Oblique Images Research Articles

Development of pupil divided optics with magnification correction for high NA oblique imaging

For manufacturing semiconductor devices and hard disks, a high sensitivity and high throughput inspection system is required to detect defects on the surface of semiconductor wafers or disk substrates. Dark-field optical microscopy with high numerical-aperture detectors in oblique directions is often used to detect weak and anisotropic scattered light from small defects. With conventional high numerical aperture (NA) optics, it is difficult to image the wide field from an oblique direction because of its short depth of focus (DOF). Here, we propose a new optics called “pupil divided optics with magnification correction.” In this optics, a low NA cylindrical lens array is placed on the pupil plane of the high NA objective lens to extend DOF. In this paper, we describe the theory and configuration of the proposed optics. Our simulation and experimental results show the capability for oblique imaging with a wide field.

Automatic Plane of Minimal Hiatal Dimensions Extraction From 3D Female Pelvic Floor Ultrasound.

There is an increasing interest in the applications of 3D ultrasound imaging of the pelvic floor to improve the diagnosis, treatment, and surgical planning of female pelvic floor dysfunction (PFD). Pelvic floor biometrics are obtained on an oblique image plane known as the plane of minimal hiatal dimensions (PMHD). Identifying this plane requires the detection of two anatomical landmarks, the pubic symphysis and anorectal angle. The manual detection of the anatomical landmarks and the PMHD in 3D pelvic ultrasound requires expert knowledge of the pelvic floor anatomy, and is challenging, time-consuming, and subject to human error. These challenges have hindered the adoption of such quantitative analysis in the clinic. This work presents an automatic approach to identify the anatomical landmarks and extract the PMHD from 3D pelvic ultrasound volumes. To demonstrate clinical utility and a complete automated clinical task, an automatic segmentation of the levator-ani muscle on the extracted PMHD images was also performed. Experiments using 73 test images of patients during a pelvic muscle resting state showed that this algorithm has the capability to accurately identify the PMHD with an average Dice of 0.89 and an average mean boundary distance of 2.25mm. Further evaluation of the PMHD detection algorithm using 35 images of patients performing pelvic muscle contraction resulted in an average Dice of 0.88 and an average mean boundary distance of 2.75mm. This work had the potential to pave the way towards the adoption of ultrasound in the clinic and development of personalized treatment for PFD.

Semi-supervised identification and mapping of surface water extent using street-level monitoring videos

ABSTRACT Urban flooding is becoming a common and devastating hazard, which causes life loss and economic damage. Monitoring and understanding urban flooding in a highly localized scale is a challenging task due to the complicated urban landscape, intricate hydraulic process, and the lack of high-quality and resolution data. The emerging smart city technology such as monitoring cameras provides an unprecedented opportunity to address the data issue. However, estimating water ponding extents on land surfaces based on monitoring footage is unreliable using the traditional segmentation technique because the boundary of the water ponding, under the influence of varying weather, background, and illumination, is usually too fuzzy to identify, and the oblique angle and image distortion in the video monitoring data prevents georeferencing and object-based measurements. This paper presents a novel semi-supervised segmentation scheme for surface water extent recognition from the footage of an oblique monitoring camera. The semi-supervised segmentation algorithm was found suitable to determine the water boundary and the monoplotting method was successfully applied to georeference the pixels of the monitoring video for the virtual quantification of the local drainage process. The correlation and mechanism-based analysis demonstrate the value of the proposed method in advancing the understanding of local drainage hydraulics. The workflow and created methods in this study have a great potential to study other street-level and earth surface processes.

The Complete and Two-Turn Cochlear Duct Length Among Filipinos


 
 
 
 ABSTRACTObjective: This study aims to measure the complete and two-turn cochlear duct lengths in a Filipino population using archived CT scan images.
 
 
 
 
 Methods: 
 Design: Retrospective Review of Records
 Setting:Tertiary Government Training Hospital
 Participants: CT Scan Images of 255 patients
 
 
 
 
 Cochlear images of patients who underwent cranial, facial, orbital, paranasal sinus and temporal bone CT scans from January 2019 to December 2019 were analyzed. Coronal oblique images from 3D multiplanar reconstructions were obtained and a single linear measurement (‘A’ value) was used as the spiral coefficient to calculate the complete cochlear duct length (CDL) and two- turn length (2TL).
 
 
 
 
 Results: A total of 510 cochlear images were obtained from the CT scan images of 255 subjects (143 males, 112 females aged 1 to 81 years; mean age = 47 years). The mean ‘A’ value was 8.81 mm (SD = 0.20). The mean complete cochlear duct length was 32.68 mm (31.01 mm – 35.50 mm; SD = 0.834) while the mean two-turn cochlear duct length was 29.61 mm (28.14 mm – 32.08 mm; SD = 0.732). The complete and two-turn cochlear duct lengths in males were found to be significantly longer than in females (p = .001). No significant difference was found between cochlear measurements for left and right ears.
 
 
 
 
 Conclusion: The mean complete cochlear duct length among Filipinos in our study measures 32.68 mm while the mean two-turn cochlear duct length measures 29.61mm. Both complete and two-turn cochlear duct lengths were longer among Filipino males than among females.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Combined Scattering, Interferometric, and Fluorescence Oblique Illumination for Live Cell Nanoscale Imaging

To determine the molecular and/or mechanical basis of cell migration using live cell imaging tools, it is necessary to correlate multiple 3D spatiotemporal events simultaneously. Fluorescence nanoscopy and label-free nanoscale imaging can complement each other by providing both molecular specificity and structural dynamics of sub-cellular structures. A combined imaging system would permit obtaining quantitative 3D spatial temporal details of individual cellular components. In this paper, we empirically determined optimal azimuthal scanning angles of rotating beams to achieve simultaneous and label-free nanoscale and fluorescence imaging. Label-free nanoscale imaging here refers to interferometric, bright-field (BF) and dark-field (DF) rotating coherence scattering (ROCS) microscopy, while fluorescence refers to high-inclined laminated oblique (HiLO) and total internal reflection fluorescence (TIRF) imaging. The combined capabilities of interferometric, scattering, and fluorescence imaging enable (1) the identification of molecular targets (substrate or organelle), (2) quantification of 3D cell morphodynamics, and (3) tracking of intracellular organelles in 3D. This combined imaging tool was then used to characterize migrating platelets and endothelial cells, both critical to the process of infection and wound healing. The combined imaging results of more than ∼1000 platelets suggest that serum albumin (bovine) is necessary for platelets to migrate and scavenge fibrin/fibrinogen. Furthermore, we identified new asynchronous membrane fluctuations between the leading and rear edges of a migrating platelet. We further demonstrated that interferometric imaging permits the quantification of mitochondrial dynamics on human lung microvascular endothelial cells. Our data suggests that axial displacement of mitochondria is minimal when it is closer to the nucleus or the leading edge of a cell membrane. Taken together, this combined nanoscopy platform helps to quantify multiple spatial temporal events of a migrating cell that will undoubtedly open ways to new quantitative correlative nanoscale live cell imaging.

High-Resolution UAV RGB Imagery Dataset for Precision Agriculture and 3D Photogrammetric Reconstruction Captured over a Pistachio Orchard (Pistacia vera L.) in Spain

A total of 248 UAV RGB images were taken in the summer of 2021 over a representative pistachio orchard in Spain (X: 341450.3, Y: 4589731.8; ETRS89/UTM zone 30N). It is a 2.03 ha plot, planted in 2016 with Pistacia vera L. cv. Kerman grafted on UCB rootstock, with a NE–SW orientation and a 7 × 6 m triangular planting pattern. The ground was kept free of any weeds that could affect image processing. The photos (provided in JPG format) were taken using a UAV DJI Phantom Advance quadcopter in two flight missions: one planned to take nadir images (β = 0°), and another to take oblique images (β = 30°), both at 55 metres above the ground. The aerial platform incorporates a DJI FC6310 RGB camera with a 20 megapixel sensor, a horizontal field of view of 84° and a mechanical shutter. In addition, GCPs (ground control points) were collected. Finally, a high-quality 3D photogrammetric reconstruction process was carried out to generate a 3D point cloud (provided in LAS, LAZ, OBJ and PLY formats), a DEM (digital elevation model) and an orthomosaic (both in TIF format). The interest in using remote sensing in precision agriculture is growing, but the availability of reliable, ready-to-work, downloadable datasets is limited. Therefore, this dataset could be useful for precision agriculture researchers interested in photogrammetric reconstruction who want to evaluate models for orthomosaic and 3D point cloud generation from UAV missions with changing flight parameters, such as camera angle.

Dense context distillation network for semantic parsing of oblique UAV images

Semantic segmentation of oblique unmanned aerial vehicle (UAV) images serves as a foundation for many modern urban applications, such as road scene monitoring and semantic 3D modeling. However, objects in UAV images can vary intensely in size and undergo severe perspective distortion because of the oblique viewing style. Existing general segmentation models designed for ground and remote sensing images rarely considered these challenges specific to UAV images. Therefore, they have large difficulties in learning discriminative representation for simultaneously reasoning the extremely large and small objects in UAV images. In this paper, we propose a dense context distillation network (DCDNet) to learn distortion-robust feature representation for semantic segmentation of UAV images. The basic DCDNet is deployed as an dual-branch encoder–decoder architecture. To accomplish the goal of dense context distillation, DCDNet is first equipped with several cross-scale context selectors at different encoding stages to densely and selectively gather the useful context from low- to high-level dual-scale feature maps. A joint supervision is then applied to reinforce the learning of shallower features for distilling more low-level contexts that are vital to the reasoning of small or thin structures. A multi-scale feature aggregator is incorporated to adaptively fuse the long-range context during decoding, which absorbs the complementary merits of the dense context captured from feature maps of different levels. With the dense context distillation, DCDNet is more capable of offering the differently scaled objects with the required context for better learning and prediction. Extensive experiments on the challenging UAVid dataset demonstrate that our DCDNet can well adapt to the oblique UAV images, achieving a state-of-the-art segmentation performance with a mIoU score of 72.38%. • A DCDNet is proposed for semantic parsing of UAV images. • DCDNet offers enough context for reasoning the large and small road scene objects. • DCDNet achieves state-of-the-art segmentation performance.

Interpretable deep learning-based hippocampal sclerosis classification.

To evaluate the performance of a deep learning model for hippocampal sclerosis classification on the clinical dataset and suggest plausible visual interpretation for the model prediction. T2-weighted oblique coronal images of the brain MRI epilepsy protocol performed on patients were used. The training set included 320 participants with 160 no, 100 left and 60 right hippocampal sclerosis, and cross-validation was implemented. The test set consisted of 302 participants with 252 no, 25 left and 25 right hippocampal sclerosis. As the test set was imbalanced, we took an average of the accuracy achieved within each group to measure a balanced accuracy for multiclass and binary classifications. The dataset was composed to include not only healthy participants but also participants with abnormalities besides hippocampal sclerosis in the control group. We visualized the reasons for the model prediction using the layer-wise relevance propagation method. When evaluated on the validation of the training set, we achieved multiclass and binary classification accuracy of 87.5% and 88.8% from the voting ensemble of six models. Evaluated on the test sets, we achieved multiclass and binary classification accuracy of 91.5% and 89.76%. The distinctly sparse visual interpretations were provided for each individual participant and group to suggest the contribution of each input voxel to the prediction on the MRI. The current interpretable deep learning-based model is promising for adapting effectively to clinical settings by utilizing commonly used data, such as MRI, with realistic abnormalities faced by neurologists to support the diagnosis of hippocampal sclerosis with plausible visual interpretation.

A study on magnetic resonance imaging in patients having perianal pathology

Introduction: Perianal region is a commonly affected area in the course of several inflammatory, infectious, and neoplastic diseases. Several imaging modalities are used in imaging evaluation of this area and magnetic resonance imaging (MRI) emerges as the imaging modality of choice due to its superb soft tissue resolution. MRI is not only useful for initial detection of perianal pathologies but also in the follow-up of these disorders. Magnetic resonance imaging (MRI), with its superb soft tissue resolution, is the imaging modality of choice for anal and perianal abnormalities.Pelvic phased array coils allow more comfortable and tolerable scans with higher signal-to-noise ratios and wider field of view. Parallel imaging techniques result in shorter scan durations T2-weighted images obtained with and without fat saturation as well as postcontrast T1-weighted images with fat suppression are the most helpful sequences. Postcontrast images are helpful, mainly for differentiating postoperative changes and chronic granulation or fibrotic tissue from active inflammation and abscess formation. Axial and coronal oblique images of the anal canal should be acquired with proper and accurate multiplanar prescription in order to get ideal images which are perpendicular and coronal to the anal canal.Materials and MethodsInclusion criteria: Patients with perianal discharge, swelling, pain or pruritus & diagnosed of having perianal pathology by MRI study (1.5 T MRI).Exclusion criteria: All patients referred to department of radiology, L G hospital, maninagar, Ahmedabad with rectal mass by MRI study (1.5 T MRI)All patients with contraindications to MRI like patients with metallic implants, claustrophobia, pacemakers and patients with contrast allergy.Study type: Retrospective study (record-based study). Consent not required for retrospective study.Results and Conclusion In our study of 50 patients, out of which 42 were males & 8 were females, the most common affected age group were between 25-35 years (42%).The most common location of fistula in our study was at 6’O – 7’O clock (36%) position followed by 12- 1’O clock (28%) position.The most common type in our study being intersphincteric type (48%) according to PARK’S classification and the most common Grade in our study being grade 1 (simple intersphincteric fistula) (26%) according to St JAMES classification.Adequate understanding of relevant pelvic anatomy and fistula classification on MRI examination is essential in providing proper assessment of perianal fistulas.Evaluation of clinically undetectable disease has significant bearing on guiding medical & surgical therapy & can help minimize recurrence & better predict outcome compared to surgical exploration.

Indoor and outdoor multi-source 3D data fusion method for ancient buildings

Ancient buildings carry important information, such as ancient politics, economy, culture, customs. However, with the course of time, ancient buildings are often damaged to different degrees, so the restoration of ancient buildings is of great importance from the historical point of view. There are three commonly used non-contact measurement methods, including UAV-based oblique photogrammetry, terrestrial laser scanning, and close-range photogrammetry. These methods can provide integrated three-dimensional surveys of open spaces, indoor and outdoor surfaces for ancient buildings. Theoretically, the combined use of the three measurement methods can provide 3D (three-dimensional) data support for the protection and repair of ancient buildings. However, data from the three methods need to be fused urgently, because if the image data is not used, it will lead to a lack of real and intuitive texture information, and if only image matching point clouds are used, their accuracy will be lower than that of terrestrial laser scanning point clouds, and it will also lead to a lack of digital expression for components with high indoor historical value of ancient buildings. Therefore, in this paper, a data fusion method is proposed to achieve multi-source and multi-scale 3D data fusion of indoor and outdoor surfaces. It takes the terrestrial laser point cloud as the core, and based on fine component texture features and building outline features, respectively, the ground close-range image matching point cloud and UAV oblique image matching point cloud are registered with the terrestrial laser point cloud. This method unifies the data from three measurements in the point cloud and realizes the high-precision fusion of these three data. Based on the indoor and outdoor 3D full-element point cloud formed by the proposed method, it will constitute a visual point cloud model in producing plans, elevations, sections, orthophotos, and other elements for the study of ancient buildings.

Drone imagery to create a common understanding of landscapes

Negotiated solutions among contrasting land use interests in the nexus of water, energy, food and ecosystems require cooperation between actors with different viewpoints and backgrounds. We suggest aerial imagery and videos, captured by drones, to be “boundary objects”, easily interpretable landscape representations that might create a common understanding across stakeholders through their universal interpretability. We collected drone imagery and videos from different angles of a wide range of landscapes in Zambia, showing agricultural areas, forests, wetlands and water infrastructure. Then, we took the imagery back to the field to probe the perceptions of multiple stakeholders, including staff from both governmental and non-governmental organizations, hydropower operators, small- and large-scale farmers. In focus group discussions, we assessed the interpretability of oblique images, taken at an angle by a video drone, compared to nadir (vertical) imagery from Google Earth and from a high-end mapping drone. We show that oblique images produced better identification results across all groups of stakeholders, but especially from small-scale farmers, suggesting this type of imagery is helpful to empower people who lack previous experience in interpreting nadir images. Overall, the appreciation of the aesthetic value and the perceived professional benefits of drone imagery are high, but technical and legal barriers impede a wider adoption of the technology. While we highlight ethical concerns and technical limitations, we suggest that conservationists and environmental planners could benefit from a critical use of affordable video drones so as to produce intuitive landscape representations useful for more effective multi-stakeholder collaborations.

Application of mammography-based radiomics signature for preoperative prediction of triple-negative breast cancer

ObjectiveThis study is aimed to explore the value of mammography-based radiomics signature for preoperative prediction of triple-negative breast cancer (TNBC).Materials and methodsInitially, the clinical and X-ray data of patients (n = 319, age of 54 ± 14) with breast cancer (triple-negative—65, non-triple-negative—254) from the First Affiliated Hospital of Soochow University (n = 211, as a training set) and Suzhou Municipal Hospital (n = 108, as a verification set) from January 2018 to February 2021 are retrospectively analyzed. Comparing the mediolateral oblique (MLO) and cranial cauda (CC) mammography images, the mammography images with larger lesion areas are selected, and the image segmentation and radiomics feature extraction are then performed by the MaZda software. Further, the Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE + ACC), and mutual information (MI) are used to select three sets of feature subsets. Moreover, the score of each patient’s radiomics signature (Radscore) is calculated. Finally, the receiver operating characteristic curve (ROC) is analyzed to calculate the AUC, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of TNBC.ResultsA significant difference in the mammography manifestation between the triple-negative and the non-triple-negative groups (P < 0.001) is observed. The (POE + ACC)-NDA method showed the highest accuracy of 88.39%. The Radscore of triple-negative and non-triple-negative groups in the training set includes − 0.678 (− 1.292, 0.088) and − 2.536 (− 3.496, − 1.324), respectively, with a statistically significant difference (Z = − 6.314, P < 0.001). In contrast, the Radscore in the validation set includes − 0.750 (− 1.332, − 0.054) and − 2.223 (− 2.963, − 1.256), with a statistically significant difference (Z = − 4.669, P < 0.001). In the training set, the AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of TNBC include 0.821 (95% confidence interval 0.752–0.890), 74.4%, 82.5%, 72.5%, 41.2%, and 94.6%, respectively. In the validation set, the AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of TNBC are of 0.809 (95% confidence interval 0.711–0.907), 80.6%, 72.0%, 80.7%, 55.5%, and 93.1%, respectively.ConclusionIn summary, we firmly believe that this mammography-based radiomics signature could be useful in the preoperative prediction of TNBC due to its high value.

An apparatus for qualitative assessment of the shading ratio of oblique illumination and real-time high-contrast imaging.

Oblique illumination imaging can significantly improve the contrast of transparent thin samples. However, in traditional oblique illumination methods, either the condenser is offset or a block is added to the condenser, which makes it complicated and challenged to build a stable oblique illumination imaging. Herein, we present a method to measure the optimal shading ratio of oblique illumination in an inverted microscope, and develop an apparatus for stable high-speed high-contrast imaging with uniform brightness. At optimal shading ratio, the oblique illumination imaging has better imaging quality than differential interference contrast, which characteristic is independent on sample. In oblique illumination with low magnification objective, the images have uneven brightness. According to target brightness, we have developed a brightness unevenness correction algorithm to form uniform background brightness for oblique illumination. Integrating the algorithm with imaging acquisition, corrected oblique illumination microscopy is appropriate to observe living cells with high contrast.

Power Line Extraction and Obstacle Inspection of Unmanned Aerial Vehicle Oblique Images Constrained by the Vertical Plane

AbstractUnmanned aerial vehicle (UAV) inspection of vegetation or other obstacles in power transmission line corridors is an economical and efficient method. Spatial power lines cannot be directly restored in the single airborne photographic geometry, and automatic robust extraction of power lines under complex background is a bottleneck problem in the current transmission line reconstruction. Thus, a robust restoration method of the power line spatial position based on the vertical plane constraint was proposed that can restore the entire power line three‐dimensional (3D) points. First, the spatial position of the hanging points at both ends of the power line was solved based on an insulator plumb, and the vertical plane of the power line was determined. A light beam passing through the photographing centre and the image point could then be determined for any image point on the image power line. The intersection point of the light beam and the vertical plane of the power line was the point of the spatial power line. The proposed method was used to select a few (three or more) power line seed points manually to calculate the corresponding 3D space points and then fit these points to obtain the dense and uniform 3D points of power line. After restoring the spatial power line, the minimum distance error line segment based on splitting was used to approximate the 3D point sequence of the power line, and it was divided into several straight‐line segments. The spatial buffer zone was established with each line segment as the centre and a safety distance as the radius to detect possible obstacles in the power line corridor. Finally, a set of simulated and actual data were used for verification and analysis. Results show that the method can effectively detect obstacles around the power line, and the accuracy is within 0.25 m.

Upper Gastrointestinal Fluoroscopic Examination: A Traditional Art Enduring into the 21st Century.

Upper Gastrointestinal Fluoroscopic Examination: A Traditional Art Enduring into the 21st Century.

Lateral Decubitus Position Enables Further Advancement of the Bronchoscope into the Lung Periphery.

Lateral Decubitus Position Enables Further Advancement of the Bronchoscope into the Lung Periphery.

SA-NET.V2: REAL-TIME VEHICLE DETECTION FROM OBLIQUE UAV IMAGES WITH USE OF UNCERTAINTY ESTIMATION IN DEEP META-LEARNING

Abstract. In recent years, unmanned aerial vehicle (UAV) imaging is a suitable solution for real-time monitoring different vehicles on the urban scale. Real-time vehicle detection with the use of uncertainty estimation in deep meta-learning for the portable platforms (e.g., UAV) potentially improves video understanding in real-world applications with a small training dataset, while many vehicle monitoring approaches appear to understand single-time detection with a big training dataset. The purpose of real-time vehicle detection from oblique UAV images is to locate the vehicle on the time series UAV images by using semantic segmentation. Real-time vehicle detection is more difficult due to the variety of depth and scale vehicles in oblique view UAV images. Motivated by these facts, in this manuscript, we consider the problem of real-time vehicle detection for oblique UAV images based on a small training dataset and deep meta-learning. The proposed architecture, called SA-Net.v2, is a developed method based on the SA-CNN for real-time vehicle detection by reformulating the squeeze-and-attention mechanism. The SA-Net.v2 is composed of two components, including the squeeze-and-attention function that extracts the high-level feature based on a small training dataset, and the gated CNN. For the real-time vehicle detection scenario, we test our model on the UAVid dataset. UAVid is a time series oblique UAV images dataset consisting of 30 video sequences. We examine the proposed method's applicability for stand real-time vehicle detection in urban environments using time series UAV images. The experiments show that the SA-Net.v2 achieves promising performance in time series oblique UAV images.

Oblique View Selection for Efficient and Accurate Building Reconstruction in Rural Areas Using Large-Scale UAV Images

3D building models are widely used in many applications. The traditional image-based 3D reconstruction pipeline without using semantic information is inefficient for building reconstruction in rural areas. An oblique view selection methodology for efficient and accurate building reconstruction in rural areas is proposed in this paper. A Mask R-CNN model is trained using satellite datasets and used to detect building instances in nadir UAV images. Then, the detected building instances and UAV images are directly georeferenced. The georeferenced building instances are used to select oblique images that cover buildings by using nearest neighbours search. Finally, precise match pairs are generated from the selected oblique images and nadir images using their georeferenced principal points. The proposed methodology is tested on a dataset containing 9775 UAV images. A total of 4441 oblique images covering 99.4% of all the buildings in the survey area are automatically selected. Experimental results show that the average precision and recall of the oblique view selection are 0.90 and 0.88, respectively. The percentage of robustly matched oblique-oblique and oblique-nadir image pairs are above 94% and 84.0%, respectively. The proposed methodology is evaluated for sparse and dense reconstruction. Experimental results show that the sparse reconstruction based on the proposed methodology reduces 68.9% of the data processing time, and it is comparably accurate and complete. Experimental results also show high consistency between the dense point clouds of buildings reconstructed by the traditional pipeline and the pipeline based on the proposed methodology.

Prevalence of Cam-Type Femoroacetabular Impingement Morphology in Hip Joint of Asymptomatic Thai Population

Objective: To determine the prevalence of cam-type femoroacetabular impingement (FAI) in the hip joints of asymptomatic Thai individuals using increased alpha angle of more than 55° on computed tomography (CT) images. Materials and Methods: The prospective study was conducted between 2019 and 2021. CT images of the 226 hip joints with no history of hip pain within three months prior to CT, no history of congenital dysplasia, Perthes disease, slipped capital femoral epiphysis, hip fracture, hip surgery, and inflammatory arthropathy, negative anterior impingement test, and no CT findings of osteoarthritis were evaluated. Alpha angles were measured in the axial oblique and radial planes at 12 o’clock, 1 o’clock, 1:30 o’clock, 2 o’clock, and 3 o’clock. Alpha angles of more than 55° were considered cam-type FAI morphology. Results: The prevalence rates of cam-type FAI morphology using increased alpha angles of more than 55° measured on axial oblique and radial plane images at 12 o’clock, 1 o’clock, 1:30 o’clock, 2 o’clock, and 3 o’clock were 0.4%, 0%, 4.9%, 8.4%, 8.0%, and 1.8% respectively. Cam-type morphology was common in the anterosuperior aspect of the femoral head-neck junction. The prevalence of cam-type morphology was significantly higher in men than in women. Conclusion: The range of prevalence rates of cam-type FAI morphology are between 0 to 8.4%. The prevalence is different in each image plane and location of the femoral head-neck junction. The prevalence of cam-type morphology in the present study differed from those in the previous studies conducted in other countries, which may be caused from variations in race and ethnicity. Keywords: Prevalence; Cam-type Femoroacetabular Impingement (FAI); Alpha angle; Computed tomography (CT)

Right auricle aneurysm in dogs can be detected with multidetector-row CT and presents with varying morphology.

Right auricle aneurysm (RAA) has been reported to be a rare congenital or acquired condition in dogs, however published CT characteristics are limited to a small number of cases. The aim of this 13-year, single-center, retrospective, cross-sectional study was to describe the prevalence and appearance of RAA in a larger group of dogs. Reports of 10,886 dogs that underwent thoracic CT examination for various reasons were reviewed. Computed tomographic data of dogs with presumed RAA (based on published imaging characteristics) were retrieved from the archive. Oblique multiplanar reformatted images were used to record RAA site, shape, and dimensions (neck, maximum height and width). Patients' sex, age, and body weight were evaluated for any association with the presence of RAA. Presumed RAA was detected in 23/10,886 dogs having thoracic CT in the selected period of time (prevalence 0.21%). Three RAA phenotypes were described: diffuse saccular dilation in 19 of 23 dogs, focal saccular dilation in three of 23 dogs, and fusiform in one of 23 dogs. The bodyweights of dogs with presumed RAA were significantly lower than those of the remaining 10836 dogs without RAA (median 8kg [IQR 5; range 37.6] and median 16kg [IQR 23; range 116.9] respectively; P<0.003. Female sex was also significantly associated with RAA (P=0.03). Findings indicated that RAA has a low prevalence in dogs, can be detected with CT, can be present in asymptomatic dogs, and can have varying appearances. In this sample of dogs, female sex and low body weight were significantly associated with RAA.