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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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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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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.
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阿尔茨海默氏症成像联盟

Margaret E Flanagan1, David Gutman2, Brittany N Dugger3

  • 1Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA.

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

大脑数字幻灯片档案 (BDSA) 是一个新的开源平台,为神经退行性疾病标准化神经病理数据共享. 它通过数字病理学和机器学习增强研究和诊断,提高准确性和协作.

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科学领域:

  • 数字病理学数字病理学
  • 神经退行性疾病研究
  • 机器学习在诊断中的应用.

背景情况:

  • 神经病理学评估是诊断神经退行性疾病的黄金标准,但在数据共享和样本准备变化方面面临挑战.
  • 数字病理学和机器学习为标准化方法和提高诊断准确性提供了解决方案.

研究的目的:

  • 开发大脑数字幻灯片档案 (BDSA),这是一个联合的开源平台,用于神经退行性疾病研究和诊断.
  • 为了支持对阿尔茨海默病 (AD) 和相关痴呆症 (ADRD) 的全幻灯片图像 (WSIs),遵守FAIR数据原则.
  • 为了实现WSIs,注释和元数据的标准化共享,用于机器学习算法开发.

主要方法:

  • 在数字幻灯片档案 (DSA) 的基础上,BDSA是NIH U24资助的倡议.
  • 该平台整合了来自9个AD/ADRD研究中心的WSIs,协调数据并实施数据共享协议.
  • 包括一个匿名化工具,以保证捐赠者的机密性,并遵守安全平台的最佳行政实践.

主要成果:

  • 最初的开发将来自九个研究中心的WSI与统一的数据和通用数据共享协议集成在一起.
  • 纳入了一个匿名化工具,以确保捐赠者的保密性.
  • 对于所有贡献的美国站点,已完成数据使用和材料转移协议,并进行软件测试和标准化协议.

结论:

  • 通过标准化,用户友好的数字存储库,BDSA实现了对神经病理学数据的访问民主化.
  • 隐私,数据共享和机器学习的综合工具将提高对神经退行性疾病的理解,并促进合作.
  • 作为一个联合的开源资源,BDSA有潜力将数字神经病理转化为AD/ADRD研究和诊断.