Proposed Reconstruction Method Research Articles

Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery.

The computation of anatomical information and laparoscope position is a fundamental block of surgical navigation in Minimally Invasive Surgery (MIS). Recovering a dense 3D structure of surgical scene using visual cues remains a challenge, and the online laparoscopic tracking primarily relies on external sensors, which increases system complexity. Here, we propose a learning-driven framework, in which an image-guided laparoscopic localization with 3D reconstructions of anatomical structures is obtained. To reconstruct the structure of the whole surgical environment, we first fine-tune a learning-based stereoscopic depth perception method, which is robust to texture-less and variant soft tissues, for depth estimation. Then, we develop a dense reconstruction algorithm to represent the scene by surfels, estimate the laparoscope poses and fuse the depth into a unified reference coordinate for tissue reconstruction. To estimate poses of new laparoscope views, we achieve a coarse-to-fine localization method, which incorporates our reconstructed model. We evaluate the reconstruction method and the localization module on three datasets, namely, the stereo correspondence and reconstruction of endoscopic data (SCARED), the ex-vivo data collected with Universal Robot (UR) and Karl Storz Laparoscope, and the in-vivo DaVinci robotic surgery dataset, where the reconstructed structures have rich details of surface texture with an error under 1.71 mm and the localization module can accurately track the laparoscope with images as input. Experimental results demonstrate the superior performance of the proposed method in anatomy reconstruction and laparoscopic localization. The proposed framework can be potentially extended to the current surgical navigation system.

Horizontal convergence reconstruction in the longitudinal direction for shield tunnels based on conditional random field

Tunnel horizontal convergence monitoring is essential to ensure the operation safety. However, only a few representative tunnel sections are chosen for monitoring due to the cost limitation. It is difficult to capture the horizontal convergence of each tunnel ring with limited measurements. Confronted with this difficulty, the paper proposes a horizontal convergence reconstruction method based on the measurements of deployed sensors. The tunnel horizontal convergence along the longitudinal direction is seen as a one-dimensional stationary and ergodic random field. The reconstruction problem is then transformed into the generation of conditional random fields. Monte Carlo simulation is adopted to generate possible realizations and the mean of realizations is considered as the maximum likelihood reconstruction. Error analysis proves the effectiveness of the proposed reconstruction method. The proposed method is proved to be applicable in reconstructing the time-variant horizontal convergence and is verified by the monitoring results of the shield tunnel of Shanghai Metro Line 2. The effect of sensor numbers is parametrically studied, and an optimal sensor placement scheme is decided. Additional sensors placed at the deformation drastically changed location can significantly improve the performance of the proposed method.

Imaging of conductivity distribution based on a combined reconstruction method in brain electrical impedance tomography

Electrical impedance tomography (EIT) is a promising technique in medical imaging. With this technique, pathology-related conductivity variation can be visualized. Nevertheless, reconstruction of conductivity distribution is a severely ill-posed inverse problem which poses a great challenge for the EIT technique. Especially in brain EIT, irregular and multi-layered head structure along with low-conductivity skull brings more difficulties for accurate reconstruction. To address such problems, a novel reconstruction method which combines Tikhonov regularization with denoising algorithm is proposed for imaging conductivity distribution in brain EIT. With the proposed method, image reconstruction of intracerebral hemorrhage in different brain lobes of a three-layer head model is conducted. Besides, simultaneous reconstruction of intracerebral hemorrhage and secondary ischemia is performed. Meanwhile, the impact of noise is investigated to evaluate the anti-noise performance. In addition, image reconstructions under head shape deformation are performed. The proposed reconstruction method is also quantitatively estimated by calculating blur radius and structural similarity. Phantom experiments are carried out to further verify the effectiveness of the proposed method. Both qualitative and quantitative results have demonstrated that the proposed combined method is superior to Tikhonov regularization in imaging conductivity distribution. This work would provide an alternative for accurate reconstruction in EIT based medical imaging.

Time-Resolved 3D cardiopulmonary MRI reconstruction using spatial transformer network.

The accurate visualization and assessment of the complex cardiac and pulmonary structures in 3D is critical for the diagnosis and treatment of cardiovascular and respiratory disorders. Conventional 3D cardiac magnetic resonance imaging (MRI) techniques suffer from long acquisition times, motion artifacts, and limited spatiotemporal resolution. This study proposes a novel time-resolved 3D cardiopulmonary MRI reconstruction method based on spatial transformer networks (STNs) to reconstruct the 3D cardiopulmonary MRI acquired using 3D center-out radial ultra-short echo time (UTE) sequences. The proposed reconstruction method employed an STN-based deep learning framework, which used a combination of data-processing, grid generator, and sampler. The reconstructed 3D images were compared against the start-of-the-art time-resolved reconstruction method. The results showed that the proposed time-resolved 3D cardiopulmonary MRI reconstruction using STNs offers a robust and efficient approach to obtain high-quality images. This method effectively overcomes the limitations of conventional 3D cardiac MRI techniques and has the potential to improve the diagnosis and treatment planning of cardiopulmonary disorders.

Proposed Methodology for Accuracy Improvement of LOD1 3D Building Models Created Based on Stereo Pléiades Satellite Imagery

Three-dimensional city models play an important role for a large number of applications in urban environments, and thus it is of high interest to create them automatically, accurately and in a cost-effective manner. This paper presents a new methodology for point cloud accuracy improvement to generate terrain topographic models and 3D building modeling with the Open Geospatial Consortium (OGC) CityGML standard, level of detail 1 (LOD1), using very high-resolution (VHR) satellite images. In that context, a number of steps are given attention (which are often (in the literature) not considered in detail), including the local geoid and the role of the digital terrain model (DTM) in the dense image matching process. The quality of the resulting models is analyzed thoroughly. For this objective, two stereo Pléiades 1 satellite images over Iasi city were acquired in September 2016, and 142 points were measured in situ by global navigation satellite system real-time kinematic positioning (GNSS-RTK) technology. First, the quasigeoid surface resulting from EGG2008 regional gravimetric model was corrected based on data from GNSS and leveling measurements using a four-parameter transformation, and the ellipsoidal heights of the 142 GNSS-RTK points were corrected based on the local quasigeoid surface. The DTM of the study area was created based on low-resolution airborne laser scanner (LR ALS) point clouds that have been filtered using the robust filter algorithm and a mask for buildings, and the ellipsoidal heights were also corrected with the local quasigeoid surface, resulting in a standard deviation of 37.3 cm for 50 levelling points and 28.1 cm for the 142 GNSS-RTK points. For the point cloud generation, two scenarios were considered: (1) no DTM and ground control points (GCPs) with uncorrected ellipsoidal heights resulting in an RMS difference (Z) for the 64 GCPs and 78 ChPs of 69.8 cm and (2) with LR ALS-DTM and GCPs with corrected ellipsoidal height values resulting in an RMS difference (Z) of 60.9 cm. The LOD1 models of 1550 buildings from the Iasi city center were created based on Pléiades-DSM point clouds (corrected and not corrected) and existing building sub-footprints, with four methods for the derivation of the building roof elevations, resulting in a standard deviation of 1.6 m against high-resolution (HR) ALS point cloud in the case of the best scenario. The proposed method for height extraction and reconstruction of the city structure performed the best compared with other studies on multiple satellite stereo imagery.

Simultaneous reconstruction of flame temperature and soot volume fraction through weighted non-negative least squares and light field imaging techniques

The flame temperature and soot volume fraction are important parameters for combustion diagnosis, which reflect the combustion reaction process and play a significant role in controlling pollutants emissions. This research introduces a direct method based on Weighted Non-Negative Least Squares (WNNLS) and light field imaging techniques to recover the flame temperature and soot volume fraction simultaneously. This method considers the flame self-absorption effect. Numerical studies were conducted on both Non-Negative Least Squares and proposed algorithms to compare the reconstruction performance. The anti-noise ability of the proposed reconstruction method was also investigated by adding various noises to radiative intensity. The effectiveness of the proposed method was also further verified by experimental studies on bimodal ethylene-diffusion flames. Thermocouple measurements were conducted and compared with the reconstructed temperatures. Both the numerical and experimental studies manifest that the proposed direct reconstruction method combing with WNNLS and light field imaging techniques is effective and accurate.

The novel continuous reconstruction approach for reconstructing anisotropic porous rocks

In this paper, a novel continuous reconstruction method is proposed to generate a numerical porous rock model that is statistically equivalent to natural rock. First, the spatial and mechanical properties of pore microstructures in rocks are obtained by computed tomography (CT) and triaxial compression tests. Then, the reconstructed rock models are generated based on the novel continuous reconstruction method. The generated rock models preserve the interest characteristics of the microstructures in rock. Compared with the exchange of discrete and isolated elements in our previous contributions, the microstructures are directly continuously reconstructed to form the reconstruction model. With the continuous approach, the natural rock sample can be efficiently represented by the reconstruction model on a large scale. We also consider reconstructing the anisotropic microstructures by different spatial correlation functions, such as the two-point probability function (TPPF), the lineal-path function (LPF), the fractal function (FF), and the two-point cluster function (TPCF). In addition, we introduce the percolating cluster volume (PCV) to improve the characterization capabilities of the proposed reconstruction method. The comparisons among the reference model, reconstructed model and rock samples show that the proposed continuous reconstruction method is able to reconstruct porous rocks while preserving mechanical and topological properties.

PGNet: Projection generative network for sparse-view reconstruction of projection-based magnetic particle imaging.

Magnetic particle imaging (MPI) is a novel tomographic imaging modality that scans the distribution of superparamagnetic iron oxide nanoparticles. However, it is time-consuming to scan multiview two-dimensional (2D) projections for three-dimensional (3D) reconstruction in projection MPI, such as computed tomography (CT). An intuitive idea is to use the sparse-view projections for reconstruction to improve the temporal resolution. Tremendous progress has been made toward addressing the sparse-view problem in CT, because of the availability of large data sets. For the novel tomography of MPI, to the best of our knowledge, studies on the sparse-view problem have not yet been reported. The acquisition of multiview projections for 3D MPI imaging is time-consuming. Our goal is to only acquire sparse-view projections for reconstruction to improve the 3D imaging temporal resolution of projection MPI. We propose to address the sparse-view problem in projection MPI by generating novel projections. The data set we constructed consists of three parts: simulation data set (including 3000 3D data), four phantoms data, and an in vivo mouse data. The simulation data set is used to train and validate the network, and the phantoms and in vivo mouse data are used to test the network. When the number of novel generated projections meets the requirements of filtered back projection, the streaking artifacts will be absent from MPI tomographic imaging. Specifically, we propose a projection generativenetwork (PGNet), that combines an attention mechanism, adversarial training strategy, and a fusion loss function and can generate novel projections based on sparse-view real projections. To the best of our knowledge, we are the first to propose a deep learning method to attempt to overcome the sparse-view problem in projection MPI. We compare our method with several sparse-view methods on phantoms and in vivo mouse data and validate the advantages and effectiveness of our proposed PGNet. Our proposed PGNet enables the 3D imaging temporal resolution of projection MPI to be improved by 6.6 times, while significantly suppressing the streaking artifacts. We proposed a deep learning method operated in projection domain to address the sparse-view reconstruction of MPI, and the data scarcity problem in projection MPI reconstruction is alleviated by constructing a sparse-dense simulated projection data set. By our proposed method, the number of acquisitions of real projections can be reduced. The advantage of our method is that it prevents the generation of streaking artifacts at the source. Our proposed sparse-view reconstruction method has great potential for application to time-sensitive in vivo 3D MPI imaging.

Numerical study of response behaviors of natural gas hydrate reservoir around wellbore induced by water jet slotting

The trial production of natural gas hydrate reservoirs remains poor. Reasonable reservoir reconstruction, which can improve formation permeability, is an important approach to increasing the efficiency and enhancing production. In this work, water jet slotting is proposed to reconstruct an natural gas hydrate reservoir near a wellbore. The spatial slots formed by water jet slotting not only directly constitute high-permeability channels, but also generate disturbances to the surrounding in-situ sediment. Water jet slotting disturbances to nearby sediment was investigated using a three dimensional flow-structure coupling model to evaluate the proposed reconstruction method. The reservoir at the SH2 site in the Shenhu area of the South China Sea was used as the reference. A horizontal slotting arrangement along the vertical well was adopted. The results demonstrate that water jet slotting can change the primary stress state of the sediment around the wellbore, and generate a dominant stress relaxation zone and small stress concentration zone. Within the stress relaxation zone, the in-situ compressive stress was remarkably reduced or even transformed into tensile stress, accompanied by sediment displacement and volumetric expansion strain. This is conducive to loosening the sediment around the wellbore and improving the permeability characteristics. In addition, the influence of the water jet slotting parameters including slot radius, spacing, and number on disturbances to the nearby sediment was studied. Reservoir responses to water jet slotting under balanced and unbalanced bottom-hole pressures were compared and analyzed. This study provides a reference for natural gas hydrate reservoir reconstruction using water jet slotting. Cited as: Huang, M., Su, D., Zhao, Z., Wu, L., Fang B., Ning, F. Numerical study of response behaviors of natural gas hydrate reservoir around wellbore induced by water jet slotting. Advances in Geo-Energy Research, 2023, 7(2): 75-89. https://doi.org/10.46690/ager.2023.02.02

Learned regularization for image reconstruction in sparse-view photoacoustic tomography.

Constrained data acquisitions, such as sparse view measurements, are sometimes used in photoacoustic computed tomography (PACT) to accelerate data acquisition. However, it is challenging to reconstruct high-quality images under such scenarios. Iterative image reconstruction with regularization is a typical choice to solve this problem but it suffers from image artifacts. In this paper, we present a learned regularization method to suppress image artifacts in model-based iterative reconstruction in sparse view PACT. A lightweight dual-path network is designed to learn regularization features from both the data and the image domains. The network is trained and tested on both simulation and in vivo datasets and compared with other methods such as Tikhonov regularization, total variation regularization, and a U-Net based post-processing approach. Results show that although the learned regularization network possesses a size of only 0.15% of a U-Net, it outperforms other methods and converges after as few as five iterations, which takes less than one-third of the time of conventional methods. Moreover, the proposed reconstruction method incorporates the physical model of photoacoustic imaging and explores structural information from training datasets. The integration of deep learning with a physical model can potentially achieve improved imaging performance in practice.

Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding.

In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.

3D velocity field reconstruction of gas-liquid two-phase flow based on space-time multi-scale binocular-PIV technology.

for particle image velocimetry (PIV) technique, the two-dimensional (2D) PIV by one camera can only obtain 2D velocity field, while three-dimensional (3D) PIV based on tomography by three or four cameras is always complex and expensive. In this work, a binocular-PIV technology based on two cameras was proposed to reconstruct the 3D velocity field of gas-liquid two-phase flow, which is a combination of the binocular stereo vision and cross-correlation based on fast Fourier transform (CC-FFT). The depth of particle was calculated by binocular stereo vision on space scale, and the plane displacement of particles was acquired by CC-FFT on time scale. Experimental results have proved the effectiveness of the proposed method in 3D reconstruction of velocity field for gas-liquid two-phase flow.

Ultrasonic Guided Wave Inversion Based on Deep Learning Restoration for Fingerprint Recognition.

As an established biometric authentication approach, fingerprint scanning has received considerable attention due to its high accuracy and reliability. In this article, the fingerprint reconstruction at any position is achieved in large physical domains, which monitors wavefield variations of plate-like structures within arrays through the ultrasonic guided wave. Accurate reconstruction and quantitative characterization of fingerprints are obtained using fast inversion tomography (FIT) based on the deep learning convolutional neural network (DLCNN). Parametric optimization is conducted to reveal submillimeter fingerprint minutiae, and a specific DLCNN model is proposed for the artifact removal in FIT reconstructions. The results prove that the FIT based on DLCNN restoration can significantly improve the imaging quality in terms of increased resolution, reduced reconstruction errors, and higher fingerprint matching confidence. The reconstruction also allows an exponential improvement in computational efficiency as a result of much-reduced sensor numbers. Several factors affecting the performance of the proposed reconstruction method are discussed at the end.

Multi-Scale Attention-Guided Non-Local Network for HDR Image Reconstruction.

High-dynamic-range (HDR) image reconstruction methods are designed to fuse multiple Low-dynamic-range (LDR) images captured with different exposure values into a single HDR image. Recent CNN-based methods mostly perform local attention- or alignment-based fusion of multiple LDR images to create HDR contents. Depending on a single attention mechanism or alignment causes failure in compensating ghosting artifacts, which can arise in the synthesized HDR images due to the motion of objects or camera movement across different LDR image inputs. In this study, we propose a multi-scale attention-guided non-local network called MSANLnet for efficient HDR image reconstruction. To mitigate the ghosting artifacts, the proposed MSANLnet performs implicit alignment of LDR image features with multi-scale spatial attention modules and then reconstructs pixel intensity values using long-range dependencies through non-local means-based fusion. These modules adaptively select useful information that is not damaged by an object’s movement or unfavorable lighting conditions for image pixel fusion. Quantitative evaluations against several current state-of-the-art methods show that the proposed approach achieves higher performance than the existing methods. Moreover, comparative visual results show the effectiveness of the proposed method in restoring saturated information from original input images and mitigating ghosting artifacts caused by large movement of objects. Ablation studies show the effectiveness of the proposed method, architectural choices, and modules for efficient HDR reconstruction.

A Subspace Approach to Sparse-Sampling-Based Multi-Attribute Data Aggregation in IoT

The emergence of the heterogeneous Internet of Things (IoT) has realized the demand for multi-attribute data collection in response to the increasing demand for information in diverse applications. Sparse sampling has been used to reduce network energy consumption in order to extend the life of energy-constrained networks. Real-time multi-attribute data aggregation under the sparse sensing framework has become a research focus. Therefore, we proposed a sparse-sampling-based IoT data aggregation approach to reduce network energy consumption and enable real-time multi-attribute reconstruction. For data collection, a sparse sampling data collection approach is proposed that can successfully collect and transmit the multi-attribute data to the sink even while certain IoT sensor nodes are in the sleep mode. For data reconstruction, a real-time multi-attribute data reconstruction method based on subspace is proposed. The proposed method arranges multi-attribute data in a tensor form in order to further utilize the correlation of multi-attribute data. Subspaces representing the spatial distributions of the multi-attribute data can be obtained from the previously reconstructed data. Incorporating total variation constraint, the proposed method reconstructs the current time slot multi-attribute data with high precision in real time. The experimental results demonstrate the effectiveness of the proposed method in real-time multi-attribute data reconstruction.

A response reconstruction method based on empirical mode decomposition and modal synthesis method

With the increasing the complexity of civil structures and diversity of the structural components, reconstructing the responses of complex civil engineering structures based on the conventional time-domain reconstruction method is very challenging. Apart from the massive computer storage needed in the calculation process, the enormous calculation times may also hinder the response reconstruction of the civil structures. This paper proposes a novel strategy of the time-domain reconstruction method by combining the modal synthesis method and empirical mode decomposition with intermittency criteria in the time domain. The response reconstruction method is conducted at two levels. The first level is performed on the reduced super-element from the interface DOFs to the internal DOFs of the critical substructure, and then at the element level to extrapolate dynamic responses at critical locations. In each level’s reconstruction, the number of calculation parameters decreases significantly. The response of the whole structure can be quickly obtained at one time, not only decreasing the computation time but also ensuring reconstruction accuracy. The numerical studies and experimental validation are both conducted on a simply supported beam and a complex cooling tower for verification, and the effects from some external parameters, including measurement noise, the order of substructural normal modes, the number of substructures and the number of sensors, are considered. The results illustrate that the proposed reconstruction method based on the modal synthesis method improves the calculation effectiveness considerably and ensures accuracy.

Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning.

Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired underwater datasets, which are difficult to obtain directly. Although the Cycle-GAN method solves the problem to some extent, the blurring degree of the fuzzy class of the paired underwater datasets generated by Cycle-GAN is relatively unitary. To solve this problem, a few-shot underwater image generative network method is proposed. Utilizing the proposed few-shot learning image generative method, the generated paired underwater datasets are better than those obtained by the Cycle-GAN method, especially under the condition of few real underwater datasets. In addition, to reconstruct high-quality results, an underwater deblurring ghost imaging method is proposed. The reconstruction method consists of two parts: reconstruction and deblurring. The experimental and simulation results show that the proposed reconstruction method has better performance in deblurring at a low sampling rate, compared with existing underwater ghost imaging methods based on deep learning. The proposed reconstruction method can effectively increase the clarity degree of the underwater reconstruction target at a low sampling rate and promotes the further applications of underwater ghost imaging.

Non-Intrusive Load Monitoring Based on the Graph Least Squares Reconstruction Method

Monitoring the operating conditions of residential appliances by collecting the total power consumption data of households has a great significance for smart power utilization. In this study, a graph least squares reconstruction approach is proposed. First, the graph signal is constructed using the collected active power consumption data, providing unique appliance signature information. The signal smoothness of the graph and an iterative least squares reconstruction algorithm are utilized for classification. The proposed graph reconstruction method relies on only low-sampling data from installed smart meters. It aims to address some existing problems of event-based NILM methods like measurement noise, indistinguishable load signatures, and inaccurate power reconstruction. Also, extensive training and associated calculations are not required. Simulation results on the REDD benchmark dataset demonstrate that the proposed method outperforms some of the current state-of-the-art techniques.

Depth-extended acoustic-resolution photoacoustic microscopy based on a two-stage deep learning network.

Acoustic resolution photoacoustic microscopy (AR-PAM) is a major modality of photoacoustic imaging. It can non-invasively provide high-resolution morphological and functional information about biological tissues. However, the image quality of AR-PAM degrades rapidly when the targets move far away from the focus. Although some works have been conducted to extend the high-resolution imaging depth of AR-PAM, most of them have a small focal point requirement, which is generally not satisfied in a regular AR-PAM system. Therefore, we propose a two-stage deep learning (DL) reconstruction strategy for AR-PAM to recover high-resolution photoacoustic images at different out-of-focus depths adaptively. The residual U-Net with attention gate was developed to implement the image reconstruction. We carried out phantom and in vivo experiments to optimize the proposed DL network and verify the performance of the proposed reconstruction method. Experimental results demonstrated that our approach extends the depth-of-focus of AR-PAM from 1mm to 3mm under the 4 mJ/cm2 light energy used in the imaging system. In addition, the imaging resolution of the region 2 mm far away from the focus can be improved, similar to the in-focus area. The proposed method effectively improves the imaging ability of AR-PAM and thus could be used in various biomedical studies needing deeper depth.

Ultrasound shear-wave computed tomography for elasticity imaging

Shear-wave elasticity imaging (SWEI) has been routinely used for measuring the elastic properties of tissues. It is potentially applicable to three-dimensional (3D) cell culture systems and may outperform existing methods such as atomic force microscopy and shear rheology in terms of being contactless and having higher spatial resolution and penetration. However, applying clinical SWEI to 3D cell culture systems requires the developments of high-frequency SWEI systems operating at >20 MHz that are compatible with the scale of cell culture systems, and C-scan 3D SWEI well suited to such observations. In this study, we implemented a computed tomography (CT) technique for SWEI (called SWCT) by leveraging the scanning scheme used in first-generation x-ray CT systems, that is, translation and rotation of a 20-MHz push probe and a 40-MHz imaging probe to obtain time-of-flight projections for multiple viewing directions. Compared with conventional B-scan SWEI, the proposed reconstruction method allows high-resolution, robust, 3D C-scan imaging of the shear-wave speed distribution. Three phantoms with different inclusions (half circle, thin strip, and cylinder) were imaged using 3D SWCT to a depth of 13 mm. The estimated shear-wave speed for the strip phantom using SWCT was 1.23 ± 0.20 m/s (mean ± standard deviation) in the background and 2.27 ± 0.11 m/s for the inclusion, which suggests the feasibility of SWCT for improving elasticity measurements of cell culture systems.