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

切換: 標籤 | MARC模式 | ISBD

Postmortem Image Analysis of the Hum...

Khandelwal, Pulkit,

FindBook

Google Book

Amazon

博客來

Postmortem Image Analysis of the Human Brain to Characterize Alzheimer's Disease and Related Dementias /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Postmortem Image Analysis of the Human Brain to Characterize Alzheimer's Disease and Related Dementias // Pulkit Khandelwal.
作者:	Khandelwal, Pulkit,
面頁冊數:	1 electronic resource (172 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
標題:	Bioengineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31997808
ISBN:	9798280758261

Postmortem Image Analysis of the Human Brain to Characterize Alzheimer's Disease and Related Dementias /
Khandelwal, Pulkit,

Postmortem Image Analysis of the Human Brain to Characterize Alzheimer's Disease and Related Dementias /Pulkit Khandelwal. - 1 electronic resource (172 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Alzheimer's disease and related disorders are heterogeneous, age-related neurodegenerative diseases characterized by the co-occurrence of multiple pathologies that manifest themselves decades before a patient becomes symptomatic. Clinical trials have focused on preclinical stages of AD to detect and diagnose a patient using in vivo imaging biomarkers. These biomarkers have been sensitive in detecting structural changes, such as cortical thickness and volumetry, during the early stages of AD. However, their potential as an imaging biomarker is limited due to their lack of sensitivity in differentiating co-morbid neurodegenerative pathologies, which simultaneously affect brain morphometry. Studies have shown that different brain regions are differentially affected by such comorbid pathologies. Therefore, there is a need for region-specific in vivo biomarkers that can detect and quantify mixed pathologies. The work presented in this dissertation is built on the hypothesis that linking ex vivo MRI-derived morphometry measurements with neuropathology would reveal stronger morphometry/pathology signatures than antemortem MRI, which would help inform the development of disease-specific in vivo biomarkers.To discover AD-specific structural patterns associated with neuropathology/neurodegeneration, we introduce and leverage a postmortem whole-hemisphere MRI dataset to link neuropathological markers derived from gold-standard histopathology examination with morphometry measurements derived from structural MRI. This dissertation presents a set of computational image analysis methods and pipelines to analyze high-resolution postmortem MRI consistently. Specifically, we present a fast, robust, and automated voxel-based tissue segmentation and surface-based anatomical parcellation pipeline for whole cerebral hemisphere 7 tesla postmortem MRI in diseased populations. Next, by constructing a population-level postmortem ex vivo MRI template, we conduct both voxel-based and surface-based point-wise statistical studies within a common coordinate system to help discover disease-specific patterns of neurodegeneration by linking morphometry measurements with neuropathology markers derived from MRI and histopathology, respectively. We then present a framework to align within-subject postmortem MRI to its corresponding antemortem MRI, which would enable the exchange of information from postmortem to corresponding antemortem MRI. This dissertation presents a one-of-a-kind analysis that compares morphometry/pathology in matched postmortem and antemortem MRI specimens. Our results suggest that high-resolution postmortem 7 tesla MRI yields localized atrophy measures that are more sensitive to tau pathology and neuronal loss in Alzheimer's disease than corresponding measures on antemortem 3 tesla MRI. The computational tools, methods, and pipelines presented in this dissertation are released as a stand-alone software, "purple-mri: Penn Utilities for Registration and ParcelLation of Ex vivo MRI", to enable systematic and continued development of the next generation of image analysis algorithms for postmortem imaging. The techniques and tools presented in this dissertation will help inform the development of AD-specific in vivo biomarkers for clinical trials.

English

ISBN: 9798280758261Subjects--Topical Terms:

657580
Bioengineering.
Subjects--Index Terms:

Alzheimer's disease

Postmortem Image Analysis of the Human Brain to Characterize Alzheimer's Disease and Related Dementias /
LDR:04796nmm a22004453i 4500 001 2432699
005 20250731101916.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798280758261
035 $a (MiAaPQD)AAI31997808
035 $a AAI31997808
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Khandelwal, Pulkit, $e author. $3 3822020
245 1 0 $a Postmortem Image Analysis of the Human Brain to Characterize Alzheimer's Disease and Related Dementias / $c Pulkit Khandelwal.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (172 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: 86-12, Section: B.
500 $a Advisors: Yushkevich, Paul A. Committee members: Wolk, David A.; Shi, Jianbo; Fischl, Bruce; Grinberg, Lea T.
502 $b Ph.D. $c University of Pennsylvania $d 2025.
520 $a Alzheimer's disease and related disorders are heterogeneous, age-related neurodegenerative diseases characterized by the co-occurrence of multiple pathologies that manifest themselves decades before a patient becomes symptomatic. Clinical trials have focused on preclinical stages of AD to detect and diagnose a patient using in vivo imaging biomarkers. These biomarkers have been sensitive in detecting structural changes, such as cortical thickness and volumetry, during the early stages of AD. However, their potential as an imaging biomarker is limited due to their lack of sensitivity in differentiating co-morbid neurodegenerative pathologies, which simultaneously affect brain morphometry. Studies have shown that different brain regions are differentially affected by such comorbid pathologies. Therefore, there is a need for region-specific in vivo biomarkers that can detect and quantify mixed pathologies. The work presented in this dissertation is built on the hypothesis that linking ex vivo MRI-derived morphometry measurements with neuropathology would reveal stronger morphometry/pathology signatures than antemortem MRI, which would help inform the development of disease-specific in vivo biomarkers.To discover AD-specific structural patterns associated with neuropathology/neurodegeneration, we introduce and leverage a postmortem whole-hemisphere MRI dataset to link neuropathological markers derived from gold-standard histopathology examination with morphometry measurements derived from structural MRI. This dissertation presents a set of computational image analysis methods and pipelines to analyze high-resolution postmortem MRI consistently. Specifically, we present a fast, robust, and automated voxel-based tissue segmentation and surface-based anatomical parcellation pipeline for whole cerebral hemisphere 7 tesla postmortem MRI in diseased populations. Next, by constructing a population-level postmortem ex vivo MRI template, we conduct both voxel-based and surface-based point-wise statistical studies within a common coordinate system to help discover disease-specific patterns of neurodegeneration by linking morphometry measurements with neuropathology markers derived from MRI and histopathology, respectively. We then present a framework to align within-subject postmortem MRI to its corresponding antemortem MRI, which would enable the exchange of information from postmortem to corresponding antemortem MRI. This dissertation presents a one-of-a-kind analysis that compares morphometry/pathology in matched postmortem and antemortem MRI specimens. Our results suggest that high-resolution postmortem 7 tesla MRI yields localized atrophy measures that are more sensitive to tau pathology and neuronal loss in Alzheimer's disease than corresponding measures on antemortem 3 tesla MRI. The computational tools, methods, and pipelines presented in this dissertation are released as a stand-alone software, "purple-mri: Penn Utilities for Registration and ParcelLation of Ex vivo MRI", to enable systematic and continued development of the next generation of image analysis algorithms for postmortem imaging. The techniques and tools presented in this dissertation will help inform the development of AD-specific in vivo biomarkers for clinical trials.
546 $a English
590 $a School code: 0175
650 4 $a Bioengineering. $3 657580
650 4 $a Biomedical engineering. $3 535387
650 4 $a Neurosciences. $3 588700
650 4 $a Medical imaging. $3 3172799
653 $a Alzheimer's disease
653 $a Deep learning
653 $a Dementia
653 $a Neuroimaging
653 $a Postmortem MRI
690 $a 0202
690 $a 0541
690 $a 0317
690 $a 0574
710 2 $a University of Pennsylvania. $b Bioengineering. $e degree granting institution. $3 3822021
720 1 $a Yushkevich, Paul A. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-12B.
790 $a 0175
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31997808