A panorama ensures a stunning wide-angle field of view up to 360° representation of a scene, exceeding the limits of a normal photograph. Panoramic cameras satisfy the single-viewpoint characteristic. There are several types of panoramic cameras for 360-degree imaging. Multi-camera panoramic imaging systems pose a difficulty in obtaining a single projection center for the cameras. In a variety of practical implementations of panoramic cameras, it is possible to calculate three-dimensional coordinates from a panoramic image, especially using the Direct Linear Transformation (DLT) method. In this study, not only a defining method of the non-uniform image coordinate system is presented by utilizing the C-Means algorithm for a single panoramic image, captured with a Ladybug2 panoramic camera in a panoramic calibration room but also the use of an elliptical panoramic projection coordinate system is defined by the Singular Value Decomposition method in a panoramic view. The results of the suggested method have been compared with the DLT algorithm for a single panoramic image which defined a conventional photogrammetric image coordinate system. It has been observed that the proposed method provides more accurate results for the 3D coordinate definition.

Photoacoustic (PA) imaging has gained much attention, providing structural and functional information in combination with clinical ultrasound (US) imaging systems. 2D PA and US imaging is easily implemented, but its heavy dependence on operator skills makes 3D imaging preferable. In this study, we propose a panoramic volumetric clinical PA and US imaging system equipping a handheld imaging scanner weighing 600 g and measuring 70 × 62 × 110 mm3. Multiple PA/US scans were performed to cover a large field-of-view (FOV), and the acquired PA/US volumes were mosaic-stitched after manually correcting the positions and rotations in a total of 6 degrees of freedom. PA and US maximum amplitude projection images were visualized online, while spectral unmixed data was quantified offline. The performance of the system was tested via tissue-mimicking phantom experiments. The system\'s potential was confirmed in vivo by panoramically imaging vascular networks in human arms and necks, with FOVs of 331 × 38 and 129 × 120 mm2, respectively. Further, we quantified hemoglobin oxygen saturation levels in the radial artery, brachial artery, carotid artery, and jugular vein. We hope that this system can be applied for various clinical fields such as cardiovascular imaging, dermatology, vascular surgery, internal medicine, and oncology.

The daily image quality assessment involves large datasets that consume a lot of time and effort. This study aims to evaluate a proposed automated calculator for image distortion analysis in 2-dimensional (2D) panoramic imaging mode for a dental cone beam computed tomography (CBCT) system in comparison with present manual calculations.
A ball phantom was scanned using panoramic mode of the Planmeca ProMax 3D Mid CBCT unit (Planmeca, Helsinki, Finland) with standard exposure settings used in clinical practice (60 kV, 2 mA, and maximum FOV). An automated calculator algorithm was developed in MATLAB platform. Two parameters associated with panoramic image distortion such as balls diameter and distance between middle and tenth balls were measured. These automated measurements were compared with manual measurement using the Planmeca Romexis and ImageJ software.
The findings showed smaller deviation in distance difference measurements by proposed automated calculator (ranged 3.83 mm) as compared to manual measurements (ranged 5.00 for Romexis and 5.12 mm for ImageJ software). There was a significant difference (p < 0.05) on the mean measured ball diameter between automated and manual measurement. For ball diameter measurement, there is a moderate positive correlation between automated measurement with the manual measurements (r = 0.6024 and r = 0.6358 for Romexis and ImageJ, respectively). However, there is a negative correlation between automated measurement for the distance difference with manual methods (r = -0.3484 and r = -0.3494 for Romexis and ImageJ, respectively). There was a good approximation between automated and ImageJ measurement of ball diameter in comparison to reference value.
In conclusion, the proposed automated calculator provides faster method with an accurate and acceptable results for daily-basis image quality test in dental panoramic mode of a Dental CBCT imaging system in comparison to current manual method.
An automated calculator is recommended for image distortion analysis on phantom images in routine image quality assessment for dental panoramic mode of Dental CBCT imaging system that may involve analysis of large image datasets. It offers improvement in routine image quality practice in term of time and accuracy.

The C-arm X-ray system is a common intraoperative imaging modality used to observe the state of a fractured bone in orthopedic surgery. Using C-arm, the bone fragments are aligned during surgery, and their lengths and angles with respect to the entire bone are measured to verify the fracture reduction. Since the field-of-view of the C-arm is too narrow to visualize the entire bone, a panoramic X-ray image is utilized to enlarge it by stitching multiple images. To achieve X-ray image stitching with feature detection, the extraction of accurate and densely matched features within the overlap region between images is imperative. However, since the features are highly affected by the properties and sizes of the overlap regions in consecutive X-ray images, the accuracy and density of matched features cannot be guaranteed. To solve this problem, a heterogeneous stitching of X-ray images was proposed. This heterogeneous stitching was completed according to the overlap region based on homographic evaluation. To acquire sufficiently matched features within the limited overlap region, integrated feature detection was used to estimate a homography. The homography was then evaluated to confirm its accuracy. When the estimated homography was incorrect, local regions around the matched feature were derived from integrated feature detection and substituted to re-estimate the homography. Successful X-ray image stitching of the C-arm was achieved by estimating the optimal homography for each image. Based on phantom and ex-vivo experiments using the proposed method, we confirmed a panoramic X-ray image construction that was robust compared to the conventional methods.

The scope of oral and maxillofacial surgery treatment and care is very broad, from dentoalveolar surgery, to pathology and reconstruction, to treatment of craniofacial deformities. The effective surgical treatment of patients requires appropriate and accurate diagnostic imaging. The various imaging modalities used in oral and maxillofacial surgery are typically for diagnostic and treatment planning purposes. With the improvements of three-dimensional imaging and software programs, surgical treatment and care have been enhanced with patient-specific guides, hardware, and implants. This article discusses the various imaging modalities used for a variety of typical oral and maxillofacial surgery procedures.

The purpose of this study was to evaluate the usefulness of adding Waters\' projection to panoramic imaging compared with panoramic imaging or Waters\' projection alone. Maxillary sinusitis in 106 patients (206 sinuses) was retrospectively assessed with panoramic imaging, Waters\' projection, and computed tomography imaging by two oral radiologists. The diagnostic performance was assessed with computed tomography imaging as the gold standard. Receiver operating characteristic curves and area under the curve values were obtained. Inter- and intra-observer agreement was quantified using weighted kappa coefficients. Observer A performed the same procedure twice (A1 and A2 for the first and second observations, respectively). The accuracies of observers A1, B, and A2 with combination imaging were 0.699, 0.636, and 0.718, respectively. Their area under the curve values with combination imaging were 0.746, 0.640, and 0.771, respectively. Inter-observer agreement was good for Waters\' projection (κ, 0.650), and poor for panoramic imaging (κ, 0281). Intra-observer agreement was good for Waters\' projection (κ, 0.752), and moderate for panoramic imaging (κ, 0.597). Panoramic imaging was equivalent to Waters\' projection for diagnosing maxillary sinusitis. Combination imaging comprising panoramic imaging and Waters\' projection can contribute to the diagnosis of odontogenic maxillary sinusitis because of its high sensitivity.

With the rapid development of the virtual reality industry, one of the bottlenecks is the scarcity of video resources. How to capture high-definition panoramic video with depth information and real-time stereo display has become a key technical problem to be solved. In this paper, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed. Combined with the real-time processing algorithm of multi detector mosaic panoramic stereo imaging image, a panoramic stereo real-time imaging system is developed. Firstly, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed, and the space coordinate calibration platform of ultra-high precision panoramic camera based on theodolite angle compensation function is constructed. The projection matrix of adjacent cameras is obtained by solving the imaging principle of binocular stereo vision. Then, a real-time registration algorithm of multi-detector mosaic image and Lucas-Kanade optical flow method based on image segmentation are proposed to realize stereo matching and depth information estimation of panoramic imaging, and the estimation results are analyzed effectively. Experimental results show that the stereo matching time of panoramic imaging is 30 ms, the registration accuracy is 0.1 pixel, the edge information of depth map is clearer, and it can meet the imaging requirements of different lighting conditions.

The ground-based arc-scanning synthetic aperture radar (ArcSAR) is capable of 360° scanning of the surroundings with the antenna fixed on a rotating arm. ArcSAR has much wider field of view when compared with conventional ground-based synthetic aperture radar (GBSAR) scanning on a linear rail. It has already been used in deformation monitoring applications. This paper mainly focuses on the accurate and fast imaging algorithms for ArcSAR. The curvature track makes the image focusing challenging and, in the classical frequency domain, fast imaging algorithms that are designed for linear rail SAR cannot be readily applied. This paper proposed an efficient frequency domain imaging algorithm for ArcSAR. The proposed algorithm takes advantage of the angular shift-invariant property of the ArcSAR signal, and it deduces the accurate matched filter in the angular-frequency domain, so panoramic images in polar coordinates with wide swath can be obtained at one time without segmenting strategy. When compared with existing ArcSAR frequency domain algorithms, the proposed algorithm is more accurate and efficient, because it has neither far range nor narrow beam antenna restrictions. The proposed method is validated by both simulation and real data. The results show that our algorithm brings the quality of image close to the time domain back-projection (BP) algorithm at a processing efficiency about two orders of magnitude better, and it has better image quality than the existing frequency domain Lee\'s algorithm at a comparable processing speed.

OBJECTIVE: We assessed fascicle behaviors of the upper extremities during isometric contractions at different joint angles in this study.
METHODS: Thirteen healthy men and women performed isometric elbow extension tasks at 50% and 75% of maximal voluntary contraction (MVC) at 60°, 90°, and 120° of elbow extension (full extension = 180°). Extended field-of-view B-mode ultrasonography was used to obtain sagittal plane panoramic images of the long head (TB-Long) and medial head (TB-Med) of the triceps brachii at rest and during contraction; fascicle length and pennation angle were measured.
RESULTS: In the TB-Long, significant fascicle shortening from rest was found during 50% and 75%MVC at 60° and during 75%MVC at 90° of extension. There was no significant fascicle shortening in the TB-Med muscle under any conditions. There was no significant pennation angle change from rest in either muscle. The pennation angle of the TB-Long was significantly greater than that of the TB-Med under all conditions.
CONCLUSIONS: These results suggest that fascicle shortening in the TB-Long muscle occurs in flexion; however, no change was found in the TB-Med. In the upper extremity muscle-tendon complex, the superficial and deeper muscles may have different force-transmission efficiency at flexed joint angles.

Objective: To apply the technique of deep learning on a small dataset of panoramic images for the detection and segmentation of the mental foramen (MF). Study design: In this study we used in-house dataset created within the School of Dental Medicine, Tel Aviv University. The dataset contained randomly chosen and anonymized 112 digital panoramic X-ray images and corresponding segmentations of MF. In order to solve the task of segmentation of the MF we used a single fully convolution neural network, that was based on U-net as well as a cascade architecture. 70% of the data were randomly chosen for training, 15% for validation and accuracy was tested on 15%. The model was trained using NVIDIA GeForce GTX 1080 GPU. The SPSS software, version 17.0 (Chicago, IL, USA) was used for the statistical analysis. The study was approved by the ethical committee of Tel Aviv University. Results: The best results of the dice similarity coefficient ( DSC), precision, recall, MF-wise true positive rate (MFTPR) and MF-wise false positive rate (MFFPR) in single networks were 49.51%, 71.13%, 68.24%, 87.81% and 14.08%, respectively. The cascade of networks has shown better results than simple networks in recall and MFTPR, which were 88.83%, 93.75%, respectively, while DSC and precision achieved the lowest values, 31.77% and 23.92%, respectively. Conclusions: Currently, the U-net, one of the most used neural network architectures for biomedical application, was effectively used in this study. Methods based on deep learning are extremely important for automatic detection and segmentation in radiology and require further development.