PurposeThe sinus lift procedure requires detailed knowledge of maxillary sinus anatomy and the possible anatomical variations. This study evaluated the location and prevalence of maxillary sinus septa using computed tomography (CT).MethodsThis study was based on the analysis of CT images for posterior maxilla which were obtained from patients who visited Chonbuk National University Dental Hospital during the period of June 2007 to December 2008. With the exclusion of cases presenting any pathological changes, 236 maxillary sinuses in 204 patients were retrospectively analyzed. The average age of the patients was 50.9. The cases were divided into two groups, an atrophy/edentulous segment and a non-atrophy/dentate segment, and maxillary sinus septa of less than 2.5 mm were not taken in-to consideration. The location of septa was also divided for analysis into 3 regions: the anterior (1st and 2nd premolar), middle (1st and 2nd molar) and posterior (behind 2nd molar) regions.ResultsIn 54 (20.9%) of the 204 patients there were pathologic findings, and those patients were excluded from the analysis. Sinus septa were present in 58 (24.6%) of the 236 maxillary sinuses and in 55 (27%) of the 204 total patients. In the atrophy/edentulous ridge group (148 maxillary sinuses), 41 cases (27.7%) were found, and 17 cases (19.3%) were found in the non-atrophy/dentulous ridge group (88 maxillary sinuses). In terms of location, septa were found in 18 cases (27.3%) in the anterior, in 33 cases (50%) in the middle and in 15 cases (22.7%) in the posterior regions.ConclusionsIn the posterior maxilla, regardless of type of ridge (atrophy/edentulous or non-atrophy/dentate), the anatomical variation of sinus septa is diverse in its prevalence and location. Thus, accurate information on the maxillary sinus of the patient is essential and should be clearly understood by the surgeon to prevent possible complications during sinus lifting.

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Recently a multiple-aperture interferometry (MAI)-based azimuth shift method has been proposed to correct the ionospheric phase on synthetic aperture radar (SAR) interferograms. This method needs to determine integral constants required for azimuth integration. The exact estimation of the integral constants plays a key role in this MAI-based method. In this paper, we propose an efficient method for improving the performance of integral constant estimation, which functions by simultaneously removing the ionospheric and orbital phase artifacts from the interferometric SAR interferogram. We validate the performance improvement of the proposed method using two Advanced Land Observation Satellite Phase Array L-band SAR (ALOS PALSAR) interferometric pairs. The proposed method is compared with a MAI-based method, which does not work well due to azimuth integration errors. The proposed method successfully corrects the ionospheric and orbital phase artifacts. In addition, we compare the performances of the previous and proposed methods using the in situ Global Positioning System velocities. The root-mean-square error (RMSE) in line-of sight velocity from the previous method is about 7.0 mm/yr, whereas the RMSE from the proposed method is about 4.3 mm/yr. An RMSE reduction of about 38.6% is achieved when using the proposed method. These results indicate: 1) that the proposed method successfully estimates and corrects the ionosphere and orbital phase distortions; and 2) that the proposed method is superior to the previous method. Index Terms-Advanced Land Observation Satellite (ALOS)Phase Array L-band SAR (PALSAR), ionospheric phase correction, multiple-aperture interferometry (MAI), orbital phase correction, SAR interferometry (InSAR), synthetic aperture radar (SAR).

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Volcanic eruptions cause pyroclastic flows, which can destroy plantations and settlements. We used image data from Landsat 7 Bands 7, 4 and 2 and Landsat 8 Bands 7, 5 and 3 to observe and analyze the distribution of pyroclastic flow deposits for two volcanos, Mount Sinabung and Merapi, over a period of 10 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). The satellite data are used in conjunction with an artificial neural network method to produce maps of pyroclastic precipitation for Landsat 7 and 8, then we calculated the pyroclastic precipitation area using an artificial neural network method after dividing the images into four classes based on color. Red, green, blue and yellow were used to indicate pyroclastic deposits, vegetation and forest, water and cloud, and farmland, respectively. The area affected by a volcanic eruption was deduced from the neural network processing, including calculating the area of pyroclastic deposits. The main differences between the pyroclastic flow deposits of Mount Sinabung and Mount Merapi are: the sediment deposits of the pyroclastic flows of Mount Sinabung tend to widen, whereas those of Merapi elongated; the direction of pyroclastic flow differed; and the area affected by an eruption was greater for Mount Merapi than Mount Sinabung because the VEI (Volcanic Explosivity Index) during the last 10 years of Mount Merapi was larger than Mount Sinabung.

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For the measurement of abrupt and large surface movements caused by earthquakes, volcanic eruption and melting glacier, Synthetic aperture radar (SAR) offset tracking method would be a feasible solution because it can provide unambiguous ground displacements in both the ground range and azimuth directions when the interferometric phase is not coherent. However, the measurement performance of the method largely depends on the kernel size, which denotes the size of search window to estimate the azimuth and range offsets between reference and target SAR images. Thus, there is a trade-off between sensitivity and measurement density depending on the search kernel size. In this study, an enhanced SAR offset tracking method based on multi-kernel processing has been developed to find an optimized measurement from the trade-off between resolution and measurement accuracy. It can obtain optimal surface displacement measurements by calculating multiple offset measurements and determining a final measurement from the statistical properties of the multiple measurements. The measurement performance of the proposed method was evaluated by using European Remote Sensing 2 (ERS-2) satellite SAR data sets of the Hector Mine earthquake event in 1999 and Advanced Land Observing Satellite-2 Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) data sets of the 2016 Kumamoto earthquake event. Our results showed that an optimized measurement from the trade-off between the observation accuracy and resolution can be effectively determined by our proposed processing strategy. The results are improved results for measurement density and accuracy over previously published results. It further confirmed that our new method is allowed for the optimal measuring the large-scale fast surface displacements that cannot be sufficiently observed with the phase-based SAR method.INDEX TERMS Synthetic aperture radar (SAR), SAR offset tracking, surface deformation measurements, the 1999 hector mine earthquake, the 2016 Kumamoto earthquake.

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