Imaging Algorithm Research Articles

Novel method of image analysis that combines Radon and Fourier image transformations for the purpose of differentiating between malignant and benign colonic biopsies.

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision-making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty-nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co-occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. RESEARCH HIGHLIGHTS: We propose a novel decision-making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist.

Virtual Sound Image Reconstruction Method for Multi-objective Optimization of Folk Music Based on Evolutionary Algorithm

At present, the exchanges in various fields such as culture, economy, and politics are becoming more and more close in the world. From the perspective of the relationship between national music and cultural development, the development of national music has also received more and more attention, and it has become an inevitable trend in the development of today's era. The purpose of this paper was to perform virtual reconstruction of the sound image of folk music through multiple objective optimizations, and to model the virtual sound image of folk music as a multi-objective optimization problem. According to the research on sound image positioning, the relevant noise factors were removed, so as to achieve the playback of ethnic music that enhanced the surround effect and visual enjoyment. The evolutionary algorithm in this paper was mainly based on the multi-objective optimized sound image localization technology. For the music virtual sound image, an improved FCM algorithm and an evolutionary multi-objective optimization algorithm combining local and non-local information of the sound image were proposed, respectively. Through the analysis of the traditional sound image algorithm method, the accuracy of the sound image localization on the horizontal plane could be effectively improved. After the conversion, the audience could feel the better stereo image and surround feeling of the folk music. Through experimental analysis, it can be seen that the system can not only perform virtual conversion of audio-visual signals of different frequencies, but also provide data for different audio playback systems. The feature point registration error is low, and the reconstruction effect is good, especially the random delay processing in the range of 0∼20m, and the performance is better than the traditional method. Finally, virtual left and right surround sound image signals were obtained, which effectively improved the three-dimensional surround feeling of folk music.

Swarm intelligence for full Stokes dynamic imaging reconstruction of interferometric data

In very long baseline interferometry (VLBI), the combination of multiple antennas permits the synthesis of a virtual telescope with a larger diameter and consequently higher resolution than the individual antennas . However, due to the sparse nature of the array recovering an image from the observed data is a challenging ill-posed inverse problem. The VLBI community is interested in not only recovering an image in total intensity from interferometric data, but also in obtaining results in the polarimetric and the temporal domain. Only a few algorithms are able to work in all these domains simultaneously . In particular, the algorithms based on optimization that consider various penalty terms specific to static total intensity imaging, time-variability and polarimetry are restricted to grids in the domain of the objective function. In this work we present a novel algorithm, multiobjective particle swarm optimization (MO-PSO), that is able to recover the optimal weights without any space-gridding, and to obtain the marginal contribution of each of the playing terms. To this end, we utilized multiobjective optimization together with particle swarm metaheuristics. We let the swarm of weights converge to the best position. We evaluate our algorithm with synthetic data sets that are representative for the main science targets and instrumental configuration of the Event Horizon Telescope Collaboration (EHTC) and its planned successors. We successfully recover the polarimetric, static, and time-dynamic signature of the ground truth movie, even with relative sparsity, and a set of realistic data corruptions. We have built a novel, fast, hyperparameter space gridding-free algorithm that successfully recovers static and dynamic polarimetric reconstructions. Compared to regularized maximum likelihood (RML) methods, it avoids the need for parameter surveys, and it is not limited to the number of pixels, unlike recently proposed multiobjective imaging algorithms. Hence, this technique is a novel useful alternative tool to characterize full Stokes time-(in)dependent signatures in a VLBI data set robustly with a minimal set of user-based choices.

A high contrast and resolution reconstruction algorithm in quantitative photoacoustic tomography

A framework for reconstruction of optical diffusion and absorption coefficients in quantitative photoacoustic tomography is presented. This framework is based on a Tikhonov-type functional with a regularization term promoting sparsity of the absorption coefficient and a prior involving a Kubelka–Munk absorption–diffusion relation that allows to obtain superior reconstructions. The reconstruction problem is formulated as the minimization of this functional subject to the differential constraint given by a photon-propagation model. The solution of this problem is obtained by a fast and robust sequential quadratic hamiltonian algorithm based on the Pontryagin maximum principle. Results of several numerical experiments demonstrate that the proposed computational strategy is able to obtain reconstructions of the optical coefficients with high contrast and resolution for a wide variety of objects.

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.

Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the plane. Leveraging cell continuity in consecutive -stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

IONOLAB-Fusion: Fusion of Radio Occultation into Computerized Ionospheric Tomography

In this study, a 4-D, computerized ionospheric tomography algorithm, IONOLAB-Fusion, is developed to reconstruct electron density using both actual and virtual vertical and horizontal paths for all ionospheric states. The user-friendly algorithm only requires the coordinates of the region of interest and range with the desired spatio-temporal resolutions. The model ionosphere is formed using spherical voxels in a lexicographical order so that a 4-D ionosphere can be mapped to a 2-D matrix. The model matrix is formed automatically using a background ionospheric model with an optimized retrospective or near-real time manner. The singular value decomposition is applied to extract a subset of significant singular values and corresponding signal subspace basis vectors. The measurement vector is filled automatically with the optimized number of ground-based and space-based paths. The reconstruction is obtained in closed form in the least squares sense. When the performance of IONOLAB-Fusion across Europe was compared with ionosonde profiles, a 26.51% and 32.33% improvement was observed over the background ionospheric model for quiet and disturbed days, respectively. When compared with GIM-TEC, the agreement of IONOLAB-Fusion was 37.89% and 31.58% better than those achieved with the background model for quiet and disturbed days, respectively.

An efficient sub-aperture millimeter-wave imaging technique based on boundary-type MIMO array

Millimeter-wave (MMW) 3-D imaging based on multiple-input-multiple-output (MIMO) radar has been widely studied due to its unique advantages in human security screening. However, more research has been done on the use of scanning 1-D MIMO array and the fast imaging methods that match them. This limits the rate of data acquisition and imaging speed to some extent. It is necessary to study frame rate imaging system as well as efficient imaging methods. In this article, a boundary-type MIMO array is designed, based on which an efficient frequency-domain imaging algorithm with sub-aperture is proposed. The proposed algorithm solves the target reflectivity function by dividing the uniform array into two sub-apertures and solving the echo signals of each sub-aperture separately. The final target reflectivity function is obtained by superposing all the solutions. Compared with the advanced RMA, FSA and FωK algorithms, the proposed sub-aperture frequency-domain imaging algorithm (SFA) does not require a large amount of zero-padding to satisfy the requirements of fast Fourier transform (FFT) in all dimensions. It greatly improves the speed of imaging and saves the space required for computation. We derive the theoretical formulation of SFA applied to MIMO array and analyze its computational complexity and phase error. Simulation and experiment verify the efficiency of the proposed SFA.

Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks

Objectives. In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging to aid in contouring of the left ventricle, right ventricle, and Myocardium (Myo). Approach. We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure. We evaluated our proposed network using public data from 100 individuals. The network performance was quantitatively evaluated using 3D volume similarity between the ground truth contours and the predictions using Dice score coefficient, sensitivity, and precision as well as 2D surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). We benchmarked the performance against two other current leading edge networks known as Dynamic UNet and Swin-UNetr on the same public dataset. Results. The proposed network achieved the following volume similarity metrics when averaged over three cardiac segments: Dice = 0.952 ± 0.017, precision = 0.948 ± 0.016, sensitivity = 0.956 ± 0.022. The average surface similarities were HD = 1.521 ± 0.121 mm, MSD = 0.266 ± 0.075 mm, and RMSD = 0.668 ± 0.288 mm. The network shows statistically significant improvement in comparison to the Dynamic UNet and Swin-UNetr algorithms for most volumetric and surface metrics with p-value less than 0.05. Overall, the proposed Swin-MLP mixer network demonstrates better or comparable performance than competing methods. Significance. The proposed Swin-MLP mixer network demonstrates more accurate segmentation performance compared to current leading edge methods. This robust method demonstrates the potential to streamline clinical workflows for multiple applications.

A novel motion compensation algorithm for spaceborne inverse synthetic aperture radar imaging of air target under low signal‐to‐noise ratio condition

A novel motion compensation algorithm for spaceborne inverse synthetic aperture radar imaging of air target under low signal‐to‐noise ratio condition

A CHEAPLY NON-DESTRUCTIVE TECHNIQUE TO ESTIMATE HONEY QUALITY: THERMAL IMAGING AND MACHINE LEARNING

The aim of this study was to estimate honey quality based on proline and Brix content using a thermal imaging and machine learning algorithm. The proline, Brix and color properties of twenty honey samples were determined. Proline and Brix values were classified and estimated using the classification and regression tree (CART) algorithm. The mean proline and Brix content in honey samples was 678.83±192.16 mg/kg and 83.2±0.79%, respectively. CART analysis revealed that high proline honey samples had L values above 48.143 and b* values below 35.416. In contrast, honey samples with low Brix values were characterized by L and a* values below 55.860 and 53.660, respectively, and were identified as freshly harvested. The CART algorithm classified the proline and Brix values with an accuracy of 95% and 100%, respectively (p< 0.001). As a result, whitish, bluish, blackish and greenish honeys are of higher quality due to high proline and low Brix content. However, to accurately assess honey quality based on its color traits, comprehensive studies with more honey samples and origin, are required.

Fidelity-aware large-scale distributed network emulation

The design and development of new network protocols, architectures, and technologies requires an evaluation phase where the researcher must provide empirical evidence for the performance of their contributions, potentially in comparison to existing solutions. In this context, network emulation has proven to be an attractive approach as it offers more flexibility compared to traditional testing platforms, and more realism compared to simulation. Network emulators provide contained, customisable, and scalable testing environments both for researchers to evaluate their contributions and for the community to reproduce their results. However, two limitations to network emulation have been identified and well documented in the literature: its scalability limits and its accuracy issues. This paper11This journal paper is an extension of a shorter paper (Elbouanani et al., 2023 [1]) previously presented in the TASIR (Testbeds for Advanced Systems Implementation and Research) workshop of Comsnets 2023 conference, and published in its proceedings. documents our attempts to address these concerns. Our findings are distilled into Hifinet: a lightweight scalable and fidelity-aware distributed network emulator. We particularly show how Hifinet outperforms its state-of-the-art counterparts in terms of scalability and efficiency by working around the flaws of their design principles and the technological limitations of the tools they rely on. Hifinet is also fidelity-enhanced, in that it implements a well-theorised fidelity monitoring framework, which passively monitors emulated packet delays to evaluate realism of network emulation and accuracy of results. Another asset of Hifinet is its ability to infer underlying causes in case of erroneous emulation and guide the user through fixing them. This is achieved by using delay tomography algorithms and heuristics.

3D visual reconstruction of corrosion in underwater structure using alternating current field measurement technique

Corrosion is a common damage form in various underwater structures. However, the corrosions are usually covered by the coatings and attachments in the underwater environment. In this paper, the 3D visual reconstruction methods for evaluating the corrosion defect using the alternating current field measurement (ACFM) technique are presented. The FEM model is set up with corrosion and pitting defects to analyze the disturbed current field and the distorted magnetic field. A gradient imaging algorithm based on the Bz signal is proposed to reconstruct the surface morphology of the corrosion and the pitting defect. The inner contour of the corrosion defect is reconstructed using the Bx signal sensitive characteristics of probe lift-off. The calibration method is proposed to measure the depth of the pitting defect. The testing system is built, and the experiments are carried out to verify the 3D visual reconstruction methods. The maximum error of the surface area reconstruction is 8.13%. The maximum error of the corrosion inner reconstruction is 10%. The maximum error of the pitting depth is 3.25%.

Revolutionizing Precision Agriculture with Drone-Based Imaging and Fuzzy Intelligent Algorithms

This paper proposes a clear understanding of drone based agricultural image processing techniques using fuzzy intelligent algorithm. This procedure is used for drone-based agricultural monitoring through intelligent calculations by various images acquired and trained from the field. The objective is to upgrade the exactness and proficiency of crop yield and the accuracy in agriculture. The proposed methods use high quality images caught by drones to remove crop-related infections and evaluate their wellbeing status. The fuzzy based calculation is applied to characterize the harvest wellbeing status and produce a choice to guide and assist the farmers of various regions to concentrate the area of infection based on the images captured from drones. The projected approach is assessed utilizing the information and the consideration and appropriate recommendation can be made after careful evaluation of infected region in the field. Graphical User Interface (GUI) is developed using fuzzy based algorithm which can segment the infectious leaves and classify the type of diseases and check the healthiness of the leaves. Results show that the proposed approach accomplishes higher exactness and improved effectiveness in distinguishing crop wellbeing status in comparable with existing the procedures. This study shows the capability of integrating drone-based agriculture with fuzzy intelligent image processing tool which makes efficient for farmers to improve the quality of crops, attain greater yield and to develop the nation with drone-based agribusiness.

Solar multiobject multiframe blind deconvolution with a spatially variant convolution neural emulator

The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post facto image correction, essential for correcting these distortions, often rely on simplifying assumptions that limit their effectiveness, particularly in the presence of spatially variant atmospheric turbulence. Such cases are often solved by partitioning the field of view into small patches, deconvolving each patch independently, and merging all patches together. This approach is often inefficient and can produce artifacts. Recent advancements in computational techniques and the advent of deep learning offer new pathways to address these limitations. This paper introduces a novel framework leveraging a deep neural network to emulate spatially variant convolutions, offering a breakthrough in the efficiency and accuracy of astronomical image deconvolution. By training on a dataset of images convolved with spatially invariant point spread functions and validating its generalizability to spatially variant conditions, this approach presents a significant advancement over traditional methods. The convolution emulator is used as a forward model in a multiobject multiframe blind deconvolution algorithm for solar images. The emulator enables the deconvolution of solar observations across large fields of view without resorting to patch-wise mosaicking, thus avoiding the artifacts associated with such techniques. This method represents a significant computational advantage, reducing processing times by orders of magnitude.

A lightweight multiscale smoke segmentation algorithm based on improved DeepLabV3+

AbstractFires not only cause devastating consequences for human life and property, but also lead to soil erosion in forests. Therefore, it is necessary to design a novel algorithm that can quickly monitor smoke from fires. Most existing smoke segmentation methods do not consider the segmentation accuracy of algorithms under limited computational resources. To address this research gap, this paper proposes a lightweight smoke segmentation algorithm based on DeepLabV3+ that achieves fast inference speed and high accuracy for different sizes smoke. To reduce the number of parameters, the feature extraction network of the DeeplabV3+ algorithm is replaced by MobileNetV2, which enhances the extraction ability of the algorithm in segment smoke images. Then, the Convolutional Block Attention Module (CBAM) is added to the encoder part to enhance the perception of the algorithm for small smoke and effectively alleviates smoke mis‐segmentation. Furthermore, a newly designed loss function is used in the network. The experimental results show that the proposed method has improved by 1.27% in Smoke IoU and 1.21% in mPA compared with other methods. The weight size has been reduced to 10.76% of the original DeepLabV3+, and the inference time is only 33.71ms. Therefore, it is a more suitable early fire detection algorithm for resource‐constrained environments.

Maize leaf disease recognition based on improved MSRCR and OSCRNet

Accurate identification of maize pests and diseases plays a crucial role in the growth and yield of maize. The recognition method of maize leaf diseases based on deep learning is currently a research hotspot. In practical applications, it is difficult to ensure a high recognition accuracy due to the impact of environmental factors (such as light intensity, background noise) and insufficient training data. To address this challenge, this paper proposes a maize image recognition method based on image enhancement and OSCRNet. Firstly, a maize leaf image enhancement framework and algorithm based on improved MSRCR were designed to enhance the characteristics of the maize leaf in a complex environment and to solve the problems of high noise and blur of maize images. Subsequently, the OSCRNet maize leaf recognition network model was established, which uses octave convolution with characteristics to accelerate network training and reduce unnecessary redundant spatial information in maize leaf images. Additionally, a self-calibrated convolution with multi-scale features was employed to realize the interactions of different feature information in the maize leaf images, enhance feature extraction, and solve the problems of similarity of maize disease features and easy learning disorders. Concurrently, batch normalization was employed to prevent network overfitting and enhance the robustness of the model. The experimental results showed that the highest identification accuracy of the proposed method for rust, gray leaf disease, northern wilt disease, and healthy maize are 96.67%, 94.86%, 92.81%, and 95.63%, respectively. Our methods were beneficial in solving the problems of slow efficiency, low accuracy and insufficient training data, and also outperformed other comparison models. The proposed method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases.

Reconstructing phase space of injection beam based on the measurement of accumulated beam: A novel tomography algorithm and its applications

The measurement of phase space has always been an important topic in the field of accelerator physics, playing an indispensable role in understanding the beam dynamics. The phase space distribution of the injected beam is crucial for optimizing the multiturn accumulation injection process in synchrotrons. However, directly and accurately measuring the phase space distribution is a challenging task. In this study, we propose an innovative tomographic algorithm based on the measurement data of the accumulated beam profile obtained from the wall current monitor (WCM) in a synchrotron, to reconstruct the longitudinal phase space of the injected beam. Simulations were conducted for various initial distribution scenarios, and the results showed that this algorithm can achieve a difference of about 4% in the rms momentum spread between the initial and reconstructed phase space distribution of the injected beam. This algorithm has been applied to the China Spallation Neutron Source and successfully measured the momentum spread of the injected beam. Machine studies considering the phase error of the injected beam showed a high consistency between the reconstructed beam profiles and the measurement results from the WCM on Rapid Cycling Synchrotron. The research results demonstrate that this algorithm can be an effective approach for measuring the momentum distribution of the injected beam in a synchrotron. Furthermore, this method also has the potential to be extended to reconstruct the transverse phase space of the injected beam. Published by the American Physical Society 2024

Modified tuna swarm optimization algorithm for brain stroke imaging with electrical impedance tomography

Stroke is a common disease characterized by high disability rate and high mortality rate. Accurate detection and continuous monitoring are vital for the treatment of stroke. As a promising medical imaging technique, electrical impedance tomography (EIT) is able to provide an alternative for brain imaging. With this technique, conductivity distribution variation caused by pathological change can be visualized. However, image reconstruction is a severely ill-posed inverse problem. Particularly in brain imaging, irregular and multi-layered head structure along with low-conductivity skull further aggravate the challenge for accurate reconstruction. To solve this problem, a novel image-reconstruction method based on modified tuna swarm optimization is proposed for visualizing the conductivity distribution in brain EIT. To evaluate the performance of the proposed method, extensive simulations are carried out on a three-layer head model. The anti-noise performance of the proposed method is estimated by considering noise with different signal-to-noise ratios. In addition to simulation, phantom experiments are conducted to further verify the effectiveness of the proposed method. Both reconstructed images and quantitative evaluations demonstrate that the proposed approach performs well in the reconstruction of simulated intracerebral hemorrhage and secondary ischemia. This work would offer an alternative for accurate reconstruction in medical imaging based on EIT.

3D imaging of magnetic domains in Nd2Fe14B using scanning hard X-ray nanotomography.

Nanoscale structural and electronic heterogeneities are prevalent in condensed matter physics. Investigating these heterogeneities in 3D has become an important task for understanding material properties. To provide a tool to unravel the connection between nanoscale heterogeneity and macroscopic emergent properties in magnetic materials, scanning transmission X-ray microscopy (STXM) is combined with X-ray magnetic circular dichroism. A vector tomography algorithm has been developed to reconstruct the full 3D magnetic vector field without any prior noise assumptions or knowledge about the sample. Two tomographic scans around the vertical axis are acquired on single-crystalline Nd2Fe14B pillars tilted at two different angles, with 2D STXM projections recorded using a focused 120 nm X-ray beam with left and right circular polarization. Image alignment and iterative registration have been implemented based on the 2D STXM projections for the two tilts. Dichroic projections obtained from difference images are used for the tomographic reconstruction to obtain the 3D magnetization distribution at the nanoscale.

EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints.

One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on and norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.