Reconstruction Artifacts Research Articles

Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications. We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data. DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm. The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration. This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.

Transport of intensity phase retrieval in the presence of intensity variations and unknown boundary conditions

The so-called Transport of Intensity Equation (TIE) phase retrieval technique is widely applied in light, X-ray and electron optics to reconstruct, e.g., refractive indices, electric and magnetic fields in solids. Here, we present a largely improved TIE reconstruction algorithm, which properly considers intensity variations as well as unknown boundary conditions in a finite difference implementation of the Transport of Intensity partial differential equation. That largely removes reconstruction artifacts encountered in state-of-the-art Poisson solvers of the TIE, and hence significantly increases the applicability of the technique.

Cryo-X-Ray Phase Contrast Imaging Enables Combined 3D Structural Quantification and Nucleic Acid Analysis of Myocardial Biopsies.

Snap-frozen biopsies serve as a valuable clinical resource of archival material for disease research, as they enable a comprehensive array of downstream analyses to be performed, including extraction and sequencing of nucleic acids. Obtaining three-dimensional (3D) structural information before multi-omics is more challenging but can potentially allow for better characterization of tissues and targeting of clinically relevant cells. Conventional histological techniques are limited in this regard due to their destructive nature and the reconstruction artifacts produced by sectioning, dehydration, and chemical processing. These limitations are particularly notable in soft tissues such as the heart. In this study, the feasibility of using synchrotron-based cryo-X-ray phase contrast imaging (cryo-X-PCI) of snap-frozen myocardial biopsies is assessed and 3D structure tensor analysis of aggregated myocytes, followed by nucleic acid (DNA and RNA) extraction and analysis. It is shown that optimal sample preparation is the key driver for successful structural and nucleic acid preservation which is unaffected by the process of cryo-X-PCI. It is proposed that cryo-X-PCI has clinical value for 3D tissue analysis of cardiac and potentially non-cardiac soft tissue biopsies before nucleic acid investigation.

Zero-echo-time sequences in highly inhomogeneous fields.

Zero-echo-time (ZTE) sequences have proven a powerful tool for MRI of ultrashort tissues, but they fail to produce useful images in the presence of strong field inhomogeneities (14 000 ppm). Here we seek a method to correct reconstruction artifacts from non-Cartesian acquisitions in highly inhomogeneous , where the standard double-shot gradient-echo approach to field mapping fails. We present a technique based on magnetic field maps obtained from two geometric distortion-free point-wise (SPRITE) acquisitions. To this end, we employ three scanners with varying field homogeneities. These maps are used for model-based image reconstruction with iterative algebraic techniques (ART). For comparison, the same prior information is fed also to widely used Conjugate Phase (CP) algorithms. Distortions and artifacts coming from severe inhomogeneities, at the level of the encoding gradient, are largely reverted by our method, as opposed to CP reconstructions. This holds even close to the limit where intra-voxel bandwidths (determined by inhomogeneities, up to 1.2 kHz) are comparable to the encoding inter-voxel bandwidth (determined by the gradient fields, 625 Hz in this work). We have benchmarked the performance of a new method for ZTE imaging in highly inhomogeneous magnetic fields. For example, this can be exploited for dental imaging in affordable low-field MRI systems, and can be expanded for arbitrary pulse sequences and extreme magnet geometries, as in, for example, single-sided MRI.

Timing offset calibration for TOF PET using stationary line source scans at multiple positions

Background. Accurate timing offset calibration is crucial for time-of-flight (TOF) positron emission tomography (PET) to mitigate image artifacts and improve quantitative accuracy. However, existing methods are often time-consuming, complex, or costly.Objective. This paper presents a method for TOF PET timing offset calibration that eliminates the need for costly equipment, phantoms, short-half-life sources, and precise source positioning.Approach. We estimate channel timing offsets using stationary scans of a68Ge line source, typically used for routine quality control, at a minimum of three non-coplanar positions, with each position scanned for two minutes. The line source positions are accurately determined by applying a simple algorithm to their reconstructed images, allowing precise calculation of arrival time differences. Channel timing offsets are estimated by solving a least squares problem. This method is assessed through analyses of phantoms and patient images using a RAYSOLUTION DigitMI 930 scanner.Main results. The estimated timing offsets ranged from -500 ps to 500 ps across all channels. Calibration with a minimum of three scanned positions was sufficient to correct these offsets, achieving less than a 1% discrepancy across various metrics of the image quality (IQ) phantom compared to 12 positions. This calibration significantly reduced edge artifacts in TOF reconstruction of both phantoms and patients. Furthermore, the IQ phantom displayed a 14% increase in average contrast recovery, a 61% reduction in average background variability across all spheres, and a 90% reduction in average residual error. Consistent with the phantom results, patient data revealed enhancements in maximum standardized uptake values (SUVmax) from 14% to 55% for lesions measuring 6 mm to 14 mm. The calibration also improved lesion-to-background contrast and eliminated artifacts caused by the spillover effect of the kidneys and bladder.Significance. The proposed method is fast, user-friendly, and cost-effective, effectively improving lesion detection and diagnostic accuracy.

Fourier Raman light field microscopy based on surface-enhanced Raman scattering.

Raman scattering, as a vibrational spectrum that carries material information, has no photobleaching that enables long-duration imaging. Raman spectra have very narrow emission peaks, and multiplex Raman imaging can be achieved by using different Raman scattering peak signals. These advantages make Raman imaging widely used in biology, cytology, and medicine, which has a wider range of application scenarios. However, obtaining a three-dimensional (3D) Raman image requires scanning for tens of minutes to several hours at present. Therefore, a fast non-scanning 3D Raman imaging method is greatly needed. In this article, we propose a Fourier Raman light field microscopy based on surface-enhanced Raman scattering (sers-FRLFM). Using flower-like gap-enhanced Raman nanoparticles (F-GERNs) to enhance Raman scattering signals, a Fourier-configured light field microscope (LFM) is capable of recording complete four-dimensional Raman field information in a single frame, facilitating the 3D reconstruction of the Raman image without generating reconstruction artifacts at the native object plan. Moreover, F-GERNs can mark specific locations and have the potential to become a new tracing method to achieve specific imaging. This imaging method has great potential in the 3D real-time Raman imaging of cells, microorganisms, and tissues with the lateral resolution of 2.40 µm and an axial resolution of 4.02 µm.

Intelligent skin-removal photoacoustic computed tomography for human based on deep learning.

Photoacoustic computed tomography (PACT) has centimeter-level imaging ability and can be used to detect the human body. However, strong photoacoustic signals from skin cover deep tissue information, hindering the frontal display and analysis of photoacoustic images of deep regions of interest. Therefore, we propose a 2.5 D deep learning model based on feature pyramid structure and single-type skin annotation to extract the skin region, and design a mask generation algorithm to remove skin automatically. PACT imaging experiments on the human periphery blood vessel verified the correctness our proposed skin-removal method. Compared with previous studies, our method exhibits high robustness to the uneven illumination, irregular skin boundary, and reconstruction artifacts in the images, and the reconstruction errors of PACT images decreased by 20% ~ 90% with a 1.65 dB improvement in the signal-to-noise ratio at the same time. This study may provide a promising way for high-definition PACT imaging of deep tissues.

ERT image reconstruction using marker region segmentation method

Abstract Inspired by image region segmentation method, a marking region segmentation iterative method is proposed to reconstruct sparse binary images of electrical resistance tomography. The grayscale matrix of the iteration process is mapped to another linear space for segmentation processing, adding the watershed thresholding of marking regions. In the iteration process for estimating conductivity distribution, the target regions are separated to avoid excessive segmentation effects. By applying this method in conjunction with the Landweber iterative model to solve the inverse problem of resistivity tomography imaging, more accurate binary images can be obtained, and the method exhibits superior convergence properties in comparison to the Landweber algorithm. To verify the reconstruction effectiveness of LW-TDIS method, numerical simulations and static experiments are conducted for comparison with three other methods. The results demonstrate that the proposed method effectively reduces reconstruction artifacts, improves reconstruction quality, and achieves better reconstruction performance.

TVNeRF: Improving few-view neural volume rendering with total variation maximization

Neural Radiance Fields (NeRF) has profoundly impacted few-shot novel view synthesis and 3D reconstruction with its innovative rendering techniques and high-quality output. However, synthesizing novel views from sparse inputs remains challenging for NeRF. With limited view inputs, NeRF tends to overfit the available views, leading to reconstruction discrepancies and artifacts. To address this issue, we propose TVNeRF, which introduces two complementary regularization methods. By maximizing the total variation of unseen rays, we control the density distribution of ray sampled points to prevent excessive sampling in non-critical areas, thereby reducing artifacts and floaters. Concurrently, minimizing the accumulated opacity of unseen rays allows for the effective selection of important rays and sampled points without additional computational overhead, while also mitigating potential over-regularization effects. These combined strategies not only prevent overfitting but also achieve effects similar to the Least Absolute Shrinkage and Selection Operator (LASSO) regularization. Moreover, TVNeRF achieves these improvements without the need for pre-trained models or prior knowledge. By utilizing these straightforward yet effective regularization techniques, TVNeRF can deliver results comparable to state-of-the-art models while maintaining computational efficiency. This approach highlights that substantial improvements in NeRF performance can be realized through targeted regularization methods, effectively addressing the challenges posed by sparse view synthesis.

Surpassing light inhomogeneities in structured-illumination microscopy with FlexSIM.

Super-resolution structured-illumination microscopy (SIM) is a powerful technique that allows one to surpass the diffraction limit by up to a factor two. Yet, its practical use is hampered by its sensitivity to imaging conditions which makes it prone to reconstruction artefacts. In this work, we present FlexSIM, a flexible SIM reconstruction method capable to handle highly challenging data. Specifically, we demonstrate the ability of FlexSIM to deal with the distortion of patterns, the high level of noise encountered in live imaging, as well as out-of-focus fluorescence. Moreover, we show that FlexSIM achieves state-of-the-art performance over a variety of open SIMdatasets.

Reduction of metal artefacts from bilateral hip prostheses during lower extremity computed tomography angiography: an experimental phantom study

AbstractIntroductionImage quality reduction due to metallic artefacts is a significant challenge during vascular computed tomography (CT) imaging of the lower extremities in patients with hip prostheses. This study aims to analyse various reconstruction algorithms' ability to reduce metal artefacts due to two types of hip prostheses during lower extremity CT angiography examinations.MethodsA pelvis phantom was fabricated with the insertion of a tube filled with contrast media to simulate the femoral artery, and the phantom was then CT scanned with and without hip prostheses. Multimodal images were acquired using different kilovoltage peak (kVp) settings and reconstructed with different algorithms, such as filtered back projection (FBP), iterative reconstruction (iDose4), iterative model‐based reconstruction (IMR) and orthopaedic metal artefact reduction (O‐MAR). Image quality was assessed based on image noise, signal‐to‐noise ratio (SNR) and Hounsfield unit (HU) deviation.ResultsThe IMR approach significantly improved image quality compared to iDose4 and FBP. For the vascular region, O‐MAR improves SNR by 5 ± 1, 23 ± 5 and 42 ± 9 for FBP, iDose4 and IMR respectively, and improves HU precision towards the baseline values by 49% and 83% for FBP and IMR, respectively. The noise reduction was 71% and 89% for FBP and IMR, and 57% for iDose4. O‐MAR greatly enhances SNR corrections among the most severe artefacts, with 29 ± 1 and 43 ± 4 for FBP and IMR, compared to iDose4 by 37 ± 7.ConclusionIMR combined with O‐MAR could improve the CT angiography of the lower extremities of patients with a hip prosthesis.

Forest 3D Radar Reflectivity Reconstruction at X-Band Using a Lidar Derived Polarimetric Coherence Tomography Basis

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis.

Deep Learning Realizes Photoacoustic Imaging Artifact Removal

Photoacoustic imaging integrates the strengths of optics and ultrasound, offering high resolution, depth penetration, and multimodal imaging capabilities. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their “view” of the imaging target, which result in image reconstruction artifacts degrading image quality. To address this problem, YOLOv8-Pix2Pix is proposed as a hybrid artifact-removal algorithm, which is advantageous in comprehensively eliminating various types of artifacts and effectively restoring image details compared to existing algorithms. The proposed algorithm demonstrates superior performance in artifact removal and segmentation of photoacoustic images of brain tumors. For the purpose of further expanding its application fields and aligning with actual clinical needs, an experimental system for photoacoustic detection is designed in this paper to be verified. The experimental results show that the processed images are better than the pre-processed images in terms of reconstruction metrics PSNR and SSIM, and also the segmentation performance is significantly improved, which provides an effective solution for the further development of photoacoustic imaging technology.

Data-Proximal Complementary ℓ1-TV Reconstruction for Limited Data Computed Tomography

In a number of tomographic applications, data cannot be fully acquired, resulting in severely underdetermined image reconstruction. Conventional methods in such cases lead to reconstructions with significant artifacts. To overcome these artifacts, regularization methods are applied that incorporate additional information. An important example is TV reconstruction, which is known to be efficient in compensating for missing data and reducing reconstruction artifacts. On the other hand, tomographic data are also contaminated by noise, which poses an additional challenge. The use of a single regularizer must therefore account for both the missing data and the noise. A particular regularizer may not be ideal for both tasks. For example, the TV regularizer is a poor choice for noise reduction over multiple scales, in which case ℓ1 curvelet regularization methods are well suited. To address this issue, in this paper, we present a novel variational regularization framework that combines the advantages of different regularizers. The basic idea of our framework is to perform reconstruction in two stages. The first stage is mainly aimed at accurate reconstruction in the presence of noise, and the second stage is aimed at artifact reduction. Both reconstruction stages are connected by a data proximity condition. The proposed method is implemented and tested for limited-view CT using a combined curvelet–TV approach. We define and implement a curvelet transform adapted to the limited-view problem and illustrate the advantages of our approach in numerical experiments.

Swin Transformer and the Unet Architecture to Correct Motion Artifacts in Magnetic Resonance Image Reconstruction.

We present a deep learning-based method that corrects motion artifacts and thus accelerates data acquisition and reconstruction of magnetic resonance images. The novel model, the Motion Artifact Correction by Swin Network (MACS-Net), uses a Swin transformer layer as the fundamental block and the Unet architecture as the neural network backbone. We employ a hierarchical transformer with shifted windows to extract multiscale contextual features during encoding. A new dual upsampling technique is employed to enhance the spatial resolutions of feature maps in the Swin transformer-based decoder layer. A raw magnetic resonance imaging dataset is used for network training and testing; the data contain various motion artifacts with ground truth images of the same subjects. The results were compared to six state-of-the-art MRI image motion correction methods using two types of motions. When motions were brief (within 5 s), the method reduced the average normalized root mean square error (NRMSE) from 45.25% to 17.51%, increased the mean structural similarity index measure (SSIM) from 79.43% to 91.72%, and increased the peak signal-to-noise ratio (PSNR) from 18.24 to 26.57 dB. Similarly, when motions were extended from 5 to 10 s, our approach decreased the average NRMSE from 60.30% to 21.04%, improved the mean SSIM from 33.86% to 90.33%, and increased the PSNR from 15.64 to 24.99 dB. The anatomical structures of the corrected images and the motion-free brain data were similar.

Hierarchical mussel farm reconstruction from video with object tracking

ABSTRACT As the mussel farming industry expands, particularly in regions such as New Zealand, there is a growing need for advanced monitoring and management solutions to ensure sustainability and operational efficiency. The current reliance on manual infrequent observation of aquaculture structures limits farmers' ability to monitor them in real time. Addressing these challenges, large-scale 3D reconstruction provides a practical solution by facilitating the creation of replicable depictions of mussel farm scenes and floatation buoys derived from recorded video, supporting off-site evaluation and enabling precise decision making. This paper introduces a novel approach to enhance the visualisation and monitoring of mussel farm floatation through a hierarchical reconstruction process. In contrast to earlier studies, we focus on recovering not only the overall environment (background) but also the finer details of key elements such as buoys to create a comprehensive representation of mussel farm geometry and appearance. We propose to segment the scene into the background and granular object instances and reconstruct them separately in a multi-stage process. The initial 3D scene reconstruction is performed using the Structure-from-Motion (SfM) technique, leveraging video footage captured by a vessel-mounted camera. This coarse reconstruction serves as the foundation for subsequent fine-grained enhancements. To recover finer details, object tracking is applied and the trajectories obtained are then conjunct with geometry triangulation to determine the real-world positions of individual buoys. A multiple-scale denoising method, grounded in dominant direction correlation, is implemented to eliminate non-reliable tracking objects and reduce reconstruction artifacts, ensuring the accuracy of the final results. This hierarchical reconstruction approach contributes to the advancement of mussel farm management by offering a powerful technology for comprehensive visualisation, enabling farmers to make informed decisions based on a detailed understanding of the mussel farm's geometry and dynamics.

Complete Neuron Reconstruction Based on Branch Confidence.

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time.

Spectral Reconstruction Improvement in a Cycloidal Coded-Aperture Mass Spectrometer.

Spatial aperture coding is a technique used to improve throughput without sacrificing resolution both in optical spectroscopy and sector mass spectrometry (MS). Previous work demonstrated that aperture coding combined with a position-sensitive array detector in a miniature cycloidal mass spectrometer was successful in providing high-throughput, high-resolution measurements. However, due to poor alignment and field nonuniformities, reconstruction artifacts were present. Recently, significant progress was made in eliminating most of the reconstruction artifacts with improved field uniformity and alignment. However, artifacts as large as 1/3 of the main peak were still observed at low mass (<17 u). Such artifacts will reduce accuracy in identification and quantification of analytes, reducing the impact of the throughput advantage gained by using a coded aperture. The artifacts were hypothesized to be a result of a mass dependent in curvature of ions in the ion source. Ions with higher mass (m/z > 17 u) and a larger curvature did not pass through all slits in the coded aperture. Therefore, when reconstructing with a system response derived from the aperture image from a higher mass m/z = 32 u ion, reconstruction artifacts appeared for m/z < 17 u. In this work, two methods were implemented to significantly reduce the presence of artifacts in reconstructed data. First, we modified the reconstruction algorithm to incorporate a mass-dependent system response function across the mass range (10-110 u). This method reduced the size of the artifacts by 82%. Second, to validate the hypothesis that the mass-dependent system response function was a result of differences in curvature of ions in the ion source, we modified the design of the ion source by shifting the coded aperture slits relative to the center of the ionization volume. This method resulted in ions of all masses passing through all slits in the coded aperture, a constant system response function across the entire mass range. Artifacts were reduced by 94%.

Motion-induced phase-corrected homodyne reconstruction for partial Fourier single-shot diffusion-weighted echo planar imaging of the liver.

Partial Fourier encoding is popular in single-shot (ss) diffusion-weighted (DW) echo planar imaging (EPI) because it enables a shorter echo time (TE) and, hence, improves the signal-to-noise-ratio. Motion during diffusion encoding causes k-space shifting and dispersion, which compromises the quality of the homodyne reconstruction. This work provides a comprehensive understanding of the artifacts in homodyne reconstruction of partial Fourier ss-DW-EPI data in the presence of motion-induced phase and proposes the motion-induced phase-corrected homodyne (mpc-hdyne) reconstruction method to ameliorate these artifacts. Simulations with different types of motion-induced phase were performed to provide an understanding of the potential artifacts that occur in the homodyne reconstruction of partial Fourier ss-DW-EPI data. To correct for the artifacts, the mpc-hdyne reconstruction is proposed. The algorithm recenters k-space, updates the partial Fourier factor according to detected global k-space shifts, and removes low-resolution nonlinear phase before the conventional homodyne reconstruction. The mpc-hdyne reconstruction is tested on both simulation and in vivo data. Motion-induced phase can cause signal overestimation, worm artifacts, and signal loss in partial Fourier ss-DW-EPI data with the conventional homodyne reconstruction. Simulation and in vivo data showed that the proposed mpc-hdyne reconstruction ameliorated artifacts, yielding higher quality DW images compared with conventional homodyne reconstruction. Based on the understanding of the artifacts in homodyne reconstruction of partial Fourier ss-DW-EPI data, the mpc-hdyne reconstruction was proposed and showed superior performance compared with the conventional homodyne reconstruction on both simulation and in vivo data.

MR-based electrical property tomography using a physics-informed network at 3 and 7 T.

Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non-invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B1 measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high-resolution EPs reconstruction based on water content maps by using a physics-informed network (PIN-wEPT). The proposed method utilizes standard clinical protocols and conventional multi-channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of and by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN-wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.