Effects Of Image Resolution Research Articles

Evaluation of Asphalt Pavement Texture Using Multiview Stereo Reconstruction Based on Deep Learning

To quickly obtain texture information and accurately evaluate the skid resistance of asphalt pavement, a multiview stereo reconstruction method based on deep learning is proposed for evaluating the texture depth of asphalt pavement. First, a depth camera and a digital camera are used to collect RGB-D (i.e., Red, Green, Blue, Depth) and RGB (i.e., Red, Green, Blue) multiview image datasets on different types of pavements for model training and validation respectively. Then, the model precision is validated by calculating the overlap (intersection over union, IoU) between the ground truth point cloud and the reconstructed point cloud. The model performances under different training strategies are compared to obtain the best model, and the effects of image resolution, number of views, and types of pavement material on the model precision are analyzed. Finally, image processing methods and texture depth characterization indexes are proposed to obtain pavement texture information from the reconstructed depth map, thus validating the effectiveness of the model. The results show that the trained model using the transfer learning strategy achieves a reconstruction precision of 0.77 when the image resolution is 3024×3024 and the number of views is 7. Furthermore, the reconstruction performance remains stable across different pavement materials, suggesting the model is suitable for accurately reconstructing depth maps for asphalt pavements. The errors between the predicted texture depth value calculated based on the volume-based index (MTDp) and the profile-based index (MPDp) using the depth maps after image processing and the measured texture depth value (MTDe) using the sand patch method are 11.72% and 18.85%, respectively. It is believed that the pavement texture depth can be effectively evaluated from the reconstructed depth map using the volume-based metric (MTDp). In contrast to traditional testing methods, this approach requires using only a digital camera and a personal computer, making it a lightweight and intelligent analysis method for obtaining pavement texture depth information.

Investigation of ConViT on COVID-19 Lung Image Classification and the Effects of Image Resolution and Number of Attention Heads

COVID-19 has beenone of the popular foci in the research community since its first outbreak in China, 2019. Radiological patternssuch as ground glass opacity (GGO) andconsolidations are often found inCT scan images ofmoderate to severe COVID-19 patients. Therefore, a deep learning model can be trained to distinguish COVID-19 patients using their CT scan images. Convolutional Neural Networks (CNNs) has been a popular choice for this type of classification task. Anotherpotential method is the use ofvisiontransformer with convolution, resulting in Convolutional Vision Transformer (ConViT), to possibly produce on par performance using less computational resources. In this study, ConViT is applied to diagnose COVID-19 cases from lung CT scan images. Particularly, we investigated the relationship of the input image pixel resolutions and the number of attention heads used in ConViT and their effects on the model’s performance.Specifically, we used 512x512, 224x224 and 128x128 pixels resolution to train the modelwith 4 (tiny), 9 (small) and 16 (base) number of attention heads used. An open access dataset consisting of 2282 COVID-19 CT images and 9776 Normal CT images from Iran is used in this study. Byusing 128x128 image pixels resolution,training using 16 attention heads, the ConViT modelhas achieved an accuracy of98.01%,sensitivity of90.83%, specificity of99.69%, positive predictive value (PPV) of95.58%, negative predictive value (NPV) of97.89%and F1-score of94.55%.The model has also achieved improvedperformance over other recent studiesthat usedthe same dataset.In conclusion, this study has shown that theConViTmodel can play a meaningful role to complement RT-PCR test on COVID-19 close contacts and patients.

Contrast solution properties and scan parameters influence the apparent diffusivity of computed tomography contrast agents in articular cartilage.

The inability to detect early degenerative changes to the articular cartilage surface that commonly precede bulk osteoarthritic degradation is an obstacle to early disease detection for research or clinical diagnosis. Leveraging a known artefact that blurs tissue boundaries in clinical arthrograms, contrast agent (CA) diffusivity can be derived from computed tomography arthrography (CTa) scans. We combined experimental and computational approaches to study protocol variations that may alter the CTa-derived apparent diffusivity. In experimental studies on bovine cartilage explants, we examined how CA dilution and transport direction (absorption versus desorption) influence the apparent diffusivity of untreated and enzymatically digested cartilage. Using multiphysics simulations, we examined mechanisms underlying experimental observations and the effects of image resolution, scan interval and early scan termination. The apparent diffusivity during absorption decreased with increasing CA concentration by an amount similar to the increase induced by tissue digestion. Models indicated that osmotically-induced fluid efflux strongly contributed to the concentration effect. Simulated changes to spatial resolution, scan spacing and total scan time all influenced the apparent diffusivity, indicating the importance of consistent protocols. With careful control of imaging protocols and interpretations guided by transport models, CTa-derived diffusivity offers promise as a biomarker for early degenerative changes.

Deep learning based techniques for flame identification in optical engines

The mandatory migration from fossil to renewable energy sources requires the characterization of new alternative fuels. One important step in fuel characterization is the test in optical engines, which allows the morphological characterization of flames. This analysis requires the post treatment of images by using segmentation. In many cases, an automatic threshold presents shortcomings as the flames may present different regions with variable luminosity, as also reflections from valves and cylinder liner. Consequently, a time-consuming manual image processing is required and, therefore, an automatic procedure would be welcome. The use of deep learning techniques for image segmentation is a promising alternative for such task, which has showed excellent results in several applications. In this study, two different models were trained to identify flames in images obtained from an optical engine operating at various conditions. The dataset used to train the models was generated by using images from tests with several types of fuels and combustion modes. The effects of image resolution and the generalization capabilities for different fuels and combustion operation were investigated. After analyzing the results, the use of deep learning methods to identify and characterize flames was validated as a mean for improving processing time.

Do image resolution and classifier choice impact island biogeographical parameters of terrestrial islands?

AbstractIsland biogeography provides concepts for conservation management as fragmented habitats are comparable to ocean islands. Remote sensing can help to extract terrestrial habitat islands on the landscape scale. However, little is known about the effects of image resolution and classification algorithms on resulting island size and related parameters. We study the combined effect of three image resolutions (2, 10, and 30 m) and three classification algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) by extracting quartz islands from WorldView‐2 imagery using image segmentation. We compared four island parameters (i.e., area, distance, shape index, and perimeter–area ratio between resolutions and classifiers). We found that in all cases, image resolution and classification algorithms had a strong effect. However, image resolution was more important for area and shape. Artificial Neural Network always provided the best performance as a classifier (OA: 0.880, kappa: 0.801, F1: 0.912). Hence, conservation strategies could lead to different results when different pattern extraction strategies are applied. Future studies which aim at extracting terrestrial habitat islands from image datasets should aim for the highest possible resolution and compare the outcomes of different classifiers to ensure the best possible results.

Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth

Enabling automated pipelines, image analysis and big data methodology in cancer clinics requires thorough understanding of the data. Automated quality assurance steps could improve the efficiency and robustness of these methods by verifying possible data biases. In particular, in head and neck (H&N) computed-tomography (CT) images, dental artifacts (DA) obscure visualization of structures and the accuracy of Hounsfield units; a challenge for image analysis tasks, including radiomics, where poor image quality can lead to systemic biases. In this work we analyze the performance of three-dimensional convolutional neural networks (CNN) trained to classify DA statuses. 1538 patient images were scored by a single observer as DA positive or negative. Stratified five-fold cross validation was performed to train and test CNNs using various isotropic resampling grids (643, 1283 and 2563), with CNN depths designed to produce 323, 163, and 83 machine generated features. These parameters were selected to determine if more computationally efficient CNNs could be utilized to achieve the same performance. The area under the precision recall curve (PR-AUC) was used to assess CNN performance. The highest PR-AUC (0.92 ± 0.03) was achieved with a CNN depth = 5, resampling grid = 256. The CNN performance with 2563 resampling grid size is not significantly better than 643 and 1283 after 20 epochs, which had PR-AUC = 0.89 ± 0.03 (p -value = 0.28) and 0.91 ± 0.02 (p -value = 0.93) at depths of 3 and 4, respectively. Our experiments demonstrate the potential to automate specific quality assurance tasks required for unbiased and robust automated pipeline and image analysis research. Additionally, we determined that there is an opportunity to simplify CNNs with smaller resampling grids to make the process more amenable to very large datasets that will be available in the future.

Image-based modelling for fluid flow and permeability prediction within continuously-evolving mushy zone during melting and solidification

Mushy zone permeability is a key parameter that characterizes the fluid flow resistance within the two-phase mixed region during solid-liquid phase transitions. It plays an important role in accurately modelling the melting and solidification processes as well as the related latent heat storage performance. In our previous research, series images of the transient microstructures within the continuously-evolving mushy zone during both paraffin’s melting and solidification have been experimentally obtained by using a confocal optical microscope system. Thus, in this study, based on those experimentally-obtained images, the liquid flow and related permeability within the transient mushy zone were calculated using a pore-scale lattice Boltzmann method. The calculated flow velocity and predicted permeability have been verified against the corresponding reported analytical solutions. In addition, the mechanism behind the microscopic flow was presented and analysed. Moreover, the effects of image resolution on the fluid flow calculation were also studied. It was found that difference in the microstructures between melting and solidification results in distinctly various distributions of local velocity fields within the mushy zone, but makes little difference to the maximum velocity and the average permeability. The results also indicate that the image resolution imposes great effects on the predicted liquid flow by blurring and omitting tiny structures within the mushy zone in lower-resolution images. In addition, the predicted permeability increases more gently with the local liquid fraction as compared with our previously proposed correlations. Therefore, this work presents detailed information on fluid flow within mushy zone with continuously-evolving microstructures, and provides more insights into the fluid dynamics of the solid-liquid phase change.

Resolution and scale controls on the accuracy of atoll island shorelines interpreted from satellite imagery

Understanding the positional uncertainty of vector objects within a GIS has been widely discussed and a range of approaches have been applied to quantify the uncertainty of such objects. Recently, a number of studies have adopted remote sensing approaches to examine the erosion of low-lying atoll islands. These studies are underpinned by a comparison of shoreline positions interpreted from remote imagery and represented as line features, and/or island areas represented as polygons. However, the effects of image resolution and scale of interpretation have rarely been considered when examining the results of such studies. In order to examine the control of image resolution and scale of interpretation on the accuracy of shoreline positions, repeated digitisation of islands was undertaken under a range of conditions similar to those applied in an operational setting. High-resolution (40 cm) satellite imagery was resampled to coarser resolutions and islands repeatedly digitised at a range of scales. Results quantify seemingly intuitive relationships, whereby shorelines digitised from coarse imagery and at small scales are less accurate than those digitised from high-resolution imagery at large scales. Results of this study provide a useful guide for selecting imagery for shoreline change studies and understanding the positional uncertainty of vector objects within a GIS.

Effects of Image Resolution on Sandstone Porosity and Permeability as Obtained from X-Ray Microscopy

Estimating porosity and permeability for porous rock is a vital component of reservoir engineering, and imaging techniques have to date focused on methodologies to match image-derived flow parameters with experimentally identified values. Less emphasis has been placed on the trade-off between imaging complexity, computational time, and error in identifying porosity and permeability. Here, the effect of image resolution on the permeability derived from micro-X-ray microscopy (micro-XRM) is discussed. A minicore plug of Bentheimer sandstone is imaged at a resolution of $$1024 \times 1024 \times 1024$$ voxels, with a voxel size of 1.53 $${\upmu }\hbox {m}$$ , and progressively rebinned to as low as 32 voxels per side (voxel size 48.96 $${\upmu }\hbox {m}$$ ). Pore-scale flow is modeled using the finite volume method in the open-source program OpenFOAM®. A sharp drop in permeability between images with a voxel size of 24 and 12 $${\upmu }\hbox {m}$$ suggests that an optimal speed/resolution trade-off may be found. The primary source of error is due to reassignment of voxels along the pore–solid interface and the subsequent change in pore connectivity. We observe the error in permeability and porosity due to both image resolution and thresholding values in order to find a method that balances an acceptable error range with reasonable computation time.

An Online Tool Temperature Monitoring Method Based on Physics-Guided Infrared Image Features and Artificial Neural Network for Dry Cutting

This paper presents an efficient method, which reconstructs the temperature field around the tool/chip interface from infrared (IR) thermal images, for online monitoring the internal peak temperature of the cutting tool. The tool temperature field is divided into two regions; namely, a far field for solving the heat-transfer coefficient between the tool and ambient temperature, and a near field where an artificial neural network (ANN) is trained to account for the unknown heat variations at the frictional contact interface. Methods to extract physics-based feature points from the IR image as ANN inputs are discussed. The effects of image resolution, feature selection, chip occlusion, contact heat variation, and measurement noises on the estimated contact temperature are analyzed numerically and experimentally. The proposed method has been verified by comparing the ANN-estimated surface temperatures against “true values” experimentally obtained using a high-resolution IR imager on a custom-designed testbed as well as numerically simulated using finite-element analysis. The concept feasibility of the temperature monitoring method is demonstrated on an industrial lathe-turning center with a commercial tool insert. Note to Practitioners —The internal tool-temperature field around the tool/chip interface during cutting offers essential information to monitor tool-wear and ensure surface-quality particularly in finishing cuts, which is difficult to be monitored because of the stringent real-time requirements (including low cost and high accuracy) in harsh working environments. This paper presents a potentially low-cost solution that combines noncontact surface temperature measurements with high-fidelity physics-based computational models for monitoring the internal peak temperature at the tool/chip frictional contact during cutting. This physics-based method requires only a small number of preselected features, and uses thermal isotherms and streamlines to detect and substitute any occlusions in the IR images. The method does not rely on high-resolution (HR) images to infer the steep temperature gradient near the tool-tip where the peak temperature occurs, and thus can be implemented with a relatively low-cost IR imager. Unlike traditional least-square methods that bases solutions to a heat-conduction partial differential equation (PDE), the ANN is trained with precomputed physics-based models based on a novel dual-field (far-field and near-field) approach. The ANN-based method not only eliminates the need to solve the time-demanding PDE in real time, but also effectively accounts for parameter variations due to the uncertain frictional heat-fluxes at the contact interface during cutting.

Joint atmospheric turbulence detection and adaptive demodulation technique using the CNN for the OAM-FSO communication

A novel joint atmospheric turbulence (AT) detection and adaptive demodulation technique based on convolutional neural network (CNN) are proposed for the OAM-based free-space optical (FSO) communication. The AT detecting accuracy (ATDA) and the adaptive demodulating accuracy (ADA) of the 4-OAM, 8-OAM, 16-OAM FSO communication systems over computer-simulated 1000-m turbulent channels with 4, 6, 10 kinds of classic ATs are investigated, respectively. Compared to previous approaches using the self-organizing mapping (SOM), deep neural network (DNN) and other CNNs, the proposed CNN achieves the highest ATDA and ADA due to the advanced multi-layer representation learning without feature extractors designed carefully by numerous experts. For the AT detection, the ATDA of CNN is near 95.2% for 6 kinds of typical ATs, in cases of both weak and strong ATs. For the adaptive demodulation of optical vortices (OV) carrying OAM modes, the ADA of CNN is about 99.8% for the 8-OAM system over the computer-simulated 1000-m free-space strong turbulent link. In addition, the effects of image resolution, iteration number, activation functions and the structure of the CNN are also studied comprehensively. The proposed technique has the potential to be embedded in charge-coupled device (CCD) cameras deployed at the receiver to improve the reliability and flexibility for the OAM-FSO communication.

Remote sensing of intertidal habitats predicts West Indian topsnail population expansion but reveals scale-dependent bias

High-resolution imagery is lacking for much of the West Indies, impeding accurate intertidal habitat assessments and conservation planning. The West Indian topsnail, which inhabits rocky shores, is an important, regional fishery. It was overfished to extinction in Bermuda but reintroduced in 1982. In this study, we estimate potential population size through habitat mapping using high-resolution imagery and ground-based survey data. We also test the effects of image resolution and map scale on intertidal habitat assessments. The coastline of Bermuda was mapped as a linear feature (1:500) using high-resolution imagery. Topsnail population size was predicted using length of preferred habitat and population density. With the comprehensive map as ground-truth, effects of scale were assessed in two ways: supervised classification of low-resolution imagery and progressive map scale coarsening (Douglas-Peuker simplification). Bermuda’s coastline is 296 km at this map scale, 50 % of which is rocky shore. Topsnail population could expand significantly if all preferred habitat is occupied. However, image resolution and map scale drastically affect mapping robustness. Unsurprisingly, automated classifiers poorly distinguished narrow intertidal habitats. More disturbingly, coarsening map scale differentially affected habitats. Fine-scale mapping enabled by high-resolution imagery is vital for intertidal conservation planning. Limitations of low-resolution imagery and scale-dependent biases are pertinent beyond intertidal habitats. Numerous predominantly linear habitats may be especially sensitive to sea-level rise and other effects of climate change, so careful consideration of the effects of scale on habitat assessments and the use of high-resolution imagery are strongly recommended.

Effects of image resolution and numerical resolution on computed permeability of consolidated packing using LB and FEM pore-scale simulations

Image-based pore-scale modeling has become an important tool for studying fluid transport and other phenomena in porous media. Spatial resolution of the digital images used for modeling is critical not only because it dictates the scale of features that can be resolved, but also because for most techniques there is at least some relationship between voxel size in the image data and numerical resolution applied to the computational simulations. In this work we investigate this relationship using a computer-generated consolidated porous medium, which was digitized at voxel resolutions in the range 2–10μm. These images, which are free of experimental and segmentation errors, are then used to compute permeability and tortuosity using lattice Boltzmann (LB) and finite elements methods (FEM). Results show how changes in computed permeability are affected by image resolution (which dictates how well the pore geometry is approximated) versus grid or mesh resolution (which changes numerical accuracy). For LB, the image and grid resolution are usually taken to be the same; we show at least one case where effects of grid and image resolution appear to counteract one another, giving the mistaken appearance of resolution-independent results. For FEM, meshing can provide certain attributes (such as better conformance to surfaces), but it also adds an extra step for error or approximation to be introduced in the workflow. Results show that performing grid coarsening on the FEM mesh caused a reduction in computed permeability, but in this particular case the effect is related to tightening of the pore space rather than loss of numerical accuracy.

SU-E-T-43: The Effects of Image Resolution and Noise on the Gamma Dose Distribution Comparison Method for IMRT QA

Purpose: To investigate the effects of image noise and resolution on gamma analysis results for clinical IMRT quality assurance (QA) treatment plans. Methods: For each clinical IMRT plan, a hybrid QA plan was created in Pinnacle3 using a solid water phantom and delivered with a Varian Clinac 21EX. A transverse dose plane was measured using Kodak EDR2 radiographic film. Gamma analyses were performed using OmniPro-I'mRT software (IBA Dosimetry, Germany), comparing the calculated dose distribution (reference) versus the measured dose distribution (evaluated). Each IMRT QA film was digitized using three different image resolutions (71, 142, and 285 dpi). In order to study the potential for noise (e.g., from the film, developer, or digitizer) to mask an error, a 4 mm positional shift and various noise levels (normally distributed with 1, 2, and 3% standard deviation) were introduced to the calculated dose distributions, and gamma analyses were performed using these noisy distributions as the measured dose distributions. Results: Our analysis showed that the percentage of passing pixels increased with both increasing image resolution and image noise. Doubling the film image resolution increased the passing rate by an average of 0.1% for ±5%/3mm acceptance criteria, 0.8% for 3%/3mm, and 2.6% for 2%/2mm for the six clinical IMRT QA plans in this study. Increasing the noise from 0% to 3% increased the passing rate of the shifted images by an average of 1.5% for the 5%/3mm acceptance criteria, 4.6% for 3%/3mm, and 8.9% for 2%/2mm. Conclusions: Image noise and high scanning resolution artificially increased the percentage of passing pixels in gamma analysis. This effect was more pronounced for tighter γ criteria (i.e. 2%/2mm). In designing IMRT QA protocols, it is important to be aware of the fact that gamma analysis is sensitive to these two parameters.

The Effects of Image Resolution on an Armored Vehicle Differentiation Task

Future usage of multiple unmanned vehicle (UV) assets by all branches of the United States military necessitates examination of the factors involved in efficient communication of information among ...

3D laser imaging for joint orientation analysis

This paper evaluates the application of three-dimensional (3D) laser imaging to measure joint orientation. A field trial was conducted at a road cut with 3 well expressed joint sets. Using 3D point cloud data, joint orientation was evaluated using two methods: a 2.5D method, commercially available, based on a triangular irregular network (TIN) and a new 3D pole density contouring method where the orientation of each triangular mesh element in the 3D model is determined. Validated against field measurements, the 2.5D and 3D methods were applied manually and the resultant average angular differences were 13.3° and 3.8°, respectively, indicating that the 3D method is very accurate. When automated, the 2.5D and 3D methods yielded results with average angular differences of 14.4° and 9.9°. The effects of image resolution (an image acquisition parameter), triangular mesh element size (an image processing parameter), and joint face geometry (a geological parameter) on the performance of the 3D pole density contouring method were assessed. Image resolution had minimal effect on measurement accuracy. Increasing triangular mesh element size had an adverse effect because holes started to appear in the 3D model.

Pore-scale modeling of electrical and fluid transport in Berea sandstone

The purpose of this paper is to test how well numerical calculations can predict transport properties of porous permeable rock, given its 3D digital microtomography [Formula: see text] image. For this study, a Berea 500 sandstone sample is used, whose [Formula: see text] images have been obtained with resolution of [Formula: see text]. Porosity, electrical conductivity, permeability, and surface area are calculated from the [Formula: see text] image and compared with laboratory-measured values. For transport properties (electrical conductivity, permeability), a finite-difference scheme is adopted. The calculated and measured properties compare quite well. Electrical transport in Berea 500 sandstone is complicated by the presence of surface conduction in the electric double layer at the grain-electrolyte boundary. A three-phase conductivity model is proposed to compute surface conduction on the rock [Formula: see text] image. Effects of image resolution and computation sample size on the accuracy of numerical predictions are also investigated. Reducing resolution (i.e., increasing the voxel dimensions) decreases the calculated values of electrical conductivity and hydraulic permeability. Increasing computation sample volume gives a better match between laboratory measurements and numerical results. Large sample provides a better representation of the rock.

Carotid Atheroma Rupture Observed In Vivo and FSI-Predicted Stress Distribution Based on Pre-rupture Imaging

Atherosclerosis at the carotid bifurcation is a major risk factor for stroke. As mechanical forces may impact lesion stability, finite element studies have been conducted on models of diseased vessels to elucidate the effects of lesion characteristics on the stresses within plaque materials. It is hoped that patient-specific biomechanical analyses may serve clinically to assess the rupture potential for any particular lesion, allowing better stratification of patients into the most appropriate treatments. Due to a sparsity of in vivo plaque rupture data, the relationship between various mechanical descriptors such as stresses or strains and rupture vulnerability is incompletely known, and the patient-specific utility of biomechanical analyses is unclear. In this article, we present a comparison between carotid atheroma rupture observed in vivo and the plaque stress distribution from fluid–structure interaction analysis based on pre-rupture medical imaging. The effects of image resolution are explored and the calculated stress fields are shown to vary by as much as 50% with sub-pixel geometric uncertainty. Within these bounds, we find a region of pronounced elevation in stress within the fibrous plaque layer of the lesion with a location and extent corresponding to that of the observed site of plaque rupture.

Matrix-Erosion Tessellation: Comparing Particle Clustering Measures Extracted from Three-Dimensional vs Two-Dimensional Images

Microvoids can nucleate from second-phase particles, grow, coalesce, and ultimately result in ductile failure. Since the rate of nucleation has been shown to be greatly increased by the clustering of second-phase particles, it is important to be able to characterize the particle distribution within an engineering alloy. Recent technological advances have made it possible to obtain three-dimensional (3-D) images of microstructural particle fields, but traditional two-dimensional (2-D) imaging of metallographic samples remains more convenient and cost-effective. Therefore, the extent to which the true nature of 3-D clustering can be quantified using only 2-D images is of genuine interest. In this study, matrix-erosion tessellation and dilational counting techniques are extended from 2-D to 3-D in order to measure the spatial distribution characteristics of various virtual 3-D particle fields. The effects of image resolution are first investigated and a minimum resolution parameter is proposed. Individual 2-D planes are then extracted from the 3-D virtual images for analysis and comparison with the 3-D results. The minimum number of features for the 2-D image to be representative of the 3-D system is then assessed. It was found that the use of 2-D images is appropriate for identifying the general distribution type (i.e., ordered, random, or clustered) and for comparing the relative amounts of clustering. The 2-D–based measures are also able to detect the presence of stringers in materials with a preferred cluster orientation (e.g., rolled sheet).

A practical approach to in vivo high-resolution diffusion tensor imaging of rhesus monkeys on a 3-T human scanner

The nonhuman primate brain study provides important supplemental means for human brain exploration since the two species share close anatomical and functional similarities. MR diffusion tensor imaging (DTI) in human brain has revealed exquisite details of brain structures especially in the brain white matter. However, most previous monkey brain DTI results lack the spatial resolution in comparison to the conventional tracing and postmortem imaging methods, especially when it is acquired in commonly available human MRI scanners of field strength of 3 T or lower. To meet the increasing demands for nonhuman primate DTI studies, we proposed an in vivo high-resolution monkey DTI acquisition protocol that is practically feasible and combined it with an improved postprocessing procedure for a 3-T human scanner. The acquisition protocol, susceptibility distortion correction method with phase reversal acquisition, and postprocessing steps were proved to be effective in our study of rhesus monkeys. Results from diffusion tensor estimations and fiber tractography at 1 x 1 x 1 mm(3) resolution were found to be comparable to previous ex vivo DTI studies with much longer acquisition times. Effects of image resolution were evaluated and it was confirmed that the partial volume effect due to the larger voxel size in low-resolution data biased the diffusion tensor estimation and produced erroneous fiber tractography. Our results suggest that in vivo high-resolution monkey brain DTI can be achieved within practical time, which allows accurate diffusion tensor estimation and fiber tractography in monkey brains, so that the complex anatomical structures within many small but important anatomic structures can be delineated.