Sparse Scanning Research Articles

Accelerated noncontact guided wave array imaging via sparse array data reconstruction

Noncontact guided wave array imaging with a scanning laser Doppler vibrometer (SLDV) is an effective tool to detect and locate defects within a plate-like structure, as it obviates the need for installing, calibrating, and maintaining a transducer array. However, it requires collecting guided wave signals through scanning across dense spatial grid points to avoid non-defect artifacts in the array image, which is time-consuming. In this paper, we present an accelerated noncontact guided wave array imaging method that does not require dense scanning array while providing defect imaging performance comparable to the dense scanning case. In our approach, sparse scanning measurements at only a small number of points are carried out first for fast guide wave data acquisition. Then, dense guided wave array data is reconstructed from these sparse array measurements using a sparsity-promoting optimization technique, followed by delay-and-sum (DAS) beamforming to image defects within a test structure. We validate this method with laboratory experiments on composite plate specimens with multiple defects. The results demonstrate that defects within a composite plate can be successfully detected and located using sparsely sampled guided wave array measurement data. Such a significant reduction in the number of required measurement points enables accelerated noncontact guided wave array imaging.

A Hybrid Schatten p-Norm and Lp-Norm With Plane Wave Expansion Method for Near–Near Field Transformation

Near field scanning is one of the most useful methods for electromagnetic interference (EMI) diagnosis. However, large scanning points and long scanning time are the major obstacles when the device under test is complex. To resolve the issue, a novel hybrid method including two steps to quickly predict the near field pattern of an unknown EMI source is proposed. The first step is to reconstruct a full field pattern from a sparse scanning using the joint Schatten <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> -norm and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lp</i> -norm method, where less sampling points are required on the original scanning plane. The second step is the near–near field transformation based on the plane wave spectrum method. It uses one single field pattern to derive the field distributions on multiplanes around the EMI source by introducing a calibration factor. The source reconstruction method in the second step can also be used to predict far field and coupled power to the victim circuit. Both simulation and experimental examples have validated the feasibility of the proposed method.

High temporal-resolution scanning transmission electron microscopy using sparse-serpentine scan pathways

Scanning transmission electron microscopy (STEM) provides structural analysis with sub-angstrom resolution. But the pixel-by-pixel scanning process is a limiting factor in acquiring high-speed data. Different strategies have been implemented to increase scanning speeds while at the same time minimizing beam damage via optimizing the scanning strategy. Here, we achieve the highest possible scanning speed by eliminating the image acquisition dead time induced by the beam flyback time combined with reducing the amount of scanning pixels via sparse imaging. A calibration procedure was developed to compensate for the hysteresis of the magnetic scan coils. A combination of sparse and serpentine scanning routines was tested for a crystalline thin film, gold nanoparticles, and in an in-situ liquid phase STEM experiment. Frame rates of 92, 23 and 5.8 s-1 were achieved for images of a width of 128, 256, and 512 pixels, respectively. The methods described here can be applied to single-particle tracking and analysis of radiation sensitive materials.

Sparse and continuous sampling approaches to fMRI of overt vocalization tasks

Sparse temporal sampling has become the dominant paradigm for functional magnetic resonance imaging studies of auditory stimuli or verbal responses, as it allows the presentation or production of stimuli during the relatively quiet periods when there is no gradient switching and ensures that task-related movements are not occurring during scan acquisitions. To date, however, there has been no direct comparison between sparse and continuous acquisition protocols for overt auditory-verbal studies (i.e., speaking or singing). The aim of this study was to determine whether sparse temporal sampling would reduce movement artefacts and show better network activation for overt singing compared to continuous imaging. Fifteen healthy adults performed the same overt singing task under both sparse and continuous scanning conditions. We noted significant variations in signal intensity between adjacent slices in our sparse acquisition, with (odd-numbered) slices acquired in the second half of each volume acquisition being of lower intensity and showing less reliable task-related activation, and thus requiring the removal of these slices prior to preprocessing. Edge artefacts, presumably due to movement, were observed in both acquisition types at a subthreshold level, although ventricular space artefacts were more apparent in the continuous data. However, statistical comparison revealed no significant differences in functional activation nor in motion correction parameters. Our results show that sparse imaging has the potential to introduce significant image artefacts affecting downstream analyses. While sparse sampling provides benefits that may be essential for certain studies (e.g., periods free from scanner noise), the technical requirements of such sequences should not be overlooked and inspection of raw data is essential. Our data also show that continuous imaging can be used for overt response auditory-verbal studies and may be of wider utility than previously appreciated.

Temporal feature prior-aided separated reconstruction method for low-dose dynamic myocardial perfusion computed tomography

Dynamic myocardial perfusion computed tomography (DMP-CT) is an effective medical imaging technique for coronary artery disease diagnosis and therapy guidance. However, the radiation dose received by the patient during repeated CT scans is a widespread concern of radiologists because of the increased risk of cancer. The sparse few-view CT scanning protocol can be a feasible approach to reduce the radiation dose of DMP-CT imaging; however, an advanced reconstruction algorithm is needed. In this paper, a temporal feature prior-aided separated reconstruction method (TFP-SR) for low-dose DMP-CT images reconstruction from sparse few-view sinograms is proposed. To implement the proposed method, the objective perfusion image is divided into the baseline fraction and the enhancement fraction introduced by the arrival of the contrast agent. The core of the proposed TFP-SR method is the utilization of the temporal evolution information that naturally exists in the DMP-CT image sequence to aid the enhancement image reconstruction from limited data. The temporal feature vector of an image pixel is defined by the intensities of this pixel in the pre-reconstructed enhancement sequence, and the connection between two related features is calculated via a zero-mean Gaussian function. A prior matrix is constructed based on the connections between the extracted temporal features and used in the iterative reconstruction of the enhancement images. To evaluate the proposed method, the conventional filtered back-projection algorithm, the total variation regularized PWLS (PWLS-TV) and the prior image constrained compressed sensing are compared in this paper based on studies on a digital extended cardiac-torso (XCAT) thoracic phantom and a preclinical porcine DMP-CT data set that take image misregistration into account. The experimental results demonstrate that the proposed TFP-SR method has superior performance in sparse DMP-CT images reconstruction in terms of image quality and the analyses of the time attenuation curve and hemodynamic parameters.

Low-dose CT reconstruction method based on prior information of normal-dose image.

Radiation risk from computed tomography (CT) is always an issue for patients, especially those in clinical conditions in which repeated CT scanning is required. For patients undergoing repeated CT scanning, a low-dose protocol, such as sparse scanning, is often used, and consequently, an advanced reconstruction algorithm is also needed. To develop a novel algorithm used for sparse-view CT reconstruction associated with the prior image. A low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) involving a transformed model for attenuation coefficients of the object to be reconstructed and prior information application in the forward-projection process was used to reconstruct CT images from sparse-view projection data. A digital extended cardiac-torso (XCAT) ventral phantom and a diagnostic head phantom were employed to evaluate the performance of the proposed PI-NDI method. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR) and mean percent absolute error (MPAE) of the reconstructed images were measured for quantitative evaluation of the proposed PI-NDI method. The reconstructed images with sparse-view projection data via the proposed PI-NDI method have higher quality by visual inspection than that via the compared methods. In terms of quantitative evaluations, the RMSE measured on the images reconstructed by the PI-NDI method with sparse projection data is comparable to that by MLEM-TV, PWLS-TV and PWLS-PICCS with fully sampled projection data. When the projection data are very sparse, images reconstructed by the PI-NDI method have higher PSNR values and lower MPAE values than those from the compared algorithms. This study presents a new low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) for sparse-view CT image reconstruction. The experimental results validate that the new method has superior performance over other state-of-art methods.

Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization.

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. This study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics.

With the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to electron radiation. Random sparse scanning can be used in the combination with image reconstruction techniques to reduce the acquisition time or electron dose in scanning electron microscopy. In this study, we demonstrate a workflow that includes data acquisition on a scanning electron microscope, followed by a sparse image reconstruction based on compressive sensing or alternatively using neural networks. Neuron structures are automatically segmented from the reconstructed images using deep learning techniques. We show that the average dwell time per pixel can be reduced by a factor of 2-3, thereby providing a real-life confirmation of previous results on simulated data in one of the key segmentation applications in connectomics and thus demonstrating the feasibility and benefit of random sparse scanning techniques for a specific real-world scenario.

Passive Intermodulation Source Localization Based on Emission Source Microscopy

Emission source microscopy (ESM) technique can be used for localization and characterization of electromagnetic interference sources by measuring the magnitude and phase of the electromagnetic field in the far-field zone. This article presents a method which uses ESM to locate sources of passive intermodulation (PIM). Compared to the traditional methods of PIM source localization techniques including mechanical manipulation of potential sources and near-field scanning, the proposed ESM-based approach does not require access to the devices under test allowing to detect the PIM sources at relatively large distances. Moreover, the influence of background PIM generated by surrounding environment can be reduced by ESM focusing. The high-quality maps of reconstructed PIM sources can be obtained by using ESM. The feasibility of manual sparse ESM scanning, which is more practical in realistic settings is also demonstrated.

Reconstruction of multi-frame semi-sparse scanning probe microscopy images using dependent Gaussian process

A major drawback of scanning probe microscopes (SPMs) is the time-consuming image acquisition, typically on the order of tens of seconds to minutes. Sparse raster scanning and non-raster scanning have been demonstrated to be effective approaches to improve the efficiency of SPMs. However, these strategies entail the challenge of reconstructing accurate images from semi-sparse sampling points, which means the distribution of the scattered sampling points is considerably sparser in the direction perpendicular to the scanning path. In this paper, we focus on the multi-frame semi-sparse video image reconstruction. In this setting, one of the frames is regarded as the reference frame while the others are regarded as the target frames. Instead of reconstructing the target frames independently, we propose a dependent Gaussian process regression model to make full use of the information provided by the reference frame and the currently processed target frame. The proposed method takes advantage of the intrinsic correlation between images. Thus, the accuracy of the reconstruction can be improved greatly. Comparisons with the renowned Delaunay triangulation-based interpolation method and the Gaussian process regression method prove that the proposed method has dramatically improved the reconstruction accuracy of semi-sparse video images.

Deformable registration and region-of-interest image reconstruction in sparse repeat CT scanning.

Repeat CT scanning is ubiquitous in many clinical situations, e.g. to follow disease progression, to evaluate treatment efficacy, and to monitor interventional CT procedures. However, it incurs in cumulative radiation to the patient which can be significantly reduced by using a region of interest (ROI) and the existing baseline scan. To obtain a high-quality reconstruction of a ROI with a significantly reduced X-ray radiation dosage that accounts for deformations. We present a new method for deformable registration and image reconstruction inside an ROI in repeat CT scans with a highly reduced X-ray radiation dose based on sparse scanning. Our method uses the existing baseline scan data, a user-defined ROI, and a new sparse repeat scan to compute a high-quality repeat scan ROI image with a significantly reduced radiation dose. Our method first performs rigid registration between the densely scanned baseline and the sparsely scanned repeat CT scans followed by deformable registration with a low-order parametric model, both in 3D Radon space and without reconstructing the repeat scan image. It then reconstructs the repeat scan ROI without computing the entire repeat scan image. Our experimental results on clinical lung and liver CT scans yield a mean × 14 computation speedup and a × 7.6-12.5 radiation dose reduction, with a minor image quality loss of 0.0157 in the NRMSE metric. Our method is considerably faster than existing methods, thereby enabling intraoperative online repeat scanning that it is accurate and accounts for position, deformation, and structure changes at a fraction of the radiation dose required by existing methods.

Interpolation based Low Dose CT Image Reconstruction

Abstract Medical imaging has grown tremendously over years. The CT and MRI are used at most. MRI is less harmful, but one cannot underestimate the side effects of CT. Current study demonstrate the serious risk of cancer, due to repeated use of harmful X-rays. Consequently the plan of low dose imaging protocol is emerging as vital solution in the present scenario. Low dose imaging is achieved by sparse view scanning. The sparse-view x-ray computed tomography (CT) is appreciated in medical diagnosis and industrial non-destructive testing. However, when the sampling is insufficient due to sparse view, the reconstructed image usually suffers from complex artifacts and noise. In order to deal such issue, we present interpolation technique to fill the sinogram as promising method to address issues of low dose imaging. Reconstructed image quality parameters are recorded, and are compared to original image and the low dose image. The evidenced results convey substantial improved performance compared to conventional low does image.

Sparse Scanning Electron Microscopy and Deep Learning for Imaging and Segmentation of Neuron Structures

Sparse Scanning Electron Microscopy and Deep Learning for Imaging and Segmentation of Neuron Structures

ADMM based low-rank and sparse matrix recovery method for sparse photoacoustic microscopy

Photoacoustic microscopy (PAM) has evolved into a new promising medical imaging tool available for both in vivo surficial and deep-tissue imaging with a high spatial resolution. However, the long data acquisition time has made real-time imaging highly challenging. This paper presents an Alternating Direction Method of Multipliers (ADMM) based low-rank and sparse matrix recovery method for a sparse optical-scanning PAM system to realize fast PAM vascular imaging.For our system, an x-y galvanometer scanner is used to achieve compressive sampling, and the associated image recovery process is formulated as a matrix completion problem. The sparse scanning scheme might be easy to integrate with several other optical-resolution PAM modalities. Further, the sparse constraint (the total variation norm) and the low-rank constraint (nuclear norm) are combined for solving the optimization program under ADMM framework for matrix recovery in order to achieve better PAM image recovery even for images that are not well-approximated by their low-rank components.A prototype PAM system has been implemented and the recovery method has been validated. From both visual effects and quantitative parameters, such as PSNR, SSIM, Rerr and MSE, comparable image qualities with conventional full sampling optical resolution PAM could be acquired by just half of the data acquisition time. Besides, the ADMM based low-rank and sparse matrix recovery method could show better results than GoDec. The obtained results demonstrate the preclinical and clinical potential of sparse PAM system in investigating vascular diseases.

Planar surface detection for sparse and heterogeneous mobile laser scanning point clouds

Plane detection and segmentation is one of the most crucial tasks in point cloud processing. The output from this process can be used as input for further processing steps, such as modelling, registration and calibration. However, the sparseness and heterogeneity of Mobile Laser Scanning (MLS) point clouds may lead to problems for existing planar surfaces detection and segmentation methods. This paper proposes a new method that can be applicable to detect and segment planar features in sparse and heterogeneous MLS point clouds. This method utilises the scan profile patterns and the planarity values between different neighbouring scan profiles to detect and segment planar surfaces from MLS point clouds. The proposed method is compared to the three most state-of-the-art segmentation methods (e.g. RANSAC, a robust segmentation method based on robust statistics and diagnostic principal component analysis – RDCPA as well as the plane detection method based on line arrangement). Three datasets are used for the validation of the results. The results show that our proposed method outperforms the existing methods in detecting and segmenting planar surfaces in sparse and heterogeneous MLS point clouds. In some instances, the state-of-the-art methods produce incorrect segmentation results for façade details which have a similar orientation, such as for windows and doors within a façade. While RDCPA produces up to 50% of outliers depending on the neighbourhood threshold, another method could not detect such features at all. When dealing with small features such as a target, some algorithms (including RANSAC) were unable to perform segmentation. However, the propose algorithm was demonstrated to detect all planes in the test data sets correctly. The paper shows that these mis-segmentations in other algorithms may lead to significant errors in the registration process of between 1.047 and 1.614 degrees in the angular parameters, whereas the propose method had only resulted in 0.462 degree angular bias. Furthermore, it is not sensitive to the required method parameters as well as the point density of the point clouds.

POLE-LIKE ROAD FURNITURE DETECTION IN SPARSE AND UNEVENLY DISTRIBUTED MOBILE LASER SCANNING DATA

Abstract. Pole-like road furniture detection received much attention due to its traffic functionality in recent years. In this paper, we develop a framework to detect pole-like road furniture from sparse mobile laser scanning data. The framework is carried out in four steps. The unorganised point cloud is first partitioned. Then above ground points are clustered and roughly classified after removing ground points. A slicing check in combination with cylinder masking is proposed to extract pole-like road furniture candidates. Pole-like road furniture are obtained after occlusion analysis in the last stage. The average completeness and correctness of pole-like road furniture in sparse and unevenly distributed mobile laser scanning data was above 0.83. It is comparable to the state of art in the field of pole-like road furniture detection in mobile laser scanning data of good quality and is potentially of practical use in the processing of point clouds collected by autonomous driving platforms.

How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?

In scanning electron microscopy, the achievable image quality is often limited by a maximum feasible acquisition time per dataset. Particularly with regard to three-dimensional or large field-of-view imaging, a compromise must be found between a high amount of shot noise, which leads to a low signal-to-noise ratio, and excessive acquisition times. Assuming a fixed acquisition time per frame, we compared three different strategies for algorithm-assisted image acquisition in scanning electron microscopy. We evaluated (1) raster scanning with a reduced dwell time per pixel followed by a state-of-the-art Denoising algorithm, (2) raster scanning with a decreased resolution in conjunction with a state-of-the-art Super Resolution algorithm, and (3) a sparse scanning approach where a fixed percentage of pixels is visited by the beam in combination with state-of-the-art inpainting algorithms. Additionally, we considered increased beam currents for each of the strategies. The experiments showed that sparse scanning using an appropriate reconstruction technique was superior to the other strategies.

Calculation, consequences and measurement of the point spread function for low-loss inelastic scattering.

We have previously derived an analytical formula for the point spread function (PSF) that describes the delocalization of low-loss inelastic scattering. Here, we modify the formula to take account variation of scattered-electron phase. The exponentially attenuated Lorentzian form is retained but its halfwidth at half maximum is chosen to provide better agreement with measurements of the median delocalization distance. For low energy losses, the 1/r2 tails of the PSF extend beyond the region of energy deposition, allowing a small-diameter electron probe to provide energy-loss data from relatively undamaged regions of a beam-sensitive specimen. Alternatively, a core-loss or elastic image can be recorded with less damage by sparse scanning, as in scanned moiré imaging. A procedure is proposed for directly measuring the PSF, using a TEM with aberration-corrected lenses and an energy-filtered imaging system.

Large tree diameter distribution modelling using sparse airborne laser scanning data in a subtropical forest in Nepal

Large-diameter trees (taking DBH > 30 cm to define large trees) dominate the dynamics, function and structure of a forest ecosystem. The aim here was to employ sparse airborne laser scanning (ALS) data with a mean point density of 0.8 m−2 and the non-parametric k-most similar neighbour (k-MSN) to predict tree diameter at breast height (DBH) distributions in a subtropical forest in southern Nepal. The specific objectives were: (1) to evaluate the accuracy of the large-tree fraction of the diameter distribution; and (2) to assess the effect of the number of training areas (sample size, n) on the accuracy of the predicted tree diameter distribution. Comparison of the predicted distributions with empirical ones indicated that the large tree diameter distribution can be derived in a mixed species forest with a RMSE% of 66% and a bias% of −1.33%. It was also feasible to downsize the sample size without losing the interpretability capacity of the model. For large-diameter trees, even a reduction of half of the training plots (n = 250), giving a marginal increase in the RMSE% (1.12–1.97%) was reported compared with the original training plots (n = 500). To be consistent with these outcomes, the sample areas should capture the entire range of spatial and feature variability in order to reduce the occurrence of error.

Comprehensive approach for building outline extraction from LiDAR data with accent to a sparse laser scanning point cloud

&lt;em&gt;The method of building outline extraction based on segmentation of airborne laser scanning data is proposed and tested on a dataset comprising 1,400 buildings typical for residential and industrial urban areas. The algorithm starts with setting a special threshold to separate building from bare earth points and low objects. Next, local planes are fitted to each point using RANSAC and further refined by least squares adjustment. A normal vector is assigned to each point. Similarities among normal vectors are evaluated in order to assemble planar or curved roof segments. Finally, building outlines are formed from detected segments using the a-shapes algorithm and further regularized. The extracted outlines were compared with reference polygons manually derived from the processed laser scanning point cloud and orthoimages. Area-based evaluation of accuracy of the proposed method revealed completeness and correctness of 87 % and 97 %, respectively, for the test dataset. The influence of parameters like number of points per roof segment, complexity of the roof structure, roof type, and overlap with vegetation on accuracy was evaluated and discussed.&lt;/em&gt;