東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Advanced Strain Retrieval for Optica...

Mazzolani, Andrea,

Linked to FindBook

Google Book

Amazon

博客來

Advanced Strain Retrieval for Optical Coherence Elastography /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced Strain Retrieval for Optical Coherence Elastography // Andrea Mazzolani.
Author:	Mazzolani, Andrea,
Description:	1 electronic resource (283 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Contained By:	Dissertations Abstracts International86-07B.
Subject:	Biomedical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31838979
ISBN:	9798302828026

Advanced Strain Retrieval for Optical Coherence Elastography /
Mazzolani, Andrea,

Advanced Strain Retrieval for Optical Coherence Elastography /Andrea Mazzolani. - 1 electronic resource (283 pages)

Source: Dissertations Abstracts International, Volume: 86-07, Section: B.

Mechanical properties of tissue, such as elasticity and stiffness, are crucial health indicators. Techniques to measure these properties, known as elastography, include ultra-sound and magnetic resonance imaging. Recently, optical coherence tomography (OCT) has been utilized for elastography, resulting in optical coherence elastography (OCE), which offers higher sensitivity to tissue deformation and superior spatial resolution.Strain retrieval in OCE involves estimating tissue deformations to determine local strains. Conventional strain retrieval models use the OCT phase difference between unloaded and loaded signals to estimate strain. However, these models face significant limitations, mainly due to strong assumptions regarding the OCT phase difference.The motivation of my work was to develop accurate and robust OCE methods that do not rely on these assumptions and can overcome the limitations of conventional models. I first analyze the state-of-the-art OCT and OCE techniques, followed by an in-depth examination of the OCT phase difference used in conventional models. I demonstrated that the assumptions of conventional OCE models lead to inaccurate strain estimations, especially in tissues with highly heterogeneous stiffness, such as cancerous tissues. To overcome these limitations, I developed the mathematical formalism of the complex-speckle recorrelation model (CSR), which does not make these strong assumptions on the phase, and is highly robust. Subsequently, I used deep learning to create ElastoCNN, a more general OCE model that is faster and more robust than CSR, capable of retrieving strains under more general mechanical loadings. ElastoCNN was trained with simulated data, and I accelerated the OCT image simulation process by using analytical approximations and a novel technique I developed, denoted as multi-spectral regression (MSR). This enabled the generation of realistic OCE data and the successful training of ElastoCNN, which surpasses conventional models in robustness and accuracy, significantly enhancing the reliability of OCE.

English

ISBN: 9798302828026Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Computational imaging

Advanced Strain Retrieval for Optical Coherence Elastography /
LDR:03571nmm a22004333i 4500 001 2434798
005 20251001062527.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798302828026
035 $a (MiAaPQD)AAI31838979
035 $a AAI31838979
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Mazzolani, Andrea, $e author. $3 3824903
245 1 0 $a Advanced Strain Retrieval for Optical Coherence Elastography / $c Andrea Mazzolani.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (283 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: 86-07, Section: B.
500 $a Advisors: Munro, Peter R. T. Committee members: Hebden, Jem; Matcher, Stephen J.
502 $b Ph.D. $c University of London, University College London (United Kingdom) $d 2025.
520 $a Mechanical properties of tissue, such as elasticity and stiffness, are crucial health indicators. Techniques to measure these properties, known as elastography, include ultra-sound and magnetic resonance imaging. Recently, optical coherence tomography (OCT) has been utilized for elastography, resulting in optical coherence elastography (OCE), which offers higher sensitivity to tissue deformation and superior spatial resolution.Strain retrieval in OCE involves estimating tissue deformations to determine local strains. Conventional strain retrieval models use the OCT phase difference between unloaded and loaded signals to estimate strain. However, these models face significant limitations, mainly due to strong assumptions regarding the OCT phase difference.The motivation of my work was to develop accurate and robust OCE methods that do not rely on these assumptions and can overcome the limitations of conventional models. I first analyze the state-of-the-art OCT and OCE techniques, followed by an in-depth examination of the OCT phase difference used in conventional models. I demonstrated that the assumptions of conventional OCE models lead to inaccurate strain estimations, especially in tissues with highly heterogeneous stiffness, such as cancerous tissues. To overcome these limitations, I developed the mathematical formalism of the complex-speckle recorrelation model (CSR), which does not make these strong assumptions on the phase, and is highly robust. Subsequently, I used deep learning to create ElastoCNN, a more general OCE model that is faster and more robust than CSR, capable of retrieving strains under more general mechanical loadings. ElastoCNN was trained with simulated data, and I accelerated the OCT image simulation process by using analytical approximations and a novel technique I developed, denoted as multi-spectral regression (MSR). This enabled the generation of realistic OCE data and the successful training of ElastoCNN, which surpasses conventional models in robustness and accuracy, significantly enhancing the reliability of OCE.
546 $a English
590 $a School code: 6022
650 4 $a Biomedical engineering. $3 535387
650 4 $a Medical imaging. $3 3172799
650 4 $a Applied physics. $3 3343996
653 $a Computational imaging
653 $a Deep learning
653 $a Optical coherence elastography
653 $a Optical coherence tomography
653 $a Strain retrieval
690 $a 0541
690 $a 0574
690 $a 0215
710 2 $a University of London, University College London (United Kingdom). $b Science and Technology Studies. $e degree granting institution. $3 3814655
720 1 $a Munro, Peter R. T. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-07B.
790 $a 6022
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31838979