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Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
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Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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Updated: Jan 8, 2026

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
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Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

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阿尔茨海默氏症成像联盟

Guanlin Guo1,2, Harinishree Sathu3, Marc D Rudolph4

  • 1University of Texas Health Science Center at Houston, Houston, TX, USA.

Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 23, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个可解释的图形卷积网络 (GCN),使用MRI数据检测早期阿尔茨海默氏病 (AD) 风险. 该模型在识别患有AD风险较高的个体和预测认知衰退方面表现有前途.

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Visualization of Amyloid β Deposits in the Human Brain with Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry
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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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科学领域:

  • 神经成像是一种神经成像.
  • 机器学习 机器学习
  • 神经学 神经学

背景情况:

  • 阿尔茨海默病 (AD) 是导致痴呆的主要原因.
  • 在T1MRI中通过皮质缩模式检测早期的AD神经退行是具有挑战性的,因为细微的,异质的变化.
  • 传统的深度学习方法很难捕捉这些早期的,微妙的模式.

研究的目的:

  • 开发一个可解释的皮质图卷积网络 (GCN).
  • 捕捉皮质表面的早期缩模式,以识别患有AD风险较高的人.
  • 为了改善预发性AD的早期诊断.

主要方法:

  • 利用了来自阿尔茨海默病神经成像计划 (ADNI) 数据集 (1645名受试者) 的T1MRI数据.
  • 使用FreeSurfer处理MRI数据,并使用皮质GCN模型将皮质表面视为图形.
  • 在90%的CN+AD数据上训练了五倍交叉验证的模型,在10%的数据上进行了测试,并评估了稳定的MCI (sMCI) 与渐进的MCI (pMCI) 的预测.

主要成果:

  • 皮质GCN模型在区分痴呆症 (AD) 和认知正常 (CN) 个体方面实现了0.736平衡的准确性.
  • 该模型获得了0.644的平均平均平衡精度,用于从pMCI预测sMCI.
  • 证明了该模型能够预测风险人群未来痴呆症发病的能力.

结论:

  • 这项研究表明,皮质GCN对于早期预测患有AD风险的个体痴呆发病的有效性.
  • 未来的工作包括对NACC/ADRC数据进行独立验证,以评估可通用性.
  • 计划使用Grad-CAM和集成梯度等技术来提高模型可解释性,以提供更深入的见解和改进诊断工具.