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Related Concept Videos

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 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.
Description of the Procedures
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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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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Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

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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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Related Experiment Video

Updated: Jul 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Integrative Bayesian tensor regression for imaging genetics applications.

Yajie Liu1, Nilanjana Chakraborty2, Zhaohui S Qin3

  • 1Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States.

Frontiers in Neuroscience
|September 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method combining brain imaging and genetic data for better Alzheimer's disease prediction. The approach improves early detection by analyzing spatial patterns and gene relationships.

Keywords:
Alzheimer's diseaseBayesian tensor regression modelscollinearityimaging genetics analysistranscriptomics

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Area of Science:

  • Neuroimaging
  • Genetics
  • Biostatistics

Background:

  • Alzheimer's disease (AD) early detection is crucial for effective treatment.
  • Medical imaging and genetics offer valuable biomarkers for AD.
  • Combining multimodal data (imaging and genetics) shows promise for improved predictive accuracy.

Purpose of the Study:

  • To develop a novel integrative Bayesian model for predicting cognitive outcomes in Alzheimer's disease.
  • To systematically combine high-dimensional, voxel-level imaging data with transcriptomic features.
  • To address limitations of existing methods that ignore spatial configurations and gene dependencies.

Main Methods:

  • Proposed a Bayesian scalar-on-image regression model integrating voxel-level imaging and transcriptomic data.
  • Utilized a tensor approach for spatial dependency modeling and dimension reduction in imaging data.
  • Employed a Graph-Laplacian prior to model dependencies within transcriptomic features.
  • Implemented the model using an efficient Markov chain Monte Carlo (MCMC) computation strategy.

Main Results:

  • The proposed integrative imaging-transcriptomics approach significantly improved prediction of cognitive scores.
  • The method outperformed models using only imaging or genetic data.
  • Accounting for inherent dependencies between imaging and genetic features enhanced prediction accuracy.

Conclusions:

  • The novel Bayesian model effectively integrates voxel-level imaging and transcriptomic data for Alzheimer's disease prediction.
  • This approach demonstrates significant advantages over single-modality or un-modeled multimodal methods.
  • The findings highlight the importance of considering spatial information and feature dependencies for accurate AD outcome prediction.