자료유형  

 학위논문

Control Number  

0017162789

International Standard Book Number  

9798382738871

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Li, Zongyu.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Michigan., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

161 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

General Note  

Advisor: Dewaraja, Yuni K.;Fessler, Jeffrey A.

Dissertation Note  

Thesis (Ph.D.)--University of Michigan, 2024.

Summary, Etc.  

요약We live in a world where many objects cannot be imaged directly and hence rely on reconstruction algorithms to solve the corresponding inverse imaging problems. However, lots of information is contaminated or even lost when samples are collected by imaging devices, so that the resulting inverse problem is ill-posed and challenging to solve. As the recorded photon arrivals by the sensor are often assumed to follow Poisson distributions, algorithms for solving Poisson inverse problems are crucial. This thesis tackles two applications where Poisson inverse problems arise: phase retrieval and single photon emission computerized tomography (SPECT).For phase retrieval, we propose novel optimization algorithms working in low-count regimes, including a novel majorize-minimize (MM) algorithm, a modified Wirtinger flow algorithm using the observed Fisher information for step size and a generative image prior based on score matching. Our proposed algorithms lead to faster convergence rate and improved reconstruction quality evaluated both qualitatively and quantitatively.For SPECT imaging, we focus on deep learning (DL) solutions including: 1) We propose end-to-end training of unrolled iterative convolutional neural network (CNN) using our memory efficient Julia toolbox for SPECT image reconstruction. 2) We propose a dl algorithm for joint dosimetry estimation and image deblurring for estimating patient's absorbed dose-rate distribution in radionuclide therapy. 3) We propose unsupervised coordinate-based learning for predicting missing SPECT projection views.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Medical imaging.

Subject Added Entry-Topical Term  

Electrical engineering.

Subject Added Entry-Topical Term  

Computer engineering.

Index Term-Uncontrolled  

Poisson inverse problems

Index Term-Uncontrolled  

Phase retrieval

Index Term-Uncontrolled  

Deep learning

Index Term-Uncontrolled  

Imaging devices

Added Entry-Corporate Name  

University of Michigan Electrical and Computer Engineering

Host Item Entry  

Dissertations Abstracts International. 85-12B.

Electronic Location and Access  

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Control Number  

joongbu:657789