<p></p><p>One-sided ultrasonic non-destructive evaluation (UNDE) is extensively
used to characterize structures that need to be inspected and maintained from
defects and flaws that could affect the performance of power plants, such as
nuclear power plants. Most UNDE systems send acoustic pulses into the structure
of interest, measure the received waveform and use an algorithm to reconstruct
the quantity of interest. The most widely used algorithm in UNDE systems is the
synthetic aperture focusing technique (SAFT) because it produces acceptable
results in real time. A few regularized inversion techniques with linear models
have been proposed which can improve on SAFT, but they tend to make simplifying
assumptions that show artifacts and do not address how to obtain
reconstructions from large real data sets. In this thesis, we present two
studies. The first study covers the model-based iterative reconstruction (MBIR)
technique which is used to resolve some of the issues in SAFT and the current
linear regularized inversion techniques, and the second study covers the direct
deep learning (DDL) technique which is used to further resolve issues related
to non-linear interactions between the ultrasound signal and the specimen.</p>
<p>In the first study, we propose a model-based iterative
reconstruction (MBIR) algorithm designed for scanning UNDE systems. MBIR
reconstructs the image by optimizing a cost function that contains two terms:
the forward model that models the measurements and the prior model that models
the object. To further reduce some of the artifacts in the results, we enhance
the forward model of MBIR to account for the direct arrival artifacts and the
isotropic artifacts. The direct arrival signals are the signals received
directly from the transmitter without being reflected. These signals contain no
useful information about the specimen and produce high amplitude artifacts in
regions close to the transducers. We resolve this issue by modeling these direct
arrival signals in the forward model to reduce their artifacts while
maintaining information from reflections of other objects. Next, the isotropic
artifacts appear when the transmitted signal is assumed to propagate in all
directions equally. Therefore, we modify our forward model to resolve this issue
by modeling the anisotropic propagation. Next, because of the significant
attenuation of the transmitted signal as it propagates through deeper regions,
the reconstruction of deeper regions tends to be much dimmer than closer
regions. Therefore, we combine the forward model with a spatially variant prior
model to account for the attenuation by reducing the regularization as the
pixel gets deeper. Next, for scanning large structures, multiple scans are
required to cover the whole field of view. Typically, these scans are performed
in raster order which makes adjacent scans share some useful correlations.
Reconstructing each scan individually and performing a conventional stitching
method is not an efficient way because this could produce stitching artifacts
and ignore extra information from adjacent scans. We present an algorithm to
jointly reconstruct measurements from large data sets that reduces the
stitching artifacts and exploits useful information from adjacent scans. Next,
using simulated and extensive experimental data, we show MBIR results and
demonstrate how we can improve over SAFT as well as existing regularized
inversion techniques. However, even with this improvement, MBIR still results
in some artifacts caused by the inherent non-linearity of the interaction
between the ultrasound signal and the specimen.</p>
<p>In the second study, we propose DDL, 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.
While the technique we are proposing can show significant enhancement compared
to the current techniques with simulated data, one issue appears with the
performance of this technique when applied to experimental data. The issue is a
modeling mismatch between the simulated training data and the real data. We
propose an effective solution that can reduce the effect of this modeling
mismatch by adding noise to the simulation input of the training set before
simulation. This solution trains the neural network on the general features of
the system rather than specific features of the simulator and can act as a
regularization to the neural network. Another issue appears similar to the
issue in MBIR caused by the attenuation of deeper reflections. Therefore, we
propose a spatially variant amplification technique applied to the
back-projection to amplify deeper regions. Next, to reconstruct from a large
field of view that requires multiple scans, we propose a joint deep neural
network technique to jointly reconstruct an image from these multiple scans.
Finally, we apply DDL to simulated and experimental ultrasound data to
demonstrate significant improvements in image quality compared to the
delay-and-sum approach and the linear model-based reconstruction approach.</p><br><p></p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7408829 |
| Date | 16 January 2019 |
| Creators | Hani A. Almansouri (5929469) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Model-Based_Iterative_Reconstruction_and_Direct_Deep_Learning_for_One-Sided_Ultrasonic_Non-Destructive_Evaluation/7408829 |
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