자료유형  

 학위논문

Control Number  

0016931457

International Standard Book Number  

9798380618816

Dewey Decimal Classification Number  

616

Main Entry-Personal Name  

Bae, Jonghyun.

Publication, Distribution, etc. (Imprint  

[S.l.] : New York University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(211 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

General Note  

Advisor: Kim, Sungheon Gene.

Dissertation Note  

Thesis (Ph.D.)--New York University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약The Blood-Brain Barrier (BBB) is a cellular barrier lining the brain capillaries, which protects the brain parenchyma by halting the entry of neurotoxins. Recent studies have shown that increased BBB permeability is associated with aging and the progression of Alzheimer's disease (AD). Among non-invasive methodologies, Dynamic Contrast-Enhanced MRI (DCE-MRI) is a non-invasive imaging approach recognized for its capacity of quantitatively assessing BBB disruption. Through acquiring T1-weighted images during and after injecting a contrast agent intravenously, DCE-MRI enables the measurement of BBB permeability to Gadolinium-based contrast agent. However, previous DCE-MRI studies of BBB permeability faced several challenges, such as long scan times and inconsistent intra- and inter-study permeability measures, thereby limiting its potential as a biomarker for AD.In this dissertation, we address these challenges to improve the measurement of subtle BBB permeability changes. First, we propose a novel pharmacokinetic model that accurately measures BBB permeability while reducing scan time. Our results demonstrate that the conventional Patlak model tends to overestimate permeability measures when the scan time is reduced from 30 min to 10 min. In contrast, our proposed model with vascular transport design yields accurate measurements with a reduced scan time.To reduce the variability in permeability measures arising from the selection of the input function, we develop a deep learning approach to automatically predict a local capillary-level input function (CIF). The feasibility of CIF is demonstrated using our breast cancer dataset, where kinetic parameters estimated with network-predicted CIF show similar diagnostic performance to that obtained from the conventional approach.In our animal experiments, we first examine the sensitivity of our measurements by systematically inducing different levels of subtle BBB disruption using focused ultrasound with varying acoustic power. The results suggest that our imaging method is capable of detecting subtle differences in BBB disruption. Subsequently, we investigate BBB permeability changes in AD pathology using a transgenic mouse model of AD. The results indicate that BBB disruption occurs in AD transgenic mice at an early age and worsens with aging.Finally, recognizing the importance of image reconstruction, we develop a digital reference object (DRO) that provides a wide variety of ground-truth data to assess image reconstruction techniques. The proposed developments in this dissertation contribute to more accurate measurements of subtle BBB permeability changes in aging and AD. Moreover, these advancements serve as a guide for developing future study designs for BBB permeability measurements.

Subject Added Entry-Topical Term  

Medical imaging.

Subject Added Entry-Topical Term  

Neurosciences.

Subject Added Entry-Topical Term  

Aging.

Index Term-Uncontrolled  

Alzheimer's disease

Index Term-Uncontrolled  

Neurotoxins

Index Term-Uncontrolled  

Brain parenchyma

Index Term-Uncontrolled  

Acoustic power

Index Term-Uncontrolled  

Brain capillaries

Added Entry-Corporate Name  

New York University Basic Medical Science

Host Item Entry  

Dissertations Abstracts International. 85-04B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642252