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Related Concept Videos

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 III: Computed Tomography01:27

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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 for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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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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Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
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Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
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Residual Conditional Variational Autoencoder for Multi-Center PET/CT Radiomic Feature Harmonization with Integrated

Bingzhen Wang1,2, Jinghua Liu3,4, Xiaolei Zhang2

  • 1Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.

Journal of Imaging Informatics in Medicine
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Summary

A new Residual Conditional Variational Autoencoder (ResCVAE-Harmonizer) model improves multi-center medical image feature harmonization. It effectively reduces batch effects and enhances survival prediction accuracy for PET and CT data.

Keywords:
CTHarmonizationMulti-centerPETRadiomics

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Area of Science:

  • Medical imaging analysis
  • Machine learning for healthcare
  • Radiomics and deep learning

Background:

  • Multi-center studies face challenges with batch effects in medical imaging data.
  • Variations in imaging protocols and equipment across centers can impact data consistency.
  • Accurate feature harmonization is crucial for reliable downstream analysis and clinical applications.

Purpose of the Study:

  • To introduce and evaluate the Residual Conditional Variational Autoencoder Harmonizer (ResCVAE-Harmonizer) for multi-center feature harmonization.
  • To compare the performance of ResCVAE-Harmonizer against traditional methods like ComBat and CovBat.
  • To assess the impact of harmonization on feature consistency and downstream tasks like classification and survival prediction.

Main Methods:

  • Development of the ResCVAE-Harmonizer model integrating batch and clinical covariate information.
  • Extraction of low-dimensional, high-dimensional radiomic, and deep learning features from PET and CT images.
  • Harmonization of extracted features using ResCVAE-Harmonizer, ComBat, and CovBat.
  • Comprehensive evaluation using variance homogeneity analysis, multi-center classification, and survival prediction tasks.

Main Results:

  • ResCVAE-Harmonizer significantly improved cross-center feature consistency, particularly for radiomic features.
  • Harmonized features demonstrated enhanced stability in multi-center classification tasks.
  • ResCVAE-harmonized PET deep learning features achieved the highest C-index for survival prediction (0.8920), outperforming original features and other methods.
  • Kaplan-Meier survival curves showed clearer risk group separation with ResCVAE-harmonized features.

Conclusions:

  • ResCVAE-Harmonizer effectively eliminates linear and nonlinear batch effects in multi-center imaging data.
  • The model significantly enhances performance in downstream survival prediction tasks.
  • ResCVAE-Harmonizer shows strong potential for improving the reliability and utility of multi-center medical imaging studies.