Reconstruction Computation Time Research Articles

Deep learning for isolated attosecond pulse reconstruction with the all-optical method

The characterization of attosecond pulses is crucial for attosecond metrology. In this work, we investigate the isolated attosecond pulse reconstruction with the all-optical method. The results show that this method can characterize isolated attosecond pulses with a duration shorter than 50 attoseconds. Moreover, we develop a deep learning scheme to characterize isolated attosecond pulses. Through supervised learning, the deep neural network learns the mapping from the photon spectrograms to attosecond pulses. It allows complete characterization of the amplitude and phase of isolated attosecond pulses. Compared to the conventional principal component generalized projections algorithm, the reconstruction with our neural network shows superior quality and robustness to noise. Also, the reconstruction computation time is significantly reduced to a few seconds.

Fast 3D localization algorithm for high-density molecules based on multiple measurement vector compressed sensing

Single-molecule localization microscopy (SMLM) achieves super-resolution based on a series of images of sparsely distributed molecules, which makes it time-consuming. To improve the temporal resolution, several high-density localization algorithms have been developed, and the computational speed is considered one of the important factors for evaluating their performance. In this study, for the first time, a three-dimensional (3D) multiple measurement vector compressed sensing (3D-MMV-CS) model was constructed and then solved using a multiple sparse Bayesian learning algorithm to realize 3D single-molecule localization and obtain 3D super-resolved fluorescence images. The 3D-MMV-CS successfully recovered a 3D super-resolved image from a series of simulated high-density fluorescence images. The computation time for reconstruction only increases marginally with an increase in the number of raw images and approximately stays constant with an increase in the density of fluorescent molecules. Simulation results demonstrated that, when the number of raw images reaches 1900, compared with the other two algorithms L1-Homotopy and compressed sensing STORM (CSSTORM) with CVX, the 3D-MMV-CS method is 100 times faster than the former and five orders of magnitude faster than the latter. This outstanding performance in computation speed would allow the 3D-MMV-CS approach to be routinely applicable to SMLM. Validation test of the 3D-MMV-CS method was performed successfully with experimental data from an astigmatism-based 3D SMLM system.

Three-Dimensional Automatic Target Recognition System Based on Optical Integral Imaging Reconstruction

In this paper, we present a three-dimensional (3-D) automatic target recognition system based on optical integral imaging reconstruction. In integral imaging, elemental images of the reference and target 3-D objects are obtained through a lenslet array or a camera array. Then, reconstructed 3-D images at various reconstruction depths can be optically generated on the output plane by back-projecting these elemental images onto a display panel. 3-D automatic target recognition can be implemented using computational integral imaging reconstruction and digital nonlinear correlation filters. However, these methods require non-trivial computation time for reconstruction and recognition. Instead, we implement 3-D automatic target recognition using optical cross-correlation between the reconstructed 3-D reference and target images at the same reconstruction depth. Our method depends on an all-optical structure to realize a real-time 3-D automatic target recognition system. In addition, we use a nonlinear correlation filter to improve recognition performance. To prove our proposed method, we carry out the optical experiments and report recognition results.

Comparison of MRI Under-Sampling Techniques for Compressed Sensing with Translation Invariant Wavelets Using FastTestCS: A Flexible Simulation Tool

A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to perform exceedingly well in CS by reducing repetitive line pattern image artifacts that may be observed when using orthogonal wavelets. To further establish its validity as a good sparsifying transform, the TIWT is comprehensively investigated and compared with Total Variation (TV), using six under-sampling patterns through simulation. Both trajectory and random mask based under-sampling of MRI data are reconstructed to demonstrate a comprehensive coverage of tests. Notably, the TIWT in CS reconstruction performs well for all varieties of under-sampling patterns tested, even for cases where TV does not improve the mean squared error. This improved Image Quality (IQ) gives confidence in applying this transform to more CS applications which will contribute to an even greater speed-up of a CS MRI scan. High vs low resolution time of flight MRI CS re-constructions are also analyzed showing how partial Fourier acquisitions must be carefully addressed in CS to prevent loss of IQ. In the spirit of reproducible research, novel software is introduced here as FastTestCS. It is a helpful tool to quickly develop and perform tests with many CS customizations. Easy integration and testing for the TIWT and TV minimization are exemplified. Simulations of 3D MRI datasets are shown to be efficiently distributed as a scalable solution for large studies. Comparisons in reconstruction computation time are made between the Wavelab toolbox and Gnu Scientific Library in FastTestCS that show a significant time savings factor of 60×. The addition of FastTestCS is proven to be a fast, flexible, portable and reproducible simulation aid for CS research.

Accelerated gradient based diffuse optical tomographic image reconstruction

Fast reconstruction of interior optical parameter distribution using a new approach called Broyden-based model iterative image reconstruction (BMOBIIR) and adjoint Broyden-based MOBIIR (ABMOBIIR) of a tissue and a tissue mimicking phantom from boundary measurement data in diffuse optical tomography (DOT). DOT is a nonlinear and ill-posed inverse problem. Newton-based MOBIIR algorithm, which is generally used, requires repeated evaluation of the Jacobian which consumes bulk of the computation time for reconstruction. In this study, we propose a Broyden approach-based accelerated scheme for Jacobian computation and it is combined with conjugate gradient scheme (CGS) for fast reconstruction. The method makes explicit use of secant and adjoint information that can be obtained from forward solution of the diffusion equation. This approach reduces the computational time many fold by approximating the system Jacobian successively through low-rank updates. Simulation studies have been carried out with single as well as multiple inhomogeneities. Algorithms are validated using an experimental study carried out on a pork tissue with fat acting as an inhomogeneity. The results obtained through the proposed BMOBIIR and ABMOBIIR approaches are compared with those of Newton-based MOBIIR algorithm. The mean squared error and execution time are used as metrics for comparing the results of reconstruction. We have shown through experimental and simulation studies that Broyden-based MOBIIR and adjoint Broyden-based methods are capable of reconstructing single as well as multiple inhomogeneities in tissue and a tissue-mimicking phantom. Broyden MOBIIR and adjoint Broyden MOBIIR methods are computationally simple and they result in much faster implementations because they avoid direct evaluation of Jacobian. The image reconstructions have been carried out with different initial values using Newton, Broyden, and adjoint Broyden approaches. These algorithms work well when the initial guess is close to the true solution. However, when initial guess is far away from true solution, Newton-based MOBIIR gives better reconstructed images. The proposed methods are found to be stable with noisy measurement data.

An efficient gridding reconstruction method for multishot non‐Cartesian imaging with correction of off‐resonance artifacts

An efficient iterative gridding reconstruction method with correction of off-resonance artifacts was developed, which is especially tailored for multiple-shot non-Cartesian imaging. The novelty of the method lies in that the transformation matrix for gridding (T) was constructed as the convolution of two sparse matrices, among which the former is determined by the sampling interval and the spatial distribution of the off-resonance frequencies and the latter by the sampling trajectory and the target grid in the Cartesian space. The resulting T matrix is also sparse and can be solved efficiently with the iterative conjugate gradient algorithm. It was shown that, with the proposed method, the reconstruction speed in multiple-shot non-Cartesian imaging can be improved significantly while retaining high reconstruction fidelity. More important, the method proposed allows tradeoff between the accuracy and the computation time of reconstruction, making customization of the use of such a method in different applications possible. The performance of the proposed method was demonstrated by numerical simulation and multiple-shot spiral imaging on rat brain at 4.7 T.

Fast reconstruction of three‐dimensional images in Fresnel transform imaging technique using optical imaging system

AbstractIn this paper, a fast reconstruction of three‐dimensional images, based on the analogy between the expression for the NMR signal in the three‐dimensional Fresnel transform NMR imaging and the expression for light diffraction, is presented. When three‐dimensional imaging is applied to MRI, the problem arises that the computation time for reconstruction is too long, due to the large amount of data. However, the reconstruction time for the spatially continuous cross‐sectional images can be greatly reduced by high‐speed optical operation if the signal is sent to a liquid‐crystal spatial light modulator as a set of Fresnel hologram signals, to be displayed continuously and illuminated by coherent light. In this study, holographic reconstruction is applied to image reconstruction in three‐dimensional Fresnel transform imaging, and an attempt is made to improve the speed of image reconstruction by high‐speed transfer of the obtained signals to the holographic reconstruction system as a set of Fresnel holograms. Thus, the MR cross‐sectional image is reconstructed with the video rate from the three‐dimensional signal data. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(5): 21–29, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1124