Image Interpretation Methods Research Articles

Assessing the Image Quality of Digitally Reconstructed Radiographs from Chest CT.

Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs. We conducted a pilot study to explore which DRR technique best approaches the image quality of chest X-Rays (CXR). We quantitatively and qualitatively compared four DRR techniques. A retrospective convenience sample of 217 patients who underwent both ultra-low-dose (ULD) chest CT and CXR was used. Four DRRs were generated per ULDCT, and CheXNet, a neural network trained to detect 14 diseases, was applied to CXRs and DRRs to compute area under the curve (AUC) scores. For qualitative assessment, six radiologists rated the image quality of the four DRRs generated from six ULDCTs on a Likert scale from 1 to 6 ('not diagnostic quality' to 'diagnostic quality') and provided feedback, which was analysed using inductive category development. CheXNet's AUC for CXRs was 0.80, while DRR techniques ranged from 0.75 to 0.82 (p > 0.26). Radiologists rated the diagnostic quality of the DRRs between 3.0 and 3.5 on average. The SoftMip technique scored highest in both the quantitative (AUC = 0.82) and the qualitative (score = 3.5) evaluation. DRRs showed comparable disease detection performance to CXRs, suggesting non-inferiority. However, radiologists expressed concerns about DRR image quality, particularly in terms of resolution, noise, and overall look-and-feel. Addressing these limitations with advanced techniques may further align DRRs with the diagnostic standards of CXRs.

The Urban Park Green Spaces Landscape Premium Functional Value Accounting System: Construction and Application

Urban park green spaces have the functions of improving the urban ecological environment and providing recreational services, and at the same time, they have a certain effect on the value of the surrounding residential property. To quantitatively assess the value of the landscape premium function of park green space, many scholars have carried out research exploration and adopted a variety of methods (such as the contingent valuation method (CVM), travel cost method (TCM) and hedonic price method (HPM)), which have developed from simple theoretical models with single factors to complex empirical models with multiple factors. Among them, the hedonic price method has become the mainstream research method, and in recent years, it has been widely adopted in combination with GIS technology. In terms of research objects, single park green space or multiple park green spaces in large cities are the main focus, while there are fewer studies on park green spaces in built-up areas of small and medium-sized cities. In terms of research content, there are more studies on the value-added coefficient of landscape premium and influence distance, and there are fewer studies on the total value of landscape premium. This article aims to calculate the total landscape premium value of all park green spaces in the built-up areas of small and medium-sized cities, proposing a complete and operable accounting system for the functional value of park green space landscape premiums by combining GIS with a hedonic pricing model and remote sensing image interpretation methods. For the first time, a method for interpreting the height of residential buildings within the benefit range of landscape premium through remote sensing images is proposed, and then the floor area ratio of residential plots is estimated, so as to estimate the total area of actual beneficial buildings. Therefore, this paper takes Chifeng City, a small and medium-sized city, as a case study, and empirically demonstrates the assessment of the landscape premium function of parks and green spaces in the built-up area of Chifeng City by using this accounting system. Research shows that this method has certain feasibility, not only calculating the total value of landscape premium but also addressing the issue in existing studies where all areas within the potential range of landscape premium function are counted as appreciated areas, leading to an overestimation of the premium. It further advances the accuracy of accounting for the value of landscape premium function of urban park green space and provides theoretical reference for the planning and construction of urban park green space.

The Effects of Anthropogenic Loads on Vegetation Cover and Natural Water Quality in the Ugii Lake Basin, Mongolia

The Ugii Lake Basin, located in central Mongolia, has many natural parks with unique formations and is part of the International Ramsar Wetland Protection Agreement. To establish the "New Kharkhorum" city, it was necessary to plan and evaluate changes to the regional geological environment, including the Ugii Lake Basin. We aimed to assess changes in the vegetation cover, soil, and surface water of the Ugii Lake Basin. Field research, geochemical analysis of soil and surface water, and remote sensing image interpretation methods were used in this study. We interpreted Moderate Resolution Imaging Spectroradiometer satellite data from 2000 to 2022 to investigate changes in vegetation cover. On the basis of this classification, soil and natural water samples were chosen for onsite observations at four locations. As a result, the boundaries of the Ugii Lake Basin have been defined; they are 1017 km long and cover an area of 14,300 km2. Six normalized difference vegetation index categories were identified while the vegetation cover quality in the Ugii Lake Basin was assessed. The pH values of the surface water exceeded the standard by 0.05–0.21 in the area with a high anthropogenic load, whereas the other factors (Ca2+, Mg2+, Na++K+, SO42-, Fe, CI-, CO32-, HCO3-, NO2-, NO3-, and physiochemical parameters) did not exceed the Mongolian National Standard. The pH values in natural water were relatively high near the lake, indicating human activity in the Ugii Lake Basin. Consequently, this should be considered when designing integrated measures to address climate change and anthropogenic impacts in the Ugii Lake Basin.

Artificial intelligence in land use prediction modeling: a review

<p><span lang="EN-US">This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.</span></p>

Land morphology analysis with LiDAR technology to increase oil palm production

Abstract The morphological condition of the land plays an important role in determining the quality of growth of oil palm plants. Integrated management of oil palm plantations is a key factor in increasing productivity. Analysis of land morphology in oil palm plantations is a crucial first step. The objectives of this study were (1) to characterize land morphology with LiDAR implementation, (2) to explain the LiDAR mechanism in the NDVI class classification used for determining the oil palm Vegetation Index, and (3) to provide ideas to optimize oil palm productivity. The study was conducted using the image interpretation method from the acquisition of LiDAR data which has a resolution of 3 x 3 cm in the form of DEM and orthophoto to be able to perform land surface morphology analysis (MPL) and NDVI flatfoot. The data needed in this study is Orthophoto, which is used to crosscheck field conditions, plant canopy conditions, and populations of plants per block. NDVI processing is used to determine the Vegetation Index to interpret the health of oil palm plants. The results showed that LiDAR technology can be used to determine the health of oil palm plants. The overall accuracy and reliability value of NDVI reached 88.33% and 88.13%, respectively. This shows that the value of NDVI can predict the health of oil palm plants and can be used to monitor them effectively.

Diagnostic Accuracy of SPECT for Mild Traumatic Brain Injury: A Systematic Review and Meta-analysis.

This study examines the diagnostic accuracy of brain perfusion SPECT for mild traumatic brain injury (mTBI). A systematic review and meta-analysis was performed according to PRISMA guidelines (PROSPERO: CRD42023484636). Five databases were searched for studies evaluating brain perfusion SPECT in adult patients with mTBI (GCS 13-15). Study quality was assessed using a modified QUADAS-2 tool. A meta-analysis was performed to pool proportions of hypoperfusion abnormalities across brain lobes. Of 4735 records, 22 studies (5 longitudinal [40% high quality], 17 cross-sectional [24% high quality]) were included totaling 800 patients (mean age, 37.4 ± 12.6 years; 36.4% female). Meta-analysis of proportions indicated that the frontal lobe most frequently showed hypoperfusion on brain perfusion SPECT (pooled proportion 40.1% [95% confidence interval, 31.2% to 49.8%], 99/254, I2 = 54.5%), followed by the temporal lobe (26.1% [95% confidence interval, 19.9% to 33.6%], 68/254, I2 = 30.7%). Several studies found that hypoperfusion abnormalities were associated with neuropsychological findings. Also, brain perfusion SPECT could detect abnormalities not seen on MRI. Abnormalities in perfusion on brain perfusion SPECT may be more readily detected with a quantitative assessment compared with a visual assessment alone, although there appears to be no consensus on the optimal method for image interpretation. Evidence evaluating the sensitivity and specificity of brain perfusion SPECT for mTBI was limited. Using the GRADE framework, the evidence was rated as low. Although perfusion abnormalities can be seen in patients with mTBI, commonly in the frontal and temporal lobes, the findings are nonspecific and may derive from various factors. Ultimately, brain perfusion SPECT provides additional information for mTBI, but the final added value for the detection of mTBI is unknown.

Vulnerability to ARI and Diarrhea after Disaster

This research was conducted in shelters in two sub-districts, namely Mantikulore and Ulujadi sub-districts, with the aim of describing the level of ARI and diarrhea vulnerability after the disaster (especially shelters in Mantikulore and Ulujadi sub-districts). This research uses weighting scoring method and image interpretation method. The population in this study was 1,138 families from temporary shelters in the two sub-districts, with a sample size of 114 families. Meanwhile, the technique used to obtain samples is quato sampling. The results obtained are (1) In the parameter of ARI sufferers and the parameter of the use of anti-mosquito coils, it can be seen that most respondents fall into the classification of the class not vulnerable. On the parameter of the physical environment of the shelter (number of shelter members and ventilation area), all shelters are classified as highly vulnerable. On the smoking habit parameter, it can be seen that most respondents fall into the highly vulnerable class classification. In the parameter of road area, it can be seen that there are some shelters that are classified as not vulnerable and some are classified as very vulnerable. (2) In the parameter of ARI patients, it can be seen that most respondents are classified as not vulnerable. In the parameter of hand washing before eating and after defecation, it can be seen that all respondents are classified as not vulnerable. For waste management, half of the respondents were classified as vulnerable. In terms of the distance from the shelter to the polling station, it can be seen that all shelters are classified as highly vulnerable.

Multi-Modal Ensemble Deep Learning in Head and Neck Cancer HPV Sub-Typing.

Oropharyngeal Squamous Cell Carcinoma (OPSCC) is one of the common forms of heterogeneity in head and neck cancer. Infection with human papillomavirus (HPV) has been identified as a major risk factor for OPSCC. Therefore, differentiating the HPV-positive and negative cases in OPSCC patients is an essential diagnostic factor influencing future treatment decisions. In this study, we investigated the accuracy of a deep learning-based method for image interpretation and automatically detected the HPV status of OPSCC in routinely acquired Computed Tomography (CT) and Positron Emission Tomography (PET) images. We introduce a 3D CNN-based multi-modal feature fusion architecture for HPV status prediction in primary tumor lesions. The architecture is composed of an ensemble of CNN networks and merges image features in a softmax classification layer. The pipeline separately learns the intensity, contrast variation, shape, texture heterogeneity, and metabolic assessment from CT and PET tumor volume regions and fuses those multi-modal features for final HPV status classification. The precision, recall, and AUC scores of the proposed method are computed, and the results are compared with other existing models. The experimental results demonstrate that the multi-modal ensemble model with soft voting outperformed single-modality PET/CT, with an AUC of 0.76 and F1 score of 0.746 on publicly available TCGA and MAASTRO datasets. In the MAASTRO dataset, our model achieved an AUC score of 0.74 over primary tumor volumes of interest (VOIs). In the future, more extensive cohort validation may suffice for better diagnostic accuracy and provide preliminary assessment before the biopsy.

Expanding the dynamic range of a measuring system that implements the phase triangulation method

The article is devoted to the development of phase triangulation and structured illumination methods for measuring the geometry of objects with arbitrary light-scattering surface properties. A method for expanding the dynamic range of a measuring system that implements the phase triangulation method is proposed. The method is based on automatic adjustment of the exposure time of the photodetector, excessive collection of experimental data, and complex regression analysis. The results of experimental studies of the effectiveness of the proposed method for expanding the dynamic range of the measuring system are presented. It is shown that the classical approach to image interpretation using a photodetector with a fi xed exposure duration almost always leads to a high level of error over the entire range of light-scattering properties of the surface. For low exposure values, there is an increase in the measurement error in the region of the dark surface, since the signal level there turns out to be signifi cantly weaker than for the bright regions of the measured surface. For large exposure values, an increase in the error is observed when measuring the bright area of the measured surface, since the signal is outside the dynamic range of the photodetector. The proposed method of image interpretation with an extended range of the measuring system demonstrates an error level less than or comparable to the results of image interpretation with a fi xed exposure for all measured light-scattering properties of the surface. The proposed method makes it possible to perform automated measurements without additional optimization of the photodetector parameters for the light-scattering properties of the surface of the measured object.

Patterns of Population Displacement in the Former Hot Cloud Area after the 2010 Merapi Volcano Eruption Along the Upper Gendol River Flow

The Merapi Volcano eruption 2010 caused land damage in several locations, including the District of Cangkringan. After the eruption, residents were relocated to temporary and permanent housing. The site of the 2010 Merapi Volcano eruption's former hot clouds is changing back into a residential area. This study aims to determine the pattern of population displacement after the 2010 Merapi Volcano eruption along the upstream Gendol River. This research uses descriptive and data collection methods of image interpretation, field surveys, and in-depth interviews. Data analysis techniques were conducted using spatial analysis and data triangulation. The pattern of population relocation follows the multiple origin - multiple destination pattern to the temporary shelter and temporary housing, then back to the original residence in the former hot cloud area of the 2010 Merapi Volcano eruption. Relocation back to the former hot cloud area is influenced by (1) being near the land where the informants work, (2) the informants' children use the shelter because they are of marriageable age, (3) taking care of the land that has been inherited or will be inherited, and (4) the bond between the residents around Merapi Volcano and Merapi Volcano itself or the nature around Merapi.

Thematic interpretation of aerial photographs of forest areas based on conceptual modeling

Earth remote sensing (ERS) is one of the main sources of information about the earth's surface. With the development of unmanned aerial vehicles (UAVs), it became possible to take aerial photographs with high spatial resolution, which can be used to more accurately identify objects. The purpose of the study is to increase the reliability of aerial imagery interpretation by developing a methodology for automated image processing based on conceptual modeling. An analysis of the methods for thematic interpretation of images showed that none of them provides sufficient quality of segmentation without additional adjustment to the subject area. The technique of conceptual modeling of subject tasks is applied, which ensures the adequacy of syntactic representations (including various images) and makes it possible to control the logic of solving problems and reduce the number of errors at the stage of its software implementation. The description of the information system for thematic interpretation of images, which implements the developed methodology, is given. An assessment was made of the reliability of thematic interpretation of images of forest areas. It is shown that the developed technique allowed to improve the identification of forest objects on aerial photographs by 18.6%. The development of this technique can be carried out to determine the characteristics of identifiable trees: age, species, height, and wood stock.

A polarimetric projection-based scattering characteristics extraction tool and its application to PolSAR image classification

It is necessary to adequately extract scattering characteristics from polarimetric synthetic aperture radar (PolSAR) data for land-cover classification. Current interpretation technologies, such as target decompositions, polarimetric signatures, and polarimetric coherence patterns, have been widely used to explore polarization information. However, because limited scattering models are used to characterize all ground targets, these methods are occasionally limited in the interpretation of PolSAR data. This may reduce the accuracy of the land-cover classification. In this study, we attempted to extract the scattering characteristics through another novel method. We understand the PolSAR image interpretation as the projection of the scattering model on the measured matrix. Current methods project only a limited series of scattering models onto a measured matrix. To complement current methods of PolSAR image interpretation, we propose a polarimetric projection-based scattering characteristics extraction (PPSCE) method. It projects scattering models consisting of four changing independent polarization parameters onto the measured matrix. These scattering models, associated with the geometric and physical properties of the ground targets, correspond to the entire polarization space. The PPSCE method, meanwhile, extracts polarization information sufficiently. L-band UAVSAR, C-band Radarsat-2, and P-band AIRSAR images of classical experimental areas are used to validate the PPSCE method. The results show that the proposed PPSCE method can present polarization information that is difficult to investigate by the existing methods. In addition, the scattering features derived using the PPSCE method are superior for land-cover classification. Compared to the classification results based on the coherency matrix and Wishart classifier, the proposed method improves the overall classification accuracy by 23.56% for UAVSAR data. Compared to target decompositions and polarization signatures, the overall classification accuracy of the proposed method is increased by 29.32% (RF) and 22.63% (SVM) for UAVSAR data, 7.96% (RF) and 4.36% (RF) for Radarsat-2 and AIRSAR datasets, respectively. Furthermore, the proposed method has significant potential for soil moisture inversion. https://rgdoi.net/10.13140/RG.2.2.14091.26409.

Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.

SARS-CoV-2 is a novel virus that has been affecting the global population by spreading rapidly and causing severe complications, which require prompt and elaborate emergency treatment. Automatic tools to diagnose COVID-19 could potentially be an important and useful aid. Radiologists and clinicians could potentially rely on interpretable AI technologies to address the diagnosis and monitoring of COVID-19 patients. This paper aims to provide a comprehensive analysis of the state-of-the-art deep learning techniques for COVID-19 classification. The previous studies are methodically evaluated, and a summary of the proposed convolutional neural network (CNN)-based classification approaches is presented. The reviewed papers have presented a variety of CNN models and architectures that were developed to provide an accurate and quick automatic tool to diagnose the COVID-19 virus based on presented CT scan or X-ray images. In this systematic review, we focused on the critical components of the deep learning approach, such as network architecture, model complexity, parameter optimization, explainability, and dataset/code availability. The literature search yielded a large number of studies over the past period of the virus spread, and we summarized their past efforts. State-of-the-art CNN architectures, with their strengths and weaknesses, are discussed with respect to diverse technical and clinical evaluation metrics to safely implement current AI studies in medical practice.

Geomorphological mapping and landform characterization of Semeru volcano after the eruption in 2021

The eruption of the Semeru volcano in 2021 has diverse impacts on the landscape and surroundings. It is essential to detect recent landscape features to promptly evacuate personnel and property near the volcano and take emergency treatment measures in case of sudden danger. This study presents a detailed geomorphological map on a 1:50.000 scale for the Semeru volcano after the eruption in 2021. The analysis of the hybrid approach and the on-screen image interpretation (OSII) method on DEM data, optical satellites, and lithology units was carried out to obtain morphological, morphochronological, and morphoarrangement information. While, morphoprocess information was obtained through field observations. We used DAS Rejali for our study area. The results of identifying geomorphological conditions divide the Rejali watershed area into 32 landforms consisting of landform units from volcanic, structural, fluvial, and marine processes. Twenty-three land units in the research location are landforms from volcanic processes. This condition causes morphological conditions, material availability, and processes that dominantly reflect the characteristics of an active volcano. In addition, the landform units from the fluvial process in the Rejali watershed area were identified as many as six units in the form of floodplains of lahar flows along the main river. Landforms with structural genesis were placed in Mandalika formation and diorite rock breakthrough. The structural formation in the Rejali watershed has retention and rock structures that are not easily eroded by water. It becomes a bottleneck formation as a barrier between the upstream and middle areas

A State-Of-The-Art Review on Coronary Artery Border Segmentation Algorithms for Intravascular Ultrasound (IVUS) Images.

Intravascular Ultrasound images (IVUS) is a useful guide for medical practitioners to identify the vascular status of coronary arteries in human beings. IVUS is a unique intracoronary imaging modality that is used as an adjunct to angioplasty to view vessel structures using a catheter with high resolutions. Segmentation of IVUS images has always remained a challenging task due to various impediments, for example, similar tissue components, vessel structures, and artifacts imposed during the acquisition process. Many researchers have applied various techniques to develop standard methods of image interpretation, however, the ultimate goal is still elusive to most researchers. This challenge was presented at the MICCAI- Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop in 2011. This paper presents a major review of recently reported work in the field, with a detailed analysis of various segmentation techniques applied in IVUS, and highlights the directions for future research. The findings recommend a reference database with a larger number of samples acquired at varied transducer frequencies with special consideration towards complex lesions, suitable validation metrics, and ground-truth definition as a standard against which to compare new and current algorithms.

Geomorphological Study of the Talumopatu Geothermal Area Gorontalo Regency Gorontalo Province

Geomorphological research is urgently needed to be carried out in areas with geothermal potential such as in Talumopatu. Geomorphological analysis was chosen to complete the data in the early stages of the geothermal exploration of the study area. The purpose of this study was to determine the geomorphological characteristics of the study area. This study uses image interpretation methods and field excursions to observe the morphology of the study area. The stages of data processing use software applications based on Geographic Information Systems (GIS). The Geomorphological analysis consists of analysis of morphography, morphometry and morphogenesis. The analysis results are then compiled to produce a geomorphological map that refers to the van Zuidam classification. In accordance with the results of the analysis, it can be concluded that the geomorphological condition of the study area is divided into three parts, namely the Alluvial Plain Unit, Peneplains Unit, and Residual Hills Unit.

Land Use Changes Toward Water Quality Status of Lake Maninjau West Sumatera

The purpose of this research is to review and analyze the relationship between land use and water quality. The research method used is remote sensing image interpretation method, survey method, and spatial based descriptive analysis. The results of land use research included settlements, forests, rice fields, water bodies and shrubs in 1992, 2002, 2012 and 2022, in 1992 settlements 3.82%, 2002 2.87%, 2012 6.9% and in 2022 settlements 8.83%. in 1992 forest 53.59%, in 2002 43.78%, in 2012 49.76% and in 2022 forest 49.17%. in 1992 paddy fields 14.59%, in 2002 21.29%, in 2012 10.96% and in 2022 paddy fields 11.21%. in 1992 water bodies 22.25%, in 2002 22.97%, in 2012 22.38% and in 2022 water bodies 22.19%. Shrubs were 5.75% in 1992, 9.09% in 2002, 10% in 2012, and 8.6% in 2022. The water analysis findings suggest that class 1 water with a score of -15 corresponds to class C water, which is significantly contaminated. Class II with a score of -15 is classified as moderately contaminated, while Class III with a score of -10 is classified as somewhat polluted. Class IV, with a score of -10, is included in Class B, along with the Slightly contaminated category.

18F-FET-PET imaging in high-grade gliomas and brain metastases: a systematic review and meta-analysis.

To provide a summary of the diagnostic performance of 18F-FET-PET in the management of patients with high-grade brain gliomas or metastases from extracranial primary malignancies. MEDLINE, EMBASE, and Cochrane Database of Systematic Reviews databases were searched for studies that reported on diagnostic test parameters in radiotherapy planning, response assessment, and tumour recurrence/treatment-related changes differentiation. Radiomic studies were excluded. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool and the GRADE approach. A bivariate, random-effects model was used to produce summary estimates of sensitivity and specificity. Twenty-six studies with a total of 1206 patients/lesions were included in the analysis. For radiotherapy planning of glioma, the pooled proportion of patients from 3 studies with 18F-FET uptake extending beyond the 20mm margin from the gadolinium enhancement on standard MRI was 39% (95% CI, 10-73%). In 3 studies, 18F-FET-PET was also shown to be predictive of early responders to treatment, whereas MRI failed to show any prognostic value. For the differentiation of glioma recurrence from treatment-related changes, the pooled sensitivity and specificity of TBRmax 1.9-2.3 from 6 studies were 91% (95% CI, 74-97%) and 84% (95% CI, 69-93%), respectively. The respective values for brain metastases from 4 studies were 82% (95% CI, 74-88%) and 82% (95% CI, 74-88%) using TBRmax 2.15-3.11. While 18F-FET shows promise as a complementary modality to standard-of-care MRI for the management of primary and metastatic brain malignancies, further validation with standardized image interpretation methods in well-designed prospective studies are warranted.

Expanding the imaging armamentarium for the diagnosis of prosthetic aortic graft infection.

In their study published in this issue of the European Journal of Cardio-Thoracic Surgery, Tsuda et al. [1] evaluated the role of 18-fluorodeoxyglucose positron emission tomography (18-F-FDG PET) in the diagnosis of prosthetic aortic graft infection. Their retrospective review of 71 patients who underwent 18-FDG PET imaging after aortic repair showed that patients with aortic graft infection had a higher maximum standardized uptake value (SUVmax) than patients without infection (8.64 vs 3.40, P < 0.01) and that SUVmax value differed between the early postoperative period and long-term follow-up. The authors propose a cut-off value for SUVmax of 4.5 for suspicion of graft infection immediately after endovascular repair and subsequently using stepwise cuff-off values of 6.5 at 1 year, 5.5 at 3 years and 5.0 thereafter. Aortic graft infection is an uncommon complication, and its diagnosis and treatment can be challenging. The recent American Heart Association aortic disease guideline [2] provides recommendations for the diagnosis and management of prosthetic aortic graft infection. The diagnostic recommendations state that the sensitivity and specificity of diagnosis are enhanced by the use of cross-sectional imaging with computed tomography angiography combined with 18-FDG PET or magnetic resonance imaging of tagged white blood cells. Furthermore, the Management of Aortic Graft Infection Collaboration (MAGIC) group has proposed clinical/surgical, radiological, and laboratory diagnostic criteria for aortic graft infection, among which suspicious metabolic activity on 18-FDG PET is a minor criterion [3]. The MAGIC criteria were shown to have with good sensitivity and specificity in the prospective Vascular Graft Cohort study (VASGRA) [3]. Importantly, FDG-PET interpretation can be limited in patients with natural inflammation or malignancy that can affect its diagnostic accuracy for infection, as well as differences among centres and practitioners in their methods of image acquisition, processing and interpretation [4].

The conversion of rubber to oil palm and other landcover types in Southeast Asia

Rubber and oil palm are two main crops threatening biodiversity and natural habitats in Southeast Asia. The expansion of these crops has been transforming landscapes in the region replacing vast areas of forests and other croplands. Here, we utilize existing crop expansion time series data and tree plantation maps combined with image interpretation methods to identify rubber-associated land cover dynamics including the extent of rubber conversion to oil palm. While existing rubber plantations in 2014 had been converted from multiple land cover types including forests, we documented more rubber conversion to oil palm and other land cover types between 2014 and 2020. The percentage conversion of existing rubber plantations to oil palm was 1.9% and 2.6% for Indonesia and Malaysia, respectively. The estimated overall rate of this conversion was 10 500 ha/year. This slow readjustment of rubber and oil palm, following their competitive advantages, may reshape agricultural threats to tropical and sub-tropical environments through indirect land-use change. Continuation of this trend could lead to displacement of rubber to countries like Cambodia, Laos, and Myanmar threatening remaining natural habitats. Sustainability measures should therefore simultaneously focus on both oil palm and rubber considering their coupled transboundary displacement trends.