자료유형  

 학위논문

Control Number  

0016932510

International Standard Book Number  

9798379664510

Dewey Decimal Classification Number  

610

Main Entry-Personal Name  

Balke, Thilo.

Publication, Distribution, etc. (Imprint  

[S.l.] : Purdue University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(91 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Bouman, Charles A.

Dissertation Note  

Thesis (Ph.D.)--Purdue University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약There is a great need for accurate image reconstruction in the context of non-destructive evaluation. Major challenges include the ever-increasing necessity for high resolution reconstruction with limited scan and reconstruction time and thus fewer and noisier measurements. In this thesis, we leverage advanced Bayesian modeling of the physical measurement process and probabilistic prior information of the image distribution in order to yield higher image quality despite limited measurement time. We demonstrate in several ways efficient computational performance through the exploitation of more efficient memory access, optimized parametrization of the system model, and multi-pixel parallelization. We demonstrate that by building high-fidelity forward models that we can generate quantitatively reliable reconstructions despite very limited measurement data.In the first chapter, we introduce an algorithm for estimating isotopic densities from neutron time-of-flight imaging data. Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material. Energy-dependent radiography image sequences can be created by utilizing neutron time-of-flight techniques. In combination with uniquely characteristic isotopic neutron cross-section spectra, isotopic areal densities can be determined on a per-pixel basis, thus resulting in a set of areal density images for each isotope present in the sample. By preforming ERNI measurements over several rotational views, an isotope decomposed 3D computed tomography is possible. We demonstrate a method involving a robust and automated background estimation based on a linear programming formulation. The extremely high noise due to low count measurements is overcome using a sparse coding approach. It allows for a significant computation time improvement, from weeks to a few hours compared to existing neutron evaluation tools, enabling at the present stage a semi-quantitative, user-friendly routine application.In the second chapter, we introduce the TRINIDI algorithm, a more refined algorithm for the same problem. Accurate reconstruction of 2D and 3D isotope densities is a desired capability with great potential impact in applications such as evaluation and development of next-generation nuclear fuels. Neutron time-of-flight (TOF) resonance imaging offers a potential approach by exploiting the characteristic neutron adsorption spectra of each isotope. However, it is a major challenge to compute quantitatively accurate images due to a variety of confounding effects such as severe Poisson noise, background scatter, beam non-uniformity, absorption non-linearity, and extended source pulse duration. We present the TRINIDI algorithm which is based on a two-step process in which we first estimate the neutron flux and background counts, and then reconstruct the areal densities of each isotope and pixel. Both components are based on the inversion of a forward model that accounts for the highly non-linear absorption, energy-dependent emission profile, and Poisson noise, while also modeling the substantial spatio-temporal variation of the background and flux. To do this, we formulate the non-linear inverse problem as two optimization problems that are solved in sequence. We demonstrate on both synthetic and measured data that TRINIDI can reconstruct quantitatively accurate 2D views of isotopic areal density that can then be reconstructed into quantitatively accurate 3D volumes of isotopic volumetric density.In the third chapter, we introduce a separable forward model for cone-beam computed tomography (CT) that enables efficient computation of a Bayesian model-based reconstruction. Cone-beam CT is an attractive tool for many kinds of non-destructive evaluation (NDE).

Subject Added Entry-Topical Term  

Tomography.

Subject Added Entry-Topical Term  

Neutrons.

Subject Added Entry-Topical Term  

Isotopes.

Subject Added Entry-Topical Term  

Maps.

Subject Added Entry-Topical Term  

Decomposition.

Subject Added Entry-Topical Term  

Linear programming.

Subject Added Entry-Topical Term  

Energy.

Subject Added Entry-Topical Term  

Algorithms.

Subject Added Entry-Topical Term  

X-rays.

Subject Added Entry-Topical Term  

Radiography.

Subject Added Entry-Topical Term  

Parameter estimation.

Subject Added Entry-Topical Term  

Atomic physics.

Subject Added Entry-Topical Term  

Medical imaging.

Subject Added Entry-Topical Term  

Physics.

Added Entry-Corporate Name  

Purdue University.

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:640919