scholarly journals Image Reconstruction with Reliability Assessment in Quantitative Photoacoustic Tomography

2018 ◽  
Vol 4 (12) ◽  
pp. 148 ◽  
Author(s):  
Niko Hänninen ◽  
Aki Pulkkinen ◽  
Tanja Tarvainen
Keyword(s):  
Approximation Error ◽  
Initial Pressure ◽  
Photoacoustic Tomography ◽  
Imaging Method ◽  
Optical Parameters ◽  
Reconstruction Method ◽  
Reconstruction Procedure ◽  
Modeling Errors ◽  
Bayesian Approximation ◽  
Error Method

Quantitative photoacoustic tomography is a novel imaging method which aims to reconstruct optical parameters of an imaged target based on initial pressure distribution, which can be obtained from ultrasound measurements. In this paper, a method for reconstructing the optical parameters in a Bayesian framework is presented. In addition, evaluating the credibility of the estimates is studied. Furthermore, a Bayesian approximation error method is utilized to compensate the modeling errors caused by coarse discretization of the forward model. The reconstruction method and the reliability of the credibility estimates are investigated with two-dimensional numerical simulations. The results suggest that the Bayesian approach can be used to obtain accurate estimates of the optical parameters and the credibility estimates of these parameters. Furthermore, the Bayesian approximation error method can be used to compensate for the modeling errors caused by a coarse discretization, which can be used to reduce the computational costs of the reconstruction procedure. In addition, taking the modeling errors into account can increase the reliability of the credibility estimates.

scholarly journals Creating a Simulated Dataset for Training Deep Convolutional Neural Networks for Use in Cardiovascular Photoacoustic Tomography

2020 ◽  
Vol 3 ◽  
Author(s):  
Hayley Chan ◽  
Craig Goergen ◽  
Katherine Leyba
Keyword(s):  
Data Augmentation ◽  
Ex Vivo ◽  
Initial Pressure ◽  
Image Resolution ◽  
Sensor Data ◽  
Photoacoustic Tomography ◽  
Multidimensional Space ◽  
Imaging Method ◽  
Ultrasound Images ◽  
Carotid Ultrasound

Background/Objective: Photoacoustic tomography possesses increasing potential as a non-invasive imaging method that combines optical and acoustic imaging to maximize the visualization of tissue. Determining the composition, orientation, and location of anatomical structures in multidimensional space requires maximizing image resolution and differentiation from noise and reflection artifacts. Using simulations to develop and improve methods for image resolution allows for flexibility and variation of numerous variables.    Methods: Binary masks were created from mouse common carotid ultrasound images using a graphical user interface for MATLAB. With the k-Wave toolbox, we performed time-reversal photoacoustic simulations using the masks. Medium properties for the simulations were assigned for sound speed and density for connective tissue (1540 m/s, 1027 kg/m3) and arterial walls (1569 m/s, 1102 kg/m3). The dataset was augmented through rotational and mirrored transformations and the addition of noise and reflection artifacts via Python open-source software.    Results: A set of 87 binary masks was generated from common carotid ultrasound images. These masks were used to simulate initial pressure distributions through the k-Wave toolbox to reconstruct the structure of the common carotid. Each simulation yielded graphs for initial pressure and sensor distribution, simulated sensor data, reconstructed initial pressure, and a comparison profile between the original and reconstructed pressure. Data augmentation was implemented using the reconstructed pressure output from the 87 simulations, each producing 12 distinct images from rotations and mirroring with the addition of noise and reflection artifacts. The final dataset yielded 1044 images.    Conclusion and Potential Impact: Future work will involve applying this dataset to a neural network to improve photoacoustic quality such that transfer learning can be applied on ex vivo and in vivo datasets. Thus, there is potential for use in diagnostic applications in patients with cardiovascular disease states like atherosclerosis and aneurysms that require high resolution visualization of tissue structure and composition. 

Global Inhomogeneity Index Evaluation of a DCT-based EIT Lung Imaging

2020 ◽  
Vol 6 (3) ◽  
pp. 36-39
Author(s):  
Rongqing Chen ◽  
Knut Möller
Keyword(s):  
Classical Method ◽  
Lung Imaging ◽  
Imaging Method ◽  
Reconstruction Method ◽  
Essential Information ◽  
Preliminary Results ◽  
Reconstruction Methods ◽  
Comparison Results ◽  
Index Evaluation ◽  
Inhomogeneity Index

AbstractPurpose: To evaluate a novel structural-functional DCT-based EIT lung imaging method against the classical EIT reconstruction. Method: Taken retrospectively from a former study, EIT data was evaluated using both reconstruction methods. For different phases of ventilation, EIT images are analyzed with respect to the global inhomogeneity (GI) index for comparison. Results: A significant less variant GI index was observed in the DCTbased method, compared to the index from classical method. Conclusion: The DCT-based method generates more accurate lung contour yet decreasing the essential information in the image which affects the GI index. These preliminary results must be consolidated with more patient data in different breathing states.

Acceleration of the EM-like reconstruction method for diffuse optical tomography with ordered-subsets method

2014 ◽  
Vol 22 (3) ◽  
Author(s):  
Caifang Wang
Keyword(s):  
Numerical Experiments ◽  
Imaging Modality ◽  
Random Medium ◽  
Optical Tomography ◽  
Back Propagation ◽  
Three Dimensional ◽  
Diffuse Optical Tomography ◽  
Optical Parameters ◽  
Reconstruction Method ◽  
Boundary Measurements

Abstract.Diffuse optical tomography (DOT) is an optical imaging modality, which provides the spatial distribution of the optical parameters inside a random medium. A propagation back-propagation method named EM-like reconstruction method for stationary DOT problem has been proposed yet. This method is really time consuming. Hence the ordered-subsets (OS) technique for this reconstruction method is studied in this paper. The boundary measurements of DOT are grouped into nonoverlapping and overlapping ordered sequence of subsets with random partition, sequential partition and periodic partition, respectively. The performance of OS methods is compared with the standard EM-like reconstruction method with two-dimensional and three-dimensional numerical experiments. The numerical experiments indicate that reconstruction of nonoverlapping subsets with periodic partition, overlapping subsets with periodic partition and standard EM-like method provide very similar acceptable reconstruction results. However, reconstruction of nonoverlapping subsets with periodic partition spends a minimum of time to get proper results.

Approximation error method for full-wave tomography

2013 ◽  
Vol 133 (5) ◽  
pp. 3230-3230 ◽  
Author(s):  
Janne Koponen ◽  
Tomi Huttunen ◽  
Tanja Tarvainen ◽  
Jari Kaipio
Keyword(s):  
Approximation Error ◽  
Full Wave ◽  
Wave Tomography ◽  
Error Method

Postoperative Complication Assessments of Different Reconstruction Procedures after Total Pharyngolaryngoesophagectomy: Tubular Gastric Pull-Up versus Whole Gastric Pull-Up

2018 ◽  
Vol 84 (12) ◽  
pp. 1927-1931
Author(s):  
Zhenbo Dai ◽  
Qinghua He ◽  
Boyu Pan ◽  
Liren Liu ◽  
Dejun Zhou
Keyword(s):  
Postoperative Complications ◽  
Hypopharyngeal Cancer ◽  
Reconstruction Method ◽  
Observation Study ◽  
Reconstruction Procedure ◽  
Intrathoracic Stomach ◽  
Group A ◽  
Group B ◽  
Medical University ◽  
Gastric Pull Up

Hypopharynx carcinoma tends to be diagnosed at advanced stage and usually has a poor prognosis because of the high incidence of submucosal spreading and lymphatic metastasis. Total pharyngolaryngoesophagectomy (PLE) is mostly used as a curative intervention for this deadly disease, and a commonly used reconstruction method after PLE is gastric pull-up, which could be further divided into tubular gastric pull-up and whole gastric pull-up procedures. Aiming to achieve a precise guidance on optimal reconstruction method after PLE, the present study evaluated the postoperative complications involving in different gastric pull-up procedures in patients with hypopharynx cancer. A total of 52 consecutive patients with hypopharyngeal cancer who underwent total PLE with gastric pull-up reconstruction in Tianjin Medical University Cancer Institute and Hospital between 1996 and 2014 were analyzed in this study. Of these patients, 28 underwent tubular gastric pull-up reconstruction procedure (Group A), whereas 24 underwent whole gastric pull-up reconstruction procedure (Group B). We compared the postoperative complications between these two groups retrospectively. Postoperative anastomotic fistulas occurred in three patients in Group A (3/28) versus eight patients in Group B (8/24), leading to an incidence rate of 10.71 and 33.33 per cent, respectively. The incidence of intrathoracic stomach syndrome was 21.43 per cent in Group A (6/28) versus 58.33 per cent in Group B (14/24), and the incidence of reflux was 35.71 per cent in Group A (10/28) versus 66.67 per cent in Group B (16/24). All of the above postoperative complications exhibited statistical differences between two groups ( P ≤ 0.05). This retrospective observation study suggests that compared with whole gastric pull-up, tubular gastric pull-up is a better reconstruction procedure of choice after PLE, evidenced by reduced incidences of postoperative anastomotic fistula, intrathoracic stomach syndrome, and reflux.

scholarly journals Stochastic modeling error reduction using Bayesian approach coupled with an adaptive Kriging-based model

2014 ◽  
Vol 33 (3) ◽  
pp. 856-867 ◽  
Author(s):  
Ahmed Abou-Elyazied Abdallh ◽  
Luc Dupré
Keyword(s):  
Inverse Problem ◽  
Stochastic Modeling ◽  
Approximation Error ◽  
Memory Storage ◽  
Computational Time ◽  
Problem Solution ◽  
Content Type ◽  
Inverse Problem Solution ◽  
Bayesian Approximation ◽  
Adaptive Kriging

Purpose – Magnetic material properties of an electromagnetic device (EMD) can be recovered by solving a coupled experimental numerical inverse problem. In order to ensure the highest possible accuracy of the inverse problem solution, all physics of the EMD need to be perfectly modeled using a complex numerical model. However, these fine models demand a high computational time. Alternatively, less accurate coarse models can be used with a demerit of the high expected recovery errors. The purpose of this paper is to present an efficient methodology to reduce the effect of stochastic modeling errors in the inverse problem solution. Design/methodology/approach – The recovery error in the electromagnetic inverse problem solution is reduced using the Bayesian approximation error approach coupled with an adaptive Kriging-based model. The accuracy of the forward model is assessed and adapted a priori using the cross-validation technique. Findings – The adaptive Kriging-based model seems to be an efficient technique for modeling EMDs used in inverse problems. Moreover, using the proposed methodology, the recovery error in the electromagnetic inverse problem solution is largely reduced in a relatively small computational time and memory storage. Originality/value – The proposed methodology is capable of not only improving the accuracy of the inverse problem solution, but also reducing the computational time as well as the memory storage. Furthermore, to the best of the authors knowledge, it is the first time to combine the adaptive Kriging-based model with the Bayesian approximation error approach for the stochastic modeling error reduction.

scholarly journals A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

2015 ◽  
Vol 8 (4) ◽  
pp. 1259-1273 ◽  
Author(s):  
J. Ray ◽  
J. Lee ◽  
V. Yadav ◽  
S. Lefantzi ◽  
A. M. Michalak ◽  
...  
Keyword(s):  
Field Model ◽  
Computational Cost ◽  
Estimation Method ◽  
Sparse Reconstruction ◽  
Model Parameters ◽  
Reconstruction Method ◽  
Reconstruction Procedure ◽  
Reconstruction Methods ◽  
Atmospheric Inversion ◽  
Atmospheric Inversions

Abstract. Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO2 (ffCO2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO2 emissions and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.

scholarly journals Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty

2021 ◽  
Vol 15 (4) ◽  
pp. 1731-1750
Author(s):  
Olalekan Babaniyi ◽  
Ruanui Nicholson ◽  
Umberto Villa ◽  
Noémi Petra
Keyword(s):  
Model Uncertainty ◽  
Approximation Error ◽  
Ice Sheet ◽  
Forward Model ◽  
Three Dimensions ◽  
Low Rank ◽  
Surface Velocity ◽  
Low Rank Approximation ◽  
Basal Sliding ◽  
Bayesian Approximation

Abstract. We consider the problem of inferring the basal sliding coefficient field for an uncertain Stokes ice sheet forward model from synthetic surface velocity measurements. The uncertainty in the forward model stems from unknown (or uncertain) auxiliary parameters (e.g., rheology parameters). This inverse problem is posed within the Bayesian framework, which provides a systematic means of quantifying uncertainty in the solution. To account for the associated model uncertainty (error), we employ the Bayesian approximation error (BAE) approach to approximately premarginalize simultaneously over both the noise in measurements and uncertainty in the forward model. We also carry out approximative posterior uncertainty quantification based on a linearization of the parameter-to-observable map centered at the maximum a posteriori (MAP) basal sliding coefficient estimate, i.e., by taking the Laplace approximation. The MAP estimate is found by minimizing the negative log posterior using an inexact Newton conjugate gradient method. The gradient and Hessian actions to vectors are efficiently computed using adjoints. Sampling from the approximate covariance is made tractable by invoking a low-rank approximation of the data misfit component of the Hessian. We study the performance of the BAE approach in the context of three numerical examples in two and three dimensions. For each example, the basal sliding coefficient field is the parameter of primary interest which we seek to infer, and the rheology parameters (e.g., the flow rate factor or the Glen's flow law exponent coefficient field) represent so-called nuisance (secondary uncertain) parameters. Our results indicate that accounting for model uncertainty stemming from the presence of nuisance parameters is crucial. Namely our findings suggest that using nominal values for these parameters, as is often done in practice, without taking into account the resulting modeling error, can lead to overconfident and heavily biased results. We also show that the BAE approach can be used to account for the additional model uncertainty at no additional cost at the online stage.

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