Back Projection Research Articles

Improving radiomics reproducibility using deep learning-based image conversion of CT reconstruction algorithms in hepatocellular carcinoma patients.

CT reconstruction algorithms affect radiomics reproducibility. In this study, we evaluate the effect of deep learning-based image conversion on CT reconstruction algorithms. This study included 78 hepatocellular carcinoma (HCC) patients who underwent four-phase liver CTs comprising non-contrast, late arterial (LAP), portal venous (PVP), and delayed phase (DP), reconstructed using both filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). PVP images were used to train a convolutional neural network (CNN) model to convert images from FBP to ADMIRE and vice versa. LAP, PVP, and DP images were used for validation and testing. Radiomic features were extracted for each patient with a semi-automatic segmentation tool. We used concordance correlation coefficients (CCCs) to evaluate the radiomics reproducibility for original FBP (oFBP) vs. original ADMIRE (oADMIRE), oFBP vs. converted FBP (cFBP), and oADMIRE vs. converted ADMIRE (cADMIRE). In the test group including 30 patients, the CCC and proportion of reproducible features (CCC ≥ 0.85) for oFBP vs. oADMIRE were 0.65 and 32.9% (524/1595) for LAP, 0.65 and 35.9% (573/1595) for PVP, and 0.69 and 43.8% (699/1595) for DP. For oFBP vs. cFBP, the values increased to 0.92 and 83.9% (1339/1595) for LAP, 0.89 and 71.0% (1133/1595) for PVP, and 0.90 and 79.7% (1271/1595) for DP. Similarly, for oADMIRE vs. cADMIRE, the values increased to 0.87 and 68.1% (1086/1595) for LAP, 0.91 and 82.1% (1309/1595) for PVP, and 0.89 and 76.2% (1216/1595) for DP. CNN-based image conversion between CT reconstruction algorithms improved the radiomics reproducibility of HCCs. This study demonstrates that using a CNN-based image conversion technique significantly improves the reproducibility of radiomic features in HCCs, highlighting its potential for enhancing radiomics research in HCC patients. Radiomics reproducibility of HCC was improved via CNN-based image conversion between two different CT reconstruction algorithms. This is the first clinical study to demonstrate improvements across a range of radiomic features in HCC patients. This study promotes the reproducibility and generalizability of different CT reconstruction algorithms in radiomics research.

Utility of follow-up ultra-high-resolution CT angiography with model-based iterative reconstruction after flow diverter treatment for cerebral aneurysms.

Follow-up examinations after flow diverter (FD) treatment for cerebral aneurysms typically involve magnetic resonance imaging (MRI) or digital subtraction angiography (DSA). However, MRI is prone to vascular defects due to metal artifacts from FD, and DSA carries a risk of ischemic complications. In the context of computed tomography angiography (CTA), this study compares the efficacy of ultra-high-resolution CT (UHRCT) and novel reconstruction techniques, such as model-based iterative reconstruction (MBIR), against conventional methods such as filtered back projection (FBP) and hybrid iterative reconstruction (IR), to determine if they are a viable alternative to DSA in clinical settings. A phantom study was conducted with the full-width half-maximum considered as the FD thickness. This study compared three reconstruction methods: MBIR, FBP, and hybrid IR. A clinical study was also conducted with 21 patients who underwent follow-up CTA after FD treatment. The FD's visibility was assessed using a 4-point scale in FBP, hybrid IR, and MBIR compared to cone-beam CT (CBCT) with angiographic systems. In the phantom study, FBP, hybrid IR, and MBIR visualized thinner FD thicknesses and improved detail rendering in that order. MBIR proved to be significantly superior in both the phantom and clinical study. UHRCT with MBIR is highly effective for follow-up evaluations after FD treatment and may become the first-choice modality in the future.

Neutron Bragg edge tomography characterisation of residual strain in a laser-welded Eurofer97 joint

Nuclear fusion is a potential source of electricity which can address the environmental problems posed by fossil fuels. Eurofer97 steel is a primary structural material for breeding blanket and divertor components in fusion Tokamaks. Assembling and maintaining the structural integrity of these in-vessel components requires remote joint techniques, such as laser welding, although it induces immersive residual stress. The interaction of the residual strain and the heterogeneous microstructure degrades the mechanical performance of fusion components. However, an inspection of bulk residual strain distribution is still challenging. This study presents the residual strain distribution in the bulk of the weldment using volumetric tomographic reconstruction. A neutron Bragg edge imaging technique is used to obtain 2D angular projections. The 3D volumetric strain map is reconstructed from 2D residual strain projections using the filtered back projection technique. It is found that the laser welding technique generates a uniform residual strain field in the through-thickness direction. The results also demonstrate the potential of reconstructing volumetric residual strain distribution in bulk materials using fewer projections to reduce data redundancy and acquisition time for the neutron Bragg edge imaging technique.

Improved sparse domain super-resolution reconstruction algorithm based on CMUT.

A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. To solve the problem, this paper proposes an improved medical image super-resolution (MeSR) method based on the sparse domain. First, we use the simultaneous algebraic reconstruction technique (SART) with high imaging accuracy to reconstruct the image into a training image in a sparse domain model. Secondly, we denoise and enhance the contrast of the SART images to obtain improved detail images before training the dictionary. Then, we use the original detail image as the guide image to further process the improved detail image. Therefore, a high-precision dictionary was obtained during the testing phase and applied to filtered back projection SR reconstruction. We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. The results showed significant improvements in peak signal-to-noise ratio, entropy, and average gradient compared to previously reported algorithms. The experimental results demonstrated that the proposed MeSR method can use noisy reconstructed images as input for the training phase of the SR algorithm and produce excellent visual effects.

Void fraction measurement of gas–liquid two-phase flow based on compressed sensing theory and capacitively coupled electrical resistance tomography system

Capacitively coupled electrical resistance tomography (CCERT) is an innovative technique for electrical resistance tomography based on capacitively coupled contactless conductivity detection (C4D). Despite its potential, there are only a few studies on image reconstruction algorithms for CCERT. To address this, a CCERT measurement system is developed, and the compressed sensing (CS) theory is applied to the inverse problem imaging of CCERT to improve the image reconstruction quality and speed. Firstly, a mathematical CCERT image reconstruction model is constructed under CS theory and three algorithms under CS theory are employed to solve the convex optimization problem of the reconstruction model. Then a thresholding operation is applied to obtain the post-processed image and compared it with the classical linear back projection and Landweber algorithms. The simulation results demonstrate that the Barzilai–Borwein gradient projection for sparse reconstruction (GPSR-BB) algorithm yields more satisfactory imaging results than the other four algorithms. Additionally, three sensitivity matrix optimization methods for GPSR-BB algorithm are compared and find that the new method of screening the rows of the sensitivity matrix to zero is more effective than the other two common optimization methods in terms of reconstructed image quality. Finally, the static experiments of void fraction measurement are conducted using the developed CCERT system. The results indicated that the absolute error of void fraction measurement by GPSR-BB algorithm was less than 6.59% in the range of void fraction from 0.90% to 66.29%.

First Experimental Demonstration of the Use of a Novel Planar Segmented HPGe Detector for Gamma Emission Tomography of Mockup Fuel Rods

Postirradiation examination of nuclear fuel is routinely performed to characterize the important properties of current and future fuel. Gamma emission tomography is a proven noninvasive technique for this purpose. Among various measurement elements of the technique, a gamma-ray detector is an important element whose spectroscopic abilities and detection efficiency affect the overall results. Finding a combination of high detection efficiency and excellent energy resolution in a single detector is often a challenge. We have designed a novel planar segmented high-purity germanium detector that offers simultaneous measurement in six lines of sight with excellent energy resolution. The simultaneous detection ability enables faster data acquisition in a tomographic measurement, which may facilitate achieving higher spatial resolution. In this work, we have demonstrated the first use of the detector by performing a full tomographic measurement of mockup fuel rods. Two methods of detector data analysis were used to make spectra, and the images (tomograms) were reconstructed using the filtered back projection algorithm. The reconstructed images validate the successful use of the detector for tomographic measurement. The use of the detector for real fuel measurement is being planned and will be performed in the near future.

A transformer-based dual-domain network for reconstructing FOV extended cone-beam CT images from truncated sinograms in radiation therapy

Background and objectiveCone-beam computed tomography (CBCT) is widely used in clinical radiotherapy, but its small field of view (sFOV) limits its application potential. In this study, a transformer-based dual-domain network (dual_swin), which combined image domain restoration and sinogram domain restoration, was proposed for the reconstruction of complete CBCT images with extended FOV from truncated sinograms. MethodsThe planning CT images with large FOV (LFOV) of 330 patients who received radiation therapy were collected. The synthetic CBCT (sCBCT) images with LFOV were generated from CT images by the trained cycleGAN network, and CBCT images with sFOV were obtained through forward projection, projection truncation, and filtered back projection (FBP), comprising the training and test data. The proposed dual_swin includes sinogram domain restoration, image domain restoration, and FBP layer, and the swin transformer blocks were used as the basic feature extraction module in the network to improve the global feature extraction ability. The proposed dual_swin was compared with the image domain method, the sinogram domain method, the U-Net based dual domain network (dual_Unet), and the traditional iterative reconstruction method based on prior image and conjugate gradient least-squares (CGLS) in the test of sCBCT images and clinical CBCT images. The HU accuracy and body contour accuracy of the predicted images by each method were evaluated. ResultsThe images generated using the CGLS method were fuzzy and obtained the lowest structural similarity (SSIM) among all methods in the test of sCBCT and clinical CBCT images. The predicted images by the image domain methods are quite different from the ground truth and have low accuracy on HU value and body contour. In comparison with image domain methods, sinogram domain methods improved the accuracy of HU value and body contour but introduced secondary artifacts and distorted bone tissue. The proposed dual_swin achieved the highest HU and contour accuracy with mean absolute error (MAE) of 23.0 HU, SSIM of 95.7%, dice similarity coefficient (DSC) of 99.6%, and Hausdorff distance (HD) of 4.1 mm in the test of sCBCT images. In the test of clinical patients, images that were predicted by dual_swin yielded MAE, SSIM, DSC, and HD of 38.2 HU, 91.7%, 99.0%, and 5.4 mm, respectively. The predicted images by the proposed dual_swin has significantly higher accuracy than the other methods (P < 0.05). ConclusionsThe proposed dual_swin can accurately reconstruct FOV extended CBCT images from the truncated sinogram to improve the application potential of CBCT images in radiotherapy.

A stopping criterion for iterative proton CT image reconstruction based on correlated noise properties.

Whereas filtered back projection algorithms for voxel-based CT image reconstruction have noise properties defined by the filter, iterative algorithms must stop at some point in their convergence and do not necessarily produce consistent noise properties for images with different degrees of heterogeneity. A least-squares iterative algorithm for proton CT (pCT) image reconstruction converges toward a unique solution for relative stopping power (RSP) that optimally fits the protons. We present a stopping criterion that delivers solutions with the property that correlations of RSP noise between voxels are relatively low. This provides a method to produce pCT images with consistent noise properties useful for proton therapy treatment planning, which relies on summing RSP along lines of voxels. Consistent noise properties will also be useful for future studies of image quality using metrics such as contrast to noise ratio, and to compare RSP noise and dose of pCT with other modalities such as dual-energyCT. With simulated and real images with varying heterogeneity from a prototype clinical proton imaging system, we calculate average RSP correlations between voxel pairs in uniform regions-of-interest versus distance between voxels. We define a parameter r, the remaining distance to the unique solution relative to estimated RSP noise, and our stopping criterion is based on r falling below a chosenvalue. We find large correlations between voxels for larger values of r, and anticorrelations for smaller values. For r in the range of 0.5-1, voxels are relatively uncorrelated, and compared to smaller values of r have lower noise with only slight loss of spatialresolution. Iterative algorithms not using a specific metric or rationale for stopping iterations may produce images with an unknown and arbitrary level of convergence or smoothing. We resolve this issue by stopping iterations of a least-squares iterative algorithm when r reaches the range of 0.5-1. This defines a pCT image reconstruction method with consistent statistical properties optimal for clinical use, including for treatment planning with pCTimages.

Using convergent half width half Maximum(c-HWHM) tilting method to determine the virtual source position of scanning-pass scattering beam in carbon ion therapy

IntroductionWe developed a half-width half-maximum tilting angle convergent method to replace the conventional measurement of the virtual source position for scanning-pass scattering beam in carbon ion therapy. Materials and methodsA homemade large-format CMOS sensor and Gaf Chromic EBT3 films were used for the virtual source position measurement. The Gaf films were embedded in a self-designed rectangular plastic frame to tighten the films and set up on a treatment couch for irradiation in the air with the film perpendicular to the carbon ion beam at the nominal source-axis-distance (SAD) as well as upstream and downstream from the SAD. The radiation rectangular field size in horizontal and vertical directions on film or detector delivered by 5 carbon ion energies were analyzed separately in this study. The virtual source position was determined with film or detector at a distance upstream or downstream from the various source-film-distance by the half width half maximum tilting angle convergent method to compare the results conducted with the same measurement condition by the back projecting the FWHM to zero methods. ResultsThe determination of virtual source position by using half width half maximum tilting angle convergent method agrees with the method by back projecting the FWHM to zero, and for higher carbon ion energy has an obvious longer virtual source position since the more carbon ion beam energy, hence, the less side scatter results in the less divergent at the downstream, then the virtual source positions is distal for high energies than low energies when back projects the field sizes to zero. ConclusionWe have developed a technique capable of dealing with the virtual source position with a geometric convergent method to avoid any mistakes in the passive scatter carbon ion beam. The method for investigating the virtual source position in the carbon ion beam in this study can also be used for electrons and proton beams.

3D Radio Imaging Under Low-Rank Constraint

Radio frequency (RF) imaging has been of interest for years due to its ability to create images of targets without being affected by weather and light conditions. The existing heuristic solutions such as back projection (BP), suffer from the sidelobe interference due to limited antenna aperture. In this paper, we propose a super-resolution 3-D imaging algorithm to enhance imaging performance. Mathematically, the reconstruction problem is an inverse problem that can be formulated as an optimization problem. The low-rank property of the object is exploited to regularize the imaging problem by nuclear norm minimization. We also develop a coarse-to-fine scan structure to reduce the computational complexity of the proposed algorithm. We evaluate the proposed algorithm with both simulations and measurements. With a frequency band range from 2.7-4.1 GHz and a 16×6 multiple-input-multiple-output (MIMO) antenna array, simulation results show high imaging quality with a median boundary keypoint precision of 2 cm, and experimental results validate the feasibility of the proposed algorithm in a real-world environment.

Safe Distance Monitoring of Live Equipment Based Upon Instance Segmentation and Pseudo-LiDAR

Electrocution accidents caused by operation and maintenance personnel and high-voltage live equipment frequently occur in substations. Although many cameras have been installed for the surveillance of critical energized equipment, they cannot complete the monitoring of safe distance. In addition, the current research based on vision or LiDAR suffers from computationally intensive, lack of real-time or expensive equipment. In order to meet the low cost, easy maintenance and real-time requirements of substation safe distance monitoring, a monocular vision method based on 2D-3D fusion is proposed in this study. Specifically, instance segmentation, depth estimation, depth reconstruction, and back projection transformation are used to predict the 3D distance of objects in 2D images. The work is mainly implemented in two aspects: a) To obtain the high-quality masks of Power Transformers and Person, SOLOv2 is optimized on three aspects: feature extraction, feature fusion and ability to cope with object geometric transformation. b) Pseudo-LiDAR output from RGB images via depth estimation and back projection techniques. Subsequently, combined with the point cloud of the object and the actual-to-virtual conversion ratio, the actual distance between Power Transformer and Person can be calculated. Experimental results show that the maximum error of ranging is within 9%, with an average error rate of 5.34% on the Power Transformer-Person dataset. It can be seen that the proposed method has achieved good ranging effects, and can realize the automatic measurement of the safe distance of Power Transformer.

Improvement of diagnostic performance of hyperacute ischemic stroke in head CT using an image-based noise reduction technique with non-black-boxed process

PurposeThis study aims to investigate whether an image-based noise reduction (INR) technique with a conventional rule-based algorithm involving no black-boxed processes can outperform an existing hybrid-type iterative reconstruction (HIR) technique, when applied to brain CT images for diagnosis of early CT signs, which generally exhibit low-contrast lesions that are difficult to detect. MethodsThe subjects comprised 27 patients having infarctions within 4.5 h of onset and 27 patients with no change in brain parenchyma. Images with thicknesses of 5 mm and 0.625 mm were reconstructed by HIR. Images with a thickness of 0.625 mm reconstructed by filter back projection (FBP) were processed by INR. The contrast-to-noise ratios (CNRs) were calculated between gray and white matters; lentiform nucleus and internal capsule; infarcted and non-infarcted areas. Two radiologists subjectively evaluated the presence of hyperdense artery signs (HASs) and infarctions and visually scored three properties regarding image quality (0.625-mm HIR images were excluded because of their notably worse noise appearances). ResultsThe CNRs of INR were significantly better than those of HIR with P < 0.001 for all the indicators. INR yielded significantly higher areas under the curve for both infarction and HAS detections than HIR (P < 0.001). Also, INR significantly improved the visual scores of all the three indicators. ConclusionThe INR incorporating a simple and reproducible algorithm was more effective than HIR in detecting early CT signs and can be potentially applied to CT images from a large variety of CT systems.

An Investigation of Microwave Tomography Technique to Image Brain Tumour Through Cross-Section Imaging with Different Number of Electrode

Brain tumours resulted from the irregular growth and cell division within the skull, indicating a highrisk for malignancies to develop and can lead to brain injury or even death. The brain tumour can affectnervoussystem’s function based on the tumour’s growth rate and location. Early detection of brain tumour is essential to improve patients’ survival rates through appropriate medical care. As the current clinical imaging has a few impediments e.g. radiation-based and expensive, tomography technique is seen possible to provide safe and inexpensive technology. The aim of this research is to investigate the feasibility of brain tumour detection using microwave tomography technique with different numbers of electrodes. The 2D finite element modelling approach is applied, and the images are reconstructed using a linear back projection (LBP) algorithm in MATLAB. A different number of rectangular sensing electrodes are arranged around the head phantom in an elliptical array, working in pairs as transmitters and receivers. The simulation shows that the system is able to detect the permittivity difference, thus detecting the existence of the tumour in the head phantom.Theimage reconstruction presented promising tumour images with an 8-antenna microwave tomography system at all locations, i.e. left, right, top, centre, and bottom, in comparison to 4-antenna and 12-antenna systems.

Effect of Low Tube Voltage (100 kV) Combined with ASIR-V on the Visualization and Image Quality of the Adamkiewicz Artery: A Comparison with 120 kV Protocol.

To evaluate the effect of low tube voltage (100 kV) combined with adaptive statistical iterative reconstruction-V (ASIR-V) on the visualization and image quality of the Adamkiewicz artery (AKA). One hundred patients were prospectively enrolled and randomly assigned into two groups (both n = 50). Group A (100 kV) was reconstructed with filtered back projection (FBP) and ASIR-V from 10% to 100% with 10% intervals. Group B (120 kV) was only reconstructed with FBP. The objective image quality was evaluated by using CT values of the aorta (CTAorta), background noise, signal-to-noise ratio of the descending aorta (SNRAorta), and contrast-to-noise ratio of the spinal cord (CNRSpinal cord). The subjective image quality and visualization scores of the AKA were assessed on a 5-point scale. CTAorta was significantly higher in Group A than in Group B (p < 0.001). When ASIR-V weights were ≥60%, significant differences were found in the background noise, SNRAorta, and CNRSpinal cord between the two groups (all p < 0.05). In Group A, compared with FBP, the subjective score gradually increased as ASIR-V increased to 80%, which decreased when ASIR-V exceeded 80%. The visualization scores of the AKA (≥60%) and the ability to detect vessel continuity (≥80%) gradually increased as the ASIR-V weights increased (p < 0.05). The effective radiation dose was reduced by about 40.36% in Group A compared to Group B. compared with conventional scanning protocol, using a combination of low tube voltage (100 kV) and 80% ASIR-V protocol could not only increase the visualization of the AKA, but also improve image quality and reduce the radiation doses.

Visual impression and texture analysis of advanced modeled iterative reconstruction (ADMIRE): improved assessment of image quality in CT for better estimation of dose reduction potential

To evaluate the image quality (IQ) of advanced modeled iterative reconstruction (ADMIRE; Siemens Healthcare, Forchheim, Germany) applying image texture and image visual impression as a supplement to physical parameters such as noise level and spatial resolution. An ACR-phantom with four modules was examined at different radiation dose levels. To characterise the image texture, two Haralick texture parameters, contrast and entropy, were assessed at different dose levels and reconstruction algorithms. The visual impression of images and the low-contrast detectability were evaluated by the structural similarity index (SSIM). The spatial resolution was determined by the modulation transfer functions and the line spread function. The Haralick texture parameters, contrast and entropy, decreased with increasing ADMIRE levels. ADMIRE III, IV and V offered a comparable contrast and entropy to those calculated by filtered back projection (FBP) with a radiation dose reduction up to 50%. SSIM (low-contrast detectability) improved with increasing ADMIRE levels. SSIM calculated by ADMIRE IV and V revealed comparable IQ to FBP with a decreased CTDIvol up to 50%. Spatial resolution was retained up to 90% dose reduction. Compared to FBP at the same dose level, the image noise decreased up to 61% with higher ADMIRE levels (σ FBP = 17.3 HU and σ ADMIRE V = 10.6 HU at 6.65 mGy). Taking texture analysis and visual perception into account, a more realistic assessment of the dose reduction potential of ADMIRE can be achieved than quality metrics based alone on physical measurements.

Restoring the imaging quality of circular transducer array-based PACT using synthetic aperture focusing technique integrated with 2nd-derivative-based back projection scheme

Circular-array-based photoacoustic computed tomography (CA-PACT) is a promising imaging tool owing to its broad acoustic detection coverage and fidelity. However, CA-PACT suffers from poor image quality outside the focal zone along both elevational and lateral dimensions. To address this challenge, we proposed a novel reconstruction strategy by integrating the synthetic aperture focusing technique (SAFT) with the 2nd derivative-based back projection (2nd D-BP) algorithm to restore the image quality outside the focal zone along both the elevational and lateral axes. The proposed solution is a two-phase reconstruction scheme. In the first phase, with the assistance of an acoustic lens, we designed a circular array-based SAFT algorithm to restore the resolution and SNR along the elevational axis. The acoustic lens pushes the boundary of the upper limit of the SAFT scheme to achieve enhanced elevational resolution. In the second phase, we proposed a 2nd D-BP scheme to improve the lateral resolution and suppress noises in 3D imaging results. The 2nd D-BP strategy enhances the image quality along the lateral dimension by up-converting the high spatial frequencies of the object’s absorption pattern. We validated the effectiveness of the proposed strategy using both phantoms and in vivo human experiments. The experimental results demonstrated superior image quality (7-fold enhancement in elevational resolution, 3-fold enhancement in lateral resolution, and an 11-dB increase in SNR). This strategy provides a new paradigm in the PACT system as it significantly enhances the spatial resolution and imaging contrast in both the elevational and lateral dimensions while maintaining a large focal zone.

Deep Fusion Network Based Sparse View CT Reconstructions for Clinical Diagnostic Scanners.

Sparse view CT scan has the advantage of reducing radiation exposure and scanning time in clinical diagnosis. However, the limited number of x-ray projections can make the reconstruction problem ill posed and result in image artifacts. To tackle the problem, we propose a novel model-based deep fusion network(DFN) satisfying the clinical set-up. It extracts fused features encoded from both the sinogram and the preliminary reconstructed image generated by filtered back projection (FBP) to improve the quality of reconstruction. The preliminary reconstructed image endows fused features with prior knowledge that facilitate the convergence of neural network to high-quality reconstruction images. We design a custom loss for training that enforces the network to learn both the pixel value and the integrity of the tissue structure. A synthetic sparse view breast CT dataset from American Association of Physicists in Medicine(AAPM) is used for training, validation and testing. The qualitative and quantitative evaluations show that the DFN reconstruction algorithm significantly improves in balancing between the image quality and reconstruction speed, hence enables fast and high quality CT reconstruction despite the sparse view limitations.

Gaussian-weighted crosscorrelation imaging condition for microseismic source localization

Time-reversal imaging is a powerful method for the localization of microseismic events. Conventional time-reversal imaging methods such as autocorrelation imaging or grouped crosscorrelation imaging may suffer from imaging artifacts, e.g., caused by less refined velocity models, noise-contaminated data, and data acquired from sparse receiver networks. These artifacts typically reduce imaging quality and may cause subsequent misinterpretations leading to false positives. To address these issues, we develop a new imaging condition that comprises three steps for each time step. First, we divide the back-propagated wavefield into parts according to their maximum absolute amplitudes. Second, the amplitudes of the back-propagated wavefield are weighted by a Gaussian function with the spatial extent of the prevailing wavelength of the event, centered at the absolute maximum of that part of the wavefield. Finally, we zero-lag crosscorrelate these weighted wavefields at each space point to obtain an image for this time step. The final image is gained by summing the images for each time step. This process collects all energy concentrations along the back projection process, and the energy on the wavefront overlaps and collapses at the hypocenter leading to high-resolution images displaying little to no imaging artifacts. Numerical examples using the Marmousi-II and the 3D SEG overthrust models and a 3D field data example indicate the performance of our method. High-resolution low-noise source images allow unique identification of sources even for source clusters, noisy data, and sparse acquisitions. The source localization errors are smaller than the dominant wavelength of the signal, where a smooth model with a mean velocity error of approximately 5% was considered in the synthetic examples, and a homogeneous model was used in the field data example.

A Positive Data Extraction Method for Electrical Impedance Tomography (EIT) Based on the Novel MSA-Net

The nonlinear and ill-posed Electrical Impedance Tomography (EIT) inverse problem leads to reconstructed images with distortion and low resolution. A positive data extraction method based on the deep learning is proposed in this study. This method realizes the accurate classification of the positive data and the general data in voltage measurements through a deep learning network called Multi Scale Attention Network (MSA-Net). The positive data is used for image reconstruction to improve the imaging quality. Multi scale feature extraction, residual learning and attention mechanism are introduced in the MSA-Net to improve classification accuracy. The Linear Back Projection (LBP) algorithm and the Tikhonov Regularization (TR) algorithm are used to verify the effectiveness of the proposed method. Both simulation and experiment are conducted around imaging of bubble flow. The simulation results indicate that there are fewer artifacts in the reconstructed images using the positive data of MSA-Net, and the shape and position of the bubbles can be better described. Moreover, the imaging quality of experimental samples can also be effectively improved. The existing test results preliminarily confirm that the distribution of the positive data and the general data in voltage measurements is related to the position of bubbles in the measurement domain.

A refined method for investigating the morphological and optical properties of a biodegradable monocryl suture

An interferometric computed tomography (ICT) technique with a refined method is proposed and analysed for investigating the morphology of the Monocryl surgical sutures during the degradation process. This method includes four integrated stages. In the first stage, the two-beam Pluta polarizing interference microscope is equipped with the rotational device for repeatedly recording the 2D interference patterns of the degraded sample at various angular rotations. The second stage in which an artificial intelligence model is designed to detect and remove the noise classes in the interference patterns. In the third stage, the computational Phase-Shifting interferometric approach (CPSI) is employed for demodulating the phase maps of the tested sample. Finally, autocorrelation and the filtered back projection (FBP) algorithms are utilized to explore the 3D phase morphology of the tested sample. The presented method is useful for determining the refractive indices and the birefringence during the whole degradation duration. Collectively, the results of the proposed method reveal that ICT technology has the potential to investigate the degradation performances of this kind of biodegradable polymer effectively.