Large Capture Range Research Articles

Dispersed fringe cophasing method based on principal component analysis.

With the success of the Webb telescope, dispersed fringe sensing (DFS), with the significant merit of a large capture range, is proving to be a promising cophasing approach for a large-aperture segmented telescope. In this Letter, a novel, to the best of our knowledge, piston error extraction method based on principal component analysis (PCA) technology is proposed. In this method, all the one-dimension intensity distributions along the dispersion axis for different interference positions are regarded as a set of random phase-shifted interference signals. PCA technology is utilized to obtain its corresponding continuous principal phase and the piston error could be directly estimated proportionally from the slope of the phase-wavenumber line. This method avoids nonlinear operations, similar to Shi's traditional framework; no active move is needed for fine cophasing, and the method is also free of characteristic constant calibration in sidelobe peak displacement- and slope-based methods. Preliminary simulations of the method's coarse-then-fine cophasing ability with high accuracy are presented here to show its potential.

Improved Kidney Stone Detection from Ultrasound Images Using GVF Active Contour

Medical image segmentation is of large significance in supporting information about human body structures which assist physicians in correct diagnosis to determine doing radiotherapy or surgeries. Therefore, accurate interest region detection in ultrasound images represents a challenging function and hence needs to apply more trusted tools to gain the best segmentation and classification of kidney stones. This challenge in ultrasound images includes many factors like low contrast, occlusions, signal deviations, and noise made it difficult to determine these stone’s boundaries. This paper applied gradient vector flow (GVF) model which has a large capture range to identify the image boundaries of kidney stones region and estimate variation in stone measurement to prepare a suitable treatment diagnosis.

Piston detection in segmented telescopes via multiple neural networks coordination of feature-enhanced images

High-precision piston detection within a large capture range is a key for segmented telescopes. In this paper, we propose a simple and efficient piston detection method based on multiple neural networks coordination. By setting a mask with a sparse multi-subpupil configuration at conjugate plane of the segmented mirror, a new dataset that is extremely sensitive to the piston is created. And two kinds of neural networks are built for different stages of detection, which ensures the method is of both large-scale and high-precision. Simulation shows that the piston can be detected in the range of the coherence length of the operating light with a sub-nanometer scale precision by this method. This method is robust and does not require complex hardware. It can be widely applied in segmented and deployable primary mirror telescopes.

Measuring the cophasing state of a segmented mirror with a wavelength sweep and a Zernike phase contrast sensor.

The demand for higher resolution telescopes leads to segmented primary mirrors that need to be phased for operation. A phasing sensor applying a wavelength sweep technique provides a large capture range without modulating the position of individual mirror segments. This technique offers the potential to monitor the phasing state of a segmented telescope in parallel to the science observations. We evaluate the performance of the wavelength sweep technique using a Zernike phase contrast sensor for coarse phasing. Test results on a dedicated bench show 112 nm rms precision. With the help of a simulation, we explain a known error of the method and we suggest ways for improvements.

Image segmentation using active contours with modified convolutional virtual electric field external force with an edge-stopping function.

The gradient vector flow (GVF) is an effective external force to deform the active contours. However, it suffers from high computational cost. For efficiency, the virtual electric field (VEF) model has been proposed, which can be implemented in real time thanks to fast Fourier transform (FFT). The VEF model has large capture range and low computation cost, but it has the limitations of sensitivity to noise and leakage on weak edge. The recently proposed CONVEF (Convolutional Virtual Electric Field) model takes the VEF model as a convolutional operation and employed another convolution kernel to overcome the drawbacks of the VEF model. In this paper, we employ an edge stopping function similar to that in the anisotropic diffusion to further improve the CONVEF model, and the proposed model is referred to as MCONVEF (Modified CONVEF) model. In addition, a piecewise constant approximation algorithm is borrowed to accelerate the calculation of the MCONVEF model. The proposed MCONVEF model is compared with the GVF and VEF models, and the experimental results are presented to demonstrate its superiority in terms of noise robustness, weak edge preserving and deep concavity convergence.

Artificial modulation-free Pound-Drever-Hall method for laser frequency stabilization.

We have proposed an artificial modulation-free Pound-Drever-Hall (PDH) method for laser frequency stabilization and demonstrated it via two-color polarization spectroscopy of Rydberg electromagnetically induced transparency (EIT) resonance in a room-temperature rubidium vapor. Due to the unique error signal profile, the conventional PDH method owns a large capture range in laser frequency locking. Here we manually construct a PDH error signal via a linear combination of polarization spectroscopies of the Rydberg EIT resonances without and with a magnetic field applied. The artificial modulation-free PDH error signal owns a subnatural linewidth dispersion curve as well as a large capture range with which we successfully stabilize the laser to an absolute atomic frequency reference in a long running time, immune to environmental fluctuation and even manmade impulse perturbation. This method can provide an absolute frequency reference based on atomic transition while keeping similar locking ability to provide corrections for frequency fluctuations over a broad bandwidth as the conventional PDH.

GVFOM: a novel external force for active contour based image segmentation

The GVF snake is effective on converging into concave areas and being insensitive to initialization. However, it fails to converge to areas with deep and narrow concavities or preserve weak edges. In this paper, a novel external force called gradient vector flow over manifold (GVFOM) is proposed for active contours. The proposed model takes the GVF vector as a 2D manifold embedded into a 4D Euclidean space, leading to the generalization of the Laplacian operator in Euclidean Space to that over manifold, and the two components of the external force are couples with each other to improve the properties of the GVF snake. The GVFOM snake has been tested with different kinds of images, and experimental results and comparison manifest that the GVFOM snake has better performance than the GVF and other state-of-the-art snakes on object separation, deep and narrow concavity convergence, weak edge protection, noise robustness while keeping some desirable properties of the GVF model such as initialization insensitivity and large capture range.

Experimental Study of a Multispectral Piston Sensing Technology

Nowadays giant segmented telescopes and multiaperture telescopes have grown out of the quest for high-resolution observations in astronomy. One of the major issues is the phasing of such telescopes. A concept of multispectral piston sensing technology is, therefore, re-examined and studied experimentally for the first time. Based on the phase-shifting “Telescope-Interferometer” technology and multispectral information, the multispectral piston sensing technology was assumed to be feasible with the segmented telescopes and multiaperture telescopes. In this paper, the optical scheme for this method has been designed and realized. Experimental tests have been carried out and demonstrated that the method is effective for piston sensing in a large capture range with high-precision despite some instrument-related limitations that can be eliminated. In our tests, the shortest coherent length of the three working spectrums we chose was less than 17.5 μm. Results show that the method successfully handled an amplitude of correction of about ±8 μm with an accuracy of about λ/30 (λ = 535 nm) RMS in our tests, and we can foresee that the capture range can be enlarged if we choose working spectrums with larger coherent lengths.

Multi-Lane Detection Using CNNs and A Novel Region-grow Algorithm

In this paper, we propose a novel approach to detect lanes robustly in different scenarios. Theoretically, HT (Hough Transform) can extract straight lines efficiently from images, but it cannot distinguish whether the straight lines are belong to lane-markings. To solve this problem, we integrate a simple convolutional neural networks modified from Lenet and geometry constraints to distinguish the types of lines. And in the following part, a region-grow algorithm is adopted in this paper to fit lanes, which is realized by growing local ROI (Region- of-Interest) gradually. We use RANSAC to extract the main direction in local ROI and guide the growth of the ROI. At the same time, in order to ensure right growth of ROI when the lane is dashed, we use a DVF (Direction Vector Field, modified from GVF [19]), which has large capture range and is generated by the edge-direction map. Tests of the proposed approach under kinds of conditions are discussed.

Extension of the virtual electric field model using bilateral-like filter for active contours

The gradient vector flow (GVF) model has been proven as an effective external force for active-contour-based image segmentation. However, it suffers from high computation cost since there are two PDEs to be solved in an iterative manner. As a remedy, the virtual electric field (VEF) model is proposed, which can be implemented in real time using the fast Fourier transform. However, the VEF model cannot preserve weak edges since it employs linear kernels. In this work, we extend the VEF model by using bilateral-like filters, and a fast algorithm is also employed for the proposed model. The proposed model is referred to as bilateral-filter-based VEF (BVEF) model. Experimental results on synthetic and real images demonstrate that the BVEF snake possesses some desired properties of the GVF, CNGGVF and VEF snakes such as large capture range and concavity convergence, and the BVEF model can be implemented in near real time, and its computation cost is comparable to that of the VEF model and much shorter than that of the GVF and CNGGVF models; it also can preserve weak edges, thanks to the bilateral-like nonlinear kernels.

Learning deep similarity metric for 3D MR-TRUS image registration.

The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR-TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimization strategy. This work proposes the use of a deep convolutional neural network to learn a similarity metric for MR-TRUS registration. We also use a composite optimization strategy that explores the solution space in order to search for a suitable initialization for the second-order optimization of the learned metric. Further, a multi-pass approach is used in order to smooth the metric for optimization. The learned similarity metric outperforms the classical mutual information and also the state-of-the-art MIND feature-based methods. The results indicate that the overall registration framework has a large capture range. The proposed deep similarity metric-based approach obtained a mean TRE of 3.86mm (with an initial TRE of 16mm) for this challenging problem. A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.

Piston alignment of segmented optical mirrors via convolutional neural networks.

Most of the methods used today for the alignment of segmented mirrors are based on Shack-Hartman wavefront sensors. Other proposed methods are based on curvature sensors. These can be used to cross-check the measurements given by the primary method. We investigate a different approach which employs convolutional neural networks. This technique allows the piston step values between segments to be measured with high accuracy, as well as a large capture range at visible wavelengths. The technique does not require special hardware, and is fast to be used at any time during the observation.

Multi-stage 3D–2D registration for correction of anatomical deformation in image-guided spine surgery

A multi-stage image-based 3D–2D registration method is presented that maps annotations in a 3D image (e.g. point labels annotating individual vertebrae in preoperative CT) to an intraoperative radiograph in which the patient has undergone non-rigid anatomical deformation due to changes in patient positioning or due to the intervention itself.The proposed method (termed msLevelCheck) extends a previous rigid registration solution (LevelCheck) to provide an accurate mapping of vertebral labels in the presence of spinal deformation. The method employs a multi-stage series of rigid 3D–2D registrations performed on sets of automatically determined and increasingly localized sub-images, with the final stage achieving a rigid mapping for each label to yield a locally rigid yet globally deformable solution. The method was evaluated first in a phantom study in which a CT image of the spine was acquired followed by a series of 7 mobile radiographs with increasing degree of deformation applied. Second, the method was validated using a clinical data set of patients exhibiting strong spinal deformation during thoracolumbar spine surgery. Registration accuracy was assessed using projection distance error (PDE) and failure rate (PDE > 20 mm—i.e. label registered outside vertebra).The msLevelCheck method was able to register all vertebrae accurately for all cases of deformation in the phantom study, improving the maximum PDE of the rigid method from 22.4 mm to 3.9 mm. The clinical study demonstrated the feasibility of the approach in real patient data by accurately registering all vertebral labels in each case, eliminating all instances of failure encountered in the conventional rigid method.The multi-stage approach demonstrated accurate mapping of vertebral labels in the presence of strong spinal deformation. The msLevelCheck method maintains other advantageous aspects of the original LevelCheck method (e.g. compatibility with standard clinical workflow, large capture range, and robustness against mismatch in image content) and extends capability to cases exhibiting strong changes in spinal curvature.

Guiding multimodal registration with learned optimization updates.

In this paper, we address the multimodal registration problem from a novel perspective, aiming to predict the transformation aligning images directly from their visual appearance. We formulate the prediction as a supervised regression task, with joint image descriptors as input and the output are the parameters of the transformation that guide the moving image towards alignment. We model the joint local appearance with context aware descriptors that capture both local and global cues simultaneously in the two modalities, while the regression function is based on the gradient boosted trees method capable of handling the very large contextual feature space. The good properties of our predictions allow us to couple them with a simple gradient-based optimization for the final registration. Our approach can be applied to any transformation parametrization as well as a broad range of modality pairs. Our method learns the relationship between the intensity distributions of a pair of modalities by using prior knowledge in the form of a small training set of aligned image pairs (in the order of 1-5 in our experiments). We demonstrate the flexibility and generality of our method by evaluating its performance on a variety of multimodal imaging pairs obtained from two publicly available datasets, RIRE (brain MR, CT and PET) and IXI (brain MR). We also show results for the very challenging deformable registration of Intravascular Ultrasound and Histology images. In these experiments, our approach has a larger capture range when compared to other state-of-the-art methods, while improving registration accuracy in complex cases.

Registration of MRI to intraoperative radiographs for target localization in spinal interventions

Decision support to assist in target vertebra localization could provide a useful aid to safe and effective spine surgery. Previous solutions have shown 3D–2D registration of preoperative CT to intraoperative radiographs to reliably annotate vertebral labels for assistance during level localization. We present an algorithm (referred to as MR-LevelCheck) to perform 3D–2D registration based on a preoperative MRI to accommodate the increasingly common clinical scenario in which MRI is used instead of CT for preoperative planning.Straightforward adaptation of gradient/intensity-based methods appropriate to CT-to-radiograph registration is confounded by large mismatch and noncorrespondence in image intensity between MRI and radiographs. The proposed method overcomes such challenges with a simple vertebrae segmentation step using vertebra centroids as seed points (automatically defined within existing workflow). Forwards projections are computed using segmented MRI and registered to radiographs via gradient orientation (GO) similarity and the CMA-ES (covariance-matrix-adaptation evolutionary-strategy) optimizer. The method was tested in an IRB-approved study involving 10 patients undergoing cervical, thoracic, or lumbar spine surgery following preoperative MRI.The method successfully registered each preoperative MRI to intraoperative radiographs and maintained desirable properties of robustness against image content mismatch and large capture range. Robust registration performance was achieved with projection distance error (PDE) (median ± IQR) = 4.3 ± 2.6 mm (median ± IQR) and 0% failure rate. Segmentation accuracy for the continuous max-flow method yielded dice coefficient = 88.1 ± 5.2, accuracy = 90.6 ± 5.7, RMSE = 1.8 ± 0.6 mm, and contour affinity ratio (CAR) = 0.82 ± 0.08. Registration performance was found to be robust for segmentation methods exhibiting RMSE <3 mm and CAR >0.50.The MR-LevelCheck method provides a potentially valuable extension to a previously developed decision support tool for spine surgery target localization by extending its utility to preoperative MRI while maintaining characteristics of accuracy and robustness.

WE‐AB‐BRA‐09: Registration of Preoperative MRI to Intraoperative Radiographs for Automatic Vertebral Target Localization

Purpose:Accurate localization of target vertebrae is essential to safe, effective spine surgery, but wrong‐level surgery occurs with surprisingly high frequency. Recent research yielded the “LevelCheck” method for 3D‐2D registration of preoperative CT to intraoperative radiographs, providing decision support for level localization. We report a new method (MR‐LevelCheck) to perform 3D‐2D registration based on preoperative MRI, presenting a solution for the increasingly common scenario in which MRI (not CT) is used for preoperative planning.Methods:Direct extension of LevelCheck is confounded by large mismatch in image intensity between MRI and radiographs. The proposed method overcomes such challenges with a simple vertebrae segmentation. Using seed points at centroids, vertebrae are segmented using continuous max‐flow method and dilated by 1.8 mm to include surrounding cortical bone (inconspicuous in T2w‐MRI). MRI projections are computed (analogous to DRR) using segmentation and registered to intraoperative radiographs. The method was tested in a retrospective IRB‐approved study involving 11 patients undergoing cervical, thoracic, or lumbar spine surgery following preoperative MRI. Registration accuracy was evaluated in terms of projection‐distance‐error (PDE) between the true and estimated location of vertebrae in each radiograph.Results:The method successfully registered each preoperative MRI to intraoperative radiographs and maintained desirable properties of robustness against image content mismatch, and large capture range. Segmentation achieved Dice coefficient = 89.2 ± 2.3 and mean‐absolute‐distance (MAD) = 1.5 ± 0.3 mm. Registration demonstrated robust performance under realistic patient variations, with PDE = 4.0 ± 1.9 mm (median ± iqr) and converged with run‐time = 23.3 ± 1.7 s.Conclusion:The MR‐LevelCheck algorithm provides an important extension to a previously validated decision support tool in spine surgery by extending its utility to preoperative MRI. With initial studies demonstrating PDE &lt;5 mm and 0% failure rate, the method is now in translation to larger scale prospective clinical studies.S. Vogt and G. Kleinszig are employees of Siemens Healthcare

Zero co-phasing reference calibration method based on dispersed interferogram for segmented mirror telescope

Astronomical telescopes with increasingly large apertures are required to upgrade the limit of diffraction and collect the light efficiently for the purpose of observing fainter and more remote objects with higher angular resolution. However, it is universally believed that traditional techniques of manufacturing, polishing and measuring large glass mirrors will soon face some practical challenges. Therefore, 10-m class or larger ground-based telescopes will need to employ arrays of several smaller segments to assemble into a large primary mirror. For a telescope with segmented mirrors, the piston errors between segments must be adjusted to nearly zero according to the requirements in order to be integrated into a single optical surface, which is known as co-phasing. One of the current co-phasing techniques, which has been successfully applied to Keck telescopes, employs an integration of edge sensors to detect the mirror shapes in real time with an optical phasing sensor to offer zero references for these sensors regularly. Another technique is demonstrated by use of a pyramid wavefront sensor (PWFS) to align and co-phase segmented mirrors in an active control close-loop with a single measurement. The co-phased best flat positions of segments are used as the zero references in order to measure the interaction matrix between the PWFS and the segmented mirrors. So it must be addressed that how the zero co-phasing reference is calibrated with high precision in a large capture range on the issues of co-phasing segmented mirrors. The current methods either lack accuracies, or just measure piston errors correctly in a small range. In order to solve the problem, a zero co-phasing reference calibration method based on dispersed interferogram is proposed. Specifically, the idea of the method is to define an appropriate cost function which is used to evaluate the piston errors between segments. Then it will be easy to determine the zero co-phasing reference position while all the cost function values are calculated based on the dispersed interferogram data with different piston errors inside the capture range. The proposed cost function is defined as the sum of the ratios of the second peak to the third peak of each of the columns of the two-dimensional dispersed interferogram, whose intensity distribution is along the dispersion direction. The precision and dynamic range of the method are analyzed theoretically and studied by simulations. Furthermore, the optical experiment is set up to demonstrate the efficacy of the method. In the experiment a scanning procedure is applied to one mirror and the dispersed interferograms between two mirrors with different piston errors are obtained. And then, the cost functions of these dispersed interferograms are computed through which the zero co-phasing reference position is located. The experimental results prove that the zero co-phasing reference between two mirrors can be calibrated within an accuracy of about 10 nm by making use of the proposed method. In addition, the novel method solves the problem of 2 ambiguity. Besides its sub-millimeter level wide capture range, this new co-phasing detecting method provides a helpful reference for relevant studies.

Guide filter-based gradient vector flow module for infrared image segmentation.

Infrared image segmentation is a challenging topic since infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow (GVF), have better segmentation performance for clear images. However, the GVF model has the drawbacks of sensitivity to noise and adaptability of the parameters, decreasing the effect of infrared image segmentation significantly. To address these problems, this paper proposes a guide filter-based gradient vector flow module for infrared image segmentation (GFGVF). First, a guide filter is exploited to construct a novel edge map, providing characteristics of the image edge while excluding the effects of noise. This alleviates the possibility of edge leakage caused by using the traditional edge map. Then, a novel weighting function is constructed to effectively handle the extended capture range and preserving the edge even with noise existing. The experimental results demonstrate that the GFGVF model possesses good properties such as large capture range, concavity convergence, noise robustness, and alleviative boundary leakage.

A New Active Contour Model Based on Distance-Weighted Potential Field

Snakes, or active contours, have been widely used in various image processing applications. Typical problems of snakes, including limited capture range, poor convergence to concavities, noise sensitivity, and initialization sensitivity, have limited their applications. For solving these problems, we propose a new potential for the active contour model. In this proposed potential field, each location's potential is computed by integrating the feature information from all the pixels in the image with the distances as the weights. The external forces are computed as the gradients of this proposed potential field, and the computed external forces are static and have global capture range. Experiments and also the comparisons with the snake using gradient vector flow (GVF) as external forces are conducted to examine the performances of this proposed snake. The results show that the proposed snake has a large capture range and an excellent convergence to boundary concavities, and also the proposed snake is more robust to noise, more time efficient, and less sensitive to the initialization, compared with the GVF snake.

Simulation and measurement of stiffness for dual beam laser trap using residual gravity method

Dual-beam optical trap is constituted by two beams of unfocused counter-propagating laser. It has advantages such as several equilibrium points, small optical power density, large capture range and easier integration with other technologies, which optical tweezers may not achieve. Dual-beam optical trap stiffness is an important parameter to describe its mechanical properties. On the basis of quartz-substrate dual-beam optical fibre trap unit, we demonstrate a simple method for measuring stiffness. Residual gravity offset by buoyancy may provide a tiny force for the optical trap. By measuring the relations between the captured particle's axial equilibrium position and residual gravity, we may calibrate the optical trap stiffness. This method simply requires measuring the relative position of the particle in the steady state of the optical trap, and dynamic measurement of the particles location is not needed, so we can get a high position accuracy by long time measuring, thus the accuracy of the stiffness calibration is improved. In this paper, we simulated and carried out experiments to measure the stiffness of a dual-beam fibre optical trap unit based on a quartz-substrate. We simulate the stiffness of several Mie particles with different materials and sizes in dual-beam optical trap as well as the relation between the waist radius of fundamental mode Gaussian beam and stiffness. Stiffness measurement experiment is conducted using the residual gravity method. Stiffness values measured in experiments are consistent with the simulation results, which verify the validity and accuracy of the method.