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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.

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.

X-ray based radiography, the main modality for diagnostic imaging of bone structures and fractures, provides sensitive images, but it inherently involves potentially harmful X-ray exposure. As non-ionizing alternatives, various optical imaging methods have been explored. Here, we demonstrate non-ionizing, label-free, multispectral photoacoustic (PA) imaging of bones in small animals in vivo. in situ. and ex vivo. Using near-infrared light excitation and acoustic detection, the spine and ribs were successfully visualized in high-resolution PA images. PA 3D volume images of the spine and ribs were clearly visualized together with blood vessels and several organs including the spleen, liver, and cecum, without using any exogenous contrast agent nor ionizing radiation. Quantification results of multispectral PA signals from blood vessels and bones were in good agreement with their absorption coefficients. Further, a rib fracture was photoacoustically imaged. Our results demonstrate PA imaging's potential as a non-ionizing and label-free technique for imaging bone tissues.

Bone has a sophisticated architecture characterized by a hierarchical organization, starting at the sub-micrometre level. Thus, the analysis of the mechanical and structural properties of bone at this scale is essential to understand the relationship between its physiology, physical properties and chemical composition. Here, we unveil the potential of Brillouin–Raman microspectroscopy (BRaMS), an emerging correlative optical approach that can simultaneously assess bone mechanics and chemistry with micrometric resolution. Correlative hyperspectral imaging, performed on a human diaphyseal ring, reveals a complex microarchitecture that is reflected in extremely rich and informative spectra. An innovative method for mechanical properties analysis is proposed, mapping the intermixing of soft and hard tissue areas and revealing the coexistence of regions involved in remodelling processes, nutrient transportation and structural support. The mineralized regions appear elastically inhomogeneous, resembling the pattern of the osteons' lamellae, while Raman and energy-dispersive X-ray images through scanning electron microscopy show an overall uniform distribution of the mineral content, suggesting that other structural factors are responsible for lamellar micromechanical heterogeneity. These results, besides giving an important insight into cortical bone tissue properties, highlight the potential of BRaMS to access the origin of anisotropic mechanical properties, which are almost ubiquitous in other biological tissues.

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.

Ultra-short echo time (UTE) MRI with post-processing is a promising technique in bone imaging that produces a similar contrast to computed tomography (CT). Here, we propose a 3D slab-selective ultrashort echo time (UTE) sequence together with image post-processing to image bone structures in the lumbar spine. We also explore the intermodality agreement between the UTE and CT images. The lumbar spines of two healthy volunteers were imaged with 3D UTE using five different resolutions to determine the best imaging protocol. Then, four patients with low back pain were imaged with both the 3D UTE sequence and CT to investigate agreement between the imaging methods. Two other patients with low back pain were then imaged with the 3D UTE sequence and clinical conventional T1-weighted and T2-weighted fast spin-echo (FSE) MRI sequences for qualitative comparison. The 3D UTE sequence together with post-processing showed high contrast images of bone and high intermodality agreement with CT images. In conclusion, post-processed slab-selective UTE imaging is a feasible approach for highlighting bone structures in the lumbar spine and demonstrates significant anatomical correlation with CT images.

Teaching Point: Synthetic CT images can improve evaluation of bony lesions on MRI and show potential for fracture evaluation, but validation is needed.

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.

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.

Nowadays, the use of a digital prototype in numerical modeling is one of the main approaches to calculating the elements of an inhomogeneous structure under the influence of external forces. The article considers a finite element analysis method based on computed tomography data. The calculations used a three-dimensional isoparametric finite element of a continuous medium developed by the authors with a linear approximation, based on weighted integration of the local stiffness matrix. The purpose of this study is to describe a general algorithm for constructing a numerical model that allows static calculation of objects with a porous structure according to its computed tomography data. Numerical modeling was carried out using kinematic boundary conditions. To evaluate the results obtained, computational and postprocessor grids were introduced. The qualitative assessment of the modeling data was based on the normalized error. Three-point bending of bone specimens of the pig forelimbs was considered as a model problem. The numerical simulation results were compared with the data obtained from a physical experiment. The relative error ranged from 3 to 15%, and the crack location, determined by the physical experiment, corresponded to the area where the ultimate strength values were exceeded, determined by numerical modeling. The results obtained reflect not only the effectiveness of the proposed approach, but also the agreement with experimental data. This method turned out to be relatively non-resource-intensive and time-efficient.

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.

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.

Pulmonary acini represent the functional gas-exchanging units of the lung. Due to technical limitations, individual acini cannot be identified on microscopic lung sections. To overcome these limitations, we imaged the right lower lobes of instillation-fixed rat lungs from postnatal days P4, P10, P21, and P60 at the TOMCAT beamline of the Swiss Light Source synchrotron facility at a voxel size of 1.48 μm. Individual acini were segmented from the three-dimensional data by closing the airways at the transition from conducting to gas exchanging airways. For a subset of acini (N = 268), we followed the acinar development by stereologically assessing their volume and their number of alveoli. We found that the mean volume of the acini increases 23 times during the observed time-frame. The coefficients of variation dropped from 1.26 to 0.49 and the difference between the mean volumes of the fraction of the 20% smallest to the 20% largest acini decreased from a factor of 27.26 (day 4) to a factor of 4.07 (day 60), i.e. shows a smaller dispersion at later time points. The acinar volumes show a large variation early in lung development and homogenize during maturation of the lung by reducing their size distribution by a factor of 7 until adulthood. The homogenization of the acinar sizes hints at an optimization of the gas-exchange region in the lungs of adult animals and that acini of different size are not evenly distributed in the lungs. This likely leads to more homogeneous ventilation at later stages in lung development.

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.

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.

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.

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.

High quality radiology reporting of chest X-ray images is of core importance for high-quality patient diagnosis and care. Automatically generated reports can assist radiologists by reducing their workload and even may prevent errors. Machine Learning (ML) models for this task take an X-ray image as input and output a sequence of words. In this work, we show that ML models for this task based on the popular encoder-decoder approach, like ‘Show, Attend and Tell’ (SA&T) have similar or worse performance than models that do not use the input image, called unconditioned baseline. An unconditioned model achieved diagnostic accuracy of 0.91 on the IU chest X-ray dataset, and significantly outperformed SA&T (0.877) and other popular ML models (p-value < 0.001). This unconditioned model also outperformed SA&T and similar ML methods on the BLEU-4 and METEOR metrics. Also, an unconditioned version of SA&T obtained by permuting the reports generated from images of the test set, achieved diagnostic accuracy of 0.862, comparable to that of SA&T (p-value ≥ 0.05).

Contains fulltext : 238626.pdf (Publisher’s version ) (Open Access) In the context of the current global pandemic and the limitations of the RT-PCR test, we propose a novel deep learning architecture, DFCN (Denoising Fully Connected Network). Since medical facilities around the world differ enormously in what laboratory tests or chest imaging may be available, DFCN is designed to be robust to missing input data. An ablation study extensively evaluates the performance benefits of the DFCN as well as its robustness to missing inputs. Data from 1088 patients with confirmed RT-PCR results are obtained from two independent medical facilities. The data includes results from 27 laboratory tests and a chest x-ray scored by a deep learning model. Training and test datasets are taken from different medical facilities. Data is made publicly available. The performance of DFCN in predicting the RT-PCR result is compared with 3 related architectures as well as a Random Forest baseline. All models are trained with varying levels of masked input data to encourage robustness to missing inputs. Missing data is simulated at test time by masking inputs randomly. DFCN outperforms all other models with statistical significance using random subsets of input data with 2-27 available inputs. When all 28 inputs are available DFCN obtains an AUC of 0.924, higher than any other model. Furthermore, with clinically meaningful subsets of parameters consisting of just 6 and 7 inputs respectively, DFCN achieves higher AUCs than any other model, with values of 0.909 and 0.919.

We compared lesion-based sensitivity of dual-time-point FDG-PET/CT, bone scintigraphy (BS), and low-dose CT (LDCT) for detection of various types of bone metastases in patients with metastatic breast cancer. Prospectively, we included 18 patients with recurrent breast cancer who underwent dual-time-point FDG-PET/CT with LDCT and BS within a median time interval of three days. A total of 488 bone lesions were detected on any of the modalities and were categorized by the LDCT into osteolytic, osteosclerotic, mixed morphologic, and CT-negative lesions. Lesion-based sensitivity was 98.2% (95.4–99.3) and 98.8% (96.8–99.5) for early and delayed FDG-PET/CT, respectively, compared with 79.9% (51.1–93.8) for LDCT, 76.0% (36.3–94.6) for BS, and 98.6% (95.4–99.6) for the combined BS+LDCT. BS detected only 51.2% of osteolytic lesions which was significantly lower than other metastatic types. SUVs were significantly higher for all lesion types on delayed scans than on early scans (P

The purpose of this Chapter is to present a detailed description of methods for performing bone Micro-Computed Tomography (microCT) scanning and analysis. MicroCT is an x-ray imaging method capable of visualizing bone at the micro-structural scale, that is, 1–100 μm resolution. MicroCT is the gold-standard method for assessment of 3D bone morphology in studies of small animals. As applied to the small bones of mice or rats, microCT can efficiently and accurately assess bone structure (e.g., cortical bone area [Ct. Ar]) and micro-structure (e.g., trabecular bone volume fraction [Tb.BV/TV]). The particular application described herein is for post mortem mouse femur specimens. The material presented should be generally applicable to many commercially available laboratory microCT systems, although some details are specific to the system used in our lab (Scanco mCT 40; SCANCO Medical AG, Bruttisellen, Switzerland).

As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented in the literature is to develop an image-based method to support COVID-19 diagnosis using convolutional neural networks (CNN). Because the availability of radiological data is rather limited due to the novelty of COVID-19, several methodologies consider reduced datasets, which may be inadequate, biasing the model. Here, we performed an analysis combining six different databases using chest X-ray images from open datasets to distinguish images of infected patients while differentiating COVID-19 and pneumonia from ‘no-findings’ images. In addition, the performance of models created from fewer databases, which may imperceptibly overestimate their results, is discussed. Two CNN-based architectures were created to process images of different sizes (512 × 512, 768 × 768, 1024 × 1024, and 1536 × 1536). Our best model achieved a balanced accuracy (BA) of 87.7% in predicting one of the three classes (‘no-findings’, ‘COVID-19’, and ‘pneumonia’) and a specific balanced precision of 97.0% for ‘COVID-19’ class. We also provided binary classification with a precision of 91.0% for detection of sick patients (i.e., with COVID-19 or pneumonia) and 98.4% for COVID-19 detection (i.e., differentiating from ‘no-findings’ or ‘pneumonia’). Indeed, despite we achieved an unrealistic 97.2% BA performance for one specific case, the proposed methodology of using multiple databases achieved better and less inflated results than from models with specific image datasets for training. Thus, this framework is promising for a low-cost, fast, and noninvasive means to support the diagnosis of COVID-19.

Patellofemoral maltracking predisposes the patellofemoral joint to instability. The purpose of this study was to provide a reliable dynamic magnetic resonance imaging (MRI) measuring technique for patellofemoral maltracking, and to investigate the influence of anatomical risk factors (AF) on patellar maltracking. Ten patients (2 males,8 females, average 19 years) with clinical maltracking and 20 controls (10 males,10 females,average 28 years) were examined with a dynamic, multi-slice gradient-echo MRI sequence, during repetitive flexion(40°) and full extension, in an open-chain-active-movement. In a 30-s time frame three simultaneous transverse slices were acquired. Dynamic mediolateral translation and dynamic tilt were measured at defined positions, by two independent examiners. Reproducibility was tested in a set of five knees. Common AFs for patellar maltracking (tibial-tuberosity-to-trochlear-groove-(TT-TG)-distances, trochlea-sulcus-angle, trochlea-sulcus-depth, lateral-trochlear-inclination and Caton-Deschamps-Index) were analyzed in consensus, using standard static MRI sequences. In patients, dynamic mediolateral translation was significantly greater in patients (12.4 ± 6.9 mm vs. − 0.1 ± 2.3 mm, p p p p

Bone is comprised of mineral, collagenous organic matrix, and water. X-ray-based techniques are the standard approach for bone evaluation in clinics, but they are unable to detect the organic matrix and water components in bone. Magnetic resonance imaging (MRI) is being used increasingly for bone evaluation. While MRI can non-invasively assess the proton pools in soft tissues, cortical bone typically appears as a signal void with clinical MR techniques because of its short T2*. New MRI techniques have been recently developed to image bone while avoiding the ionizing radiation present in x-ray-based methods. Qualitative bone imaging can be achieved using ultrashort echo time (UTE), single inversion recovery UTE (IR-UTE), dual-inversion recovery UTE (Dual-IR-UTE), double-inversion recovery UTE (Double-IR-UTE), and zero echo time (ZTE) sequences. The contrast mechanisms as well as the advantages and disadvantages of each technique are discussed.

Metrology has been successfully used in the last decade to quantify use-wear on stone tools. Such techniques have been mostly applied to fine-grained rocks (chert), while studies on coarse-grained raw materials have been relatively infrequent. In this study, confocal microscopy was employed to investigate polished surfaces on a coarse-grained lithology, quartzite. Wear originating from contact with five different worked materials were classified in a data-driven approach using machine learning. Two different classifiers, a decision tree and a support-vector machine, were used to assign the different textures to a worked material based on a selected number of parameters (Mean density of furrows,Mean depth of furrows,Core material volume-Vmc). The method proved successful, presenting high scores for bone and hide (100%). The obtained classification rates are satisfactory for the other worked materials, with the only exception of cane, which shows overlaps with other materials. Although the results presented here are preliminary, they can be used to develop future studies on quartzite including enlarged sample sizes.

Humanized mouse models are used extensively in research involving human pathogens and diseases. However, most of these models require preconditioning. Radio-active sources have been used routinely for this purpose but safety issues have motivated researchers to transition to chemical or X-ray based preconditioning. In this study, we directly compare 350 kV X-ray and Cs-137 low-dose precondition of NOG mice before human stem cell transplantation. Based on flow cytometry data, we found that engraftment of human cells into the mouse bone marrow was similar between radiation sources. Likewise, human engraftment in the peripheral blood was comparable between Cs-137 and three different X-ray doses with equal chimerization kinetics. In primary lymphoid organs such as the thymus and lymph nodes, and spleen, liver and lung, human-to-mouse chimerization was also comparable between irradiation sources. Development of different CD4 and CD8 T cells as well as these cells’ maturation stages, i.e. from naïve to effector and memory subsets were generally analogous. Based on our results, we conclude that there are no discernable differences between the two sources in the low-dose spectrum investigated. However, while we encourage the transition to X-ray-based sources, we recommend all research groups to consider technical specifications and dose-finding studies. Humanized mouse models are used extensively in research involving human pathogens and diseases. However, most of these models require preconditioning. Radio-active sources have been used routinely for this purpose but safety issues have motivated researchers to transition to chemical or X-ray based preconditioning. In this study, we directly compare 350 kV X-ray and Cs-137 low-dose precondition of NOG mice before human stem cell transplantation. Based on flow cytometry data, we found that engraftment of human cells into the mouse bone marrow was similar between radiation sources. Likewise, human engraftment in the peripheral blood was comparable between Cs-137 and three different X-ray doses with equal chimerization kinetics. In primary lymphoid organs such as the thymus and lymph nodes, and spleen, liver and lung, human-to-mouse chimerization was also comparable between irradiation sources. Development of different CD4 and CD8 T cells as well as these cells' maturation stages, i.e. from naïve to effector and memory subsets were generally analogous. Based on our results, we conclude that there are no discernable differences between the two sources in the low-dose spectrum investigated. However, while we encourage the transition to X-ray-based sources, we recommend all research groups to consider technical specifications and dose-finding studies.

In recent phantom studies low-contrast detectability was shown to be independent from variations in tube voltage in digital radiography (DR) systems. To investigate the transferability to a clinical setting, the lower extremities of human cadavers were exposed at constant detector doses with different tube voltages in a certain range, as proposed in the phantom studies. Three radiologists independently graded different aspects of image quality (IQ) in a comparative analysis. The grades show no correlation between IQ and kV, which means that the readers were not able to recognize a significant IQ difference at different kV. Signal-to-noise and contrast-to-noise ratios showed no significant differences in IQ despite the kV-setting variations. These findings were observed from a limited kV range setting. Higher kV-settings resulted in lowest patient exposure at constant IQ. These results confirm the potential of DR-systems to contribute to standardization of examination protocols comparable to computed tomography. This may prevent the trend to overexpose. Further investigations in other body regions and other DR-systems are encouraged to determine transferability.

In vivo micro-CT has already been used to monitor microstructural changes of bone in mice of different ages and in models of age-related diseases such as osteoporosis. However, as aging is accompanied by frailty and subsequent increased sensitivity to external stimuli such as handling and anesthesia, the extent to which longitudinal imaging can be applied in aging studies remains unclear. Consequently, the potential of monitoring individual mice during the entire aging process–from healthy to frail status–has not yet been exploited. In this study, we assessed the effects of long-term in vivo micro-CT imaging—consisting of 11 imaging sessions over 20 weeks—on hallmarks of aging both on a local (i.e., static and dynamic bone morphometry) and systemic (i.e., frailty index (FI) and body weight) level at various stages of the aging process. Furthermore, using a premature aging model (PolgA(D257A/D257A)), we assessed whether these effects differ between genotypes. The 6th caudal vertebrae of 4 groups of mice (PolgA(D257A/D257A) and PolgA(+/+)) were monitored by in vivo micro-CT every 2 weeks. One group was subjected to 11 scans between weeks 20 and 40 of age, whereas the other groups were subjected to 5 scans between weeks 26–34, 32–40 and 40–46, respectively. The long-term monitoring approach showed small but significant changes in the static bone morphometric parameters compared to the other groups. However, no interaction effect between groups and genotype was found, suggesting that PolgA mutation does not render bone more or less susceptible to long-term micro-CT imaging. The differences between groups observed in the static morphometric parameters were less pronounced in the dynamic morphometric parameters. Moreover, the body weight and FI were not affected by more frequent imaging sessions. Finally, we observed that longitudinal designs including baseline measurements at young adult age are more powerful at detecting effects of in vivo micro-CT imaging on hallmarks of aging than cross-sectional comparisons between multiple groups of aged mice subjected to fewer imaging sessions.

This study was to analyze ankle fractures for determining the epidemiology, types, distribution, possible mechanisms and diagnosis precision. Between January 2013 and December 2017, all Chinese patients older than 16 years of age with ankle fractures excluding old ankle fractures and pathological fractures in a tertiary care hospital were analyzed by using the Danis-Weber and Lauge-Hansen classification systems. Among 3952 patients with ankle fractures, 1225 fractures (31%) were Danis-Weber type A, 1640 (42%) were type B, 751 (19%) were type C, and 336 (9%) were perpendicular compression fracture. There were 1949 fractures on the left side and 2003 on the right with no significant difference (P > 0.05). Male patients between 16 and 50 years of age and women over 50 years had a higher incidence of ankle fractures accounting for 38.4% (1517/3952) and 22.2% (800/3952), respectively. Posterior malleolar fractures, fibular fractures above the inferior tibiofibular joint and Tillaux fractures were easily missed in the diagnosis, with 38 fractures (0.96%) being missed in the diagnosis. In conclusion, young and middle-aged men and older women have a higher incidence of ankle fractures, and use of the Lauge-Hansen and Danis-Weber classification systems can better help assessing the varied and complex ankle fractures, predicting the injuries, increasing diagnostic precision and decreasing misdiagnosis rate.

OBJECTIVE:To compare changes in foraminal motion at two time points post-surgery between artificial disc replacement (ADR) and anterior cervical discectomy and fusion (ACDF). METHODS:Eight ACDF and 6 ADR patients (all single-level C5-6) were tested at 2 years (T1) and 6.5 years (T2) post-surgery. The minimum foraminal height (FH.Min) and width (FW.Min) achieved during neck axial rotation and extension, and the range of these dimensions during motion (FH.Rn and FW.Rn, respectively) were measured using a biplane dynamic x-ray system, CT imaging and model-based tracking while patients performed neck axial rotation and extension tasks. Two-way mixed ANOVA was employed for analysis. RESULTS:In neck extension, significant interactions were found between year post-surgery and type of surgery for FW.Rn at C5-6 (p0.7). CONCLUSIONS:Changes were observed in the range of foraminal geometry at adjacent levels from 2 years to 6.5 years post-surgery that were different between ACDF and ADR for neck extension. These changes are contrary to the notion that motion at adjacent levels continue to increase following ACDF as compared to ADR over the long term.

PURPOSE:Anatomically preshaped implants are needed for exact restoration of the anatomy after fractures of the proximal ulna and ulnar shaft, which enables a good functional outcome. Aim of this computed tomographic analysis was to identify specific characteristics of the ulna. The data serve for the development of a new intramedullary implant for stabilisation of proximal and diaphyseal ulna fractures. METHODS:With a standardized research method 100 CT scans of the ulna were evaluated regarding anatomic parameters like width of the medullary canal, proximal ulna dorsal angulation and varus angulation. Also, correlations of these parameters were analyzed statistically. RESULTS:The mean proximal ulna dorsal angulation (PUDA) was 6.4° (SD 2.8°), while the mean varus angulation of the proximal ulna was 12.4° (SD 3.3°). The length of the ulna bone was 253.6 mm (SD 19.9 mm) on average. The average minimum diameter of the medullary canal was 4.2 mm (SD 1.1 mm) located at 141.3 mm (SD 19.7 mm) from the olecranon tip. There is a positive correlation between age and minimum diameter in our patient cohort (p< 0.001). CONCLUSION:Our study described the anatomy of the proximal ulna and the ulna shaft with a reproducible research method in a representative patient cohort. The knowledge of the evaluated anatomic parameters can lead to an improvement of any implant design for the fixation of proximal and diaphyseal ulna fractures.

Fractures of the wrist are common in Emergency Departments, where some patients are treated with a procedure called Manipulation under Anaesthesia. In some cases, this procedure is unsuccessful and patients need to revisit the hospital where they undergo surgery to treat the fracture. This work describes a geometric semi-automatic image analysis algorithm to analyse and compare the x-rays of healthy controls and patients with dorsally displaced wrist fractures (Colles’ fractures) who were treated with Manipulation under Anaesthesia. A series of 161 posterior-anterior radiographs from healthy controls and patients with Colles’ fractures were acquired and analysed. The patients’ group was further subdivided according to the outcome of the procedure (successful/unsuccessful) and pre- or post-intervention creating five groups in total (healthy, pre-successful, pre-unsuccessful, post-successful, post-unsuccessful). The semi-automatic analysis consisted of manual location of three landmarks (finger, lunate and radial styloid) and automatic processing to generate 32 geometric and texture measurements, which may be related to conditions such as osteoporosis and swelling of the wrist. Statistical differences were found between patients and controls, as well as between pre- and post-intervention, but not between the procedures. The most distinct measurements were those of texture. Although the study includes a relatively low number of cases and measurements, the statistical differences are encouraging.

Microwave imaging can be used as an alternate modality for monitoring bone health. Dielectrically accurate, anthropomorphic phantoms play vital role in testing of imaging prototype prior to clinical applications. However, no study to date has proposed cortical and trabecular hone phantoms. This paper presents a multilayered 3D-printed human calcaneus structure. Further, we have proposed liquid based tissue phantoms that mimic the dielectric properties of skin, muscle, cortical bone and trabecular bone. Tissue phantoms are composed of Trition X-100, water and salt. The dielectric properties were measured across 0.5-8.5 GHz. Each layer of the 3D-printed structure was filled with corresponding tissue phantom. The combined average percentage difference between dielectric properties of reference data and proposed tissue phantoms was found to be 2.9% for trabecular bone, 7.3% for cortical hone, 7.1% for muscle, and 8.7% for skin over the full measured frequency band. These tissue phantoms and 3D printed human calcaneus structure can be used as a valuable test platform for microwave diagnostic studies. The research leading to these results has received funding from the European Research Council under the European Union's Horizon 2020 Programme/ ERC Grant Agreement BioElecPro n. 637780. peer-reviewed

This retrospective study investigated the performance of whole-body bone scintigraphy combined with SPECT/CT and established a grading diagnostic criterion for bone metastases. We summarized signs of whole-body bone imaging combined with SPECT in nuclear medicine and CT signs of corresponding parts. Then we established a diagnostic criterion for bone metastases by using whole-body bone scintigraphy combined with local SPECT/CT. The criterion is classified into five grades. 120 patients with a total of 141 lesions underwent whole-body bone scintigraphy and SPECT/CT. Two reviewers read the images according to the diagnostic criterion. With pathological diagnosis as the “gold standard”, the diagnostic efficacy for bone metastases and the diagnostic agreement between the two reviewers were analyzed to validate the feasibility of the criterion. Diagnostic effectiveness was expressed in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. The diagnostic accuracy of the two reviewers was 90.1% and 92.9% respectively, sensitivity was both 100%, specificity was 41.7% and 58.3%, positive predictive value was 89.3% and 92.1%, and negative predictive value was both 100%. The kappa value of the diagnostic tests performed by the two reviewers on whole-body bone scintigraphy combined with SPECT/CT was 0.919 (P

In orthognathic surgery, the use of patient-specific osteosynthesis devices is a novel approach used to transfer the virtual surgical plan to the patient. The aim of this study is to analyse the quality of mandibular anatomy reproduction using a mandible-first mandibular-PSI guided procedure on 22 patients. Three different positioning guide designs were compared in terms of osteosynthesis plate positioning and mandibular anatomical outcome. PSIs and positioning guides were designed according to virtual surgical plan and 3D printed using biocompatible materials. A CBCT scan was performed 1 month after surgery and postoperative mandibular models were segmented for comparison against the surgical plan. A precision comparison was carried out among the three groups. Correlations between obtained rami and plates discrepancies and between planned rami displacements and obtained rami discrepancies were calculated. Intraoperatively, all PSIs were successfully applied. The procedure was found to be accurate in planned mandibular anatomy reproduction. Different guide designs did not differ in mandibular outcome precision. Plate positional discrepancies influenced the corresponding ramus position, mainly in roll angle and vertical translation. Ramus planned displacement was found to be a further potential source of inaccuracy, possibly due to osteosynthesis surface interference.

We report 3 cases of scurvy in children that occurred during a short period (2018) in a general pediatrics unit of a tertiary hospital for children in Paris. All children were around 3 years of age and were admitted for skeletal pain and altered general state, which mimicked infectious or malignant diseases. Their selective diet was not the prominent issue. The diagnosis of scurvy was delayed, after too many unnecessary examinations and medications. Bone imaging findings (X-ray and MRI) were a posteriori considered typical, but lesions were not easily identified as scurvy lesions because scurvy is not well-known by pediatricians and radiologists who should be mindful of this historical diagnosis.

99mTc-HDP (hydroxymethylene diphosphonate) pinhole bone scanning is an ordinary bone imaging technique upgraded by the additional use of a 4-mm aperture pinhole collimator (Bahk et al. 1987; Kim et al. 1999). However, its remarkably enriched image contents are clinically well demonstrated and histopathologically proven beyond question. Indeed, one may at once recognize that the image contents of pinhole scan are “too different” from those of the parallel hole collimator scan when tested by gamma correction (Fig. 2.1) (Bahk et al. 1987). Indeed, there is a radical difference between the findings of parallel-hole and pinhole collimator scans. For example, in vertebral compression fracture parallel-hole scan shows just a block-like 99mTc-HDP uptake, but pinhole scan shows intense tracer uptake sharply localized to the L3 upper endplate, which is slightly compressed. The parallel-hole bone scan of infective spondylitis also shows the same block-like tracer uptake in L3 vertebra, but again pinhole scan shows the entirely different finding of apposing tracer uptake in the L2 lower and L3 upper endplates. This is a case of acute infective spondylitis. Essentially, such a difference is resulted from the shaper and finer imaging feasibility of pinhole collimator. It is not related at all with the simple image size enlargement. Very importantly, it is further to be realized that the gamma correction does not work in the parallel bone scan as in pinhole scan (Fig. 2.2). Gamma correction is practically useless in multiple parallel-hole 99mTc-HDP bone scan.

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.