Local Region Of Interest Research Articles

Three‐dimensional quadrature array coil elements for improved parallel magnetic resonance imaging performance at 1.5 Tesla

AbstractIn this study, a special concept of 3D quadrature array coil elements is presented. It allows for doubling the number of phased‐array coil elements over a localized region of interest (ROI) without the necessity of reducing the sizes of the elements below optimal dimensions and thus losing performance due to domination of the noise of the elements. In this study, a 3D quadrature array coil element is formed of a planar coil element in combination with a perpendicular orientated, geometrically decoupled, second coil element. Simulation results of such a perpendicular arrangement of single coil elements are presented demonstrating the improvement in spatial sensitivity distribution and signal‐to‐noise ratio (SNR) over the localized ROI. In addition, an eight‐channel prototype array coil was built out of 3D quadrature array elements approving the advantageous encoding capabilities and SNR performance of the presented concept by in vivo T2‐weighted spin‐echo imaging studies on the thoracic human spinal cord. © 2011 Wiley Periodicals, Inc. Concepts Magn Reson Part A 38A: 61–73, 2011.

An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA)

BackgroundSegmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction.MethodsA proposed method with anisotropic diffusion as pre-processing and a novel Bounded Area Elimination (BAE) post-processing algorithm to improve the algorithm of ossification site localization technique are designed with the intent of improving the adaptive segmentation result and the region-of interest (ROI) localization accuracy.ResultsThe results are then evaluated by quantitative analysis and qualitative analysis using texture feature evaluation. The result indicates that the image homogeneity after anisotropic diffusion has improved averagely on each age group for 17.59%. Results of experiments showed that the smoothness has been improved averagely 35% after BAE algorithm and the improvement of ROI localization has improved for averagely 8.19%. The MSSIM has improved averagely 10.49% after performing the BAE algorithm on the adaptive segmented hand radiograph.ConclusionsThe result indicated that hand radiographs which have undergone anisotropic diffusion have greatly reduced the noise in the segmented image and the result as well indicated that the BAE algorithm proposed is capable of removing the artifacts generated in adaptive segmentation.

An experimental study of pathologist's navigation patterns in virtual microscopy

In virtual microscopy, a sequential process of captures of microscopical fields, allows to construct a virtual slide which is visualized using a specialized software, called the virtual microscopy viewer. This tool allows useful exploration of images, composed of thousands of microscopical fields of view at different levels of magnification, emulating an actual microscopical examination. The aim of this study was to establish the main pathologist's navigation patterns when exploring virtual microscopy slides, using a graphical user interface, adapted to the pathologist's workflow. Four pathologists with a similar level of experience, graduated from the same pathology program, navigated six virtual slides. Different issues were evaluated, namely, the percentage of common visited image regions, the time spent at each and its coincidence level, that is to say, the region of interest location. In addition, navigation patterns were also assessed, i.e., mouse movement velocities and linearity of the diagnostic paths. Results suggest that regions of interest are determined by a complex combination of the visited area, the time spent at each visit and the coincidence level among pathologists. Additionally, linear trajectories and particular velocity patterns were found for the registered diagnostic paths.

Feasibility of FAIR imaging for evaluating tumor perfusion

To evaluate the feasibility of flow-sensitive alternating inversion recovery (FAIR) for measuring blood flow in tumor models. In eight mice tumor models, FAIR and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed. The reliability for measuring blood flow on FAIR was evaluated using the coefficient of variation of blood flow on psoas muscle. Three regions of interest (ROIs) were drawn in the peripheral, intermediate, and central portions within each tumor. The location of ROI was the same on FAIR and DCE-MR images. The correlation between the blood flow on FAIR and perfusion-related parameters on DCE-MRI was evaluated using the Pearson correlation coefficient. The coefficient of variation for measuring blood flow was 9.8%. Blood flow on FAIR showed a strong correlation with Kep (r = 0.77), percent relative enhancement (r = 0.73), and percent enhancement ratio (r = 0.81). The mean values of blood flow (mL/100 g/min) (358 vs. 207), Kep (sec(-) (1)) (7.46 vs. 1.31), percent relative enhancement (179% vs. 134%), and percent enhancement ratio (42% vs. 26%) were greater in the peripheral portion than in the central portion (P < 0.01). As blood flow measurement on FAIR is reliable and closely related with that on DCE-MR, FAIR is feasible for measuring tumor blood flow.

GRID-INDEPENDENT METROPOLIS SAMPLING FOR VOLUME VISUALIZATION

We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables "importance sampling" of local regions of interest in the visualization by generating sample points intensively in regions where a user-specified transfer function takes the peak values. The generated sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. We demonstrate the effectiveness of our method by applying it to regular cubic grids and irregular tetrahedral grids with adaptive cell sizes. We visualize volume data by projecting the generated sample points onto the 2D image plane. We tested our sampling with three rendering models: an X-ray model, a simple illuminant particle model, and an illuminant particle model with light-attenuation effects. The grid-independency and the efficiency in the parallel processing mean that our method is suitable for visualizing large-scale volume data. The former means that the required number of sample points is proportional to the number of 2D pixels, not the number of 3D voxels. The latter means that our method can be easily accelerated on the multiple-CPU and/or GPU platforms. We also show that our method can work with adaptive space partitioning of volume data, which also enables us to treat large-scale/complex volume data easily.

Scene categorization via contextual visual words

In this paper, we propose a novel scene categorization method based on contextual visual words. In the proposed method, we extend the traditional ‘bags of visual words’ model by introducing contextual information from the coarser scale and neighborhood regions to the local region of interest based on unsupervised learning. The introduced contextual information provides useful information or cue about the region of interest, which can reduce the ambiguity when employing visual words to represent the local regions. The improved visual words representation of the scene image is capable of enhancing the categorization performance. The proposed method is evaluated over three scene classification datasets, with 8, 13 and 15 scene categories, respectively, using 10-fold cross-validation. The experimental results show that the proposed method achieves 90.30%, 87.63% and 85.16% recognition success for Dataset 1, 2 and 3, respectively, which significantly outperforms the methods based on the visual words that only represent the local information in the statistical manner. We also compared the proposed method with three representative scene categorization methods. The result confirms the superiority of the proposed method.

Fatigue life prediction of corrosion-damaged high-strength steel using an equivalent stress riser (ESR) model. Part II: Model development and results

The fatigue life of metallic aircraft structural components can be significantly reduced by environmentally induced corrosion. However, there have historically been no analytical methods to quantify the specific fatigue life reduction of individual unfailed corroded components with any reasonable degree of confidence. As part of a NAVAIR high-strength steel corrosion–fatigue assessment program, methods were studied to predict the impact that corrosion-induced surface roughness has on the fatigue life of high-strength steel aircraft components. The steels of interest produce general corrosion in patches as well as localized material loss similar to pitting. In addition, this type of corrosion has characteristic features over a wide range of scales. Consequently, traditional finite element analysis approaches are not well suited to this problem, since the mesh required to accurately reflect the fine details distributed over the entire corrosion patch make computation unrealistic. Therefore, approximate methods were developed that allow localized regions of interest of high stress to be identified. Subsequently, a simple notch metric formula is employed to approximate the stress riser in these regions of interest. Finally, an extension of Peterson’s fatigue notch sensitivity theory is applied to these small “notches” that has the result of suppressing the effect of smaller notches compared to larger notches in the prediction of life. Each region of interest is assigned a probability of crack initiation as a function of fatigue cycles, based on a probabilistic strain–life analysis using the predicted notch factor. The net life (to crack initiation) for the component is then the product of the survivabilities of all of the individual regions of interest on the component surface. Tests on corroded fatigue specimens have been conducted to both calibrate the parameters in the Peterson model as well as to test the life prediction capability of the approach. Predictions from the resulting model have demonstrated that an empirical approach to corrosion surface damage can be utilized to generate probabilistic life predictions that have substantial engineering value in assessing the residual fatigue life of corroded AF1410 steel components, and that the modeling technique can capture the significant corrosion features that cause fatigue cracking in most cases, especially for more severely corroded surfaces.

Preprocessing of region of interest localization based on local surface curvature analysis for three-dimensional reconstruction with multiresolution

We present an approach to integrate a preprocessing step of the region of interest (ROI) localization into 3-D scanners (laser or stereoscopic). The definite objective is to make the 3-D scanner intelligent enough to localize rapidly in the scene, during the preprocessing phase, the regions with high surface curvature, so that precise scanning will be done only in these regions instead of in the whole scene. In this way, the scanning time can be largely reduced, and the results contain only pertinent data. To test its feasibility and efficiency, we simulated the preprocessing process under an active stereoscopic system composed of two cameras and a video projector. The ROI localization is done in an iterative way. First, the video projector projects a regular point pattern in the scene, and then the pattern is modified iteratively according to the local surface curvature of each reconstructed 3-D point. Finally, the last pattern is used to determine the ROI. Our experiments showed that with this approach, the system is capable to localize all types of objects, including small objects with small depth.

Compressed sensing based interior tomography

While conventional wisdom is that the interior problem does not have a unique solution, by analytic continuation we recently showed that the interior problem can be uniquely and stably solved if we have a known sub-region inside a region of interest (ROI). However, such a known sub-region is not always readily available, and it is even impossible to find in some cases. Based on compressed sensing theory, here we prove that if an object under reconstruction is essentially piecewise constant, a local ROI can be exactly and stably reconstructed via the total variation minimization. Because many objects in computed tomography (CT) applications can be approximately modeled as piecewise constant, our approach is practically useful and suggests a new research direction for interior tomography. To illustrate the merits of our finding, we develop an iterative interior reconstruction algorithm that minimizes the total variation of a reconstructed image and evaluate the performance in numerical simulation.

Identifying Likely PM2.5 Sources on Days of Elevated Concentration: A Simple Statistical Approach

A simple statistical method is described for identifying the likely importance of local sources of PM2.5 in a region on days when the National Ambient Air Quality Standard is exceeded. The method requires only PM2.5 mass concentration and wind direction data, and makes use of the EPA database on PM2.5 emissions in the local region of interest. The method has been illustrated using data from the Pittsburgh Air Quality Study, and suggests that local sources can be very important in affecting PM2.5 exceedances. The results have implications for many of the urban areas in the eastern United States downwind of large sources in the Midwest, and shows that simple statistical tests can be of value in identifying regions where further testing with sophisticated air quality dispersion models and source-receptor models is warranted.

3D MDCT-based system for planning peripheral bronchoscopic procedures

The diagnosis and staging of lung cancer often begins with the assessment of a suspect peripheral chest site. Such suspicious peripheral sites may be solitary pulmonary nodules or other abnormally appearing regions of interest (ROIs). The state-of-the-art process for assessing such peripheral ROIs involves off-line procedure planning using a three-dimensional (3D) multidetector computed tomography (MDCT) chest scan followed by bronchoscopy with an ultrathin bronchoscope. We present an integrated computer-based system for planning peripheral bronchoscopic procedures. The system takes a 3D MDCT chest image as input and performs nearly all operations automatically. The only interaction required by the physician is the selection of ROI locations. The system is computationally efficient and fits smoothly within the clinical work flow. Integrated into the system and described in detail in the paper is a new surface-definition method, which is vital for effective analysis and planning to peripheral sites. Results demonstrate the efficacy of the system and its usage for the live guidance of ultrathin bronchoscopy to the periphery.

Multiple Description Coding Using Data Hiding and Regions of Interest for Broadcasting Applications

We propose an innovative scheme for multiple description coding (MDC) with regions of interest (ROI) support to be adopted in high-quality television. The scheme proposes to split the stream into two separate descriptors and to preserve the quality of the region of interest, even in case one descriptor is completely lost. The residual part of the frame (the background) is instead modeled through a checkerboard pattern, alternating the strength of the quantization. The decoder is provided with the necessary side-information to reconstruct the frame properly, namely, the ROI parameters and location, via a suitable data hiding procedure. Using data hiding, reconstruction parameters are embedded in the transform coefficients, thus allowing an improvement in PSNR of the single descriptions at the cost of a negligible overhead. To demonstrate its effectiveness, the algorithm has been implemented in two different scenarios, using the reference H.264/AVC codec and an MJPEG framework to evaluate the performance in absence of motion-compensated frames on 720p video sequences.

Mapping the Zonal Organization of Tumor Perfusion and Permeability in a Rat Glioma Model by Using Dynamic Contrast-enhanced Synchrotron Radiation CT

To depict and analyze in vivo the tumor zone organization of C6 gliomas depicted on quantitative parametric maps obtained with dynamic contrast material-enhanced synchrotron radiation computed tomography (CT) in a tightly controlled data-processing protocol. Animal use was compliant with official French guidelines and was assessed by the local Internal Evaluation Committee for Animal Welfare and Rights. Fifteen Wistar rats with orthotopically implanted gliomas were studied at monochromatic synchrotron radiation CT after receiving a bolus injection of contrast material. The iodine concentration maps were analyzed by using a compartmental model selected from among a package of models. Choice of model and assessment of the relevance of the model were guided by quality criteria. Tissue blood flow (F(T)), tissue blood volume fraction (V(T)), permeability-surface area product (PS), artery-to-tissue delay (D(A-T)), and vascular mean transit time (MTT) maps were obtained. Parametric map findings were compared with histologic findings. Local regions of interest were selected in the contralateral hemisphere and in several tumor structures to characterize the tumor microvasculature. Differences in parameter values between regions were assessed with the Wilcoxon method. Whole-tumor parameters were expressed as means +/- standard errors of the mean: Mean F(T), V(T), PS, and D(A-T) values and MTT were 61.4 mL/min/100 mL +/- 15.3, 2.4% +/- 0.4, 0.37 mL/min/100 mL +/- 0.11, 0.24 second +/- 0.06; and 3.9 seconds +/- 0.83, respectively. MTT and mean PS were significantly lower (P < .01) in the normal contralateral tissue: 1.10 seconds +/- 0.06 and < or = 10(-5) mL/min/100 mL, respectively. Tumor regions were characterized by significantly different (P < .05) F(T) and V(T) pairs: 108 mL/min/100 mL and 3.66%, respectively, at the periphery; 45.9 mL/min/100 mL and 1.91%, respectively, in the intermediate zone; 5.1 mL/min/100 mL and 0.42%, respectively, in the center; and 210 mL/min/100 mL and 6.82%, respectively, in the maximal value region. Fine mapping of the glioma microcirculation is feasible with dynamic contrast-enhanced synchrotron radiation CT performed with well-controlled analytic protocols. http://radiology.rsnajnls.org/cgi/content/full/2501071929/DC1.

Structural dissociation of attentional control and memory in adults with and without mild traumatic brain injury

Memory and attentional control impairments are the two most common forms of dysfunction following mild traumatic brain injury (TBI) and lead to significant morbidity in patients, yet these functions are thought to be supported by different brain networks. This 3 T magnetic resonance diffusion tensor imaging (DTI) study investigates whether microstructural integrity of white matter, as measured by fractional anisotropy (FA) within a small set of individually localized regions of interest (ROIs), is associated with these cognitive domains in normal adults and adults with mild TBI. Results in a sample of 23 normal controls reveal a significant correlation between attentional control and FA within a ROI in the left hemisphere anterior corona radiata. Furthermore, the controls demonstrate a correlation between memory performance and FA in a ROI placed in the uncinate fasciculus. Next, to examine whether these relationships are found in the pathological ranges of attention, memory and microstructural white matter integrity associated with mild TBI, these analyses were applied to a group of 43 mild TBI patients. Results, which generally demonstrated a wider range of attention, memory and FA scores, replicated the correlation between attentional control and FA in left hemisphere anterior corona radiata, as well as the correlation between memory performance and FA in the uncinate fasciculus. These two sets of brain-behaviour relationships were highly specific, as shown by a lack of correlation between attention and uncinate fasciculus FA and the lack of correlation between memory performance and anterior corona radiata FA. Furthermore, a 'correlational double dissociation' was demonstrated to exist between two distinct frontal structures independently associated with attention and memory, respectively, via a series of multiple regression analyses in both normal controls and adults with mild TBI. The results of the multiple regression analyses provide direct evidence that tract-specific variation in microstructural white matter integrity among normal controls and among mild TBI patients can account for much of the variation in performance in specific cognitive domains. More generally, such findings suggest that diffusion anisotropy measurement can be used as a quantitative biomarker for neurocognitive function and dysfunction.

Improved waveform fidelity using local HYPR reconstruction (HYPR LR).

The recently introduced HYPR (HighlY constrained backPRojection) method allows reconstruction of serial images from highly undersampled data. In HYPR, individual timeframes are obtained via unfiltered backprojections of normalized sinograms using anatomical constraints provided by a composite image. Here we develop the idea of constraining the backprojected data further to a series of local regions of interest in order to decrease the corruption of local information by distant signals. HYPR LR (local reconstruction) permits the use of a longer temporal window in the formation of the composite image, resulting in increased signal-to-noise ratio and quantitative reconstruction accuracy. Unlike HYPR, the new HYPR LR method can be applied to images acquired with arbitrary k-space trajectories. It is suitable for a broad range of medical imaging applications involving serial changes in image sequence, offering exciting new opportunities in the future.

Image reconstruction for fan-beam differential phase contrast computed tomography

Recently, x-ray differential phase contrast computed tomography (DPC-CT) has been experimentally implemented using a conventional tube combined with gratings. Images were reconstructed using a parallel-beam reconstruction formula. However, parallel-beam reconstruction formulae are not applicable when the parallel-beam approximation fails. In this paper, we present a new image reconstruction formula for fan-beam DPC-CT. There are several novel features of the new image reconstruction formula: (i) when the scanning angular range of data acquisition is larger than π + γm (γm is the full fan angle), the entire field of view can be exactly reconstructed; (ii) when the scanning angular range is smaller than π + γm, a local region of interest (ROI) can be exactly reconstructed; (iii) it enables an exact reconstruction for a local ROI when the projection data are truncated at some view angles; (iv) it enlarges the imaging field of view when the detector is asymmetrically placed. In this last case, the data are truncated from every view angle. Numerical simulations have been conducted to validate the new reconstruction formula.

Extended field of view soft x-ray Fourier transform holography: toward imaging ultrafast evolution in a single shot

Panoramic full-field imaging is demonstrated by applying spatial multiplexing to Fourier transform holography. Multiple object and reference waves extend the effective field of view for lensless imaging without compromising the spatial resolution. In this way, local regions of interest distributed throughout a sample can be simultaneously imaged with high spatial resolution. A method is proposed for capturing multiple ultrafast images of a sample with a single x-ray pulse.

A local region of interest image reconstruction via filtered backprojection for fan-beam differential phase-contrast computed tomography

Recently, x-ray differential phase contrast computed tomography (DPC-CT) has been experimentally implemented using a conventional source combined with several gratings. Images were reconstructed using a parallel-beam reconstruction formula. However, parallel-beam reconstruction formulae are not directly applicable for a large image object where the parallel-beam approximation fails. In this note, we present a new image reconstruction formula for fan-beam DPC-CT. There are two major features in this algorithm: (1) it enables the reconstruction of a local region of interest (ROI) using data acquired from an angular interval shorter than 180°+ fan angle and (2) it still preserves the filtered backprojection structure. Numerical simulations have been conducted to validate the image reconstruction algorithm.

Development of Portable Real-time Instrument for Three-Dimensional Measurement and Analysis of Spinal Curve

Non-rigid object class recognition is a challenging computer vision problem. Using descriptors extracted from local interest regions has important advantages like robustness to occlusion and photometric effects. In this work we compare different local interest region detectors for non-rigid object class recognition through the success-rate of a Generalized Hough Transform based recognition system and a database of 29 non-rigid object classes. The results of the experiments show that the Edge–Laplace (Mikolajczyk et al., 2006, Mikolajczyk et al., 2003) interest region detector leads. We also evaluate interest regions based on a novel discriminancy measure we introduce. This measure compares success-rates of detectors to success-rates of our novel random region generator, ExpRand. By this respect, ExpRand attain success-rate on par with best detector, and is more discriminant than most detectors.

Visualization and Analysis of Multi-terabyte Geophysical Datasets in an Interactive Setting with Remote Webcam Capabilities

Visualizing and analyzing datasets in the geosciences is becoming increasingly complicated as their volumes are growing explosively. This poses a challenging problem for researchers who must sift through terabytes of data to discover useful relationships inside the information. There is a great need for geophysicists to interactively explore their data sets. Conventional visualization systems lack adequate bandwidth and rendering capabilities necessary for the largest data sets. CAVE and Powerwall display devices are necessary for researchers to explore their data sets in an immersive setting. We describe a utilitarian system targeted specifically at the cost-effective interactive exploration of data sets tens of terabytes in size and harness this system for visualization and analysis of geophysical simulations. Webcams can be used as a steering device to track a local region of interest, which is useful for remote visualization of large data sets. This system will be employed as a web-service under the auspices of Narada-Brokering, while using webcam technologies to enable remote visualization for collaborating researchers. Webcams can be incorporated in a point-to-point network for rapid exchange of information and quickly announcing natural disasters, such as tsunamis, landslides and earthquakes.