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Advanced Strain Retrieval for Optica...

Mazzolani, Andrea,

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Advanced Strain Retrieval for Optical Coherence Elastography /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced Strain Retrieval for Optical Coherence Elastography // Andrea Mazzolani.
作者:	Mazzolani, Andrea,
面頁冊數:	1 electronic resource (283 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Contained By:	Dissertations Abstracts International86-07B.
標題:	Biomedical engineering. -
電子資源:	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 /
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