자료유형  

 학위논문

Control Number  

0017164302

International Standard Book Number  

9798342144230

Dewey Decimal Classification Number  

610

Main Entry-Personal Name  

Jadue, Natalie.

Publication, Distribution, etc. (Imprint  

[S.l.] : Purdue University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

66 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Buzzard, Gregery T.;Bouman, Charles A.;Yip, Nung K.;Peterson, Jonathon.

Dissertation Note  

Thesis (Ph.D.)--Purdue University, 2024.

Summary, Etc.  

요약X-ray computed tomography (CT) based on photon counting detectors (PCD) extends standard CT by counting detected photons in multiple energy bins. PCD data can be used to increase the contrast-to-noise ratio (CNR), increase spatial resolution, reduce radiation dose, reduce injected contrast dose, and compute a material decomposition using a specified set of basis materials [1]. Current commercial and prototype clinical photon counting CT systems utilize PCD-CT reconstruction methods that either reconstruct from each spectral bin separately, or first create an estimate of a material sinogram using a specified set of basis materials and then reconstruct from these material sinograms. However, existing methods are not able to utilize simultaneously and in a modular fashion both the measured spectral information and advanced prior models in order to produce a material decomposition.We describe an efficient, modular framework for PCD-based CT reconstruction and material decomposition using Multi-Agent Consensus Equilibrium (MACE). Portions of this dissertation appear in [2]. Our method employs a detector proximal map or agent that uses PCD measurements to update an estimate of the path length sinogram. We also create a prior agent in the form of a sinogram denoiser that enforces both physical and empirical knowledge about the material-decomposed sinogram. The sinogram reconstruction is computed using the MACE algorithm, which finds an equilibrium solution between the two agents, and the final image is reconstructed from the estimated sinogram. Importantly, the modularity of our method allows the two agents to be designed, implemented, and optimized independently. Our results on simulated data show a substantial (2-3 times) noise reduction vs conventional maximum likelihood reconstruction when applied to a phantom used to evaluate low contrast detectability. Our results with measured data show an even higher reduction (2-12 times) in noise standard deviation. Lastly, we demonstrate our method on a Lungman phantom that more realistically represents the human body.

Subject Added Entry-Topical Term  

Tomography.

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Iodine.

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Iterative methods.

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Optimization techniques.

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Adaptation.

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Decomposition.

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Energy.

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Contrast agents.

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Geometry.

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Radiation.

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X-rays.

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Mathematics.

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Medical imaging.

Added Entry-Corporate Name  

Purdue University.

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

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Control Number  

joongbu:657653