Accurate Image Reconstruction Research Articles

Dual-domain reconstruction network with V-Net and K-Net for fast MRI.

To introduce a dual-domain reconstruction network with V-Net and K-Net for accurate MR image reconstruction from undersampled k-space data. Most state-of-the-art reconstruction methods apply U-Net or cascaded U-Nets in the image domain and/or k-space domain. Nevertheless, these methods have the following problems: (1) directly applying U-Net in the k-space domain is not optimal for extracting features; (2) classical image-domain-oriented U-Net is heavyweighted and hence inefficient when cascaded many times to yield good reconstruction accuracy; (3) classical image-domain-oriented U-Net does not make full use of information of the encoder network for extracting features in the decoder network; and (4) existing methods are ineffective in simultaneously extracting and fusing features in the image domain and its dual k-space domain. To tackle these problems, we present 3 different methods: (1) V-Net, an image-domain encoder-decoder subnetwork that is more lightweight for cascading and effective in fully utilizing features in the encoder for decoding; (2) K-Net, a k-space domain subnetwork that is more suitable for extracting hierarchical features in the k-space domain, and (3) KV-Net, a dual-domain reconstruction network in which V-Nets and K-Nets are effectively combined and cascaded. Extensive experimental results on the fastMRI dataset demonstrate that the proposed KV-Net can reconstruct high-quality images and outperform state-of-the-art approaches with fewer parameters. To reconstruct images effectively and efficiently from incomplete k-space data, we have presented a dual-domain KV-Net to combine K-Nets and V-Nets. The KV-Net achieves better results with 9% and 5% parameters than comparable methods (XPD-Net and i-RIM).

Pyramid Convolutional RNN for MRI Image Reconstruction.

Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical practice. Deep learning based reconstruction methods have shown promising advances in recent years. However, recovering fine details from undersampled data is still challenging. In this paper, we introduce a novel deep learning based method, Pyramid Convolutional RNN (PC-RNN), to reconstruct images from multiple scales. Based on the formulation of MRI reconstruction as an inverse problem, we design the PC-RNN model with three convolutional RNN (ConvRNN) modules to iteratively learn the features in multiple scales. Each ConvRNN module reconstructs images at different scales and the reconstructed images are combined by a final CNN module in a pyramid fashion. The multi-scale ConvRNN modules learn a coarse-to-fine image reconstruction. Unlike other common reconstruction methods for parallel imaging, PC-RNN does not employ coil sensitive maps for multi-coil data and directly model the multiple coils as multi-channel inputs. The coil compression technique is applied to standardize data with various coil numbers, leading to more efficient training. We evaluate our model on the fastMRI knee and brain datasets and the results show that the proposed model outperforms other methods and can recover more details. The proposed method is one of the winner solutions in the 2019 fastMRI competition.

Fast and efficient PAT image reconstruction algorithms: A comparative performance analysis

Medical imaging technology efficiency is determined by how accurate, rapid, invasive, or non-invasive it is. Photoacoustic tomography (PAT) is a noninvasive emerging technology for medical imaging that is able to produce images with high resolution and high contrast in long penetration depths. In PAT, image reconstruction accuracy relies on the accuracy of the system model. Accurate model-based methods can be made use of for efficiently reconstructing a PAT image from the sensor measurements using a pseudo-spectral method which is suggested to produce exactly the same results as that of the k-Wave toolbox in MATLAB. Compressive sensing has been successfully applied for medical image reconstruction as it allows for sparse sampling and sparse reconstruction. Compressive sensing combined with PAT allows for deep tissue imaging at higher resolution at a much lower computational time. A fast and efficient PAT image reconstruction compressive sensing algorithm is needed to be developed, which can image dense tissues (such as breast tissue) rapidly, for early detection of cancer. In this paper, relaxed- Basis pursuit using alternating direction method of multipliers and split Bregman formulation of the total variation regularized least-squares problem is proposed and is observed to work under practical scenarios with fewer samples and sensors. Moreover, a comparative study for the performance analysis of the different compressive sensing algorithms, both iterative and non-iterative, is carried out for a decreased number of sensors and samples. The proposed split Bregman total variation algorithm is fast and produces a high-resolution sparsity preserving image compared to the state-of-the-art algorithms. Moreover, for a reduced number of samples, the proposed relaxed-Basis Pursuit is desired and performs much better than all the algorithms discussed. Implementing Cholesky factorization to the proposed algorithms further reduces the computation time of the split-Bregman algorithm by 90%. The new methods outperform existing ones in practical circumstances recreating a high-resolution image, even when the number of sensors and samples is reduced.

Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network.

Electrical impedance tomography (EIT) is a non-invasive, radiation-free imaging technique with a lot of promise in clinical monitoring. However, since EIT image reconstruction is a non-linear, pathological, and ill-posed issue, the quality of the reconstructed images needs constant improvement. To increase image reconstruction accuracy, a grey wolf optimized radial basis function neural network (GWO-RBFNN) is proposed in this paper. The grey wolf algorithm is used to optimize the weights in the radial base neural network, determine the mapping between the weights and the initial position of the grey wolf, and calculate the optimal position of the grey wolf to find the optimal solution for the weights, thus improving the image resolution of EIT imaging. COMSOL and MATLAB were used to numerically simulate the EIT system with 16 electrodes, producing 1700 simulation samples. The standard Landweber, RBFNN, and GWO-RBFNN approaches were used to train the sets separately. The obtained image correlation coefficient (ICC) of the test set after training with GWO-RBFNN is 0.9551. After adding 30, 40, and 50 dB of Gaussian white noise to the test set, the attained ICCs with GWO-RBFNN are 0.8966, 0.9197, and 0.9319, respectively. The findings reveal that the proposed GWO-RBFNN approach outperforms the existing methods when it comes to image reconstruction.

DVS-Net: Dual-domain Variable Splitting Network for Accelerated Parallel MRI Data.

Parallel imaging is an important method to accel-erate the acquisition of magnetic resonance imaging data, which can shorten the breath-hold times and reduce motion artifacts. In this paper, we propose a joint frequency domain and image domain (dual-domain) reconstruction method by introducing the full sampling condition for the undersampled multi-coil MR data. The motivation is that the dual domain method can provide more information for accurate image reconstruction. An efficient iterative algorithm is developed based on the variable splitting technique and alternating direction method of multipliers, which is unrolled into an end-to-end trainable deep neural network. We evaluate the proposed network on complex valued multi-coil knee images for both 6-fold and 8-fold acceleration factors, and compare with both variational and deep learning based reconstruction algorithms. The numerical results demonstrate that our method provides better reconstruction accuracy and perceptual quality by making using of the dual domain information. Clinical relevance: This improves the reconstruction quality for accelerated parallel MRI data both visually and quantitatively.

Neural network regularization in the problem of few-view computed tomography

The computed tomography allows to reconstruct the inner morphological structure of an object without physical destructing. The accuracy of digital image reconstruction directly depends on the measurement conditions of tomographic projections, in particular, on the number of recorded projections. In medicine, to reduce the dose of the patient load there try to reduce the number of measured projections. However, in a few-view computed tomography, when we have a small number of projections, using standard reconstruction algorithms leads to the reconstructed images degradation. The main feature of our approach for few-view tomography is that algebraic reconstruction is being finalized by a neural network with keeping measured projection data because the additive result is in zero space of the forward projection operator. The final reconstruction presents the sum of the additive calculated with the neural network and the algebraic reconstruction. First is an element of zero space of the forward projection operator. The second is an element of orthogonal addition to the zero space. Last is the result of applying the algebraic reconstruction method to a few-angle sinogram. The dependency model between elements of zero space of forward projection operator and algebraic reconstruction is built with neural networks. It demonstrated that realization of the suggested approach allows achieving better reconstruction accuracy and better computation time than state-of-the-art approaches on test data from the Low Dose CT Challenge dataset without increasing reprojection error.

Application of Sensor Path Weighting RAPID Algorithm on Pitting Corrosion Monitoring of Aluminum Plate.

Aluminum alloy is widely used in aerospace structures. However, it often suffers from a harsh corrosion environment, resulting in different damage such as pitting corrosion, which leads to a reduction in the service life of aerospace structures. In the present study, the pitting corrosion with a radius of 1 mm and a depth of 0.6 mm was manufactured using hydrofluoric (HF) acid on a 2024-T3 aluminum alloy plate (400 mm × 400 mm × 2 mm) to simulate the corrosion state of equipment. A signal acquisition system with a square sensor network of 12 piezoelectric transducers (PZTs) was established. The sensor path weighting reconstruction algorithm for the probabilistic inspection of defects (SPW-RAPID) is proposed based on corrosion damage characteristic parameters including signal correlation coefficient (SDC), root mean squared error (RMSE), and signal energy damage index (E1) to explore the monitoring efficacy of pitting corrosion. The sensor path weight w, which is the product of value coefficient a and impact factor l, is established to modify the corrosion damage characteristic parameters. The results indicate that the SPW-RAPID algorithm can improve the accuracy and clarity of image reconstruction results based on SDC, RMSE and E1, which can locate the pitting corrosion with a radius of 1 mm and a depth of 0.6 mm, and the positioning error is controlled within 0.1 mm. The research work may provide an available way to monitor tiny corrosion damage on an aluminum alloy structure.

On the limits of Conditional Generative Adversarial Neural Networks to reconstruct the identification of inhabitants from IoT low-resolution thermal sensors

One of the main objectives of smart homes is to facilitate daily life by increasing user comfort, with the potential to play a key role in revolutionizing healthcare for the elderly, the disabled and people with functional limitations. To achieve this end, smart homes will have to be able to distinguish the identity of users, their location and the activities they are performing, while also being implemented in a non-invasive way that protects the privacy of these users. Computer vision is one of the main technologies included in smart homes. However, there are drawbacks to traditional cameras, given their dependence on light and privacy-related concerns. Thermal cameras provide a solution, as they operate regardless of light conditions (e.g. at night) while respecting users’ privacy. In this work, image reconstruction and identification of inhabitants from facial images collected by low-resolution thermal sensors has been carried out by using Conditional Generative Adversarial Neural Networks (CGANs). The system has been implemented through an IoT device with raspberry Pi and dual-vision thermal and visible-spectrum sensors installed in a real smart home to automatically collect paired visible-spectrum and thermal images. Thus, different configurations of CGANs have been implemented and analyzed to achieve the following outcomes: (1) inhabitant identification (normal and masked face) with enhanced user privacy and (2) transfer from thermal to color images in the visible spectrum. Results show that the proposed CGAN achieves a recognition rate of 95% and 94% for uncovered and masked faces. This enables user identification without registering accurate facial expressions in the color image reconstruction, protecting user privacy. Furthermore, the developed system outperformed similar approaches using low-resolution datasets and has demonstrated that the accuracy of image reconstruction depends on the resolution of the input visible-spectrum images. In addition, a contribution to high-performance computing has been made by designing a CGAN that runs efficiently on multiple GPUs, achieving increased performance and response speed of the network, as well as applicability to larger problems.

Adoption of Artificial Intelligence (AI)-Based Computerized Tomography (CT) Evaluation of Comprehensive Nursing in the Operation Room in Laparoscopy-Guided Radical Surgery of Colon Cancer.

This research aimed to discuss the application of traditional nonlocal mean (NLM) algorithm-based computerized tomography (CT) images in intervention evaluation of the nursing for patients performing laparoscopy-guided radical surgery of colon cancer. A total of 100 patients who were diagnosed with colon cancer after enteroscopy and performed laparoscopic radical surgery were chosen as the research objects. They were divided into an observation group (comprehensive nursing in operation room) and a control group (routine nursing), each of which included 50 cases. All cases received CT examination. Meanwhile, the improved traditional NLM (INLM) algorithm was proposed, and the effects of image reconstruction were analyzed to improve the quality of CT images. The result showed that structural similarity index measure (SSIM) and figure of merit (FOM) of INLM were obviously higher than those of filtered back projection (FBP) algorithm and NLM algorithm, and the average running time was significantly less than that of FBP algorithm and NLM algorithm (P < 0.05). The operation time and the amount of intraoperative blood loss of patients in the observation group were both less than those of patients in the control group, and differences had statistical significance (P < 0.05). Besides, the time of getting out of bed, ventilation recovery time, postoperative meal time, stomach tube encumbrance time, and catheter encumbrance time of patients in the observation group were all less than those of patients in the control group, and the differences had statistical significance (P < 0.05). In the observation group, there were 3 cases with postoperative complications, and 2 out of them got incision infection while 1 suffered from constipation. In contrast, there were 9 cases with postoperative complications in the control group, 3 of which were patients with incision infection, and 2 suffered from urinary retention while the other 4 suffered from constipation. According to the above results, the INLM algorithm proposed in this research could improve the image reconstruction accuracy of traditional algorithm, shorten the running time, and enhance the overall diagnostic efficiency. The comprehensive nursing in operation room with laparoscopic radical surgery of colon cancer could improve the cure rate and prognosis of patients, so it was worthy of clinical promotion and application.

Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography.

Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconstruction complexity. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain an accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Applying our method, we find that we can reconstruct and localize lesions faithfully while allowing real-time reconstruction. We also demonstrate that the present algorithm can reconstruct multiple cancer lesions. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.

TELET: A monotonic algorithm to design large dimensional equiangular tight frames for applications in compressed sensing

An Equiangular tight frame (ETF) - also known as the Welch-bound-equality sequences - consists of a sequence of unit norm vectors whose absolute inner product is identical and minimal. Due to this unique property, these frames are preferred in different applications such as in constructing sensing matrices for compressed sensing systems, robust transmission and quantum computing. Construction of ETFs involves solving a challenging non-convex minimax optimization problem, and only a few methods were successful in constructing them, albeit only for smaller dimensions. In this paper, we propose an iterative algorithm named TEchnique to devise Large dimensional Equiangular Tight-frames (TELET-frames) based on the majorization minimization (MM) procedure - in which we design and minimize a tight upper bound for the ETF cost function at every iteration. Since TELET is designed using the MM approach, it inherits useful properties of MM such as monotonicity and guaranteed convergence to a stationary point. Subsequently, we use the derived frames to construct optimized sensing matrix for compressed sensing systems. In the numerical simulations, we show that the proposed algorithm can generate complex and real frames (in the order of hundreds) with very low mutual coherence value when compared to the state-of-the-art algorithm, with a slight increase in computational cost. Experiments using synthetic data and real images reveal that the optimized sensing matrix obtained through the frames constructed by TELET performs better, in terms of image reconstruction accuracy, than the sensing matrix constructed using state-of-the-art methods.

Physical model-driven deep networks for through-the-wall radar imaging

AbstractIn order to merge the advantages of the traditional compressed sensing (CS) methodology and the data-driven deep network scheme, this paper proposes a physical model-driven deep network, termed CS-Net, for solving target image reconstruction problems in through-the-wall radar imaging. The proposed method consists of two consequent steps. First, a learned convolutional neural network prior is introduced to replace the regularization term in the traditional iterative CS-based method to capture the redundancy of the radar echo signal. Moreover, the physical model of the radar signal is used in the data consistency layer to encourage consistency with the measurements. Second, the iterative CS optimization is unrolled to yield a deep learning network, where the weight, regularization parameter, and the other parameters are learnable. A quantity of training data enables the network to extract high-dimensional characteristics of the radar echo signal to reconstruct the spatial target image. Simulation results demonstrated that the proposed method can achieve accurate target image reconstruction and was superior to the traditional CS method, in terms of mean squared error and the target texture details.

Image reconstruction from data over two orthogonal arcs of limited-angular ranges.

Computed tomography (CT) scanning over limited-angular ranges (LARs) is of practical interest in possible reduction of imaging dose and time and in design of nonstandard scans. This work aims to investigate image reconstruction for two nonoverlapping arcs of LARs, and to demonstrate that they may allow more accurate image reconstruction than may a single arc ofLAR. We consider a configuration with two nonoverlapping arcs of LARs and , whose centers are separated by , and refer to it as a two-orthogonal-arc configuration. Data are generated from a chest phantom with two-orthogonal-arc configurations over total angular coverage ranging from to , and images are reconstructed subsequently by use of the directional-total-variation (DTV) algorithm. For comparison, we also consider image reconstruction for a single-arc configuration of angular range . Quantitative metrics such as the normalized root-mean-square-error (nRMSE) are used for evaluation of image reconstructionaccuracy. Visual inspection and quantitative analysis of images reconstructed reveal that a two-orthogonal-arc configuration generally yields more accurate image reconstruction than does its single-arc counterpart. As total angular range increases, the DTV algorithm yields image reconstruction with enhanced accuracy, as expected. Also, if remains constant, the two-orthogonal-arc configuration with generally leads to image reconstruction more accurate than those of two-orthogonal-arc configurations with , as the nRMSE of the former can be lower than that of the latter for up to more than one order ofmagnitude. Appropriately designed two-orthogonal-arc configurations may be exploited for improving image-reconstruction accuracy in CT imaging with reduced angular coverage. This study may yield insights into the design of innovative CT scans for lowering scan time and radiation dose, and/or for avoiding scan collision in, for example, C-armCT.

Quantitative analysis of the effect of radiation on mitochondria structure using coherent diffraction imaging with a clustering algorithm.

Radiation damage and a low signal-to-noise ratio are the primary factors that limit spatial resolution in coherent diffraction imaging (CDI) of biomaterials using X-ray sources. Introduced here is a clustering algorithm named ConvRe based on deep learning, and it is applied to obtain accurate and consistent image reconstruction from noisy diffraction patterns of weakly scattering biomaterials. To investigate the impact of X-ray radiation on soft biomaterials, CDI experiments were performed on mitochondria from human embryonic kidney cells using synchrotron radiation. Benefiting from the new algorithm, structural changes in the mitochondria induced by X-ray radiation damage were quantitatively characterized and analysed at the nanoscale with different radiation doses. This work also provides a promising approach for improving the imaging quality of biomaterials with XFEL-based plane-wave CDI.

Highly accurate THz-CT including refraction effects

The principles of algebraic image reconstruction are applied to THz computed tomography (THz-CT) in order to account for refraction within the sample. Using the nominal sample geometry as a priori knowledge, a highly accurate and robust image reconstruction algorithm based on the physics of geometric optics is presented. The validity of the geometric forward model is verified by a numerical simulation of Maxwell's equations. Furthermore, the developed method is experimentally tested using measurements performed with a fast THz-CT system based on a THz time-domain spectrometer in transmission mode. Automated evaluations of the reconstructed sample cross sections showed an accuracy of <150 μm.

Deep Task-Based Analog-to-Digital Conversion

Analog-to-digital converters (ADCs) allow physical signals to be processed using digital hardware. Their conversion consists of two stages: Sampling, which maps a continuous-time signal into discrete-time, and quantization, i.e., representing the continuous-amplitude quantities using a finite number of bits. ADCs typically implement generic uniform conversion mappings that are ignorant of the task for which the signal is acquired, and can be costly when operating in high rates and fine resolutions. In this work we design task-oriented ADCs which learn from data how to map an analog signal into a digital representation such that the system task can be efficiently carried out. We propose a model for sampling and quantization that facilitates the learning of non-uniform mappings from data. Based on this learnable ADC mapping, we present a mechanism for optimizing a hybrid acquisition system comprised of analog combining, tunable ADCs with fixed rates, and digital processing, by jointly learning its components end-to-end. Then, we show how one can exploit the representation of hybrid acquisition systems as deep networks to optimize the sampling and quantization rates given the task by utilizing Bayesian meta-learning techniques. We evaluate the proposed deep task-based ADC in two case studies: the first considers synthetic multi-variate symbol detection, where multiple analog signals are simultaneously acquired in order to recover a set of discrete symbols. The second application is beamforming of analog channel data acquired in ultrasound imaging. Our numerical results demonstrate that the proposed approach achieves performance which is comparable to operating with high sampling rates and fine resolution quantization, while operating with reduced overall bit rate. For instance, we demonstrate that deep task-based ADCs enable accurate reconstruction of ultrasound images while using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$12.5\%$</tex-math></inline-formula> of the overall number of bits used by conventional ADCs.

Spatial and Temporal Regularized Iterative Deep Neural Network for Non-Stationary Image Reconstruction of Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) is a promising technique for monitoring complex dynamics. However, due to the ill-posedness and non-linearity of EIT, its spatial resolution is low, especially when the target dynamics are too fast to be observed under the ’stationary’ condition. The traditional ’non-stationary’ image reconstruction methods for EIT, like Kalman filter, suffers from low spatial resolution and high computational complexity. To achieve fast and accurate ’non-stationary’ image reconstruction of EIT, a spatial-temporal regularized learned gradient descent (STLGD) algorithm is proposed. A novel iterative neural network is constructed for numerically implementing the proposed algorithm. A two-dimensional stochastic data interpolation method is proposed for generating a large scale ’non-stationary’ dataset using ’stationary’ experiments, which is crucial from the numerical implementation of the learning based methods. A series of experimental tests with a water tank model are conducted. Ablation and augmentation tests are conducted to prove the optimization of the proposed network. The comparison between the proposed method and the baseline methods shows that the STLGD can automatically learn the spatial-temporal correlations among the targets from the training dataset, and achieve the fast and accurate conductivity image reconstructions which are much beyond the traditional methods.

Fast and Accurate Thoracic SPECT Image Reconstruction

In Single-Photon Emission Computed Tomography (SPECT), the reconstructed image has insufficient contrast, poor resolution and inaccurate volume of the tumor size due to physical degradation factors. Generally, nonstationary filtering of the projection or the slice is one of the strategies for correcting the resolution and therefore improving the quality of the reconstructed SPECT images. This paper presents a new 3D algorithm that enhances the quality of reconstructed thoracic SPECT images and reduces the noise level with the best degree of accuracy. The suggested algorithm is composed of three steps. The first one consists of denoising the acquired projections using the benefits of the complementary properties of both the Curvelet transform and the Wavelet transforms to provide the best noise reduction. The second step is a simultaneous reconstruction of the axial slices using the 3D Ordered Subset Expectation Maximization (OSEM) algorithm. The last step is post-processing of the reconstructed axial slices using one of the newest anisotropic diffusion models named Partial Differential Equation (PDE). The method is tested on two digital phantoms and clinical bone SPECT images. A comparative study with four algorithms reviewed on state of the art proves the significance of the proposed method. In simulated data, experimental results show that the plot profile of the proposed model keeps close to the original one compared to the other algorithms. Furthermore, it presents a notable gain in terms of contrast to noise ratio (CNR) and execution time. The proposed model shows better results in the computation of contrast metric with a value of 0.68 ± 7.2 and the highest signal to noise ratio (SNR) with a value of 78.56 ± 6.4 in real data. The experimental results prove that the proposed algorithm is more accurate and robust in reconstructing SPECT images than the other algorithms. It could be considered a valuable candidate to correct the resolution of bone in the SPECT images.

Thefhdpolarised imaging pipeline: A new approach to widefield interferometric polarimetry

AbstractWe describe a new polarised imaging pipeline implemented in thefhdsoftware package. The pipeline is based on the optimal mapmaking imaging approach and performs horizon-to-horizon image reconstruction in all polarisation modes. We discuss the formalism behind the pipeline’s polarised analysis, describing equivalent representations of the polarised beam response, or Jones matrix. We show that, for arrays where antennas have uniform polarisation alignments, defining a non-orthogonal instrumental polarisation basis enables accurate and efficient image reconstruction. Finally, we present a new calibration approach that leverages widefield effects to perform fully polarised calibration. This analysis pipeline underlies the analysis of Murchison Widefield Array data in Byrne et al. (2022, MNRAS, 510, 2011).

Fast image scanning microscopy with efficient image reconstruction

Image Scanning Microscopy is an advanced linear super-resolution fluorescence microscopy technique. Unlike traditional methods that introduce complex optical system designs, ISM is capble of taking the advantage of computational method to achieve super-resolution. As ISM involves large-scale localizations to all scanned-positions in most experiments, robust and accurate algorithms are required to avoid artifacts. Meanwhile, the existing ISM image reconstruction method is mainly based on post-processing technique due to high computing complexity. Therefore, large-scale images processing in ISM image reconstruction becomes too computationally intensive, which impedes the development of fast ISM. In this article, a robust ISM scanning-position localization technique and an efficient accurate image reconstruction technique for large scale ISM reconstruction are proposed. In particular, the localization of the ISM scanning position is realized by efficient image correlation and accurate robust Gaussian fitting. For simplicity and accuracy, the image reconstruction step introduces an efficient sub-pixel reassignment technique, by which image up-sampling is completely avoided. The simulation results show high performance in the proposed localization technique. The experimental result shows that the proposed image reconstruction technique can reach 20 times faster than existing method, which makes it very suitable for large-scale image processing in ISM image reconstruction, especially in a real-time ISM system.