Likelihood Image Research Articles

A U-Net Model for Urban Land Cover Classification Using VHR Satellite Images

Urban settings are dynamic, constantly changing, and presenting a wide range of surface materials with high diversity in both spatial and spectral variation. As a result, mapping urban growth, evaluating infrastructure, managing water resources, and monitoring natural land cover become more complex tasks. Urban applications have made considerable progress thanks to the abundance of VHR orbital data and the recent development of artificial intelligence strategies especially neural networks. Convolutional neural networks have the potential to significantly enhance the analysis of urban land cover by addressing the limitations of traditional techniques. U-Net is a popular neural network for land cover analysis in remote sensing images. The current research presents a CNN model employing U-Net for image semantic segmentation in urban study area using both spectral and spatial context of VHR satellite data. The proposed model is trained, validated, and tested for VHR satellite image classification into five urban classes: water, vegetation, bare soil, road, and building. The CNN semantic segmentation results are compared to maximum likelihood image classification outcomes for validation and stability evaluation. A confusion matrix is applied to the classified scenes to determine the overall accuracy, producer's and user's accuracy, and Kappa coefficient using 400 random points with their corresponding ground truth. The U-Net image semantic segmentation technique achieved an overall accuracy of 87.50% and Kappa coefficient of 0.8395 which outperforms the maximum likelihood classification method with an overall accuracy of 83.25% and Kappa coefficient of 0.7812.

Remotely sensed land‐cover change and floodplain disturbance following upstream‐migrating river avulsions in tropical rainforests

AbstractThe displacement of a river to a new position within its adjacent floodplain is called avulsion, and here we examine how a newly recognized style, called retrogradational avulsion, affects the surrounding floodplain in tropical rainforests using remote sensing. Retrogradational avulsions begin with a channel blockage that causes self‐propagating upstream dechannelization and flooding. While this flooding results in vegetation die‐off and floodplain sedimentation, few quantitative measurements of disturbance by retrogradational avulsions exist. Here, we first focus on land‐cover change following a single retrogradational avulsion in Papua New Guinea from 2012 to 2021. During the avulsion, the river dechannelized 892 m upstream, and the parent channel width doubled. Using maximum likelihood image classification, we observed healthy vegetation fluctuated around 4.3 km2, vegetation regrowth peaked in 2017 at 3.2 km2, dead vegetation peaked in 2013 at 2.1 km2, and visible extent of deposited sediment was greatest in 2015 at 0.44 km2. We also examined 19 other retrogradational avulsions in Papua New Guinea and South America using NDVI. The area of floodplain disturbance (i.e., vegetation die‐off and possible sedimentation) for each avulsion ranged from <1 to >13 km2 and scaled with the dechannelization area. Comparing our plan‐view disturbance results with FABDEM digital‐elevation data and ICESat‐2 surface elevation measurements, we hypothesize floodplain disturbance extent is a function of topographic relief. Our results also suggest that retrogradational avulsions, on average, perturb larger areas of forest compared to blowdowns, suggesting this might be an important disturbance regime that influences gap‐filling regeneration in tropical rainforests.

Geo-spatial assessment of pluvial floods in city district Lahore, Pakistan.

This study is an effort of geo-spatial assessment of pluvial floods in District Lahore, Pakistan, caused by urban expansion and the growing frequency and intensity of high-intensity rainfall events. The use of geospatial techniques such as watershed modeling, maximum likelihood image classification, and weighted overlay analysis based on secondary data has enabled the researchers to assess the extent and severity of pluvial floods in the study area. The study's findings highlight the high risk of pluvial floods in the central part of the study area, which is dominated by built-up land and concrete roads. The increase in the area of built-up land from 34.913 km2 in 2018 to 37.442 km2 in 2022 has further intensified the risk of pluvial floods. The findings of this study can assist policymakers in developing effective strategies to reduce the risks associated with pluvial floods. Alongside, it also highlights the importance of geospatial techniques to better understand and address the complex challenges of urbanization and climate change. Flood risk zone-specific strategies are recommended to reduce the risk of pluvial floods.

RFI-Aware and Low-Cost Maximum Likelihood Imaging for High-Sensitivity Radio Telescopes

RFI-Aware and Low-Cost Maximum Likelihood Imaging for High-Sensitivity Radio Telescopes

Latent Transformer Models for out-of-distribution detection.

Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and are not presented to the downstream predictive model. In this work, we evaluate several approaches to segmentation with uncertainty for the task of segmenting bleeds in 3D CT of the head. We show that these models can fail catastrophically when operating in the far out-of-distribution domain, often providing predictions that are both highly confident and wrong. We propose to instead perform out-of-distribution detection using the Latent Transformer Model: a VQ-GAN is used to provide a highly compressed latent representation of the input volume, and a transformer is then used to estimate the likelihood of this compressed representation of the input. We demonstrate this approach can identify images that are both far- and near- out-of-distribution, as well as provide spatial maps that highlight the regions considered to be out-of-distribution. Furthermore, we find a strong relationship between an image's likelihood and the quality of a model's segmentation on it, demonstrating that this approach is viable for filtering out unsuitable images.

A multi-scale assessment of shoreline changes at an undeveloped beach in southern Brazil

This research aims to investigate shoreline variability at an undeveloped beach in southern Brazil on interannual and millennial scales. The long-term coastal evolution was investigated by GPR, whereas the interannual scale shoreline changes were analyzed by the Digital Shoreline Analysis System (DSAS). Simultaneously, a supervised Maximum Likelihood image classification was applied to the dune systems to differentiate vegetation from sand. The results show that the coastline in the Praia das Cabras region is retreating at different scales. The stratigraphic record in GPR shows that the coast has a retrogradational stacking pattern behavior in geological time. At the same time, the results obtained from the digital shoreline analysis revealed that the coast has suffered short-term erosion in the analyzed period, and this is attributed to natural causes. Another point is the higher values of erosion in the extreme parts of the study area, which are related to the presence of washouts. There is also a differentiation in the stage of the dunes and vegetation between the northern and southern parts of the stretch. The northern area is more densely vegetated and has a continuous foredune ridge with incipient dunes forming a ramp, whereas in the southern part, the vegetation is more dispersed, with interrupted dunes forming nebkhas. Between 2019 and 2023 there was a recovery of the dune field, by filling in the blowouts and minimizing the indentations, forming a more homogeneous foredune line.

Regularized Maximum Likelihood Image Synthesis and Validation for ALMA Continuum Observations of Protoplanetary Disks

Regularized Maximum Likelihood (RML) techniques are a class of image synthesis methods that achieve better angular resolution and image fidelity than traditional methods like CLEAN for sub-mm interferometric observations. To identify best practices for RML imaging, we used the GPU-accelerated open source Python package MPoL, a machine learning-based RML approach, to explore the influence of common RML regularizers (maximum entropy, sparsity, total variation, and total squared variation) on images reconstructed from real and synthetic Atacama Large millimeter/submillimeter Array (ALMA) continuum observations of protoplanetary disks. We tested two different cross-validation (CV) procedures to characterize their performance and determine optimal prior strengths, and found that CV over a coarse grid of regularization strengths easily identifies a range of models with comparably strong predictive power. To evaluate the performance of RML techniques against a ground truth image, we used MPoL on a synthetic protoplanetary disk data set and found that RML methods successfully resolve structures at fine spatial scales present in the original simulation. We used ALMA DSHARP observations of the protoplanetary disk around HD 143006 to compare the performance of MPoL and CLEAN, finding that RML imaging improved the spatial resolution of the image by up to a factor of 3 without sacrificing sensitivity. We provide general recommendations for building an RML workflow for image synthesis of ALMA protoplanetary disk observations, including effective use of CV. Using these techniques to improve the imaging resolution of protoplanetary disk observations will enable new science, including the detection of protoplanets embedded in disks.

Needle detection using ultrasound B-mode and power Doppler analyses.

Ultrasound is employed in needle interventions to visualize the anatomical structures and track the needle. Nevertheless, needle detection in ultrasound images is a difficult task, specifically at steep insertionangles. A new method is presented to enable effective needle detection using ultrasound B-mode and power Doppleranalyses. A small buzzer is used to excite the needle and an ultrasound system is utilized to acquire B-mode and power Doppler images for the needle. The B-mode and power Doppler images are processed using Radon transform and local-phase analysis to initially detect the axis of the needle. The detection of the needle axis is improved by processing the power Doppler image using alpha shape analysis to define a region of interest (ROI) that contains the needle. Also, a set of feature maps is extracted from the ROI in the B-mode image. The feature maps are processed using a machine learning classifier to construct a likelihood image that visualizes the posterior needle likelihoods of the pixels. Radon transform is applied to the likelihood image to achieve an improved needle axis detection. Additionally, the region in the B-mode image surrounding the needle axis is analyzed to identify the needle tip using a custom-made probabilistic approach. Our method was utilized to detect needles inserted in ex vivo animal tissues at shallow [ ), moderate [ ), and steep [ ]angles. Our method detected the needles with failure rates equal to 0% and mean angle, axis, and tip errors less than or equal to 0.7°, 0.6mm, and 0.7 mm, respectively. Additionally, our method achieved favorable results compared to two recently introduced needle detectionmethods. The results indicate the potential of applying our method to achieve effective needle detection in ultrasoundimages.

Towards a concrete landscape: Assessing the efficiency of land consumption in the Greater Accra Region, Ghana.

Most existing studies on land consumption have used a reactive approach to assess the phenomenon. However, for evidence-based policies, an initiative-taking forecast has been touted to be more appropriate. This study, therefore, assessed current trends and efficiency of land consumption in the Greater Accra Region from 1987 to 2017, and predicted a 30-year future land consumption in a "business-as-usual" scenario. The study adopted maximum likelihood image classification techniques and "combinatorial or" to model land cover change for Greater Accra from 1987 to 2017 while the UN-Habitat land efficiency index was employed to model efficiency of land consumption. In addition, Leo-Breiman Forest based regression, was used to model a future land cover by using the 30 years land cover change as a dependant variable and a series of natural and anthropogenic factors as independent variables. Results showed that artificial surfaces increased from 4.2% to 33.1%, with an annual growth rate of 22.1% in 30 years. Land consumption was highly inefficient as only 4.2% of the region had a good proportion of population per land area. Factors which influenced artificial surface growth were population, distance from water bodies, poverty index, distance from sacred groves, proportion of agriculture population with a small margin of influence from soil and geology type. Landscape prediction showed that artificial surfaces will increase to 92.6% as more places are coated with concrete. The high rate of land inefficiency provides an opportunity for re-zoning by the Land Use and Spatial Planning Authority of Ghana to accommodate the growing population.

Unraveling the Innermost Jet Structure of OJ 287 with the First GMVA + ALMA Observations

We present the first very long baseline interferometric (VLBI) observations of the blazar OJ 287 carried out jointly with the Global Millimeter VLBI Array (GMVA) and the phased Atacama Large Millimeter/submillimeter Array (ALMA) at 3.5 mm on 2017 April 2. The participation of phased ALMA has not only improved the GMVA north–south resolution by a factor of ∼3, but has also enabled fringe detections with signal-to-noise ratios up to 300 at baselines longer than 2 Gλ. The high sensitivity has motivated us to image the data with newly developed regularized maximum likelihood imaging methods, revealing the innermost jet structure with unprecedentedly high angular resolution. Our images reveal a compact and twisted jet extending along the northwest direction, with two bends within the inner 200 μas, resembling a precessing jet in projection. The component at the southeastern end shows a compact morphology and high brightness temperature, and is identified as the VLBI core. An extended jet feature that lies at ∼200 μas northwest of the core shows a conical shape, in both total and linearly polarized intensity, and a bimodal distribution of the linear polarization electric vector position angle. We discuss the nature of this feature by comparing our observations with models and simulations of oblique and recollimation shocks with various magnetic field configurations. Our high-fidelity images also enabled us to search for possible jet features from the secondary supermassive black hole (SMBH) and test the SMBH binary hypothesis proposed for this source.

Land use and land cover change, and analysis of its drivers in Ojoje watershed, Southern Ethiopia

There have been incredible changes that have taken place in the land use pattern globally over the last 50 years, which resulted from environmental degradation and climate change impacts. Quantitative analysis of the LULC dynamics helps in land-use management and ecosystem degradation at large. The study was conducted in the Doyogena district, southern Ethiopia to identify LULC change dynamics, and analyze the driving forces using combined approaches: remote sensing, field observations, in-depth household interviews, key informants, and Focus Group Discussions (FGDs). A supervised maximum likelihood image cataloging method was employed in conjunction with feature extraction of satellite images to categorize and map LULC classes of the study area. Satellite image handing out, classification technique, and remotely sensed data were processed using ArcGIS map 10.6, and ERDAS Imagine 2014. Common LULC categories were identified, and a change analysis was conducted. Accordingly, seven LULC categories were determined. The result showed a considerable decline in forestland from 1756.7 ha (38.8%) in 1973 to 71.6 ha (1.6%) in 2020. Similarly, wetlands have declined successively from 16.8 in 2000–2010 to 6.3 in 1986–2020 ha/year over the last three and half decades respectively. On the other hand, cropland has increased from 34.1% in 1986–2000 to 46.3% between 1986–2020, which is linked to population growth, settlement, and expansion of farmlands. The study watershed has experienced a considerable change in LULC change over the last >3 decades. Hence, local and national regimes should implement sustainable land planning, management strategies including integrated land- use planning, and policy reform into development projects and programs.

Quantitatively Assessing the Future Land-Use/Land-Cover Changes and Their Driving Factors in the Upper Stream of the Awash River Based on the CA–Markov Model and Their Implications for Water Resources Management

Despite the rapid economic and population growth, the risks related to the current dynamics of land use and land cover (LULC) have attracted a lot of attention in Ethiopia. Therefore, a complete investigation of past and future LULC changes is essential for sustainable water resources and land-use planning and management. Since the 1980s, LULC change has been detected in the upper stream of the Awash River basin. The main purpose of this research was to investigate the current dynamics of LULC and use the combined application of the cellular automata and the Markov chain (CA–Markov) model to simulate the year 2038 LULC in the future; key informant interviews, household surveys, focus group discussions, and field observations were used to assess the consequences and drivers of LULC changes in the upstream Awash basin (USAB). This research highlighted the importance of remote sensing (RS) and geographic information system (GIS) techniques for analyzing the LULC changes in the USAB. Multi-temporal cloud-free Landsat images of three sequential data sets for the periods (1984, 2000, and 2019) were employed to classify based on supervised classification and map LULC changes. Satellite imagery enhancement techniques were performed to improve and visualize the image for interpretation. ArcGIS10.4 and IDRISI software was used for LULC classification, data processing, and analyses. Based on Landsat 5 TM-GLS 1984, Landsat 7 ETM-GLS 2000, and Landsat 8 2019 OLI-TIRS, the supervised maximum likelihood image classification method was used to map the LULC dynamics. Landsat images from 1984, 2000, and 2019 were classified to simulate possible LULC in 2019 and 2038. The result reveals that the maximum area is covered by agricultural land and shrubland. It showed, to the areal extent, a substantial increase in agricultural land and urbanization and a decrease in shrubland, forest, grassland, and water. The LULC dynamics showed that those larger change rates were observed from forest and shrubland to agricultural areas. The results of the study show the radical changes in LULC during 1984–2019; the main reasons for this were agricultural expansion and urbanization. From 1984 to 2019, agriculture increased by 62%, urban area increased by 570.5%, and forest decreased by 88.7%. In the same year, the area of shrubland decreased by 68.6%, the area of water decreased by 65.5%, and the area of grassland decreased by 57.7%. In view of the greater increase in agricultural land and urbanization, as well as the decrease in shrubland, it means that the LULC of the region has changed. This research provides valuable information for water resources managers and land-use planners to make changes in the improvement of future LULC policies and development of sub-basin management strategies in the context of sustainable water resources and land-use planning and management.

Land-use and land-cover change in the lowlands of Bale Zone, Ethiopia: its driving factors and impacts of rangeland dynamics in livestock mobility.

Rangeland in Bale lowlands has been seriously degraded due to human-induced problems and natural factors. The study was conducted to analyze LULC change and its deriving factors and evaluate the impacts of rangeland dynamics on livestock mobility in Bale lowlands from 1990-2020. Landsat Thematic Mapper (TM) 1990, Enhanced Thematic Mapper Plus (ETM +) 2000, and Operational Land Imager (OLI) 2014 and 2020 were chosen to derive LULC classes using maximum likelihood image classifier. Besides, a household survey was used to understand the major causes of LULC change, as well as the impacts of rangeland dynamics on livestock mobility. The accuracy reports of classified LULC classes of the study were 88.2% (1990), 89.19% (2000), 93.8% (2014), and 95.2% (2020). The result of the study revealed that there was extreme bush encroachment (545.54%), expansion of settlement (19,166%), and farmland (171.27%) while forest cover has slightly decreased (-8.76%) from 1990 to 2020. On the other hand, shrubland (-72.74%) and grassland (-59.2%) have extremely declined. During the study period, rangeland of Bale lowlands was degraded with annual rate of -0.8%. The study also revealed that expansion of farmland, settlement, communal land, and bush encroachment was the main driving factors for LULC change in Bale lowlands. Bale pastoralists are vulnerable to the death of their livestock, and they need to travel long distances because of rapid rangeland degradation. Therefore, suitable land-use and management policies for pastoral communities should be formulated and implemented so as to permanently mitigate the problem.

Temporal and Spatial Analysis of Mangrove Forest Change in the North Coast of East Java

The north coast of East Java has high human activities and needs. These human needs include opening new land for housing areas, so that some mangrove areas have begun to be converted into residential areas. This can directly cause ecological impacts that threaten the sustainability of the coastal environment, but on the other hand, mangrove areas on the north coast of East Java are also developed as conservation and tourism areas. The purpose of this study is to determine changes in mangrove area and changes in coastlines in the northern coastal region of East Java during the period 2013 to 2019 based on Geographical Information Systems (GIS) using the Supervised-Maximum Likelihood image classification method. The results of this study showed that the largest change in mangrove forest area was in Sidoarjo Regency by 95%, while the lowest change in mangrove forest area was in Tuban Regency at 7.20%.

Human – Ear Recognition Using Scale Invariant Feature Transform

Biometrics techniques are the standard of a wide group of many applications for a human’s identification and verification issues. Because of this reason, a high scale of security needs to search for a new way to identify the person arises. In this paper, establish a human ear recognition system is proposed. This system combines four main phases: ear detection, ear feature extraction, ear recognition, and confirmation. The essential of the proposed system is to divide the ear image into the skin and non-skin pixels using a likelihood skin detector. The likelihood image processes by morphological operations to complete ear regions. Scale-invariant feature transform uses for extracting the fixed features of the ear. Ear recognition includes two modes identification mode and verification mode. Euclidean Distance Measure (EDM) uses for similarity measure between the first image in the database and a new image. According to the three experiments conducted in this paper, the results of the different datasets, the accuracy ratio are 100%, 92%.and 92% respectively.

Hydrological Response of Dry Afromontane Forest to Changes in Land Use and Land Cover in Northern Ethiopia

This study analyzes the impact of land use/land cover (LULC) changes on the hydrology of the dry Afromontane forest landscape in northern Ethiopia. Landsat satellite images of thematic mapper (TM) (1986), TM (2001), and Operational Land Imager (OLI) (2018) were employed to assess LULC. All of the images were classified while using the maximum likelihood image classification technique, and the changes were assessed by post-classification comparison. Seven LULC classes were defined with an overall accuracy 83–90% and a Kappa coefficient of 0.82–0.92. The classification result for 1986 revealed dominance of shrublands (48.5%), followed by cultivated land (42%). Between 1986 and 2018, cultivated land became the dominant (39.6%) LULC type, accompanied by a decrease in shrubland to 32.2%, as well as increases in forestland (from 4.8% to 21.4%) and bare land (from 0% to 0.96%). The soil conservation systems curve number model (SCS-CN) was consequently employed to simulate forest hydrological response to climatic variations and land-cover changes during three selected years. The observed changes in direct surface runoff, the runoff coefficient, and storage capacity of the soil were partially linked to the changes in LULC that were associated with expanding bare land and built-up areas. This change in land use aggravates the runoff potential of the study area by 31.6 mm per year on average. Runoff coefficients ranged from 25.3% to 47.2% with varied storm rainfall intensities of 26.1–45.4 mm/ha. The temporal variability of climate change and potential evapotranspiration increased by 1% during 1981–2018. The observed rainfall and modelled runoff showed a strong positive correlation (R2 = 0.78; p < 0.001). Regression analysis between runoff and rainfall intensity indicates their high and significant correlation (R2 = 0.89; p < 0.0001). Changes were also common along the slope gradient and agro-ecological zones at varying proportions. The observed changes in land degradation and surface runoff are highly linked to the change in LULC. Further study is suggested on climate scenario-based modeling of hydrological processes that are related to land use changes to understand the hydrological variability of the dry Afromontane forest ecosystems.

After the revolution: how is Cryo-EM contributing to muscle research?

The technique of electron microscopy (EM) has been fundamental to muscle research since the days of Huxley and Hanson. Direct observation of how proteins in the sarcomere are arranged and visualising the changes that occur upon activation have greatly increased our understanding of function. In the 1980s specimen preparation techniques for biological EM moved away from traditional fixing and staining. The technique known as cryo-electron microscopy (Cryo-EM) was developed, which involves rapidly freezing proteins in liquid ethane which maintains them in a near native state. Within the last 5 years there has been a step change in the achievable resolution using Cryo-EM. This ‘resolution revolution’ can be attributed to advances in detector technology, microscope automation and maximum likelihood image processing. In this article we look at how Cryo-EM has contributed to the field of muscle research in this post revolution era, focussing on recently published high resolution structures of sarcomeric proteins.

Spectral image scanning microscopy.

For decades, the confocal microscope has represented one of the dominant imaging systems in biomedical imaging at sub-cellular lengthscales. Recently, however, it has increasingly been replaced by a related, but more powerful successor technique termed image scanning microscopy (ISM). In this article, we present ISM capable of measuring spectroscopic information such as that contained in fluorescence or Raman images. Compared to established confocal spectroscopic imaging systems, our implementation offers similar spectral resolution, but higher spatial resolution and detection efficiency. Color sensitivity is achieved by a grating placed in the detection path in conjunction with a camera collecting both spatial and spectral information. The multidimensional data is processed using multi-view maximum likelihood image reconstruction. Our findings are supported by numerical simulations and experiments on micro beads and double-stained HeLa cells.

Accurate tooth segmentation with improved hybrid active contour model

In orthodontic diagnosis and oral treatment planning, 3D tooth model constructed by dental computed tomography (CT) images is an essential and useful assisted tool. In virtue of the higher spatial resolution and lower radiation of x-ray, cone beam computed tomography (CBCT) has been widely used in dental application. However, due to lower signal to noise ratio, vague and weak edge between tooth root and sockets as well as intensity inhomogeneity, the tooth root is easy to be under-segmented and appears false boundary. This paper presents a new hybrid active contour model in a variational level set formulation to segment the tooth root accurately. Initial shape and intensity information from the upper layer is used for next layer’s enhancement and shape constraint. The hybrid level set model is constituted by multi-scale local likelihood image fitting (LLIF) energy term, prior shape constraint energy term with adaptive weight and reaction–diffusion (RD) regularization energy term. For detailed interpretation of this hybrid energy model, the intensity information in a narrowband region outside the contour was used to enhance the contrast between tooth dentine and sockets. The LLIF energy term was incorporated into the level set function to overcome the edge fuzziness and intensity inhomogeneity. The shape prior energy term with adaptive weight was used to differentiate the constraint of the contour evolution inside and outside the level set function to improve the capability of curve topology changes. The RD energy term was introduced to effectively regularize the level set evolution. A new measurement for tooth segmentation evaluation was proposed for quantitative validation. The experimental result of the proposed method was compared with two other typical approaches, and was demonstrated to achieve a higher segmentation accuracy.

Theoretical study of the benefit of long axial field-of-view PET on region of interest quantification

The aim of this study is to evaluate the benefit of long axial field-of-view (AFOV) PET scanners on region of interest (ROI) quantification. We simulated a series of PET scanners with an AFOV ranging from 22 cm to 220 cm. A theoretical framework was used to predict the contrast recovery coefficient (CRC) and the variance of ROI quantification in penalized maximum likelihood (ML) image reconstruction, in which the resolution and noise tradeoff was controlled by a regularization parameter with a quadratic penalty function. The characterization was based on the converged penalized ML reconstruction with an accurate system model. We examined quantification of a 2 mm ROI and 10 mm ROI in a clinically relevant scan range of 110 cm. Multiple bed positions with 50% overlap were used for scanners with shorter AFOV to provide a relatively uniform sensitivity across the 110 cm axial range. A uniform water cylinder of 20 cm in diameter and 230 cm in length was chosen to model the attenuation and background activity. We computed the variance reduction factor at fixed resolution. Effects of different detector capabilities, including TOF (time-of-flight) resolution (320 ps, 500 ps, and non-TOF) and DOI (depth-of-interaction) resolution (4 mm, 10 mm, and no DOI), were evaluated. The results show that at a normal activity level (370 MBq), the 220 cm AFOV scanner offers a ∼17-fold variance reduction for the 2 mm ROI and ∼26-fold variance reduction for the 10 mm ROI (both measured at CRC = 0.5) over the 22 cm AFOV scanner when both using detectors with 500 ps TOF resolution no DOI capability. The variance reduction factors of trues-only are higher than those of including scatters and randoms. Combining 320 ps TOF and 4 mm DOI, the 220 cm long scanner offers a ∼45-fold variance reduction over the 22 cm long reference scanner (500 ps TOF, no DOI) for imaging 2 mm and 10 mm ROIs. The variance reduction factors are higher at a lower activity level due to lower random fraction. In conclusion, our study demonstrates that a long AFOV scanner can greatly improve the quantitative accuracy of PET imaging compared to current state-of-the-art clinical PET scanners.