1 month were retrospectively selected. The volumes of interest (VOIs) from the femoral head to the lesser trochanter and the femoral neck were expressed as 3DFemur. Round regions of interest (ROIs) of image plane drawn over the femoral neck touching the outer cortex were determined as 2Dcoronal. In HU histogram analysis (HUHA), the percentages of HU histogram ranges related to the ROI or VOI were classified as HUHAFat (Bone (126 HU≤). Diagnostic performance, correlation analysis and measurement reliability were analyzed by receiver operating characteristic curves, correlation coefficient and interobserver correlation coefficient (ICC), respectively. Results AUCs of each HUHA and mean-HU measurement on 2D-ROI and 3D-VOI were 0.94 or higher (P < 0.001). Both 3DFemur-Mean-HU and 3DFemur-HUHABone showed the highest AUC (0.96). The cut-off value of 3DFemur-Mean-HU was 231HU or less, (sensitivity: 94.8%; specificity: 85.0%; correlation coefficient: −0.65; P P > 0.05). Reliability of the 3D-VOI measurement showed perfect agreement (ICC ≥ 0.94), and 2D-ROI showed moderate to good agreement (ICC range: 0.63~0.84). Conclusions CT indexes on 3D-VOI for predicting femoral osteoporosis showed similar diagnostic accuracy with better reproducibility of measurement, compared with 2D-ROI.

Objectives To assess the distribution of bone erosions and two erosion scores in the feet of patients with gout and analyze the association between erosion scores and monosodium urate (MSU) crystal deposition using dual-energy computed tomography (DECT). Materials and methods We included all patients who underwent DECT of both feet between 2016 and 2019 in our radiology department, with positive detection of MSU deposits. Data on sex, age, treatment, serum urate, and DECT urate volumes were obtained. CT images were analyzed to score bone erosions in 31 sites per foot by using the semi-quantitative method based on the Rheumatoid Arthritis MRI Scoring (RAMRIS) system and the Dalbeth-simplified score. Reproducibility for the two scores was calculated with intraclass correlation coefficients (ICCs). Correlations between clinical features, erosion scores and urate crystal volume were analyzed by the Spearman correlation coefficient (r). Results We studied 61 patients (mean age 62.0 years); 3,751 bones were scored. The first metatarsophalangeal joint and the midfoot were the most involved in terms of frequency and severity of bone erosions. The distribution of bone erosions was not asymmetrical. The intra- and inter-observer reproducibility was similar for the RAMRIS and Dalbeth-simplified scores (ICC 0.93 vs 0.94 and 0.96 vs 0.90). DECT urate volume was significantly correlated with each of the two erosion scores (r = 0.58–0.63, p < 0.001). There was a high correlation between the two scores (r = 0.96, p < 0.001). Conclusions DECT demonstrates that foot erosions are not asymmetric in distribution and predominate at the first ray and midfoot. The two erosion scores are significantly correlated with DECT urate volume. An almost perfect correlation between the RAMRIS and Dalbeth-simplified scores is observed.

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.

The most important precondition for correction of the affected forearm using data from the contralateral side is that the left and right bone features must be similar, in order to develop patient-specific instruments (PSIs) and/or utilize computer-assisted orthopedic surgery (CAOS). The forearm has complex anatomical structure, and most people use their dominant hand more than their less dominant hand, sometimes resulting in asymmetry of the upper limbs. The aim of this study is to investigate differences of the bilateral forearm bones through a quantitative comparison of whole bone parameters including length, volume, bowing, and twisting parameters, and regional shape differences of the forearm bones. In total, 132 bilateral 3D radii and ulnae 3D models were obtained from CT images, whole bone parameters and regional shape were analyzed. Statistically significant differences in whole bone parameters were not shown. Regionally, the radius shows asymmetry in the upper section of the central part to the upper section of the distal part. The ulna shows asymmetry in the lower section of the proximal part to the lower section of the central part. Utilizing contralateral side forearm bones to correct the affected side may be feasible despite regional differences in the forearm bones of around 0.5 mm.

Thermal therapies such as radiofrequency ablation (RFA) are gaining widespread clinical adoption in the local treatment of skeletal metastases. RFA has been shown to successfully destroy tumor cells, yet the impact of RFA on the quality of the surrounding bone has not been well characterized. RFA treatment was performed on femora of rats with bone metastases (osteolytic and osteoblastic) and healthy age matched rats. Histopathology, second harmonic generation imaging and backscatter electron imaging were used to characterize changes in the structure, organic and mineral components of the bone after RFA. RFA treatment was shown to be effective in targeting tumor cells and promoting subsequent new bone formation without impacting the surrounding bone negatively. Mineralization profiles of metastatic models were significantly improved post-RFA treatment with respect to mineral content and homogeneity, suggesting a positive impact of RFA treatment on the quality of cancer involved bone. Evaluating the impact of RFA on bone quality is important in directing the growth of this minimally invasive therapeutic approach with respect to fracture risk assessment, patient selection, and multimodal treatment planning.

BackgroundThe COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, especially in underdeveloped countries. There is a clear need to develop novel computer-assisted diagnosis tools to provide rapid and cost-effective screening in places where massive traditional testing is not feasible. Lung ultrasound is a portable, easy to disinfect, low cost and non-invasive tool that can be used to identify lung diseases. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions, being a viable alternative for screening and diagnosing COVID-19.ObjectiveTo evaluate and compare the performance of deep-learning techniques for detecting COVID-19 infections from lung ultrasound imagery.MethodsWe adapted different pre-trained deep learning architectures, including VGG19, InceptionV3, Xception, and ResNet50. We used the publicly available POCUS dataset comprising 3326 lung ultrasound frames of healthy, COVID-19, and pneumonia patients for training and fine-tuning. We conducted two experiments considering three classes (COVID-19, pneumonia, and healthy) and two classes (COVID-19 versus pneumonia and COVID-19 versus non-COVID-19) of predictive models. The obtained results were also compared with the POCOVID-net model. For performance evaluation, we calculated per-class classification metrics (Precision, Recall, and F1-score) and overall metrics (Accuracy, Balanced Accuracy, and Area Under the Receiver Operating Characteristic Curve). Lastly, we performed a statistical analysis of performance results using ANOVA and Friedman tests followed by post-hoc analysis using the Wilcoxon signed-rank test with the Holm’s step-down correction.ResultsInceptionV3 network achieved the best average accuracy (89.1%), balanced accuracy (89.3%), and area under the receiver operating curve (97.1%) for COVID-19 detection from bacterial pneumonia and healthy lung ultrasound data. The ANOVA and Friedman tests found statistically significant performance differences between models for accuracy, balanced accuracy and area under the receiver operating curve. Post-hoc analysis showed statistically significant differences between the performance obtained with the InceptionV3-based model and POCOVID-net, VGG19-, and ResNet50-based models. No statistically significant differences were found in the performance obtained with InceptionV3- and Xception-based models.ConclusionsDeep learning techniques for computer-assisted analysis of lung ultrasound imagery provide a promising avenue for COVID-19 screening and diagnosis. Particularly, we found that the InceptionV3 network provides the most promising predictive results from all AI-based techniques evaluated in this work. InceptionV3- and Xception-based models can be used to further develop a viable computer-assisted screening tool for COVID-19 based on ultrasound imagery.

Structural and thermodynamic factors which may influence burnt bone survivorship in archaeological contexts have not been fully described. A highly controlled experimental reference collection of fresh, modern bone burned in temperature increments 100–1200˚C is presented here to document the changes to bone tissue relevant to preservation using Fourier transform infrared spectroscopy and X-ray diffraction. Specific parameters investigated here include the rate of organic loss, amount of bone mineral recrystallization, and average growth in bone mineral crystallite size. An archaeological faunal assemblage ca. 30,000 years ago from Tolbor-17 (Mongolia) is additionally considered to confirm visibility of changes seen in the modern reference sample and to relate structural changes to commonly used zooarchaeological scales of burning intensity. The timing of our results indicates that the loss of organic components in both modern and archaeological bone burnt to temperatures up to 700˚C are not accompanied by growth changes in the average crystallite size of bone mineral bioapatite, leaving the small and reactive bioapatite crystals of charred and carbonized bone exposed to diagenetic agents in depositional contexts. For bones burnt to temperatures of 700˚C and above, two major increases in average crystallite size are noted which effectively decrease the available surface area of bone mineral crystals, decreasing reactivity and offering greater thermodynamic stability despite the mechanical fragility of calcined bone. We discuss the archaeological implications of these observations within the context of Tolbor-17 and the challenges of identifying anthropogenic fire.

Analysis using human body models has been performed to reduce the impact of accidents; however, no analysis has shown a relationship between lumbar and pelvic/spine angle and seat belts in reducing human damage from accidents. Lumbar and pelvic/spine angles were measured in 75 individuals and the measurements were used to create three different angles for the Total Human Model for Safety model. In the present study, we focused on lumber lordosis (LL) and pelvic angle (PA). A normal distribution and histogram were used for analysis of PA (01, 10, and 50). The Total Human Model for Safety, including LL and PA, was corrected using finite element software. Simulations were conducted under the conditions of the Japan New Car Assessment Programme (JNCAP) 56 kph full lap frontal impact. Using the results of the FEM, the amount of lap-belt cranial sliding-up, anterior movement of the pelvis, posterior tilt of the pelvis, head injury criterion (HIC), second cervical vertebrae (C2) compressive load, C2 moment, chest deflectiou (upper, middle, and lower), left and right femur load, and shoulder belt force were measured. The lap-belt cranial sliding-up was 1.91 and 2.37 for PA10 and PA01, respectively, compared to PA50; the anterior movement of the pelvis was 1.08 and 1.12 for PA10 and PA01, respectively; and the posterior tilt of the pelvis was 1.1 and 1.18 for PA10 and PA01, respectively. HIC was 1.13 for PA10 and 1.58 for PA01; there was no difference in C2 compressive load by PA, but C2 moment increased to 1.59 for PA10 and 2.72 for PA01. It was found that as LL increases and the PA decreases, the seat belt becomes likely to catch the iliac bone, making it harder to cause injury. This study could help to reconsider the safe seat and seatbelt position in the future.

The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models’ loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.

In longitudinal studies, in vivo micro-Computed Tomography (microCT) imaging is used to investigate bone changes over time due to interventions in mice. However, ionising radiation can provoke significant variations in bone morphometric parameters. In a previous study, we evaluated the effect of reducing the integration time on the properties of the mouse tibia measured from microCT images. A scanning procedure (100 ms integration time, 256 mGy nominal radiation dose) was selected as the best compromise between image quality and radiation dose induced on the animal. In this work, the effect of repeated in vivo scans has been evaluated using the selected procedure. The right tibia of twelve female C57BL/6 (six wild type, WT, six ovariectomised, OVX) and twelve BALB/c (six WT, six OVX) mice was scanned every two weeks, starting at week 14 of age. At week 24, mice were sacrificed and both tibiae were scanned. Standard trabecular and cortical morphometric parameters were calculated. The spatial distribution of densitometric parameters (e.g. bone mineral content) was obtained by dividing each tibia in 40 partitions. Stiffness and strength in compression were estimated using homogeneous linear elastic microCT-based micro-Finite Element models. Differences between right (irradiated) and left (non-irradiated control) tibiae were evaluated for each parameter. The irradiated tibiae had higher Tb.Th (+3.3%) and Tb.Sp (+11.6%), and lower Tb.N (-14.2%) compared to non-irradiated tibiae, consistently across both strains and intervention groups. A reduction in Tb.BV/TV (-14.9%) was also observed in the C57BL/6 strain. In the OVX group, a small reduction was also observed in Tt.Ar (-5.0%). In conclusion, repeated microCT scans (at 256 mGy, 5 scans, every two weeks) had limited effects on the mouse tibia, compared to the expected changes induced by bone treatments. Therefore, the selected scanning protocol is acceptable for measuring the effect of bone interventions in vivo.

ObjectTo compare the clinical and radiological outcomes between patients with long posterior cervical fusion (PCF) in which fusion stopped at C7 versus patients in which fusion crossed the cervicothoracic junction (CTJ).MethodsThe patients were divided into 2 groups on the basis of the lower-most instrumented vertebra (LIV); C7 group patients (n = 25) and upper thoracic (UT) group (n = 21). We analyzed the visual analogue scale of arm/neck pain, Japanese Orthopedic Association (JOA) score, and neck disability index (NDI). And we also measured the following parameters: (1) pseudomotion of fused segments; (2) C2-C7 sagittal vertical axis; (3) T1 slope; and (4) C2-C7 lordosis.ResultsArm and neck pain were similar in both groups pre- and postoperatively. Interestingly, mean postoperative NDI score in the UT group was significant worse when compared with the C7 group (9.7±4.6 vs. 14.2±3.7, p = 0.006). Although UT patients had longer fusion levels, the fusion rates were not significantly different between the C7 and UT groups (96.0% vs. 90.5%; p = 0.577). The radiographic parameters did not show any significant differences between the groups at final follow-up.ConclusionsOur study demonstrates that multi-level PCF stopping at C7 does not negatively affect C7-T1 segment failure, fusion rate, neck pain, neurologic outcomes, and global sagittal alignment of the cervical spine. Hence, it is unnecessary to extend the long PCF levels caudally across the healthy CTJ for fear of development of adjacent segmental disease (ASD) at the C7-T1 segment.

This study aimed to identify the differentiating parameters of the spinal curves’ 2D projections through a hierarchical classification of the 3D spinal curve in adolescent idiopathic scoliosis (AIS). A total number of 103 right thoracic left lumbar pre-operative AIS patients were included retrospectively and consecutively. A total number of 20 non-scoliotic adolescents were included as the control group. All patients had biplanar X-rays and 3D reconstructions of the spine. The 3D spinal curve was calculated by interpolating the center of vertebrae and was isotropically normalized. A hierarchical classification of the normalized spinal curves was developed to group the patients based on the similarity of their 3D spinal curve. The spinal curves’ 2D projections and clinical spinal measurements in the three anatomical planes were then statistically compared between these groups and between the scoliotic subtypes and the non-scoliotic controls. A total of 5 patient groups of right thoracic left lumbar AIS patients were identified. The characteristics of the posterior-anterior and sagittal views of the spines were: Type 1: Normal sagittal profile and S shape axial view. T1 is leveled or tilted to the right in the posterior view. Type 2: Hypokyphotic and a V shape axial view. T1 is tilted to the left in the posterior view. Type 3: Hypokyphotic (only T5-T10) and frontal imbalance, S shape axial view. T1 is leveled or tilted to the right, and 3 frontal curves. Type 4: Flat sagittal profile (T1-L2), slight frontal imbalance with a V shape axial view, T1 tilted to the left. Type 5: flat sagittal profile and forward trunk shift with a proximal kyphosis and S shape axial view. T1 is leveled or tilted to the right. In conclusion, a hierarchical classification of the 3D scoliotic spine allowed identifying various distinguishing features of the spinal curves in patients with a right thoracic curve in an orderly fashion. The subtypes’ characteristics resulting from this 3D classification can be identified from the pairs of the frontal and sagittal spinal curves i.e. X-rays in right thoracic AIS patients.

Gastroretentive (GR) systems are designed to prolong gastric residence time to allow sustained absorption and improve the oral bioavailability of drugs with a narrow absorption window in the upper part of the gastrointestinal tract. The present study aimed to develop a GR system for acyclovir using 3D printing technology and evaluate its in vivo pharmacokinetics after oral administration in Beagle dogs. The system consisted of a gastro-floating device, which can float in the gastric fluid, prepared by a fused deposition modeling 3D printer and conventional acyclovir sustained-release (SR) tablet. The acyclovir SR tablet was inserted to the floating device to allow sustained release of the drug in the stomach. The buoyancy and sustained-release property of the developed GR system were determined using an in vitro dissolution test, in vivo pharmacokinetic study, and abdominal X-ray imaging in Beagle dogs. The in vivo dissolution profiles of the GR system were also predicted based on the in vivo pharmacokinetic data using a population pharmacokinetic (POP-PK) model. In the dissolution test, the sustained-release characteristic of the GR system was identified with a time corresponding to 80% dissolution (T80) of 2.52 h. Following oral administration of the GR system, the time to reach the maximum concentration (Tmax) of acyclovir was significantly prolonged, whereas the maximum concentration (Cmax) decreased and the area under the curve increased compared with those obtained after the administration of immediate-release and SR tablets, indicating prolonged absorption. By X-ray imaging, we showed that the developed GR system stayed in the stomach for more than 12 h. The POP-PK model successfully described the observed plasma concentration-time data and predicted the in vivo biphasic dissolution profiles of the GR system, which was significantly different from the in vitro dissolution. The developed GR system could be applied to various drugs and had great prospects in the design and development of novel controlled-release formulations.

The present study aimed to develop a denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm for decreasing metal objects in digital tomosynthesis (DT) for arthroplasty by using projection data. For metal artifact reduction (MAR), we implemented a DnCNN-MARHR algorithm based on a training network (mini-batch stochastic gradient descent algorithm with momentum) to estimate the residual reference (140 keV virtual monochromatic [VM]) and object (70 kV with metal artifacts) images. For this, we used projection data and subtracted the estimated residual images from the object images, involving hybrid and subjectively reconstructed image usage (back projection and maximum likelihood expectation maximization [MLEM]). The DnCNN-MARHR algorithm was compared with the dual-energy material decomposition reconstruction algorithm (DEMDRA), VM, MLEM, established and commonly used filtered back projection (FBP), and a simultaneous algebraic reconstruction technique-total variation (SART-TV) with MAR processing. MAR was compared using artifact index (AI) and texture analysis. Artifact spread functions (ASFs) for images that were out-of-plane and in-focus were evaluated using a prosthesis phantom. The overall performance of the DnCNN-MARHR algorithm was adequate with regard to the ASF, and the derived images showed better results, without being influenced by the metal type (AI was almost equal to the best value for the DEMDRA). In the ASF analysis, the DnCNN-MARHR algorithm generated better MAR compared with that obtained employing usual algorithms for reconstruction using MAR processing. In addition, comparison of the difference (mean square error) between DnCNN-MARHR and the conventional algorithm resulted in the smallest VM. The DnCNN-MARHR algorithm showed the best performance with regard to image homogeneity in the texture analysis. The proposed algorithm is particularly useful for reducing artifacts in the longitudinal direction, and it is not affected by tissue misclassification.