Reconstruction In Spatial Domain Research Articles

Parameter-free super-resolution structured illumination microscopy via a physics-enhanced neural network.

With full-field imaging and high photon efficiency advantages, structured illumination microscopy (SIM) is one of the most potent super-resolution (SR) modalities in bioscience. Regarding SR reconstruction for SIM, spatial domain reconstruction (SDR) has been proven to be faster than traditional frequency domain reconstruction (FDR), facilitating real-time imaging of live cells. Nevertheless, SDR relies on high-precision parameter estimation for reconstruction, which tends to suffer from low signal-to-noise ratio (SNR) conditions and inevitably leads to artifacts that seriously affect the accuracy of SR reconstruction. In this Letter, a physics-enhanced neural network-based parameter-free SDR (PNNP-SDR) is proposed, which can achieve SR reconstruction directly in the spatial domain. As a result, the peak-SNR (PSNR) of PNNP-SDR is improved by about 4 dB compared to the cross-correlation (COR) SR reconstruction; meanwhile, the reconstruction speed of PNNP-SDR is even about five times faster than the fast approach based on principal component analysis (PCA). Given its capability of achieving parameter-free imaging, noise robustness, and high-fidelity and high-speed SR reconstruction over conventional SIM microscope hardware, the proposed PNNP-SDR is expected to be widely adopted in biomedical SR imaging scenarios.

Large-field and fast super-resolution microscopic imaging method based on laser interferometry

In recent years, structured illumination microscopy (SIM) has been drawing great attention for both technique development and application. However, conventional SIM, which uses a spatial light modulator (SLM) for fringe projection, often has a limited field of view. To meet the demand for high-throughput microscopic imaging in biomedicine research, a large-field super-resolution (SR) fluorescence microscopic imaging method based on laser interferometry was proposed. The method that combines a two-dimensional (2D) grating for fringe pattern projection and an SLM for selecting fringe orientation can break the limitation of fringe number limited by the digital projection devices. A spatial-domain reconstruction algorithm was developed to improve the computational speed of super-resolution imaging. Finally, an experimental platform for SIM microscopy was established. A large-field view of 1380 μm × 1035 μm under a 20×/NA0.75 objective is experimentally demonstrated, and an enhancement of 1.8-fold resolution is realized. The spatial-domain reconstruction algorithm can significantly improve the computational speed by approximately 10 times faster compared to the traditional frequency-domain algorithm.

Learning the Imaging Model of Speed-of-Sound Reconstruction via a Convolutional Formulation.

Speed-of-sound (SoS) is an emerging ultrasound contrast modality, where pulse-echo techniques using conventional transducers offer multiple benefits. For estimating tissue SoS distributions, spatial domain reconstruction from relative speckle shifts between different beamforming sequences is a promising approach. This operates based on a forward model that relates the sought local values of SoS to observed speckle shifts, for which the associated image reconstruction inverse problem is solved. The reconstruction accuracy thus highly depends on the hand-crafted forward imaging model. In this work, we propose to learn the SoS imaging model based on data. We introduce a convolutional formulation of the pulse-echo SoS imaging problem such that the entire field-of-view requires a single unified kernel, the learning of which is then tractable and robust. We present least-squares estimation of such convolutional kernel, which can further be constrained and regularized for numerical stability. In experiments, we show that a forward model learned from k-Wave simulations reduces the contrast error of SoS reconstructions by 38%, compared to a conventional hand-crafted line-based wave-path model. This simulation-learned model generalizes successfully to acquired phantom data, reducing the contrast error compared to the conventional hand-crafted alternative. We successfully demonstrate the feasibility of learning machine-specific kernels as well as one-shot learning from a single image. On in-vivo data of a cancerous breast tumor, the phantom-learned model exhibits an SoS contrast of 34.6 m/s, as an impressive improvement over the conventional model contrast of merely 3.4 m/s.

Image Reconstruction for Accelerated MR Scan With Faster Fourier Convolutional Neural Networks.

High quality image reconstruction from undersampled k -space data is key to accelerating MR scanning. Current deep learning methods are limited by the small receptive fields in reconstruction networks, which restrict the exploitation of long-range information, and impede the mitigation of full-image artifacts, particularly in 3D reconstruction tasks. Additionally, the substantial computational demands of 3D reconstruction considerably hinder advancements in related fields. To tackle these challenges, we propose the following: 1) A novel convolution operator named Faster Fourier Convolution (FasterFC), aims at providing an adaptable broad receptive field for spatial domain reconstruction networks with fast computational speed. 2) A split-slice strategy that substantially reduces the computational load of 3D reconstruction, enabling high-resolution, multi-coil, 3D MR image reconstruction while fully utilizing inter-layer and intra-layer information. 3) A single-to-group algorithm that efficiently utilizes scan-specific and data-driven priors to enhance k -space interpolation effects. 4) A multi-stage, multi-coil, 3D fast MRI method, called the faster Fourier convolution based single-to-group network (FAS-Net), comprising a single-to-group k -space interpolation algorithm and a FasterFC-based image domain reconstruction module, significantly minimizes the computational demands of 3D reconstruction through split-slice strategy. Experimental evaluations conducted on the NYU fastMRI and Stanford MRI Data datasets reveal that the FasterFC significantly enhances the quality of both 2D and 3D reconstruction results. Moreover, FAS-Net, characterized as a method that can achieve high-resolution (320, 320, 256), multi-coil, (8 coils), 3D fast MRI, exhibits superior reconstruction performance compared to other state-of-the-art 2D and 3D methods.

Spectrum-optimized direct image reconstruction of super-resolution structured illumination microscopy

Super-resolution structured illumination microscopy (SR-SIM) has become a widely used nanoscopy technique for rapid, long-term, and multi-color imaging of live cells. Precise but troublesome determination of the illumination pattern parameters is a prerequisite for Wiener-deconvolution-based SR-SIM image reconstruction. Here, we present a direct reconstruction SIM algorithm (direct-SIM) with an initial spatial-domain reconstruction followed by frequency-domain spectrum optimization. Without any prior knowledge of illumination patterns and bypassing the artifact-sensitive Wiener deconvolution procedures, resolution-doubled SR images could be reconstructed by direct-SIM free of common artifacts, even for the raw images with large pattern variance in the field of view (FOV). Direct-SIM can be applied to previously difficult scenarios such as very sparse samples, periodic samples, very small FOV imaging, and stitched large FOV imaging.

Advances in High-Speed Structured Illumination Microscopy

Super-resolution microscopy surpasses the diffraction limit to enable the observation of the fine details in sub-cellular structures and their dynamics in diverse biological processes within living cells. Structured illumination microscopy (SIM) uses a relatively low illumination light power compared with other super-resolution microscopies and has great potential to meet the demands of live-cell imaging. However, the imaging acquisition and reconstruction speeds limit its further applications. In this article, recent developments all targeted at improving the overall speed of SIM are reviewed. These comprise both hardware and software improvements, which include a reduction in the number of raw images, GPU acceleration, deep learning and the spatial domain reconstruction. We also discuss the application of these developments in live-cell imaging.

Comments on “Rapid Image Reconstruction of Structured Illumination Microscopy Directly in the Spatial Domain”

This comment shows that the formulation used in the above paper has been presented in virtually the same form in a previous paper. The comment also emphasizes that spatial domain reconstruction for structured light microscopy is a useful supplement to Fourier based algorithms.

Rapid Image Reconstruction of Structured Illumination Microscopy Directly in the Spatial Domain.

Super-resolution structured illumination microscopy (SIM) routinely performs image reconstruction in the frequency domain using an approach termed frequency-domain reconstruction (FDR). Due to multiple Fourier transforms between the spatial and frequency domains, SIM suffers from low reconstruction speed, constraining its applications in real-time, dynamic imaging. To overcome this limitation, we developed a new method for SIM image reconstruction, termed spatial domain reconstruction (SDR). SDR is intrinsically simpler than FDR, does not require Fourier transforms and the theory predicts it to be a rapid image reconstruction method. Results show that SDR reconstructs a super-resolution image 7-fold faster than FDR, producing images that are equal to either FDR or the widely-used FairSIM. We provide a proof-of-principle using mobile fluorescent beads to demonstrate the utility of SDR in imaging moving objects. Consequently, replacement of the FDR approach with SDR significantly enhances SIM as the desired method for live-cell, instant super-resolution imaging. This means that SDR-SIM is a “What You See Is What You Get” approach to super-resolution imaging.

Breast Cancer Assessment With Pulse-Echo Speed of Sound Ultrasound From Intrinsic Tissue Reflections: Proof-of-Concept.

The aim of this study was to differentiate malignant and benign solid breast lesions with a novel ultrasound (US) technique, which measures speed of sound (SoS) using standard US transducers and intrinsic tissue reflections and scattering (speckles) as internal reference. This prospective, institutional review board-approved, Health Insurance Portability and Accountability Act-compliant prospective comparison study was performed with prior written informed consent from 20 women. Ten women with histological proven breast cancer and 10 with fibroadenoma were measured. A conventional US system with a linear probe was used for SoS-US (SonixTouch; Ultrasonix, Richmond, British Columbia, Canada). Tissue speckle reflections served as a timing reference for the US signals transmitted through the breasts. Relative phase inconsistencies were detected using plane wave measurements from different angular directions, and SoS images with 0.5-mm resolution were generated using a spatial domain reconstruction algorithm. The SoS of tumors were compared with the breast density of a larger cohort of 106 healthy women. Breast lesions show focal increments ΔSoS (meters per second) with respect to the tissue background. Peak ΔSoS values were evaluated. Breast carcinoma showed significantly higher ΔSoS than fibroadenomas ([INCREMENT]SoS > 41.64 m/s: sensitivity, 90%; specificity, 80%; area under curve, 0.910) and healthy breast tissue of different densities (area under curve, 0.938; sensitivity, 90%; specificity, 96.5%). The lesion localization in SoS-US images was consistent with B-mode imaging and repeated SoS-US measurements were reproducible. Using SoS-US, based on conventional US and tissue speckles as timing reference, breast carcinoma showed significantly higher SoS values than fibroadenoma and healthy breast tissue of different densities. The SoS presents a promising technique for differentiating solid breast lesions.

Spatial domain reconstruction for imaging speed-of-sound with pulse-echo ultrasound: simulation and in vivo study

Despite many uses of ultrasound, some pathologies such as breast cancer still cannot reliably be diagnosed in either conventional B-mode ultrasound imaging nor with more recent ultrasound elastography methods. Speed-of-sound (SoS) is a quantitative imaging biomarker, which is sensitive to structural changes due to pathology, and hence could facilitate diagnosis. Full-angle ultrasound computed tomography (USCT) was proposed to obtain spatially-resolved SoS images, however, its water-bath setup involves practical limitations. To increase clinical utility and for widespread use, recently, a limited-angle Fourier-domain SoS reconstruction was proposed, however, it suffers from significant image reconstruction artifacts. In this work, we present a SoS reconstruction strategy, where the forward problem is formulated using differential time-of-flight measurements based on apparent displacements along different ultrasound wave propagation paths, and the inverse problem is solved in spatial-domain using a proposed total-variation scheme with spatially-varying anisotropic weighting to compensate for geometric bias from limited angle imaging setup. This is shown to be robust to missing displacement data and easily allow for incorporating any prior geometric information. In numerical simulations, SoS values in inclusions are accurately reconstructed with 90% accuracy up to a noise level of 50%. With respect to Fourier-domain reconstruction, our proposed method improved contrast ratio from 0.37 to 0.67 for even high noise levels such as 50%. Numerical full-wave simulation and our preliminary in vivo results illustrate the clinical applicability of our method in a breast cancer imaging setting. Our proposed method requires single-sided access to the tissue and can be implemented as an add-on to conventional ultrasound equipment, applicable to a range of transducers and applications.

Phase distribution analysis of tissues based on the off-axis digital holographic hybrid reconstruction algorithm.

Off-axis digital holography (DH) has great potential in histopathology for its high efficiency and precision. Phase distribution, usually extracted by the angular spectrum (AS) algorithm from a digital hologram, reflects important structural information of biological tissues. However, the complex structure of tissues introduces spectrum aliasing of the hologram, making the AS algorithm hard to realize and accurate phase analysis difficult to conduct. Here, we present a hybrid reconstruction algorithm, combining Fresnel reconstruction in spatial domain with the AS algorithm in frequency domain, to solve aliasing by spatial filtering. Through simulation, we demonstrate the feasibility and superiority of the hybrid algorithm and verified the precision (10-3 rad) of the hybrid algorithm with spectrum aliasing. We extract phase distributions from normal urothelial and bladder cancer tissues by the hybrid algorithm and make quantitative analysis through histogram and standard deviation. The result shows the hybrid algorithm in off-axis DH has great advantage for the high-precision phase extraction of tissues and supplies significant information for cancer diagnosis.

The post-Doppler adaptive processing method based on the spatial domain reconstruction

Space-time adaptive processing (STAP) has a huge computational complexity and a large training samples requirement, which limit its practical applications. The traditional post-Doppler adaptive processing methods such as factored approach (FA) and extended factored approach (EFA) can significantly reduce the computational complexity and the training sample requirement in adaptive processing, and maintain nearly the same performance as the optimal STAP. However, because training samples are restricted in real-world environments, their performances can be considerably degraded in the large-scale antenna array. To solve this problem, the post-Doppler adaptive processing method based on the spatial domain reconstruction is proposed. In this method, the spatial clutter data after Doppler filtering is reconstructed as a matrix that has close columns and rows. The spatial weights vector in FA or EFA is also re-expressed as the product of two shorter weight vectors. Then the cyclic minimizer is applied to find the desired solution. Experimental results show that the proposed method has the advantages of fast convergence and small training samples requirement. It has greater moving target detection ability especially under the condition for large-scale antenna array and small training samples support than FA and EFA.

A combined finite element-nonlinear conjugate gradient spatial method for the reconstruction of unknown scatterer profiles

The inversion of microwave imaging data is treated by means of a novel iterative spatial domain reconstruction technique. The latter combines the Finite Element Method (FEM) and a Nonlinear Conjugate Gradient (NCG) optimization scheme. The method is applied to the reconstruction of the complex permittivity profile of cylindrical inhomogeneous objects, that are illuminated by transverse magnetic incident waves, and is based on scattered far field measurements. Starting from an initial guess of the scatterer profile, the FEM is used to solve the forward problem, resulting in an estimate of the scattered field. The inverse process of updating the constitutive parameters of the scatterer is performed by the Polak-Ribiere algorithm by the use of which the difference between the measured and the estimated scattered data is minimized. The minimization procedure is carried out by a sensitivity analysis scheme based on the FEM and the introduction of the adjoint state vector. The proposed method is applied to various cases of scatterer profiles.