Crosstalk Artifacts Research Articles

Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation.

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echotime and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.

A novel and efficient engine for P-/S-wave-mode vector decomposition for vertical transverse isotropic elastic reverse time migration

Wave-mode decomposition plays a very important role in elastic reverse time migration (ERTM). Improved imaging quality can be achieved due to reduced wave-mode crosstalk artifacts. The current state-of-the-art methods for anisotropic wavefield separation are based on either splitting model strategy, low-rank approximation, or lower-upper (LU) factorization. Most of these involve expensive matrix computation and Fourier transforms with strong model assumptions. Based on the anisotropic-Helmholtz (ani-Helmholtz) decomposition operator and decoupled formulations, we develop a novel and efficient P-/S-wave-mode vector decomposition method in vertical transverse isotropic (VTI) media with application in ERTM. We first review the basics of classical Helmholtz decomposition and isotropic decoupled formulations. In addition, the ani-Helmholtz decomposition operator is built from the P- and S-wave polarizations of the Christoffel equation in VTI media. We then derive novel decoupled formulations of anisotropic P-/S-waves based on the obtained ani-Helmholtz operator. Moreover, we use the first-order Taylor expansion to approximate the normalization term from the derived decoupled formulations and obtain an efficient ani-Helmholtz decomposition approach, which produces vector P- and S-wavefields with correct units, phases, and amplitudes. Compared with the previous studies, our approach mitigates model assumptions, avoids intricate calculations, such as LU factorization or low-rank approximation, and only needs three fast Fourier transforms at each time step. In addition, the graphic processing unit technique is used to dramatically accelerate various functions of ERTM, such as wavefields extrapolation, decomposition, and imaging. Three synthetic examples demonstrate the effectiveness and feasibility of our proposed approach.

Review on data analysis methods for mesoscale neural imaging in vivo.

.Significance: Mesoscale neural imaging in vivo has gained extreme popularity in neuroscience for its capacity of recording large-scale neurons in action. Optical imaging with single-cell resolution and millimeter-level field of view in vivo has been providing an accumulated database of neuron-behavior correspondence. Meanwhile, optical detection of neuron signals is easily contaminated by noises, background, crosstalk, and motion artifacts, while neural-level signal processing and network-level coordinate are extremely complicated, leading to laborious and challenging signal processing demands. The existing data analysis procedure remains unstandardized, which could be daunting to neophytes or neuroscientists without computational background.Aim: We hope to provide a general data analysis pipeline of mesoscale neural imaging shared between imaging modalities and systems.Approach: We divide the pipeline into two main stages. The first stage focuses on extracting high-fidelity neural responses at single-cell level from raw images, including motion registration, image denoising, neuron segmentation, and signal extraction. The second stage focuses on data mining, including neural functional mapping, clustering, and brain-wide network deduction.Results: Here, we introduce the general pipeline of processing the mesoscale neural images. We explain the principles of these procedures and compare different approaches and their application scopes with detailed discussions about the shortcomings and remaining challenges.Conclusions: There are great challenges and opportunities brought by the large-scale mesoscale data, such as the balance between fidelity and efficiency, increasing computational load, and neural network interpretability. We believe that global circuits on single-neuron level will be more extensively explored in the future.

Double photon coincidence crosstalk reduction method for multi-nuclide Compton imaging

Compton imaging based on Compton scattering kinematics has the potential to visualize multi-nuclides by discriminating the total energy of Compton scattering and photoelectric absorption events. This feature enables us to perform multi-tracer imaging that reflects different functional information in nuclear medicine, resulting in a definitive diagnosis and being useful for biological and medical research. One of the challenges with multi-nuclide imaging is the crosstalk artifacts caused by scattered photons of higher energy gamma-rays. In this study, we investigated the potential benefits of the double photon coincidence detection as a drastic crosstalk reduction method. Coincidence detection of successive gamma-rays can differentiate nuclides and reduce the background caused by other nuclides' gamma-rays because some nuclides emit two or more gamma-rays in rapid succession. In this study, we focused on the coincidence detection of a Compton event and a photoelectric absorption event, and we showed simultaneous double photon emitter imaging of 111In and 177Lu with a ring-type Compton imaging system. The artifacts caused by other nuclides' gamma-rays were reduced by extracting Compton events coincident with photoelectric absorption events. The coincidence Compton images demonstrated a signal-to-background ratio improvement of 1.1–1.7 times over the one of no-coincidence Compton images, despite a drop in intrinsic detection efficiency of the order of 10-2. This strategy of directly reducing crosstalk will be useful in other combinations imaging such as of 111In (or 177Lu) and a positron emission tomography nuclide.

Reverse time migration of phase-encoded all-order multiples

Multiples follow long paths and carry more information on the subsurface than primary reflections, making them particularly useful for imaging. However, seismic migration using multiples can generate crosstalk artifacts in the resulting images because multiples of different orders interfere with each other and crosstalk artifacts greatly degrade the quality of an image. We form a supergather by applying phase-encoding functions to image multiples and stacking several encoded controlled-order multiples. The multiples are separated into different orders using multiple decomposition strategies. The method is referred to as the phase-encoded migration of all-order multiples (PEM). The new migration can be performed by applying only two finite-difference solutions to the wave equation. The solutions include backward extrapolating the blended virtual receiver data and forward propagating the summed virtual source data. Our approach can significantly attenuate crosstalk artifacts and also significantly reduce computational costs. Numerical examples demonstrate that PEM can remove relatively strong crosstalk artifacts generated by multiples and is a promising approach for imaging subsurface targets.

Deep Learning for Enhancing Multisource Reverse Time Migration

Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built over 500 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±10°, ±20°, and ±30° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.

Elastic Full-Waveform Inversion Using Both the Multiparametric Approximate Hessian and the Discrete Cosine Transform

We propose seismic elastic full-waveform inversion (EFWI) using both the multiparametric approximate Hessian and the discrete cosine transform (DCT). EFWI is a promising technology for obtaining physical parameters. EFWI using the multiparametric approximate Hessian can suppress crosstalk artifacts for each physical parameter, but the computational burden increases as the number of physical parameters considered increases. In this study, to reduce the computational burden, DCT was used to compress the model parameters and accelerate EFWI in the truncated DCT domain. EFWI using DCT is possible to increase the calculation efficiency by reducing the total number of unknown variables. We implemented EFWI using monoparametric Hessian and multiparametric Hessian, which reduced the computational burden using DCT and conducted tests through numerical experiments using each Hessian. In numerical result, despite using a small number of DCT coefficients, we confirmed that contamination due to crosstalk artifacts was suppressed when EFWI was performed using the multiparametric approximate Hessian.

Well-Guided Multisource Elastic Full-Waveform Inversion

Full waveform inversion (FWI) has been considered one of the most promising approaches to estimating the high-resolution subsurface parameters, which takes advantage of the kinematics and dynamics information of seismic data. However, FWI is greatly dependent on the accuracy of the initial model and vulnerable to the issue of local minimum. Moreover, the multi-source and multi-parameter crosstalk artifacts make multi-source elastic FWI (MS-EFWI) more likely to trap into a suboptimal inversion result. To remedy this defect, this study proposes an efficient elastic FWI (EFWI) paradigm that combines the crosstalk-free MS-EFWI method and a well-guided initial model-building algorithm. Specifically, we apply a harmonic wavelet encoding technology to MS-EFWI, by which the multi-source wavefields can be completely deblended without crosstalk noise. The well-guided structure-oriented interpolation, with the aid of the dip information derived from the initial migration images, is designed to build a satisfactory initial model and therefore reduce the risk of cycle skipping. Numerical examples based on the 2D Overthrust model and Marmousi model further demonstrate the feasibility and robustness of the proposed method with a relatively little number of iterations.

The P- and S-Wave Decomposition in a Multicomponent Elastic Wavefield Based on the Divergence and Curl Operators and Their Applications in Elastic Reverse Time Migration

The P- and S-wave separation is an important step in the elastic reverse-time migration (ERTM). It not only removes crosstalk artifacts in the image but also provides additional constrains to subsurface structures by providing images between different wave types. Traditional Helmholtz decomposition based on divergence and curl operations can separate the P and S wave modes but also modify the amplitude, phase and physical dimension of the original coupled wavefields. To recover the separated P- and S-waves to their original forms, we propose a method, in which the corrections to the distorted separated wavefields, including their phase, amplitude and vector polarizations, are organized into two spatial-time domain partial differential equations. Solving these equations can generate the separated P and S wavefields. The method can be conveniently applied in the time-space domain and consistent with most time-domain finite-difference based elastic reverse-time migration method. To verify the quality of the decomposed P- and S-wave components, we apply them to the ERTM using different elastic image conditions. For the PP image, we use the scalar imaging condition by crosscorrelating the corrected scalar Helmholtz potential wavefield from both source- and receiver-sides. For the PS image, we use the magnitude- and sign-based vector imaging condition by crosscorrelating the magnitudes of the source-side vector P wave and the receiver-side vector S wave, whereas the sign is determined through their dot-product results. Thus, accurate PP- and PS-reflectivity images can be obtained. Several numerical examples are used to verify our elastic wavefield separation method and the ERTM workflow.

Attenuation compensation for wavefield‐separation‐based least‐squares reverse time migration in viscoelastic media

ABSTRACTElastic least‐squares reverse time migration can image the multicomponent seismic recordings. However, on the one hand, without considering the intrinsic attenuation of the subsurface, it may produce blurred reflectivity images with incorrect positions of reflectors for seismic recordings with strong attenuation. On the other hand, the crosstalk artefacts created by the different wave modes may severely degrade the imaging quality. To alleviate these crosstalk artefacts and compensate for the attenuation effects, we have developed a time‐domain wavefield‐separation‐based least‐squares reverse time migration approach in viscoelastic media, which utilizes the viscoelastic wave equation on the basis of the standard linear solid model to simulate intrinsic subsurface attenuation. The key point of the proposed approach is that we utilize the separated gradient contribution of PP‐ and PS‐wave modes based on the wavefield separation technique in viscoelastic media to construct the P‐wave and S‐wave velocity images, respectively. Unlike the conventional wavefield‐separation‐based least‐squares reverse time migration approach, which generally uses the new stress–velocity equations to formulate, our proposed wavefield separation scheme fully depends on the conventional viscoelastic wave equation and its adjoint wave equation. By introducing the pure P‐wave stress in the forward and adjoint wavefields, the coupled wavefields can be well decomposed, which requires much less computational costs than the wavenumber‐domain wavefield separation scheme. Numerical examples using the layered and Marmousi‐2 models have shown that the proposed approach can improve the migration image quality under geological conditions with strong attenuation where elastic least‐squares reverse time migration may produce blurred and unfocused events. Meanwhile, the proposed least‐squares reverse time migration approach with the wavefield separation scheme has a better convergence rate and produces fewer crosstalk artefacts than that without the wavefield separation scheme.

First-order multiples imaging aided by water bottom

Surface-related multiples frequently propagate into the subsurface and contain abundant information on small reflection angles. Compared with the conventional migration of primaries, migration of multiples offers complementary illumination and a higher vertical resolution. However, crosstalk artifacts caused by unrelated multiples during reverse time migration (RTM) using multiples severely degrade the reliability and interpretation of the final migration images. Therefore, we proposed RTM using first-order receiver-side water-bottom-related multiples for eliminating crosstalk artifacts and enhancing vertical resolution. We first backward propagate the first-order receiver-side water-bottom-related multiples using a water-layer model, followed by saving the upper boundary wavefield. Then we produce the source wavefield using a seismic wavelet and the receiver wavefield by back-extrapolating the saved boundary. Finally, the cross-correlation imaging condition is applied to generate the final image. This method transforms the receiver-side multiples into primaries, followed by the conventional migration processing procedures. Numerical examples using synthetic datasets demonstrate that our method significantly enhances the imaging quality by eliminating crosstalk artifacts and improving the resolution.

Elastic Reverse-Time Migration with Complex Topography

Migration is an important step in seismic data processing for oil and gas exploration. The accuracy of migration directly affects the accuracy of subsequent oil and gas reservoir characterization. Reverse-time migration is one of the most accurate migration methods at present. Multi-wave and multicomponent seismic data contain more P- and S-wave information. Making full use of multi-wave and multicomponent seismic data can offer more information about underground structure and lithology, as well as improve the accuracy of seismic exploration. Elastic reverse-time migration (ERTM) has no dip restriction and can be applied to image multi-wave and multicomponent seismic data in complex structural areas and some special lithology structures. However, the surface topography of complex regions has an influence on wavefield and seriously degrades the quality of ERTM’s migration results. We developed a new ERTM method to migrate multi-wave and multicomponent seismic data in the region with complex surface topography. We first fill the layers between the highest and lowest undulating surface with near-surface elastic parameters in a complex topography model to obtain a new model with a horizontal surface. This allows the finite difference (FD) method based on the regular rectangular grid to be used to numerically solve elastic wave equations in the model with complex topography. The decoupled wave equations are used to generate source P- and S-waves and receiver P- and S-waves to reduce crosstalk artefacts in ERTM. A topography-related filter is further used to remove the influence of surface topography on migration results. The scalar imaging condition is also applied to generate PP and PS migration images. Some numerical examples with different complex topographies demonstrate that our proposed ERTM method can remove the influence of complex topography on ERTM’s images and effectively generate high-quality ERTM images.

Prestack correlative elastic least-squares reverse time migration based on wavefield decomposition

Standard elastic least-squares reverse time migration (ELSRTM) can iteratively improve the quality of stacked reflection images of P- and S-wave impedance by minimizing the L2 norm of waveform residual. However, the resulting images are sensitive to the velocity errors and amplitude errors in the observed data. Moreover, the crosstalk artifacts created by the different wave modes may degrade the imaging quality. To mitigate these problems, a correlative ELSRTM approach based on wavefield decomposition and prestack implementation is developed. This approach includes two key points. The first is that the proposed approach aims at finding the optimal reflection images for each shot recording via minimizing the normalized zero-lag cross-correlation objective function. The final reflection images can be produced by summing the optimal reflection images for all shot recordings. The second is that we develop a wavefield decomposition scheme, which is compatible with the correlative ELSRTM approach, as a gradient precondition to reduce crosstalk artifacts and accelerate the convergence. Numerical examples using several 2D experiments have determined that the proposed ELSRTM approach can provide higher quality images with fewer crosstalk artifacts, compared to the correlative ELSRTM approach without wavefield decomposition. Furthermore, it is less sensitive to velocity errors than the poststack correlative ELSRTM approach.

New Ocean Subsurface Optical Properties From Space Lidars: CALIOP/CALIPSO and ATLAS/ICESat‐2

AbstractRemote sensing from Earth‐observing satellites is now providing valuable information about ocean phytoplankton distributions. This paper presents the new ocean subsurface optical properties obtained from two space‐based lidars: the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite and the Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite‐2 satellite. Obtaining reliable estimates of subsurface biomass necessitates removing instrument artifacts peculiar to each sensor, that is, polarization crosstalk artifacts in the CALIOP signals and after pulsing effects arising from the ATLAS photodetectors. We validate the optical properties derived from the corrected lidar backscatter signals using MODerate‐resolution Imaging Spectroradiometer ocean color measurements and autonomous biogeochemical Argo float profiles. Our results support the continued use of present and future spaceborne lidars to study the global plankton system and characterize its vertical structures in the upper ocean.

Elastic least-squares reverse time migration based on decoupled wave equations

Elastic reverse time migration (ERTM) is developed for better characterization of complex structures by imaging multicomponent seismic data. However, conventional ERTM is subject to limitations such as finite recording aperture, limited bandwidth, and imperfect illumination. Elastic least-squares reverse time migration (ELSRTM) can improve imaging accuracy gradually with iterations by minimizing the residuals between the observed and calculated multicomponent data. Conventional ELSRTM suffers from crosstalk artifacts caused by coupled elastic wavefields with different wave modes. Decomposing the coupled elastic wavefields into pure P- and S-waves is an effective method to suppress these crosstalk artifacts. Considering the trade-off between calculation accuracy and efficiency, we have developed a new ELSRTM scheme for isotropic media based on decoupled wave equations to suppress these wave mode-related crosstalk artifacts in the images of conventional ELSRTM. Pure wavefields are obtained by solving the decoupled wave equations using the finite-difference method in our new ELSRTM method. We also derive new decoupled adjoint-state wave equations that are suitable for the elastic velocity-stress equations in isotropic media. Furthermore, we use the gradient equations based on pure wavefields to update the reflectivity images. Synthetic examples demonstrate that our new ELSRTM method can generate images that better represent the subsurface when compared with conventional ERTM and conventional ELSRTM.

Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

Binarization for eliminating calibration in fiberscope image processing

High-density coherent fiber bundles, mainly used for high-resolution fiberscopes, consist of non-circular cores and have various optical noises such as auto-fluorescence, crosstalk, and honeycomb artifacts that may require excessive image processing. Binarization was applied to post-image processing to eliminate a calibration used as pre-image processing for traditional fiberscope image reconstruction. It was able to significantly reduce the complexity of implementing core peak detection and effectively reduce the overall processing time. For binarization, the global thresholding suggested by Otsu and the local thresholdings suggested by Bernsen, Niblack, and White and Rohrer were used. By applying a dynamic method to our method, we can further simplify the process and apply it to image sequencing. When evaluating the results comprehensively, the local thresholding proposed by Bernsen was best suited for interpolating our fiberscope images. By introducing binarization combined with local thresholding in post-image processing, we were able to successfully eliminate the preliminary calibration process and reduce the burden of overall image processing for image reconstruction in a high-resolution fiberscope.

A Double-Envelope Mitral Inflow Spectral Doppler Profile After MitraClip

A Double-Envelope Mitral Inflow Spectral Doppler Profile After MitraClip

Using a modified double deep image prior for crosstalk mitigation in multislice ptychography.

Multislice ptychography is a high-resolution microscopy technique used to image multiple separate axial planes using a single illumination direction. However, multislice ptychography reconstructions are often degraded by crosstalk, where some features on one plane erroneously contribute to the reconstructed image of another plane. Here, the use of a modified `double deep image prior' (DDIP) architecture is demonstrated in mitigating crosstalk artifacts in multislice ptychography. Utilizing the tendency of generative neural networks to produce natural images, a modified DDIP method yielded good results on experimental data. For one of the datasets, it is shown that using DDIP could remove the need of using additional experimental data, such as from X-ray fluorescence, to suppress the crosstalk. This method may help X-ray multislice ptychography work for more general experimental scenarios.

Sparsity-promoting multiparameter pseudoinverse Born inversion in acoustic media

Least-squares reverse time migration (RTM) has become the method of choice for quantitative seismic imaging. The main drawback of such a scheme is that it requires many migration/modeling cycles. The convergence of least-squares RTM can be accelerated by using a suitable preconditioner. In the context of an extended domain in variable-density acoustic media, the pseudoinverse Born operator is the recommended preconditioner, providing quantitative results within a single iteration. This method consists of two steps: application of the pseudoinverse Born operator and inversion of two parameters using an efficient weighted least-squares approach based on the Radon transform. As expected, crosstalk artifacts are generated in the second step due to limited acquisition. We have developed a variable-density pseudoinverse Born operator constrained with the [Formula: see text]-norm for each model parameter to suppress the artifacts. The fast iterative shrinkage-thresholding algorithm is used to carry out the optimization problem. In classic iterative least-squares migration, the [Formula: see text]-norm constraints would affect the whole imaging process. Because the imaging method is split into two steps, only the Radon transform part is modified, where no wave-based operators are involved. Through numerical experiments, we verify the robustness of our method against different migration artifacts including the parameter crosstalk, interfaces with abrupt truncations, sparse shot acquisition geometry, noisy data, and high-contrast complex structures.