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ObjectivesDiffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy.MethodsWhole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated.ResultsDistortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p−20). On average (±SD), participants’ largest error reduction was 11.8 mm (±3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI: 5.1-7.0); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p−12).DiscussionThese findings support the use of distortion correction techniques to improve localization of bone on DWI.Key Points-Diffusion weighted images of bone tissue undergo substantial spatial distortions when acquired with echo-planar imaging.-These distortions can be efficiently corrected with the reverse polarity gradient technique to generate diffusion images that more accurately reflect underlying anatomy.-In the context of bone tumor imaging where precise localization may be required, distortion correction techniques, such as reverse polarity gradient, should be applied.

This report describes the significance of the kinetic parameters (k-values) obtained from the analysis of dynamic positron emission tomography (PET) scans using the Hawkins model describing the pharmacokinetics of sodium fluoride ([18F]NaF) to understand bone physiology. Dynamic [18F]NaF PET scans may be useful as an imaging biomarker in early phase clinical trials of novel drugs in development by permitting early detection of treatment-response signals that may help avoid late-stage attrition.

Advance diagnostic and treatment modalities have improved outcomes for renal cell carcinoma (RCC) patients, but the prognosis for those with metastatic disease (mRCC) remains poor. As given metastatic distribution is critical in guiding treatment decisions for mRCC patients, we evaluated evolving metastatic patterns to assess if our current practice standards effectively address patient needs. A systematic literature review was performed to identify all publicly available prospective clinical trials in metastatic renal cell carcinoma (mRCC) from 1990 to 2018. A total of 16,899 mRCC patients from 127 qualified phase I–III clinical trials with metastatic site documentations were included for analysis for incidence of metastases to lung, liver, bone, and lymph nodes (LNs) over time. Studies were categorized into three treatment eras based on the timing of regulatory approval: Cytokine Era (1990-2004), vascular endothelial growth factor/tyrosine kinase inhibitor (TKI) Era (2005-2016), and immune checkpoint inhibitor/TKI Era (ICI-TKI, 2017-2018) and also classified as first-line only (FLO) or second-line and beyond (SLB). Overall, an increase in the incidence of bone and LNs metastases in FLO and SLB, and lung metastases in FLO, was seen over the three treatment eras. Generally, the burden of disease is higher in SLB when compared with FLO. Importantly, in the ICI-TKI era, the incidences of bone metastasis are 28% in FLO and 29% in SLB settings. The disease burden in patients with mRCC has increased steadily over the past three decades. Given the unexpectedly high rate of bone metastasis, routine dedicated bone imaging should be considered in all patients with mRCC.

In this study, phosphate-buffered saline solution (PBS) was successfully radiolabeled with technetium-99m. The biodistribution profile and bone accumulation of the [99mTc]Tc-PBS system were evaluated in healthy swiss mice. The radiolabeling of the system was successfully obtained, yielding around 90 % of radiochemical purity. The system stability was tested in the presence of saline medium at 25 oC and mouse plasma at 37 oC, and results revealed that [99mTc]Tc-PBS showed excellent stability over time, in both media. It was observed a biphasic clearance profile, with fast elimination from the bloodstream by renal filtration. The radiolabeled system revealed a high specificity bone accumulation was observed, demonstrating an uptake behavior from 20 to 50 times higher than muscle, used as control. From all results obtained, the system constituted by [99mTc]Tc-PBS can be considered a potential selective agent for bone imaging.

Pigmented villonodular synovitis (PVNS) is a rare proliferative synovial benign disorder, which is characterized by villonodular hyperplasia of joints, tendon sheath, and synovium; invasion of adjacent tissue; and sometimes visible hemosiderin deposition. Studies regarding bone scan findings of PVNS were relatively limited. Here, we report our findings on 99mTc-MDP 3-phase bone scan with SPECT/CT images on delayed phase in 3 patients with joint PVNS.

Background: BSI calculated from bone scintigraphy using 99mtechnetium-methylene diphosphonate (99mTc-MDP) is used as a quantitative indicator of metastatic bone involvement in bone metastasis diagnosis, therapeutic effect assessment, and prognosis prediction. However, the BONE NAVI, which calculates BSI, only supports bone scintigraphy using 99mTc-MDP. Aims: We developed a method in collaboration with the Tokyo University of Agriculture and Technology to calculate bone scan index (BSI) employing deep learning algorithms with bone scintigraphy images using 99mtechnetium-hydroxymethylene diphosphonate (99mTc-HMDP). We used a convolutional neural network (CNN), enabling the simultaneous processing of anterior and posterior bone scintigraphy images named CNNapis. Objectives: The purpose of this study is to investigate the usefulness of the BSI calculated by CNNapis as bone imaging and bone metabolic biomarkers in patients with bone metastases from prostate cancer. Methods: At our hospital, 121 bone scintigraphy scans using 99mTc-HMDP were performed and analyzed to examine bone metastases from prostate cancer, revealing the abnormal accumulation of radioisotope (RI) at bone metastasis sites. Blood tests for serum prostate-specific antigen (PSA) and alkaline phosphatase (ALP) were performed concurrently. BSI values calculated by CNNapis were used to quantify the metastatic bone tumor involvement. Correlations between BSI and PSA and between BSI and ALP were calculated. Subjects were divided into four groups by BSI values (Group 1, 0 to 10), and the PSA and ALP values in each group were statistically compared. Results: Patients diagnosed with bone metastases after bone scintigraphy were also diagnosed with bone metastases using CNNapis. BSI corresponding to the range of abnormal RI accumulation was calculated. PSA and BSI (r = 0.2791) and ALP and BSI (r = 0.6814) correlated positively. Significant intergroup differences in PSA between Groups 1 and 2, Groups 1 and 4, Groups 2 and 3, and Groups 3 and 4 and in ALP between Groups 1 and 4, Groups 2 and 4, and Groups 3 and 4 were found. Conclusion: BSI calculated using CNNapis correlated with ALP and PSA values and is useful as bone imaging and bone metabolic biomarkers, indicative of the activity and spread of bone metastases from prostate cancer.

To develop an electronic navigation system to increase reliability and comparability in the ultrasound screening of developmental dysplasia of the hip (DDH). The impact of the navigation system on transducer positioning and on sonographic measurements according to Graf was analyzed. Twenty hips in newborns were examined sonographically using a new navigation system capable of detecting the transducer and pelvis position in order to calculate the relative tilt in the frontal, axial, and sagittal-plane. In each newborn an ultrasound image was obtained conventionally according to Graf and a second image using the sonographic navigation system. Relative roll and pitch angles and sonographic measurements were analyzed using paired T-tests and Levene-tests. Relative tilt angles in the conventional group ranged from − 8.9° to 14.3° (frontal-plane) and − 23.8° to 14.2° (axial-plane). In the navigation-assisted group ranges from − 3.0° to 3.5° and − 2.8° to 4.5° were observed. Variances were significantly lower in the navigation-assisted group (p p = 0.004 respectively). The navigation system allowed for a significant reduction of relative tilt angles between the transducer and the newborn pelvis, thus supporting an optimal transducer positioning. This is a promising approach to improve reproducibility and reliability in the ultrasound screening for DDH.

Offset restoration in total hip arthroplasty (THA) is associated with postoperative range of motion (ROM) and gait kinematics. We aimed to research into the impact of high offset (HO) and standard stems on postoperative ROM. 121 patients received cementless THA through a minimally-invasive anterolateral approach. A 360° hip ROM analysis software calculated impingement-free hip movement based on postoperative 3D-CTs compared to ROM values necessary for activities of daily living (ADL). The same model was then run a second time after changing the stem geometry between standard and HO configuration with the implants in the same position. HO stems showed higher ROM for all directions between 4.6 and 8.9° (p 0.03). Patient-individual restoration of offset is crucial for free ROM in THA. Both over and underrestoration of offset should be avoided.

Purpose We identified the risk predictors related to prostate cancer (PCa) metastasis using contemporary data in a community setting. Then, we assessed the performance of indications for bone imaging recommended from the NCCN, AUA and EAU guidelines. Methods Using the Surveillance, Epidemiology, and End Results database (2010–2015), we collected clinicopathological information from PCa patients. The associated risk factors found by multivariate analyses were used to establish forest plots and nomograms for distant metastasis (DM) and bone(s)-only metastasis (BM). We next evaluated the NCCN, AUA and EAU guidelines indications for the discovery of certain subgroups of patients who should receive bone imaging. Results A total of 120,136 patients were eligible for analysis, of which 96.7% had no metastasis. The odds ratios of positive DM and BM results were 13.90 times and 15.87 times higher in patients with a histologic grade group (GG) 5 than in the reference group. The concordance index of the nomograms based on race, age, T/N stage, PSA, GG, percentage of positive scores for predicting DM and BM was 0.942 and 0.928, respectively. Performance of the NCCN, AUA and EAU guidelines was high and relatively similar in terms of sensitivity (93.2–96.9%) and negative predictive value (99.8–99.9%). NCCN guidelines had the highest accuracy, specificity and positive likelihood ratio, while negative likelihood ratio was lowest in AUA guideline. Conclusion Histologic GG 5 was the foremost factor for DM and BM. NCCN-based recommendations may be more rational in clinical practice. Nomograms predicting metastasis demonstrate high accuracy.

One challenge in applying deep learning to medical imaging is the lack of labeled data. Although large amounts of clinical data are available, acquiring labeled image data is difficult, especially for bone scintigraphy (i.e., 2D bone imaging) images. Bone scintigraphy images are generally noisy, and ground-truth or gold standard information from surgical or pathological reports may not be available. We propose a novel neural network model that can segment abnormal hotspots and classify bone cancer metastases in the chest area in a semisupervised manner. Our proposed model, called MaligNet, is an instance segmentation model that incorporates ladder networks to harness both labeled and unlabeled data. Unlike deep learning segmentation models that classify each instance independently, MaligNet utilizes global information via an additional connection from the core network. To evaluate the performance of our model, we created a dataset for bone lesion instance segmentation using labeled and unlabeled example data from 544 and 9,280 patients, respectively. Our proposed model achieved mean precision, mean sensitivity, and mean F1-score of 0.852, 0.856, and 0.848, respectively, and outperformed the baseline mask region-based convolutional neural network (Mask R-CNN) by 3.92%. Further analysis showed that incorporating global information also helps the model classify specific instances that require information from other regions. On the metastasis classification task, our model achieves a sensitivity of 0.657 and a specificity of 0.857, demonstrating its great potential for automated diagnosis using bone scintigraphy in clinical practice.

Human-directed selective breeding has modified the phenotype of the modern Persian cat towards an extreme brachycephalic phenotype (‘peke-face’ Persian), which originates from a spontaneous mutation that first appeared in the 1950s in traditional Persian types. It was suggested that the peke-face phenotype results from pathologic skull development and might represent a craniosynostosis of the coronal sutures. We followed this hypothesis and investigated the time dependent status of the neurocranial sutures and synchondroses in an ontogenetic series of doll-faced and peke-faced Persian cats compared to Domestic Shorthair cats (DSHs). Cranial suture closure was assessed by examining an ontogenetic series of formalin-fixed head specimens (n = 55) and dry skulls (n = 32) using micro-computed tomography. Sagittal, metopic, coronal and lambdoid sutures as well as intersphenoidal, spheno-occipital and spheno-ethmoid synchondroses were examined. Logistic regression analysis was performed to test the global effect of age on suture closure within a group of peke-face Persians, doll-face Persians and DSHs and the 50% probability of having a closed suture was calculated and compared between groups. Age was a perfect predictor for the condition of the coronal sutures in peke-face Persians. Coronal sutures were found to be closed at 0–0.3 months. In doll-face and DSHs, coronal sutures were open throughout the lifetime with the exception of a few very old cats. Results of this study confirmed a coronal craniosynostosis that likely causes the extreme brachycephalic skull morphology in the peke-face Persian.

The aim of the study was to assess the quality and reproducibility of reducing the injected [18F] sodium fluoride ([18F]NaF) dose while maintaining diagnostic imaging quality in bone imaging in a preclinical skeletal model using digital photon counting PET (dPET) detector technology. Beagles (n = 9) were administered three different [18F]NaF doses: 111 MBq (n = 5), 20 MBq (n = 5), and 1.9 MBq (n = 9). Imaging started ≃45 min post-injection for ≃30 min total acquisition time. Images were reconstructed using Time-of-Flight, ultra-high definition (voxel size of 1 × 1 × 1 mm3), with 3 iterations and 3 subsets. Point spread function was modeled and Gaussian filtering was applied. Skeleton qualitative and quantitative molecular image assessment was performed. The overall diagnostic quality of all images scored excellent (61%) and acceptable (39%) by all the reviewers. [18F]NaF SUVmean showed no statistically significant differences among the three doses in any of the region of interest assessed. This study demonstrated that a 60-fold [18F]NaF dose reduction was not significantly different from the highest dose, and it had not significant effect on overall image quality and quantitative accuracy. In the future, ultra-low dose [18F]NaF dPET/CT imaging may significantly decrease PET radiation exposure to preclinical subjects and personnel.

Cementum, the tissue attaching mammal tooth roots to the periodontal ligament, grows appositionally throughout life, displaying a series of circum-annual incremental features. These have been studied for decades as a direct record of chronological lifespan. The majority of previous studies on cementum have used traditional thin-section histological methods to image and analyse increments. However, several caveats have been raised in terms of studying cementum increments in thin-sections. Firstly, the limited number of thin-sections and the two-dimensional perspective they impart provide an incomplete interpretation of cementum structure, and studies often struggle or fail to overcome complications in increment patterns that complicate or inhibit increment counting. Increments have been repeatedly shown to both split and coalesce, creating accessory increments that can bias increment counts. Secondly, identification and counting of cementum increments using human vision is subjective, and it has led to inaccurate readings in several experiments studying individuals of known age. Here, we have attempted to optimise a recently introduced imaging modality for cementum imaging; X-ray propagation-based phase-contrast imaging (PPCI). X-ray PPCI was performed for a sample of rhesus macaque (Macaca mulatta) lower first molars (n = 10) from a laboratory population of known age. PPCI allowed the qualitative identification of primary/annual versus intermittent secondary increments formed by splitting/coalescence. A new method for semi-automatic increment counting was then integrated into a purpose-built software package for studying cementum increments, to count increments in regions with minimal complications. Qualitative comparison with data from conventional cementochronology, based on histological examination of tissue thin-sections, confirmed that X-ray PPCI reliably and non-destructively records cementum increments (given the appropriate preparation of specimens prior to X-ray imaging). Validation of the increment counting algorithm suggests that it is robust and provides accurate estimates of increment counts. In summary, we show that our new increment counting method has the potential to overcome caveats of conventional cementochronology approaches, when used to analyse three-dimensional images provided by X-ray PPCI.

Bone is a preferred site for both primary and metastasis tumors. Current diagnosis of osteopathia typically relies on noninvasive skeleton radiography technology. However, due to the limited resolution of ionizing radiation, accurate diagnosis and effective identification impairment areas are still lacking. Near-infrared (NIR) bioimaging, especially in the NIR-II (1000-1700 nm) regions, can provide high sensitivity and spatiotemporal resolution bioimaging compared to the conventional radiography. Thus, NIR bioimaging affords intraoperative visualization and imaging-guided surgery, aiming to overcome challenges associated with theranostics of osteopathia and bone tumors. The present review aimed to summarize the latest evidence on the use of NIR probes for the targeting bone imaging. We further highlight the recent advances in bone photoX (X presents thermal, dynamic, and immuno) therapy through NIR probes, in particular combination with other customized therapeutic agents could provide high-efficiency treatment for bone tumors.

Objectives68Ga-P15-041 (68Ga-HBED-CC-BP) is a novel bone-seeking PET radiotracer, which can be readily prepared by using a simple kit formulation and an in-house 68Ga/68Ge generator. The aim of this study is to assess the potential human application of 68Ga-P15-041 for clinical PET/CT imaging and to compare its efficacy to detect bone metastases of different cancers with 99mTc-MDP whole-body bone scintigraphy (WBBS).MethodsInitial kinetic study using Patlak analysis and parametric maps were performed in five histopathologically proven cancer patients (three males, two females) using 68Ga-P15-041 PET/CT scan only. Another group of 51 histopathologically proven cancer patients (22 males, 29 females) underwent both 99mTc-MDP WBBS and 68Ga-P15-041 PET/CT scans within a week, sequentially. Using either pathology examination or follow-up CT or MRI scans as the gold standard, the diagnostic efficacy and receiver operating characteristic curve (ROC) of the two methods in identifying bone metastases were compared (p ResultsFifty-one patients were imaged, and 174 bone metastatic sites were identified. 68Ga-P15-041 PET/CT and 99mTc-MDP WBBS detected 162 and 81 metastases, respectively. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 68Ga-P15-041 PET/CT and 99mTc-MDP WBBS were 93.1% vs 81.8%, 89.8% vs 90.7%, 77.5% vs 69.2%, 97.2% vs 93.4% and 90.7% vs 88.4%, respectively. Our results showed that the mean of SUVmax was significantly higher in metastases than that in benign lesions, 15.1 ± 6.9 vs. 5.6 ± 1.3 (P Conclusions68Ga-P15-041 PET/CT, offering a rapid bone scan and high contrast images in minutes, is superior to the current method of choice in detecting bone metastases. It is reasonable to suggest that 68Ga-P15-041 PET/CT could become a valuable routine nuclear medicine procedure in providing excellent images for detecting bone metastases in cancer patients. 68Ga-P15-041 could become a valuable addition expanding the collection of 68Ga-based routine nuclear medicine procedures where 18F fluoride is not currently available.

A major cause of bone mass loss worldwide is osteoporosis. X-ray is considered to be the gold-standard technique to diagnose this disease. However, there is currently a need for an alternative modality due to the ionizing radiations used in X-rays. In this vein, we conducted a numerical study herein to investigate the feasibility of using microwave tomography (MWT) to detect bone density variations that are correlated to variations in the complex relative permittivity within the reconstructed images. This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). Three anatomically-realistic human leg models based on magnetic resonance imaging reconstructions were created. Each model represents a leg with a distinct fat layer thickness, thus, the three models are for legs with thin, medium, and thick fat layers. In addition to using conventional matching media in the numerical study, the use of commercially available and cheap ultrasound gel was evaluated prior to bone image analysis. The inversion algorithm successfully localized bones in the thin and medium fat scenarios. In addition, bone volume variations were found to be inversely proportional to their relative permittivity in the reconstructed images with the root mean square error as low as 2.54. The observations found in this study suggest MWT as a promising bone imaging modality owing to its safe and non-ionizing radiations used in imaging objects with high quality.

Grading individual knee osteoarthritis (OA) features is a fine-grained knee OA severity assessment. Existing methods ignore following problems: (1) more accurately located knee joints benefit subsequent grades prediction; (2) they do not consider knee joints’ symmetry and semantic information, which help to improve grades prediction performance. To this end, we propose a SE-ResNext50-32x4d-based Siamese network with adaptive gated feature fusion method to simultaneously assess eight tasks. In our method, two cascaded small convolution neural networks are designed to locate more accurate knee joints. Detected knee joints are further cropped and split into left and right patches via their symmetry, which are fed into SE-ResNext50-32x4d-based Siamese network with shared weights, extracting more detailed knee features. The adaptive gated feature fusion method is used to capture richer semantic information for better feature representation here. Meanwhile, knee OA/non-knee OA classification task is added, which helps extract richer features. We specially introduce a new evaluation metric (top±1 accuracy) aiming to measure model performance with ambiguous data labels. Our model is evaluated on two public datasets: OAI and MOST datasets, achieving the state-of-the-art results comparing to competing approaches. It has the potential to be a tool to assist clinical decision making.

Purpose: This study aimed to analyze the reproducibility and reliability of the alignment parameters measured using the EOS image system in both limbs while standing with an even weight-bearing posture.Methods: Overall, 104 lower extremities in 52 patients were analyzed retrospectively. The patients stood with an even load over both lower extremities then rotated 15° in both directions. Two EOS images were acquired and 104 pairs of lower extremities were compared according to the position of the indexed lower extremities. Then, the inter-observer reliability of the EOS system and the inter-modality reliability between EOS and computed tomography (CT) were evaluated.Results: Femoro-tibial rotation (FTR) and tibial torsion demonstrated a significant difference between the anterior and posterior positions of the indexed lower extremity. All values except for FTR and tibial torsion demonstrated good or very good reliability. In the anterior position, FTR demonstrated moderate, and tibial torsion demonstrated poor reliability. In the posterior position, both FTR and tibial torsion demonstrated poor reliability. In the reliability analysis between the three-dimensional (3D) EOS model and 3D CT images, all measurements of the femur demonstrated very good reliability, but measurements of the tibia did not.Conclusions: For the coronal and sagittal alignment parameters measured by the EOS 3D system with rotated standing posture, except for the measurement including tibial torsion., there were no significant difference for either position of the indexed extremities with high agreement between the observers as well as with the CT 3D model.

Objective To investigate the impact of the use of different imaging units and projections on radiation dose and image quality during chest digital radiography (DR) in 3- and 4-year-old children. Methods Two hundred forty 3- and 4-year-old participants requiring chest DR were included; they were divided into three groups: supine anterior-posterior projection (APP), standing APP and standing posterior-anterior projection (PAP). Each group included 40 participants who were evaluated using the same imaging unit. The dose area product (DAP) and the entrance surface dose (ESD) were recorded after each exposure. The visual grading analysis score (VGAS) was used to evaluate image quality, and the longitudinal distance (LD) from the apex of the right lung to the apex of the right diaphragm was used to evaluate the inspiration extent. Results DAP and ESD were significantly lower in the standing PAP and APP groups than in the supine APP group (PPPPPP>0.05). Conclusion The radiation dose to superficial organs may be lower with standing PAP than with standing APP during chest DR. Standing PAP should be selected for chest DR in 3- and 4-year-old children, as it may decrease the required radiation dose.

Acquired adult flatfoot is a frequent deformity which implies multiple, complex and combined 3D modifications of the foot skeletal structure. The difficult thorough evaluation of the degree of severity pre-op and the corresponding assessment post-op can now be overcome by cone-beam (CBCT) technology, which can provide access to the 3D skeletal structure in weight-bearing. This study aims to report flatfoot deformities originally in 3D and in weight-bearing, with measurements taken using two different bone segmentation techniques. 21 such patients, with indication for surgical corrections, underwent CBCT (Carestream, US) while standing on one leg. From these scans, 3D models of each bone of the foot were reconstructed by using two different state-of-the-art segmentation tools: a semi-automatic (Mimics Innovation Suite, Materialise, Belgium), and an automatic (Bonelogic Ortho Foot and Ankle, Disior, Finland). From both reconstructed models, Principal Component Analysis was used to define anatomical reference frames, and original foot and ankle angles and other parameters were calculated mostly based on the longitudinal axis of the bones, in anatomical plane projections and in 3D. Both bone model reconstructions revealed a considerable valgus of the calcareous, plantarflexion and internal rotation of the talus, and typical Meary’s angles in the lateral and transverse plane projections. The mean difference from these angles between semi-automatic and automatic segmentations was larger than 3.5 degrees for only 3 of the 32 measurements, and a large number of these differences were not statistically significant. CBCT and the present techniques for bone shape reconstruction finally provide a novel and valuable 3D assessment of complex foot deformities in weight-bearing, eliminating previous limitations associated to unloaded feet and bidimensional measures. Corresponding measurements on the bone models from the two segmentation tools compared well. Other more representative measurements can be defined in the future using CBCT and these techniques.

Pneumonia remains the leading infectious cause of death in children under the age of five, killing about 700,000 children each year and affecting 7% of the world’s population. X-ray images of lung become the key to the diagnosis of this disease, skilled doctors in the diagnosis of a certain degree of subjectivity, if the use of computer-aided medical diagnosis to automatically detect lung abnormalities, will improve the accuracy of diagnosis. This research aims to introduce a deep learning technology based on the combination of Xception neural network and long-term short-term memory (LSTM), which can realize automatic diagnosis of patients with pneumonia in X-ray images. First, the model uses the Xception network to extract the deep features of the data, passes the extracted features to the LSTM, and then the LSTM detects the extracted features, and finally selects the most needed features. Secondly, in the training set samples, the traditional cross-entropy loss cannot more balance the mismatch between categories. Therefore, this research combines Pearson’s feature selection ideas, fusion of the correlation between the two loss functions, and optimizes the problem. The experimental results show that the accuracy rate of this paper is 96%, the receiver operator characteristic curve accuracy rate is 99%, the precision rate is 98%, the recall rate is 91%, and the F1 score accuracy rate is 94%. Compared with the existing technical methods, the research has achieved expected results on the currently available datasets. And assist doctors to provide higher reliability in the classification task of childhood pneumonia.

Cone beam computed tomography (CBCT) is now widely used in dentistry and growing areas of medical imaging. The presence of strong metal artifacts is however a major concern of using CBCT especially in dentistry due to the presence of highly attenuating dental restorations, fixed appliances, and implants. Virtual monoenergetic images (VMIs) synthesized from dual energy CT (DECT) datasets are known to reduce metal artifacts. Although several techniques exist for DECT imaging, they in general come with significantly increased equipment cost and not available in dental clinics. The objectives of this study were to investigate the feasibility of developing a low-cost dual energy CBCT (DE-CBCT) by retrofitting a regular CBCT scanner with a carbon nanotube (CNT) x-ray source with dual focal spots and corresponding low-energy (LE) and high-energy (HE) spectral filters. A testbed with a CNT field emission x-ray source (NuRay Technology, Chang Zhou, China), a flat panel detector (Teledyne, Waterloo, Canada), and a rotating object stage was used for this feasibility study. Two distinct polychromatic x-ray spectra with the mean photon energies of 66.7keV and 86.3keV were produced at a fixed 120kVp x-ray tube voltage by using Al+Au and Al+Sn foils as the respective LE and HE filters attached to the exist window of the x-ray source. The HE filter attenuated the x-ray photons more than the LE filter. The calculated post-object air kerma rate of the HE beam was 31.7% of the LE beam. An anthropomorphic head phantom (RANDO, Nuclear Associates, Hicksville, NY) with metal beads was imaged using the testbed and the images were reconstructed using an iterative volumetric CT reconstruction algorithm. The VMIs were synthesized using an image-domain basis materials decomposition method with energy ranging from 30 to 150keV. The results were compared to the reconstructed images from a single energy clinical dental CBCT scanner (CS9300, Carestream Dental, Atlanta, GA). A significant reduction of the metal artifacts was observed in the VMI images synthesized at high energies compared to those from the same object imaged by the clinical dental CBCT scanner. The ability of the CNT x-ray source to generate the output needed to compensate the reduction of photon flux due to attenuation from the spectral filters and to maintain the CT imaging time was evaluated. The results demonstrated the feasibility of DE-CBCT imaging using the proposed approach. Metal artifact reduction was achieved in VMIs synthesized. The x-ray output needed for the proposed DE-CBCT can be generated by a fixed-anode CNT x-ray source.

Background Vascular calcification (VC) is a common finding in chronic kidney disease (CKD) patients and predicts subsequent cardiovascular morbidity and mortality in this population. Vascular calcification is linked to disordered mineral metabolism and has been associated with bone histomorphometry changes in CKD. However, data on predialysis patients is scarce. Methods A cross-sectional study was conducted on a cohort of 56 CKD patients not yet on dialysis, who underwent a transiliac bone biopsy for histomorphometric evaluation after double tetracycline labeling. Patients had no previous exposure to calcium salts, vitamin D agents, steroids or bisphosphonates. Vascular calcification was assessed at the time of biopsy, using Kauppila (plain X-ray of the lateral lumbar spine) and Adragão (plain X-ray of the pelvis and hands) scores. Results Vascular calcification was seen in two-thirds of the cohort. Subjects with VC were more likely to be male and have diabetes, and had significantly higher sclerostin and osteoprotegerin circulating levels than those without VC. The histomorphometric analysis showed that bone formation rate was significantly lower in VC compared to non-VC patients. In the multivariable logistic regression analysis, bone formation rate was independently associated with the presence of VC. Conclusions Vascular calcification is highly prevalent in predialysis patients, especially in those with diabetes. The independent association between bone formation rate and VC provides evidence of an important interaction between bone and vessel in CKD. Our results suggest that low bone turnover is a non-traditional risk factor for cardiovascular disease in predialysis patients.

The mandibular proximal ramus segments should be moved and rotated during orthognathic surgery-based skeletofacial reconstruction for the correction of challenging patients with facial asymmetry and malocclusion, but quantitative data regarding this rotation were not sufficient to date. This 3D computer-assisted study measured the proximal ramus segment rotation after 3D simulation-guided two-jaw surgery in patients with facial asymmetric deformity and class III malocclusion (n = 31). Using 3D mandible models and a reliable proximal ramus segment-related plane, angular changes in pitch, roll and yaw directions were measured before and one month after surgery. Significant rotational changes (p

Periprosthetic loosening, either aseptic or induced by periprosthetic joint infection remains a major long term complication and challenge in orthopedics and trauma surgery. Sensitivity of potential loosening of the material and other causes of a painful prosthesis is essential for choosing the respective treatment option and providing the needed resources. 18F-Fluoride is a radiopharmaceutical which shows a high affinity to bone and a rapid blood clearance. The objective of this study was to assess 18F-Fluoride PET/CT´s sensitivity and specificity in diagnosing periprosthetic loosening in total hip and knee arthroplasty. We included 26 patients with 24 hip and 13 knee prostheses in our retrospective study with radiological or clinical suspicion of peri-prosthetic loosening at least one year after implantation. Results of 18F-Fluoride PET/CT imaging were compared with surgical results or clinical follow-up if surgery was not performed. On the basis of our data we found a sensitivity of 95.00 %, a specificity of 87.04 % and an accuracy of 89.19 % for 18F-Fluoride PET/CT. The results of our study show that 18F-Fluoride PET/CT is a useful and promising technique in diagnosing periprosthetic loosening of total hip and knee arthroplasties. Further investigation should focus on different uptake patterns of the isotope in periprosthetic joint infection and therefore distinguishing aseptic from septic loosening and enhancing the diagnostic value of this imaging method.

Bones provide essential functions and are sites of unique biochemistry and specialized cells, but can also be sites of disease. The treatment of bone disorders and neoplasia has presented difficulties in the past, and improved delivery of drugs to bone remains an important goal for achieving effective treatments. Drug targeting strategies have improved drug localization to bone by taking advantage of the high mineral concentration unique to the bone hydroxyapatite matrix, as well as tissue-specific cell types. The bisphosphonate molecule class binds specifically to hydroxyapatite and inhibits osteoclast resorption of bone, providing direct treatment for degenerative bone disorders, and as emerging evidence suggests, cancer. These bone-binding molecules also provide the opportunity to deliver other drugs specifically to bone by bisphosphonate conjugation. Bisphosphonate bone-targeted therapies have been successful in treatment of osteoporosis, primary and metastatic neoplasms of the bone, and other bone disorders, as well as refining bone imaging. In this review, we focus upon the use of bisphosphonate conjugates with antineoplastic agents, and overview bisphosphonate based imaging agents, nanoparticles, and other drugs. We also discuss linker design potential and the current state of bisphosphonate conjugate research progress. Ongoing investigations continue to expand the possibilities for bone-targeted therapeutics and for extending their reach into clinical practice., Highlights • The ideal bone-targeting drug treats disease and preserves normal bone function. • Bisphosphonate affinity for bone mineral is an ideal foundation for specific targeting of drug to the bone. • Three critical elements: (i) drug payload, (ii) bisphosphonate, and (iii) type of conjugation determine PK and PD activity. • Two promising bisphosphonate-conjugates are currently in clinical oncology trials. • Understanding the payload, bisphophonate and conjugate PK/PD metrics is needed to realize the full potential of this approach.

SPECT bone imaging is an effective method for early diagnosis of bone metastases from malignant tumors. In addition to bone metastases, arthritis and other diseases will also appear as abnormal metabolic distribution areas in SPECT images. Early diagnosis of arthritis based on SPECT bone imaging is of great significance for the treatment of patients. This article takes SPECT bone imaging as the object to study and construct an arthritis disease classifier based on convolutional neural network. Firstly, data preprocessing is carried out to deal with the impact of small amount of medical image data and individual differences between imaging data on the classification effect of deep learning model. Secondly, a classifier suitable for low resolution and large-scale SPECT images is constructed based on VGGNet. Finally, based on a set of real SPECT bone imaging data sets to compare the arthritis diagnosis results of different depth classification models. The experimental results show that the classifier constructed in this paper can effectively detect arthritis diseases, and the accuracy, recall and F-1 values are 0.9309, 0.9511 and 0.9284, respectively.

In some cases of vertebroplasty for adjacent fractures, we observed a cement bridging phenomenon, in which the injected cement flowed from the newly fractured vertebra to the previously cement-augmented vertebra through the space between the abutting anterior longitudinal ligament and the vertebral column. The purpose of this retrospective study was to investigate this phenomenon. From January 2012 to December 2014, patients who sustained new-onset adjacent vertebral compression fracture and who were again treated with vertebroplasty were enrolled. We divided the patients into two groups, the bridging group and the nonbridging group, to analyze the difference between them. Results showed that the cement bridging phenomenon occurred in 18 (22.8%) of the 79 patients. Significant differences between the bridging and nonbridging groups were identified in the following 3 imaging features: severe loss of the anterior vertebral body height at the new-onset adjacent vertebra on plain film (odds ratio [OR] = 4.46, p = 0.014), fluid accumulation (OR = 36.27, p

The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.

Zero-echo time (ZTE) magnetic resonance imaging (MRI) is the newest in a family of MRI pulse sequences that involve ultrafast sequence readouts, permitting visualization of short-T2 tissues such as cortical bone. Inherent sequence properties enable rapid, high-resolution, quiet, and artifact-resistant imaging. ZTE can be performed as part of a "one-stop-shop" MRI examination for comprehensive evaluation of head and neck pathology. As a potential alternative to computed tomography for bone imaging, this approach could help reduce patient exposure to ionizing radiation and improve radiology resource utilization. Because ZTE is not yet widely used clinically, it is important to understand the technical limitations and pitfalls for diagnosis. Imaging cases are presented to demonstrate potential applications of ZTE for imaging of oral cavity, oropharynx, and jaw anatomy and pathology in adult and pediatric patients. Emerging studies indicate promise for future clinical implementation based on synthetic computed tomography image generation, 3D printing, and interventional applications.

Musculoskeletal disorders affect the locomotor system and are the leading contributor to disability worldwide. Patients suffer chronic pain and limitations in mobility, dexterity, and functional ability. Musculoskeletal (bone) X-ray is an essential tool in diagnosing the abnormalities. In recent years, deep learning algorithms have increasingly been applied in musculoskeletal radiology and have produced remarkable results. In our study, we introduce a new calibrated ensemble of deep learners for the task of identifying abnormal musculoskeletal radiographs. Our model leverages the strengths of three baseline deep neural networks (ConvNet, ResNet, and DenseNet), which are typically employed either directly or as the backbone architecture in the existing deep learning-based approaches in this domain. Experimental results based on the public MURA dataset demonstrate that our proposed model outperforms three individual models and a traditional ensemble learner, achieving an overall performance of (AUC: 0.93, Accuracy: 0.87, Precision: 0.93, Recall: 0.81, Cohen’s kappa: 0.74). The model also outperforms expert radiologists in three out of the seven upper extremity anatomical regions with a leading performance of (AUC: 0.97, Accuracy: 0.93, Precision: 0.90, Recall:0.97, Cohen’s kappa: 0.85) in the humerus region. We further apply the class activation map technique to highlight the areas essential to our model’s decision-making process. Given that the best radiologist performance is between 0.73 and 0.78 in Cohen’s kappa statistic, our study provides convincing results supporting the utility of a calibrated ensemble approach for assessing abnormalities in musculoskeletal X-rays.

Analysis of scoliosis requires thorough radiographic evaluation by spinal curvature estimation to completely assess the spinal deformity. Spinal curvature estimation gives orthopaedic surgeons an idea of severity of spinal deformity for therapeutic purposes. Manual intervention has always been an issue to ensure accuracy and repeatability. Computer assisted systems are semi-automatic and is still influenced by surgeon’s expertise. Spinal curvature estimation completely relies on accurate identification of required end vertebrae like superior end-vertebra, inferior end-vertebra and apical vertebra. In the present work, automatic extraction of spinal information central sacral line and medial axis by computerized image understanding system has been proposed. The inter-observer variability in the anatomical landmark identification is quantified using Kappa statistic. The resultant Kappa value computed between proposed algorithm and observer lies in the range 0.7 and 0.9, which shows good accuracy. Identification of the required end vertebra is automated by the extracted spinal information. Difference in inter and intra-observer variability for the state of the art computer assisted and proposed system are quantified in terms of mean absolute difference for the various types (Type-I, Type-II, Type-III, Type-IV, and Type-V) of scoliosis.

This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne’s bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne’s bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.

Chronic obstructive Eustachian tube dysfunction (ETD) is a common disorder of the middle ear. In recent years, two main diagnostic tools have become available: Eustachian tube score (ETS-7) and computed tomography (CT) combined with Valsalva maneuver. The aim of this study is to evaluate the outcomes of ETS-7 and CT in a group of patients affected by middle ear atelectasis with a strong suspicion of ETD. Three males and nine females, affected by middle ear atelectasis with retraction of the TM were enrolled. Each patient underwent to Eustachian tube dysfunction evaluation adopting the ETS-7 score and a temporal bone CT with Valsalva maneuver. The ears analyzed at steady state were divided into 2 groups: ETS

Current guidelines by the National Comprehensive Cancer Network recommend that, in addition to routine computed tomography (CT) imaging, bone imaging and brain magnetic resonance imaging (MRI) should be obtained when clinically indicated. In this issue of the Journal of Kidney Cancer and VHL, a systematic literature review of clinical trials of metastatic renal cell carcinoma (mRCC) patients evaluates the incidence of osseous, lymph node and lung metastases. In particular, the analysis focuses on the changes in incidence over time. The study finds that the incidence of bone, lymph node, and lung metastases has increased over time, and that increases is significant in osseous metastases specifically. These results lead to two provocative questions. First, why have osseous metastases increased in incidence over time? Second, does this finding warrant a more aggressive and uniform approach to imaging to identify osseous metastases sooner?

Patients at high risk of fracture due to metabolic diseases frequently undergo long-term antiresorptive therapy. However, in some patients treatment is unsuccessful in preventing fractures or causes severe adverse health outcomes. Understanding load driven bone remodelling, i.e. mechanoregulation, is critical to understand which patients are at risk for progressive bone degeneration and may enable better patient selection or adaptive therapeutic intervention strategies. Bone microarchitecture assessment using high-resolution peripheral quantitative computed tomography (HR-pQCT) combined with computed mechanical loads has successfully been used to investigate bone mechanoregulation at the trabecular level. To obtain the required mechanical loads that induce local variances in mechanical strain and cause bone remodelling, estimation of physiological loading is essential. Yet, current models homogenise strain patterns throughout the bone to estimate load distribution in vivo, which may be a flawed assumption for investigating alterations in bone mechanoregulation. By further utilising available spatiotemporal information of time-lapsed bone imaging studies, we developed a mechanoregulation-based load estimation algorithm (MR). MR calculates organ scale loads by scaling and superimposing a set of predefined independent unit loads to optimise measured bone formation in high, quiescence in medium and resorption in low strain regions. We benchmarked our algorithm against a previously published load history algorithm (LH) using synthetic data, micro-CT images of murine vertebrae under defined experimental in vivo loadings and HR-pQCT images from seven patients. Our algorithm consistently outperformed LH in all three datasets. In silico generated time evolutions of distal radius geometries (n = 5) indicated significantly higher sensitivity, specificity, and accuracy for MR than LH (p < 0.01). This increased performance led to substantially better discrimination between physiological and extra-physiological loading in mice (n = 8). Moreover, a significantly (p < 0.01) higher association between remodelling events and computed local mechanical signals were found using MR (CCR = 0.42) than LH (CCR = 0.38) to estimate human distal radius loading. Future applications of MR may enable clinicians to link subtle changes in bone strength to changes in day-to-day loading, identifying weak spots in the bone micro-structure for local intervention and personalised treatment approaches.

There is considerable interpersonal variation in the size and shape of the human cochlea, with evident consequences for cochlear implantation. The ability to characterize a specific cochlea, from preoperative computed tomography (CT) images, would allow the clinician to personalize the choice of electrode, surgical approach and postoperative programming. In this study, we present a fast, practicable and freely available method for estimating cochlear size and shape from clinical CT. The approach taken is to fit a template surface to the CT data, using either a statistical shape model or a locally affine deformation (LAD). After fitting, we measure cochlear size, duct length and a novel measure of basal turn non-planarity, which we suggest might correlate with the risk of insertion trauma. Gold-standard measurements from a convenience sample of 18 micro-CT scans are compared with the same quantities estimated from low-resolution, noisy, pseudo-clinical data synthesized from the same micro-CT scans. The best results were obtained using the LAD method, with an expected error of 8–17% of the gold-standard sample range for non-planarity, cochlear size and duct length.

The objective of this experimental study was to develop and evaluate a three-dimensionally printed custom surgical guide system for performing bipolar coxofemoral osteochondral allograft transplantation in dogs. Five cadaver dogs, weighing 20–38 kg were used in the study. Custom surgical guides were designed and three-dimensionally printed to facilitate accurate execution of a surgical plan for bipolar coxofemoral osteochondral allograft transplantation. Guide-assisted technique was compared to freehand technique in each cadaver. Surgical time was recorded and postoperative computed tomography and three-dimensional segmentation was performed. Femoral version and inclination angles, femoral neck length, and gap present at the femoral and acetabular donor-recipient interface was compared between the virtual surgical plan and postoperative outcome for both techniques. One-tailed paired t-test (P < .05) was used for statistical analysis. When compared to free-hand preparation, mean donor femoral preparation time was 10 minutes longer and mean recipient preparation time was 2 minutes longer when using guides (p = 0.011 and p = 0.001, respectively). No difference in acetabular preparation time was noted between groups. Gap volume at the acetabular and femoral donor-recipient interface was not different between groups. Mean difference between the planned and postoperative version angle was 6.2° lower for the guide group when compared to the freehand group (p = 0.025). Mean femoral neck length was 2 mm closer to the plan when using guides than when performing surgery freehand (p = 0.037). Accuracy for femoral angle of inclination was not different between groups. Custom surgical guides warrants consideration in developing bipolar coxofemoral osteochondral allograft transplantation as an alternative surgical technique for managing hip disorders in dogs.

Objectives The aim of this in vitro study was to evaluate the stress distribution on three implant models with narrow and extra-narrow diameters using the finite element method (FEA). Materials and methods Dental implants of extra-narrow diameter of 2.5 mm for a one-piece implant (group G1), a narrow diameter of 3.0 mm for a one-piece implant (group G2) and a narrow diameter of 3.5 mm for a two-piece implant with a Morse taper connection (group G3). A three-dimensional model was designed with cortical and cancellous bone, a crown and an implant/abutment set of each group. Axial and angled (30°) loads of 150 N was applied. The equivalent von Mises stress was used for the implants and peri-implant bone plus the Mohr-Coulomb analysis to confirm the data of the peri-implant bone. Results In the axial load, the maximum stress value of the cortical bone for the group G1 was 22.35% higher than that the group G2 and 321.23% than the group G3. Whereas in angled load, the groups G1 and G2 showing a similar value (# 3.5%) and a highest difference for the group G3 (391.8%). In the implant structure, the group G1 showed a value of 2188MPa, 93.6% higher than the limit. Conclusions The results of this study show that the extra-narrow one-piece implant should be used with great caution, especially in areas of non-axial loads, whereas the one- and two-piece narrow-diameter implants show adequate behavior in both directions of the applied load.

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.

Objective Psoriatic arthritis (PsA) is an inflammatory rheumatic disease, mediated in part by TNFα and associated with bone loss. Anti-TNFα treatment should inhibit this phenomenon and reduce the systemic bone loss. Ultra-high field MRI (UHF MRI) may be used to quantify bone microarchitecture (BM) in-vivo. In this study, we quantified BM using UHF MRI in a PsA patient and followed up the changes related to anti-TNFα treatment. Subjects and methods A non-treated PsA patient with knee arthritis and 7 gender-matched controls were scanned using a gradient re-echo sequence at UHF MRI. After a year of Adalimumab treatment, the patient underwent a second UHF MRI. A PET-FNa imaging was performed before and after treatment to identify and localize the abnormal metabolic areas. BM was characterized using typical morphological parameters quantified in 32 regions of interest (ROIs) located in the patella, proximal tibia, and distal femur. Results Before treatment, the BM parameters were statistically different from controls in 24/32 ROIs with differences reaching up to 38%. After treatment, BM parameters were normalized for 15 out of 24 ROIs. The hypermetabolic areas disclosed by PET-FNa before the treatment partly resumed after the treatment. Conclusion Thanks to UHF MRI, we quantified in vivo BM anomalies in a PsA patient and we illustrated a major reversion after one year of treatment. Moreover, BM results highlighted that the abnormalities were not only localized in hypermetabolic regions identified by PET-FNa, suggesting that the bone loss was global and not related to inflammation.

ObjectiveDespite the high risk of missing lesions in mammography, the missed lesion rate is yet to be clinically established. Further, no breast phantoms with adjustable breast density currently exist. We developed a novel, adjustable-density breast phantom with a composition identical to that of actual breasts, and determined the quantitative relationship between breast density and the missed lesion rate in mammography.MethodsAn original breast phantom consisting of adipose- and fibroglandular-equivalent materials was developed, and a receiver operating characteristic (ROC) study was performed. Breast density, which is the fraction by weight of fibroglandular to total tissue, was adjusted to 25%, 50%, and 75% by arbitrarily mixing the two materials. Microcalcification, mass lesions, and spiculated lesions, each with unique characteristics, were inserted into the phantom. For the above-mentioned fibroglandular densities, 50 positive and 50 negative images for each lesion type were used as case samples for the ROC study. Five certified radiological technologists participated in lesion detection.ResultsThe mass-lesion detection rate, according to the area under the curve, decreased by 18.0% (p= 0.0001, 95% Confidence intervals [CI] = 0.1258 to 0.1822) and 37.8% (p= 0.0003, 95% CI = 0.2453 to 0.4031) for breast densities of 50% and 75%, respectively, compared to that for a 25% breast density. A similar tendency was observed with microcalcification; however, spiculated lesions did not follow this tendency.ConclusionsWe quantified the missed lesion rate in different densities of breast tissue using a novel breast phantom, which is imperative for advancing individualized screening mammography.

Setting The 3rd national tuberculosis (TB) survey in the Philippines in 2007 reported a significant decline in the prevalence of TB. Since then, more significant investments for TB control have been made, yet TB burden estimates from routine surveillance data remain relatively stable. Objective To estimate the prevalence of bacteriologically confirmed pulmonary TB in the Philippines amongst individuals aged ≥15 years in 2016. Design In March–December 2016, we conducted a population-based survey with stratified, multi-stage cluster sampling of residents in 106 clusters aged ≥15 years. Survey participants were screened for TB by symptom-based interview and digital chest X-ray. Those with cough ≥2 weeks and/or haemoptysis and/or chest X-ray suggestive of TB were requested to submit 2 sputum specimens for Xpert MTB/RIF, direct sputum smear microscopy using LED fluorescent microscopy, and mycobacterial solid culture (Ogawa method). Bacteriologically confirmed pulmonary TB was defined as MTB culture positive and/or Xpert positive. Results There were 46,689 individuals interviewed, and 41,444 (88.8%) consented to a chest X-ray. There were 18,597 (39.8%) eligible for sputum examination and 16,242 (87.3%) submitted at least one specimen. Out of 16,058 sputum-eligible participants, 183 (1.1%) were smear-positive. There were 466 bacteriologically confirmed TB cases: 238 (51.1%) Xpert positive, 69 (14.8%) culture positive, and 159 (34.1%) positive by both Xpert and culture. The estimated TB prevalence per 100,000 population aged ≥15 years was 434 (95% CI: 350−518) for smear-positive TB, and 1,159 (95% CI: 1,016−1,301) for bacteriologically confirmed TB. Conclusion This nationally representative survey found that the TB burden in the Philippines in 2016 was higher than estimated from routine TB surveillance data. There was no evidence of a decline in smear and culture positive TB from the 2007 survey despite significant investments in TB control. New strategies for case-finding and patient-centered care must be intensified and expanded.

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.