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BACKGROUND AND AIM: Increased maternal age at the time of pregnancy and labor is believed to have potential complications. To evaluate the effects of maternal age on the mode of delivery among nulliparous women with term pregnancies who underwent labor induction. METHODS: In this retrospective cohort study, 313 women with the gestational age of at least ≥37 weeks, were enrolled. They were divided based on their maternal ages as: Group A

Existing techniques of 3-D reconstruction of buildings from SAR images are mostly based on multibaseline SAR interferometry, such as PSI and SAR tomography (TomoSAR). However, these techniques require tens of images for a reliable reconstruction, which limits the application in various scenarios, such as emergency response. Therefore, alternatives that use a single SAR image and the building footprints from GIS data show their great potential in 3-D reconstruction. The combination of GIS data and SAR images requires a precise registration, which is challenging due to the unknown terrain height, and the difficulty in finding and extracting the correspondence. In this paper, we propose a framework to automatically register GIS building footprints to a SAR image by exploiting the features representing the intersection of ground and visible building facades, specifically the near-range boundaries in the building polygons, and the double bounce lines in the SAR image. Based on those features, the two data sets are registered progressively in multiple resolutions, allowing the algorithm to cope with variations in the local terrain. The proposed framework was tested in Berlin using one TerraSAR-X High Resolution SpotLight image and GIS building footprints of the area. Comparing to the ground truth, the proposed algorithm reduced the average distance error from 5.91 m before the registration to −0.08 m, and the standard deviation from 2.77 m to 1.12 m. Such accuracy, better than half of the typical urban floor height (3 m), is significant for precise building height reconstruction on a large scale. The proposed registration framework has great potential in assisting SAR image interpretation in typical urban areas and building model reconstruction from SAR images.

Background: In this study, we aimed at evaluating the frequency of chemoradiotherapy‑induced mucositis (CIM, RIM) and its related risk factors in patients with the head‑and‑neck cancers (HNCs). Materials and Methods: In this cross‑sectional prospective descriptive study, we evaluated a total of 54 HNCs patients referred to the Oncology Chemoradiotherapy Department of Razi and Amiralmomenin University Hospitals of Rasht, for site and grade (WHO classification) of CIM/RIM, based on their demographic features and possible risk factors. SPSS software version 18 and statistical tests (i.e., Fisher’s exact test) were used, and the level of significance was considered as P < 0.05. Results: Finally, patients enrolled in the study were 34 men and 20 women (40–72‑year‑old), of which 43 cases (79.6%) showed signs and symptoms of CIM/RIM (mostly aged 60–69‑year‑old). Grade 2 mucositis was the most frequent type (18 cases: 33.3%) and mouth floor was the most common site of CIM/RIM (15 patients: 34.9%). Age (P = 0.023), gender (P = 0.012), and the severity of pain (P = 0.018) were significantly related to mucositis in patients with HNCs who underwent chemotherapy or radiotherapy. Conclusion: Mucositis, especially in the oral cavity, is a common complication of chemoradiotherapy in HNCs patients. Age, gender, and the severity of pain are significantly associated with the frequency of CIM/RIM. Key Words: Chemoradiotherapy, mucositis, neoplasms

Uterine fibroids (UFs) are the most common benign tumors of the uterus with an unknown etiology, affecting many women of reproductive age. We aimed to evaluate the association between UFs and anthropometric features, subcutaneous and preperitoneal fat thickness and lipid profile.This is a case-control study conducted on 212 women who were available in the Al-Zahra specialized referral hospital from March 2018 to March 2019. Study variables including weight, height, waist and hip circumference were measured for all individuals. For patients with UFs, the size, number and location of fibroids were recorded. Also, subcutaneous and preperitoneal fat thicknesses were measured. Finally, the data were analyzed using the SPSS software ver.16.0.The most common complaint was abnormal uterine bleeding (AUB) in both groups. Most of the patients had a body mass index (BMI) of 25-30. There were significant differences between the two groups in terms of age (P = 0.0001) and waist circumference (P = 0.011). Cholesterol levels were much higher in the case group. Only age and low-density lipoprotein-cholesterol (LDL-C) levels in the case group were positively related to developing UFs, such that with advancing aging and increasing levels of LDL-C, the likelihood of experiencing UFs rose by 10% and 1.1%, respectively. Also, there were no significant differences between the two groups regarding in either preperitoneal fat thickness (PFT) or subcutaneous fat thickness (SFT).Our findings suggest that with aging and higher levels of LDL-C, the likelihood of developing UFs rises.

In this paper, we propose a framework for geocoding error correction of point clouds obtained from Persistent Scatterer Interferometry (PSI) in urban areas. The horizontal positioning bias of Persistent Scatterers (PS) is mitigated by applying geodetic and atmospheric corrections to the PS azimuth and range timings. Furthermore, the vertical positioning bias, caused due to the unknown height of the PSI reference point, is estimated and compensated for by the use of Synthetic Aperture Radar (SAR)-based Ground Control Point (GCP)s. Experimental results based on cross-heading pairs of TerraSAR-X (TS-X) high resolution spotlight images over the city of Berlin, Germany and in Oulu, Finland are presented. For selected test sites, the localization accuracy of the point clouds is analyzed with respect to a reference LiDAR data, which demonstrates the applicability of the proposed correction approach.

Objective: This article is aimed at introducing Sono-Guided Endostripping Technique of the Great Saphenous vein and comparing it with radiofrequency ablation in 104 patients. Methods: 52 patients underwent Sono-Guided Endostripping and 52 patients had radiofrequency ablation with Celon Olympus device. They were followed for 3 months with physical examination and Doppler Sonography for possible complications and recanalization. Results: Recanalization rate was 11.5% in RFAs while it was 0.0% in the Endostripping group. There were no significant differences for other complications (hematoma, DVT, paresthesia) but pain, which had 1.21 and 3.15 VAS scores for RFA and Endostripping groups, respectively. Recanalization rate was more in RFA comparing with the Endostripping group (11.5% vs. 0.0%, P < 0.013). RFA was more expensive. Conclusion: Sono-Guided Endostripping is a safe and cost-effective method (due to no need for using RFA probe); however, the patient must be admitted to the hospital and take appropriate anesthesia. Cosmetic result of the technique is fairly comparable with RFA scars in the thigh and leg.

Objective: Nasolacrimal duct obstruction is an annoying medical condition in both ophthalmology and otolaryngology practices. As prophylactic antibiotic therapy decreases the rates of infections, understanding the prompt microbial spectrum of the pathogens has been revealed to be highly effective in such patients. We evaluated the microbial growth and bacterial resistance patterns of Bodkin tube samples obtained from patients who underwent dacryocystorhinostomy (DCR).Methods: A total number of 72 patients were enrolled in the study, and their Bodkin tube samples were cultured 8–12 weeks after DCR at Amir-Al Momenin University Hospital of Rasht, in the north of Iran.Results: Staphylococcus epidermidis and Aspergillus fumigatus were the most common bacterial and fungal species derived from the cultures, and 23.6% and 36.1% of the patients had neither bacterial nor fungal growth, respectively. The highest antibiotic resistance rates were toward cefazolin and cloxacillin among all the studied antibiotics.Conclusion: Majority of cases had positive bacterial or fungal growth in their samples. Considering microbial resistance, proper antibiotic selection is recommended for the patients undergoing DCR.

In this paper, we propose a framework referred to as “geodetic synthetic aperture radar (SAR) tomography” that fuses the SAR imaging geodesy and tomographic SAR inversion (TomoSAR) approaches to obtain absolute 3-D positions of a large amount of natural scatterers. The methodology is applied on four very high resolution TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since all the TomoSAR estimates are relative to the same reference point object whose absolute 3-D positions are retrieved by means of stereo SAR, the point clouds reconstructed using data acquired from different viewing angles can be geodetically fused. To assess the accuracy of the position estimates, the resulting absolute shadow-free 3-D TomoSAR point clouds are compared with a digital surface model obtained by airborne LiDAR. It is demonstrated that an absolute positioning accuracy of around 20 cm and a meter-order relative positioning accuracy can be achieved by the proposed framework using TerraSAR-X data.

Differential Synthetic Aperture Radar Tomography (D-TomoSAR), similar to its conventional counterparts, such as Differential Interferometric SAR (D-InSAR) and Persistent Scatterer Interferometry (PSI) is only capable to capture one-dimensional deformation along the satellite’s line-of-sight (LOS). In this paper we propose a method based on L1-norm minimization within local spatial cubes, to reconstruct three-dimensional displacement vectors from TomoSAR point clouds available from, at least, three different viewing geometries. The methodology is applied on two pair of cross-heading - combination of ascending and descending - TerraSAR-X (TS-X) spotlight image stacks over the city of Berlin. The linear deformation rate and the amplitude of seasonal deformation are decomposed and the results from two test sites with remarkable deformation pattern are discussed in detail. The results, to our knowledge, demonstrate the first attempt for motion decomposition using TomoSAR data from multiple viewing geometries.

Synthetic Aperture Radar Tomography (TomoSAR) coupled with data from modern SAR sensors, such as the German TerraSAR-X (TS-X) produces the most detailed three-dimensional (3D) maps by distinguishing among multiple scatterers within a resolution cell. Furthermore, multi-temporal TomoSAR allows for recording the underlying deformation phenomenon of each individual scatterer. One of the limitations of using InSAR techniques, including TomoSAR, is that they only measure deformation along the radar Line-of-Sight (LOS). In order to enhance the understanding of deformation, a decomposition of the observed LOS displacement into the 3D deformation vector in the local coordinate system is desired. In this paper we propose a method, based on L1 norm minimization within local spatial cubes, to reconstruct 3D deformation vectors from TomoSAR point clouds available from, at least, three different viewing geometries. The methodology is applied on two pair of cross-heading TS-X spotlight image stacks over the city of Berlin. The linear deformation rate and amplitude of seasonal deformation are decomposed and the results from two individual test sites with remarkable deformation patterns are discussed in details.

In this paper, we propose a framework referred to as “geodetic TomoSAR“ that fuses the SAR image geodesy and TomoSAR approaches to obtain absolute 3D positions of a large amount of natural scatterers. The methodology is applied on four Very High Resolution (VHR) TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since the TomoSAR estimates are referred to the identical reference point whose absolute 3D positions are retrieved by means of Stereo-SAR, the point clouds from ascending and descending orbits are automatically fused. To assess the accuracy of the position estimates, the resulting absolute shadow-free 3D TomoSAR point clouds are compared to a DSM obtained by airborne LiDAR.

Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this paper, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state-of-the-art of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.

Geodetic stereo SAR is capable of absolute 3-D localization of natural persistent scatterers (PS)s which allows for Ground Control Point (GCP) generation using only SAR data. The prerequisite for the method to achieve high precision results is the correct detection of common scatterers in SAR images acquired from different viewing geometries. In this contribution, we describe three strategies for automatic detection of identical point targets in SAR images of urban areas taken from different orbit tracks. Moreover, a complete work-flow for automatic generation of large number of GCPs using SAR data is presented and its applicability is shown by exploiting TerraSAR-X high resolution spotlight images over the city of Oulu, Finland and a test site in Berlin, Germany.