東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Model-Based Approaches to Ill-Condit...

Li, Wenrui,

FindBook

Google Book

Amazon

博客來

Model-Based Approaches to Ill-Conditioned Inverse Problems in X-Ray Imaging /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Model-Based Approaches to Ill-Conditioned Inverse Problems in X-Ray Imaging // Wenrui Li.
作者:	Li, Wenrui,
面頁冊數:	1 electronic resource (106 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-10, Section: B.
Contained By:	Dissertations Abstracts International86-10B.
標題:	Physics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31968608
ISBN:	9798311942591

Model-Based Approaches to Ill-Conditioned Inverse Problems in X-Ray Imaging /
Li, Wenrui,

Model-Based Approaches to Ill-Conditioned Inverse Problems in X-Ray Imaging /Wenrui Li. - 1 electronic resource (106 pages)

Source: Dissertations Abstracts International, Volume: 86-10, Section: B.

X-ray Computed Tomography (XCT) is a widely used non-destructive imaging technique. With advancements in hardware technology, including X-ray source and detector improvements, there is increasing demand for higher spatial resolution, faster acquisition times, and enhanced imaging quality. Moreover, customers seek better performance by addressing challenges such as metal artifacts and imaging low-attenuation objects. These evolving requirements have intensified the need to solve many ill-conditioned inversion problems inherent in X-ray imaging, driving innovations in algorithm development.This thesis introduces model-based approaches to address two ill-conditioned inverse problems in X-ray imaging. In the first portion of the thesis, we address the inverse problem of estimating the effective spectrum for different X-ray systems using transmission CT measurement of homogeneous metal rods with known material and dimensions. We proposed two approaches to estimate the effective spectrum. First, we propose a dictionary-based spectral estimation (DictSE) algorithm that represents the unknown spectral response using an overcomplete dictionary and finds the optimal sparse representation of the spectrum. Second, we propose a model-based spectral calibration (MBSC) algorithm that models the effective spectrum as a function of some physically meaningful parameters and estimates the spectral response by estimating a limited number of parameters with multi-voltage or multi-filtration dataset. Using simulated and experimental data, we demonstrate that MBSC outperforms other methods' accuracy and robustness. The implementation of both spectral calibration methods is publicly available at https://github.com/cabouman/xspec.In the second portion of the thesis, we address the inverse problem of reconstructing images from sparse-view CT scans, a critical task in many applications to achieve faster acquisition times. We propose a direct reconstruction method called Recurrent Stacked Back Projection (RSBP), which leverages a deep recurrent neural network on the Stacked Back Projections (SBP) to improve reconstruction quality. Using simulated and experimental data, we demonstrate that our method produces more high-quality reconstructions from sparse measurements than the reconstructions from filter backprojection (FBP) and modelbased iterative reconstruction (MBIR).

English

ISBN: 9798311942591Subjects--Topical Terms:

516296
Physics.

Model-Based Approaches to Ill-Conditioned Inverse Problems in X-Ray Imaging /
LDR:03760nmm a22003973i 4500 001 2434864
005 20251001062543.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798311942591
035 $a (MiAaPQD)AAI31968608
035 $a (MiAaPQD)Purdue28548923
035 $a AAI31968608
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Li, Wenrui, $e author. $3 3824994
245 1 0 $a Model-Based Approaches to Ill-Conditioned Inverse Problems in X-Ray Imaging / $c Wenrui Li.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (106 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-10, Section: B.
500 $a Advisors: Bouman, Charles A.; Buzzard, Gregery T. Committee members: Kak, Avinash C.; Chan, Stanley H.
502 $b Ph.D. $c Purdue University $d 2025.
520 $a X-ray Computed Tomography (XCT) is a widely used non-destructive imaging technique. With advancements in hardware technology, including X-ray source and detector improvements, there is increasing demand for higher spatial resolution, faster acquisition times, and enhanced imaging quality. Moreover, customers seek better performance by addressing challenges such as metal artifacts and imaging low-attenuation objects. These evolving requirements have intensified the need to solve many ill-conditioned inversion problems inherent in X-ray imaging, driving innovations in algorithm development.This thesis introduces model-based approaches to address two ill-conditioned inverse problems in X-ray imaging. In the first portion of the thesis, we address the inverse problem of estimating the effective spectrum for different X-ray systems using transmission CT measurement of homogeneous metal rods with known material and dimensions. We proposed two approaches to estimate the effective spectrum. First, we propose a dictionary-based spectral estimation (DictSE) algorithm that represents the unknown spectral response using an overcomplete dictionary and finds the optimal sparse representation of the spectrum. Second, we propose a model-based spectral calibration (MBSC) algorithm that models the effective spectrum as a function of some physically meaningful parameters and estimates the spectral response by estimating a limited number of parameters with multi-voltage or multi-filtration dataset. Using simulated and experimental data, we demonstrate that MBSC outperforms other methods' accuracy and robustness. The implementation of both spectral calibration methods is publicly available at https://github.com/cabouman/xspec.In the second portion of the thesis, we address the inverse problem of reconstructing images from sparse-view CT scans, a critical task in many applications to achieve faster acquisition times. We propose a direct reconstruction method called Recurrent Stacked Back Projection (RSBP), which leverages a deep recurrent neural network on the Stacked Back Projections (SBP) to improve reconstruction quality. Using simulated and experimental data, we demonstrate that our method produces more high-quality reconstructions from sparse measurements than the reconstructions from filter backprojection (FBP) and modelbased iterative reconstruction (MBIR).
546 $a English
590 $a School code: 0183
650 4 $a Physics. $3 516296
650 4 $a Success. $3 518195
650 4 $a Inverse problems. $3 3686950
650 4 $a Medical research. $2 bicssc $3 1556686
650 4 $a Energy. $3 876794
650 4 $a Medical imaging. $3 3172799
650 4 $a X-rays. $3 604695
650 4 $a Parameter estimation. $3 567557
690 $a 0791
690 $a 0574
690 $a 0605
710 2 $a Purdue University. $e degree granting institution. $3 3759105
720 1 $a Bouman, Charles A. $e degree supervisor.
720 1 $a Buzzard, Gregery T. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-10B.
790 $a 0183
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31968608