The Experts below are selected from a list of 24762 Experts worldwide ranked by ideXlab platform
Mark A. Anastasio - One of the best experts on this subject based on the ideXlab platform.
-
A framework for Ultrasound Computed Tomography virtual imaging trials that employs anatomically realistic numerical breast phantoms
Medical Imaging 2021: Ultrasonic Imaging and Tomography, 2021Co-Authors: Umberto Villa, Seonyeong Park, Mark A. AnastasioAbstract:In silico studies for Ultrasound Computed Tomography (USCT) can allow to explore imaging system parameters and reconstruction methods, without the economic burden and ethical concerns of clinical trials. A framework is proposed for virtual imaging trials of USCT. First, an ensemble of three-dimensional numerical breast phantoms consisting of anatomically realistic tissue structures and lesions is created. Next, acoustic properties are assigned to each tissue-type within physiological ranges. Finally, USCT measurement data are Computed by simulating acoustic wave propagation. The proposed framework will establish a standard pipeline for USCT virtual imaging trials and provide publicly available large-scale datasets
-
Regularized Dual Averaging Image Reconstruction for Full-Wave Ultrasound Computed Tomography
IEEE transactions on ultrasonics ferroelectrics and frequency control, 2017Co-Authors: Thomas P. Matthews, Kun Wang, Neb Duric, Mark A. AnastasioAbstract:Ultrasound Computed Tomography (USCT) holds great promise for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally demanding. Recently, a source encoding technique has been combined with stochastic gradient descent (SGD) to greatly reduce image reconstruction times. However, this method bundles the stochastic data fidelity term with the deterministic regularization term. This limitation can be overcome by replacing SGD with a structured optimization method, such as the regularized dual averaging method, that exploits knowledge of the composition of the cost function. In this paper, the dual averaging method is combined with source encoding techniques to improve the effectiveness of regularization while maintaining the reduced reconstruction times afforded by source encoding. It is demonstrated that each iteration can be decomposed into a gradient descent step based on the data fidelity term and a proximal update step corresponding to the regularization term. Furthermore, the regularization term is never explicitly differentiated, allowing nonsmooth regularization penalties to be naturally incorporated. The wave equation is solved by the use of a time-domain method. The effectiveness of this approach is demonstrated through computer simulation and experimental studies. The results suggest that the dual averaging method can produce images with less noise and comparable resolution to those obtained by the use of SGD.
-
Image reconstruction for Ultrasound Computed Tomography by use of the regularized dual averaging method
Medical Imaging 2017: Ultrasonic Imaging and Tomography, 2017Co-Authors: Thomas P. Matthews, Kun Wang, Neb Duric, Mark A. AnastasioAbstract:Waveform inversion methods can produce high-resolution reconstructed sound speed images for Ultrasound Computed Tomography; however, they are very computational expensive. Source encoding methods can reduce this computational cost by formulating the image reconstruction problem as a stochastic optimization problem. Here, we solve this optimization problem by the regularized dual averaging method instead of the more commonly used stochastic gradient descent. This new optimization method allows use of non-smooth regularization functions and treats the stochastic data fidelity term in the objective function separately from the deterministic regularization function. This allows noise to be mitigated more effectively. The method further exhibits lower variance in the estimated sound speed distributions across iterations when line search methods are employed.
-
Joint reconstruction of the sound speed and initial pressure distributions for Ultrasound Computed Tomography and photoacoustic Computed Tomography
Medical Imaging 2017: Ultrasonic Imaging and Tomography, 2017Co-Authors: Thomas P. Matthews, Mark A. AnastasioAbstract:Accurate reconstruction of the initial pressure distribution in photoacoustic Computed Tomography (PACT), in general, requires knowledge of the sound speed distribution of the medium. However, the sound speed distri- bution is often unknown, and estimating both the sound speed and initial pressure from PACT measurements alone is unstable. An alternative is to estimate the sound speed from Ultrasound Computed Tomography (USCT) measurements. This approach fails to exploit the acoustic information in the PACT measurements and may require many USCT measurements to accurately reconstruct the sound speed. Here, we propose a joint recon- struction method where the sound speed and initial pressure distributions are simultaneously estimated from combined PACT/USCT measurements. This approach effectively overcomes the instability of the PACT joint reconstruction problem while requiring few USCT measurements.
-
Nonlinear waveform inversion by use of the regularized dual averaging method for Ultrasound Computed Tomography
2016 Progress in Electromagnetic Research Symposium (PIERS), 2016Co-Authors: Thomas P. Matthews, Kun Wang, Neb Duric, Mark A. AnastasioAbstract:Ultrasound Computed Tomography (USCT) is a promising medical imaging modality that offers several novel tissue contrasts and holds great potential for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally demanding. Recently, a source encoding technique was combined with stochastic gradient descent (SGD) to greatly reduce image reconstruction times. However, this method bundles the stochastic data fidelity term with the deterministic regularization term. This non-optimal approach can be overcome by replacing stochastic gradient descent with a structured optimization method, such as the regularized dual averaging (RDA) method, that exploits knowledge of the composition of the cost function. In this work, the dual averaging method is combined with source encoding techniques to improve the effectiveness of regularization while maintaining the reduced reconstruction times afforded by source encoding. It is demonstrated that, for a particular choice of regularization function, each iteration can be decomposed into a gradient descent step based on the data fidelity term and a proximal update step corresponding to the regularization term. Furthermore, the regularization term is never explicitly differentiated, allowing non-smooth regularization penalties to be naturally incorporated. The wave equation is solved by use of a time-domain k-space pseudo-spectral method, implemented on general-purpose graphics processing units. The effectiveness of this approach is demonstrated through computer simulation and experimental studies. The results suggest that the dual averaging method can produce images with less noise and comparable resolution to those obtained by use of stochastic gradient descent.
Philippe Lasaygues - One of the best experts on this subject based on the ideXlab platform.
-
Ultrasound Computed Tomography on standing trees: accounting for wood anisotropy permits a more accurate detection of defects
Annals of Forest Science, 2020Co-Authors: Luis Espinosa, Loïc Brancheriau, Yolima Cortes, Flavio Prieto, Philippe LasayguesAbstract:Key message Considering anisotropy in image reconstruction algorithm for Ultrasound Computed Tomography of trees resulted in a more accurate detection of defects compared to common approaches used. Context Ultrasound Computed Tomography is a suitable tool for nondestructive evaluation of standing trees. Until now, to simplify the image reconstruction process, the transverse cross-section of trees has been considered as quasi-isotropic and therefore limiting the defect identification capability. Aims An approach to solve the inverse problem for tree imaging is presented, using an Ultrasound-based method (travel-time Computed Tomography) suited to the anisotropy of wood material and validated experimentally. Methods The proposed iterative method focused on finding a polynomial approximation of the slowness in each pixel of the image depending on the angle of propagation, modifying the curved trajectories by means of a raytracing method. This method allowed a mapping of specific elastic constants using nonlinear regression. Experimental validation was performed using sections of green wood from a pine tree ( Pinus pinea L. ), with configurations that include a healthy case, a centered, and an off-centered defect. Results Images obtained using the proposed method led to a more accurate location of the defects compared to the filtered backprojection algorithm (isotropic hypothesis), considered as reference. Conclusion The performed experiments demonstrated that considering the wood anisotropy in the imaging process led to a better defect detection compared to the use of a common imaging technique.
-
Pipe two-phase flow non-invasive imaging using Ultrasound Computed Tomography: A two-dimensional numerical and experimental performance assessment
Flow Measurement and Instrumentation, 2020Co-Authors: William Cailly, Henri Walaszek, Sébastien Brzuchacz, Fan Zhang, Philippe LasayguesAbstract:Abstract Pipe two-phase flow non-invasive imaging is of great interest in the field of industry. In particular, small bubble flow imaging through opaque pipes is challenging. Ultrasound Computed Tomography can be a relevant technique for this purpose. However, perturbation phenomena that are inherent to the configuration (acoustic impedance mismatching, circumferential propagation, reverberation) limit two aspects: the performance of the technique and the use of conventional inversion algorithms. The objectives of the presented work are: (i) to predict the effects of the pipe wall on ultrasonic waves for both metallic and plastic pipe, (ii) to define a consistent inversion algorithm taking into account those effects, (iii) to validate and to assess the limitations of the designed imaging numerical tool using an experimental setup. The benchmark configuration consists of 150 mm diameter 3 mm thick pipes containing 6 mm diameter rods acting as reference scatterers. Two materials of very different acoustical properties were tested: aluminum and PMMA. The results highlighted that the quality of the reconstructed image is very dependent on the pipe material. The results showed that, using an adapted inversion model, consistent target reconstruction is obtained. Based on numerical predictions, performance limitations are reached for metallic pipes.
-
Automatic recognition processing in Ultrasound Computed Tomography of bone
2019Co-Authors: Fradi Marwa, Wajih Elhadj Youssef, Mohsen Machhout, Philippe Petit, Cécile Baron, Régine Guillermin, Philippe LasayguesAbstract:Ultrasound Computed Tomography (USCT) of soft biological tissues today provides images with a high-level of resolution. The signal acquisition system using multichannel and/or multifrequency arrays performs in circular mode and the main (linear) inversion algorithms are based on compression wave propagation modeling. The main limits of these methods for bone imaging are due to the large impedance contrast between tissues, and to propagative phenomena generated through periosteal interfaces (mode conversion, attenuation). The linear inversion methods fail to provide high-level resolution images. Despite their performance and robustness, the non-linear methods are still today unsuitable for clinical applications because of the high computation time required. However, in the special case of children bone imaging, acquisition steps must be as fast as possible, with short-time exposure and low-intensity sonication. In this context, we have developed a fast-acquisition setup (1 sec.) based on a cylindrical-focusing ring antenna, and a protocol (< 5 sec.) using classical Born approximation and spatial Fourier transform. Unfortunately, the result today is a poor contrast-to-noise ratio (CNR) image. Previous work done to improve CNR used signal and image processing. This work focuses on this last point, and an automatic edge detection procedure, using Haar wavelet 2D-decompositon, combining k-means and Ostu algorithms. Results will be presented on ex vivo real bone samples and on geometrical mimicking bone phantom (Sawbones TM). An example of bone defect imaging will be presented and discussed.
Lawrence H. Staib - One of the best experts on this subject based on the ideXlab platform.
-
MICCAI (2) - Physical-Space Refraction-Corrected Transmission Ultrasound Computed Tomography Made Computationally Practical
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2008Co-Authors: Klaus Mueller, Marcel P. Jackowski, Donald P. Dione, Lawrence H. StaibAbstract:Transmission Ultrasound Computed Tomography (CT) is strongly affected by the acoustic refraction properties of the imaged tissue, and proper modeling and correction of these effects is crucial to achieving high-quality image reconstructions. A method that can account for these refractive effects solves the governing Eikonal equation within an iterative reconstruction framework, using a wave-front tracking approach. Excellent results can be obtained, but at considerable computational expense. Here, we report on the acceleration of three Eikonal solvers (Fast Marching Method (FMM), Fast Sweeping Method (FSM), Fast Iterative Method (FIM)) on three computational platforms (commodity graphics hardware (GPUs), multi-core and cluster CPUs), within this refractive Transmission Ultrasound CT framework. Our efforts provide insight into the capabilities of the various architectures for acoustic wave-front tracking, and they also yield a framework that meets the interactive demands of clinical practice, without a loss in reconstruction quality.
-
ISBI - Fast marching method to correct for refraction in Ultrasound Computed Tomography
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano 2006., 1Co-Authors: Klaus Mueller, Marcel P. Jackowski, D.p. Dione, Lawrence H. StaibAbstract:A significant obstacle in the advancement of Ultrasound Computed Tomography has been the lack of efficient and precise methods for the tracing of the bent rays that result from the interaction of sound with refractive media. In this paper, we propose the use of the fast marching method (FMM) to solve the eikonal equation which governs the propagation of sound waves. The FMM enables us to determine with great accuracy and ease the distorted paths that the sound rays take from an emitter to the receivers. We show that knowledge of the accurate path proves crucial for an object reconstruction at high fidelity and accurate geometry. We employ a two-phase approach with an iterative method, SART, to faithfully reconstruct two tissue properties relevant in clinical diagnosis, such as mammography: speed of sound and sound attenuation. We demonstrate our results by ways of a newly designed analytical Ultrasound breast phantom.
Thomas P. Matthews - One of the best experts on this subject based on the ideXlab platform.
-
Regularized Dual Averaging Image Reconstruction for Full-Wave Ultrasound Computed Tomography
IEEE transactions on ultrasonics ferroelectrics and frequency control, 2017Co-Authors: Thomas P. Matthews, Kun Wang, Neb Duric, Mark A. AnastasioAbstract:Ultrasound Computed Tomography (USCT) holds great promise for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally demanding. Recently, a source encoding technique has been combined with stochastic gradient descent (SGD) to greatly reduce image reconstruction times. However, this method bundles the stochastic data fidelity term with the deterministic regularization term. This limitation can be overcome by replacing SGD with a structured optimization method, such as the regularized dual averaging method, that exploits knowledge of the composition of the cost function. In this paper, the dual averaging method is combined with source encoding techniques to improve the effectiveness of regularization while maintaining the reduced reconstruction times afforded by source encoding. It is demonstrated that each iteration can be decomposed into a gradient descent step based on the data fidelity term and a proximal update step corresponding to the regularization term. Furthermore, the regularization term is never explicitly differentiated, allowing nonsmooth regularization penalties to be naturally incorporated. The wave equation is solved by the use of a time-domain method. The effectiveness of this approach is demonstrated through computer simulation and experimental studies. The results suggest that the dual averaging method can produce images with less noise and comparable resolution to those obtained by the use of SGD.
-
Image reconstruction for Ultrasound Computed Tomography by use of the regularized dual averaging method
Medical Imaging 2017: Ultrasonic Imaging and Tomography, 2017Co-Authors: Thomas P. Matthews, Kun Wang, Neb Duric, Mark A. AnastasioAbstract:Waveform inversion methods can produce high-resolution reconstructed sound speed images for Ultrasound Computed Tomography; however, they are very computational expensive. Source encoding methods can reduce this computational cost by formulating the image reconstruction problem as a stochastic optimization problem. Here, we solve this optimization problem by the regularized dual averaging method instead of the more commonly used stochastic gradient descent. This new optimization method allows use of non-smooth regularization functions and treats the stochastic data fidelity term in the objective function separately from the deterministic regularization function. This allows noise to be mitigated more effectively. The method further exhibits lower variance in the estimated sound speed distributions across iterations when line search methods are employed.
-
Joint reconstruction of the sound speed and initial pressure distributions for Ultrasound Computed Tomography and photoacoustic Computed Tomography
Medical Imaging 2017: Ultrasonic Imaging and Tomography, 2017Co-Authors: Thomas P. Matthews, Mark A. AnastasioAbstract:Accurate reconstruction of the initial pressure distribution in photoacoustic Computed Tomography (PACT), in general, requires knowledge of the sound speed distribution of the medium. However, the sound speed distri- bution is often unknown, and estimating both the sound speed and initial pressure from PACT measurements alone is unstable. An alternative is to estimate the sound speed from Ultrasound Computed Tomography (USCT) measurements. This approach fails to exploit the acoustic information in the PACT measurements and may require many USCT measurements to accurately reconstruct the sound speed. Here, we propose a joint recon- struction method where the sound speed and initial pressure distributions are simultaneously estimated from combined PACT/USCT measurements. This approach effectively overcomes the instability of the PACT joint reconstruction problem while requiring few USCT measurements.
-
Nonlinear waveform inversion by use of the regularized dual averaging method for Ultrasound Computed Tomography
2016 Progress in Electromagnetic Research Symposium (PIERS), 2016Co-Authors: Thomas P. Matthews, Kun Wang, Neb Duric, Mark A. AnastasioAbstract:Ultrasound Computed Tomography (USCT) is a promising medical imaging modality that offers several novel tissue contrasts and holds great potential for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally demanding. Recently, a source encoding technique was combined with stochastic gradient descent (SGD) to greatly reduce image reconstruction times. However, this method bundles the stochastic data fidelity term with the deterministic regularization term. This non-optimal approach can be overcome by replacing stochastic gradient descent with a structured optimization method, such as the regularized dual averaging (RDA) method, that exploits knowledge of the composition of the cost function. In this work, the dual averaging method is combined with source encoding techniques to improve the effectiveness of regularization while maintaining the reduced reconstruction times afforded by source encoding. It is demonstrated that, for a particular choice of regularization function, each iteration can be decomposed into a gradient descent step based on the data fidelity term and a proximal update step corresponding to the regularization term. Furthermore, the regularization term is never explicitly differentiated, allowing non-smooth regularization penalties to be naturally incorporated. The wave equation is solved by use of a time-domain k-space pseudo-spectral method, implemented on general-purpose graphics processing units. The effectiveness of this approach is demonstrated through computer simulation and experimental studies. The results suggest that the dual averaging method can produce images with less noise and comparable resolution to those obtained by use of stochastic gradient descent.
-
Breast Ultrasound Computed Tomography using waveform inversion with source encoding
Medical Imaging 2015: Ultrasonic Imaging and Tomography, 2015Co-Authors: Kun Wang, Thomas P. Matthews, Neb Duric, Fatima Anis, Mark A. AnastasioAbstract:Ultrasound Computed Tomography (USCT) holds great promise for improving the detection and management of breast cancer. Because they are based on the acoustic wave equation, waveform inversion-based reconstruction methods can produce images that possess improved spatial resolution properties over those produced by ray-based methods. However, waveform inversion methods are computationally demanding and have not been applied widely in USCT breast imaging. In this work, source encoding concepts are employed to develop an accelerated USCT reconstruction method that circumvents the large computational burden of conventional waveform inversion methods. This method, referred to as the waveform inversion with source encoding (WISE) method, encodes the measurement data using a random encoding vector and determines an estimate of the speed-of-sound distribution by solving a stochastic optimization problem by use of a stochastic gradient descent algorithm. Computer-simulation studies are conducted to demonstrate the use of the WISE method. Using a single graphics processing unit card, each iteration can be completed within 25 seconds for a 128 × 128 mm2 reconstruction region. The results suggest that the WISE method maintains the high spatial resolution of waveform inversion methods while significantly reducing the computational burden.
Luis Espinosa - One of the best experts on this subject based on the ideXlab platform.
-
Ultrasound Computed Tomography on standing trees: accounting for wood anisotropy permits a more accurate detection of defects
Annals of Forest Science, 2020Co-Authors: Luis Espinosa, Loïc Brancheriau, Yolima Cortes, Flavio Prieto, Philippe LasayguesAbstract:Key message Considering anisotropy in image reconstruction algorithm for Ultrasound Computed Tomography of trees resulted in a more accurate detection of defects compared to common approaches used. Context Ultrasound Computed Tomography is a suitable tool for nondestructive evaluation of standing trees. Until now, to simplify the image reconstruction process, the transverse cross-section of trees has been considered as quasi-isotropic and therefore limiting the defect identification capability. Aims An approach to solve the inverse problem for tree imaging is presented, using an Ultrasound-based method (travel-time Computed Tomography) suited to the anisotropy of wood material and validated experimentally. Methods The proposed iterative method focused on finding a polynomial approximation of the slowness in each pixel of the image depending on the angle of propagation, modifying the curved trajectories by means of a raytracing method. This method allowed a mapping of specific elastic constants using nonlinear regression. Experimental validation was performed using sections of green wood from a pine tree ( Pinus pinea L. ), with configurations that include a healthy case, a centered, and an off-centered defect. Results Images obtained using the proposed method led to a more accurate location of the defects compared to the filtered backprojection algorithm (isotropic hypothesis), considered as reference. Conclusion The performed experiments demonstrated that considering the wood anisotropy in the imaging process led to a better defect detection compared to the use of a common imaging technique.
