Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions.

For the 3D localization problem using point spread function (PSF) engineering, we propose an enhancement of our previously introduced localization neural network, LocNet. The improved network is a physics-informed neural network that we call PiLocNet. Previous works on the localization problem may be categorized separately into model-based optimization and neural network approaches. Our PiLocNet combines the strengths of both approaches by incorporating forward-model-based information into the network via a data-fitting loss term that constrains the neural network to yield results that are physically sensible. We additionally incorporate certain regularization terms from the variational method, which further improves the robustness of the network in the presence of image noise, as we show for the Poisson and Gaussian noise models. This framework accords interpretability to the neural network, and the results we obtain show its superiority. Although this paper focuses on the use of a single-lobe rotating PSF to encode the full 3D source location, we expect the method to be widely applicable to other PSFs and imaging problems that are constrained by well-modeled forward processes.

The recent development of single-molecule imaging techniques has enabled not only high accuracy spatial resolution imaging but also information rich functional imaging. Abundant information about single molecules can be encoded in its diffraction pattern and be extracted precisely (e.g. 3D position, wavelength, dipole orientation). However, sophisticated high dimensional point spread function (PSF) modeling and analyzing methods have greatly impeded the broad accessibility of these techniques. Here, we present a graphics processing unit (GPU) -based B-spline PSF modeling method that could flexibly model high dimensional PSFs with arbitrary shape without greatly increasing the model parameters. Our B-spline fitter achieves 100 times speed improvement and minimal uncertainty for each dimension, enabling efficient high dimensional single-molecule analysis. We demonstrated, both in simulations and experiments, the universality and flexibility of our B-spline fitter to accurately extract the abundant information from different types of high dimensional single-molecule data, including multicolor PSF (3D + color), multi-channel four-dimensional 4Pi-PSF (3D + interference phase) and five-dimensional vortex PSF (3D + dipole orientation).

pH-sensitive fractal structured polyaniline in the honeycomb-patterned porous polymer film for the detection of dopamine and glucose

As a criterion used to describe the performance of an optical system, optical transfer function (OTF) can be calculated by autocorrelation of pupil function, dual Fourier transform, Zernike polynomial fitting, and so on. A fast OTF calculation method based on dual Fourier transform and graphics processing unit (GPU), in which the redundant computations of point spread function (PSF) and OTF are removed, is proposed in this paper. For an optical system with the pupil function of 512 9 512 pixels, the OTF computation is implemented on GPU platform. The result shows that the proposed method using the 128 9 128 pixels central regions of the PSF enhances the computational efficiency of about 4.88 times than the method using standard CUFFT.

Superresolution localization microscopy strongly relies on robust identification algorithms for accurate reconstruction of the biological systems it is used to measure. The fields of machine learning and computer vision have provided promising solutions for automated object identification, but usually rely on well represented training sets to learn object features. However, using a static training set can result in the learned identification algorithm making mistakes on data that is not well represented by the training set. Here, we present a method for training an artificial neural network without providing a training set in advance. This method uses the data to be analyzed, and the fitting algorithm to train an artificial neural network tailored to that data set. We show that the same artificial neural network can learn to identify at least two types of molecular emissions: the regular point spread functions (PSFs), and the astigmatism PSF. Simulations indicate that this method can be extremely reliable in extracting molecular emission signatures. Additionally, we implemented the artificial neural network calculation to be performed on a graphics processing unit (GPU) for massively parallelized calculation which drastically reduces the time required for the identification process. By implementing the neural identification on a GPU, we allow this method of identification to be used in a real time analysis algorithm. RESEARCH HIGHLIGHTS: Here, we present a machine learning algorithm for identifying point-spread functions without the need for an a priori training set. We show that this method can detect over 90% of molecules with less than 1% false positive identification in simulations. We further show that because this algorithm does not make assumptions of about the shape of molecular emission, it is compatible with models beyond the symmetric 2D Gaussian.

In this study, we introduce a novel real-time measurement and correction method for time-varying wavefront aberrations. Central to this method is a graphics processing unit (GPU)-accelerated parallel algorithm based on phase diversity images. We apply an approximate model for the point spread function (PSF) to reduce the computational load of error metric minimization. We forge a parallel framework that independently measures each aberration mode by deriving an object-independent error metric and its gradient. Numerical experiments with actual Kolmogorov model-based data were conducted to assess the measurement performance and real-time feasibility of the proposed method. When juxtaposed with the global optimization algorithm, the proposed method improved the computation speed by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1300\times $ </tex-math></inline-formula>, while maintaining measurement accuracy. Moreover, we executed benchmark tests on diverse hardware configurations, thereby verifying the real-time viability of GPU acceleration. The GPU achieved a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.8\times $ </tex-math></inline-formula> improvement in computational speed compared to the CPU. Seamlessly integrating a linear quadratic regulator (LQR) controller into the adaptive optics (AO) system, we zeroed in on the real-time correction of dynamic aberrations. The empirical results exhibited an operational speed of 90 Hz in a realistic environment for correcting only three types of aberrations (astigmatism, defocus, and coma). Furthermore, we demonstrated the correction capability for large-scale aberrations, proving that the proposed method is scalable relative to the intensity of aberrations. In conclusion, this study paves the way for a combination of real-time execution and precise wavefront aberration correction in a sensorless AO, establishing a novel standard for future development and enhancements in wavefront sensing technology.

Porous film fabricated by a thermoresponsive polymer poly( N-isopropylacrylamide-co-butylmethacrylate) with enhanced hydrophobicity

Controllable growth of porous morphology in low dielectric polyimides via thermal-driven spontaneous phase separation

In the phase-field simulation of dendrite growth during the solidification of an alloy, the computational cost becomes extremely high when the diffusion length is significantly larger than the curvature radius of a dendrite tip. In such cases, the adaptive mesh refinement (AMR) method is effective for improving the computational performance. In this study, we perform a three-dimensional dendrite growth phase-field simulation in which AMR is implemented via parallel computing using multiple graphics processing units (GPUs), which provide high parallel computation performance. In the parallel GPU computation, we apply dynamic load balancing to parallel computing to equalize the computational cost per GPU. The accuracy of an AMR refinement condition is confirmed through the single-GPU computations of columnar dendrite growth during the directional solidification of a binary alloy. Next, we evaluate the efficiency of dynamic load balancing by performing multiple-GPU parallel computations for three different directional solidification simulations using a moving frame algorithm. Finally, weak scaling tests are performed to confirm the parallel efficiency of the developed code.

Point spread function (PSF) engineering is an important technique to encode the properties (e.g., 3D positions, color, and orientation) of a single molecule in the shape of the PSF, often with the help of a programmable phase modulator. A deformable mirror (DM) is currently the most widely used phase modulator for fluorescence detection as it shows negligible photon loss. However, it relies on careful calibration for precise wavefront control. Therefore, design of an optimal PSF not only relies on the theoretical calculation of the maximum information content, but also the physical behavior of the phase modulator, which is often ignored during the optimization process. Here, we develop a framework for PSF engineering which could generate a device specific optimal PSF for 3D super-resolution imaging using a DM. We use our method to generate two types of PSFs with depths of field comparable to the widely used astigmatism and tetrapod PSFs, respectively. We demonstrate the superior performance of the DM specific optimal PSF over the conventional astigmatism and tetrapod PSF both theoretically and experimentally.

MSA Probs is a parallel algorithm developed to align multiple sequence alignment using a central processing unit (CPU). Whereas the CPU has some limitations, such as the inability to parallelize tasks in the processor (latency-oriented). To overcome these limitations, this paper proposes an improved version of MSA Probs that is compatible with a graphical Processing Unit (GPU). This idea helps in enhancing the performance of our algorithm (MSAprobs). To parallelize the sequential algorithm, Compute Unified Device Architecture (CUDA) or OpenCL is commonly used on GPUs. The NIVIDIA API is used to investigate the GPU's computing power. The results of using CPU only in MSAprobs versus the CPU and GPU are compared using two data sets from the Bali Base and OX Bench. The evaluation of the CPU and GPU is done using Threads 1,2 and 4. The results showed that by combining CPU and GPU, performance is improved and execution time is reduced.

For a phase-sensitive optical time-domain reflectometer (ϕ-OTDR) distributed sensor system, space-frequency analysis can reduce the false alarm by analyzing the frequency distribution compared with the traditional difference value method. We propose a graphics processing unit (GPU)-based parallel computing method to perform multichannel fast Fourier transform (FFT) and realize the real-time space-frequency analysis. The experiment results show that the time taken by the multichannel FFT decreased considerably based on this GPU parallel computing. The method can be completed with a sensing fiber up to 16 km long and an entry-level GPU. Meanwhile, the GPU can reduce the computing load of the central processing unit from 70% down to less than 20%. We carried out an experiment on a two-point space-frequency analysis, and the results clearly and simultaneously show the vibration point locations and frequency components. The sensor system outputs the real-time space-frequency spectra continuously with a spatial resolution of 16.3 m and frequency resolution of 2.25 Hz.

Offset computation for expanding a polyhedral object by an offset radius is a fundamental geometric process frequently used in manufacturing applications. This process combined with the triple-dexel representation solid model has become popular because of its robustness and compatibility with parallel processing using a graphics processing unit (GPU). In parallel geometric processing, conflicts between threads must be avoided. Thus, we propose a novel parallel offsetting algorithm free from conflicts between threads. The triple-dexel model is a combination of x-, y-, and z-axis-aligned dexel models. Each dexel model is defined based on an orthogonal grid given on a coordinate plane. We subdivide the grid into several sub-grids of a fixed size in advance. For each sub-grid, a block of GPU threads is assigned. As each GPU thread always processes different dexel elements from the other threads in this method, no conflict occurs. Our research group has previously presented a parallel offset computation algorithm for a polyhedral solid model that also uses a triple-dexel representation model and a GPU. In the previous algorithm, the surface polygons of the model are classified into several groups in advance. The parallel offset computation of multiple polygon groups is realized by selecting groups of polygon in which the offset processing of the polygons does not affect one another. This selection process is time-consuming. Computational experiments were performed to analyze the performance difference between the current algorithm and our previous algorithm. In our experiments, the current algorithm achieved speedups of 1.4 times to 3.2 times compared to our previous offsetting algorithm.

Vergence ability is an important visual behavior observed on living creatures when they use vision to interact with the environment. The notion of active observer is equally useful for robotic vision systems on tasks like object tracking, fixation and 3D environment structure recovery. Humanoid robotics are a potential playground for such behaviors. This paper describes the implementation of a real time binocular vergence behavior using cepstral filtering to estimate stereo disparities. By implementing the cepstral filter on a graphics processing unit (GPU) using Compute Unified Device Architecture (CUDA) we demonstrate that robust parallel algorithms that used to require dedicated hardware are now available on common computers. The cepstral filtering algorithm speed up is more than sixteen times than on a current CPU. The overall system is implemented in the binocular vision system IMPEP (IMPEP Integrated Multimodal Perception Experimental Platform) to illustrate the system performance experimentally.

To investigate algebraic reconstruction technique (ART) for parallel imaging reconstruction of radial data, applied to accelerated cardiac cine. A graphics processing unit (GPU)-accelerated ART reconstruction was implemented and applied to simulations, point spread functions and in 12 subjects imaged with radial cardiac cine acquisitions. Cine images were reconstructed with radial ART at multiple undersampling levels (192 Nr × Np = 96 to 16). Images were qualitatively and quantitatively analyzed for sharpness and artifacts, and compared to filtered back-projection, and conjugate gradient SENSE. Radial ART provided reduced artifacts and mainly preserved spatial resolution, for both simulations and in vivo data. Artifacts were qualitatively and quantitatively less with ART than filtered back-projection using 48, 32, and 24 Np , although filtered back-projection provided quantitatively sharper images at undersampling levels of 48-24 Np (all P < 0.05). Use of undersampled radial data for generating auto-calibrated coil-sensitivity profiles resulted in slightly reduced quality. ART was comparable to conjugate gradient SENSE. GPU-acceleration increased ART reconstruction speed 15-fold, with little impact on the images. GPU-accelerated ART is an alternative approach to image reconstruction for parallel radial MR imaging, providing reduced artifacts while mainly maintaining sharpness compared to filtered back-projection, as shown by its first application in cardiac studies.