Anisotropic modeling and joint-MAP stitching for improved ultrasound model-based iterative reconstruction of large and thick specimens

Almansouri, Hani; Venkatakrishnan, Singanallur; Clayton, Dwight A; Polsky, Yarom; Bouman, Charles A.; Santos-Villalobos, Hector

doi:10.1063/1.5031525

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…We compare the proposed DDL algorithm to SAFT [26] and L-MBIR [6]. We used a ten transducer system with an acquisition geometry in which one of the transducers transmits while the others receive.…”

Section: Resultsmentioning

confidence: 99%

“…However, the inversion of the underlying quantities from the received signals based on this model is challenging because of the complicated and non-linear nature of the forward model. To address these challenges, we developed a simplified linear model for the measurements [6], given bỹ…”

Section: Ultrasound Forward Model and Linear Model-based Inversionmentioning

confidence: 99%

“…whereỹ i,j is the measurement at the transmit-receive pair (i, j), ν is a point in the field of view,Ã i,j is a response function that accounts for the time-shift and attenuation of the transmitted pulse,x is the reflection coefficient, τ i,j is the time delay of the transmitted signal for point ν and the measurement pair (i, j),d i,j is the direct arrival signal. Using this model, we designed a fast model-based reconstruction approach [6] (L-MBIR) which works by minimizing the…”

Section: Ultrasound Forward Model and Linear Model-based Inversionmentioning

confidence: 99%

“…To address these challenges, we developed a simplified linear model for the measurements [6], given by ỹi,j (t) =…”

Section: Ultrasound Forward Model and Linear Model-based Inversionmentioning

confidence: 99%

“…[2] are not practical for ultrasound NDE and hence analytic algorithms based on a delay-and-sum approach, such as the synthetic aperture focusing technique (SAFT), are routinely used for reconstructions of ultrasound reflection mode data [3]- [5]. Recently, we have developed a model-based iterative reconstruction [6] approach using a simplified linear model and demonstrated significant improvements in reconstruction performance compared to SAFT while still being able to produce a reconstruction in near real-time. However, this linear-MBIR (L-MBIR) method still results in artifacts, such as reverberation and shadowing, due to the inherent nonlinearity of the ultrasound system.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Deep Neural Networks for Non-Linear Model-Based Ultrasound Reconstruction

Almansouri

Venkatakrishnan

Buzzard

et al. 2018

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Ultrasound reflection tomography is widely used to image large complex specimens that are only accessible from a single side, such as well systems and nuclear power plant containment walls. Typical methods for inverting the measurement rely on delay-and-sum algorithms that rapidly produce reconstructions but with significant artifacts. Recently, model-based reconstruction approaches using a linear forward model have been shown to significantly improve image quality compared to the conventional approach. However, even these techniques result in artifacts for complex objects because of the inherent non-linearity of the ultrasound forward model.In this paper, we propose a non-iterative model-based reconstruction method for inverting measurements that are based on non-linear forward models for ultrasound imaging. Our approach involves obtaining an approximate estimate of the reconstruction using a simple linear back-projection and training a deep neural network to refine this to the actual reconstruction. We apply our method to simulated and experimental ultrasound data to demonstrate dramatic improvements in image quality compared to the delay-andsum approach and the linear model-based reconstruction approach.

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Section: Resultsmentioning

confidence: 99%

Section: Ultrasound Forward Model and Linear Model-based Inversionmentioning

confidence: 99%

Section: Ultrasound Forward Model and Linear Model-based Inversionmentioning

confidence: 99%

“…To address these challenges, we developed a simplified linear model for the measurements [6], given by ỹi,j (t) =…”

Section: Ultrasound Forward Model and Linear Model-based Inversionmentioning

confidence: 99%