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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.
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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.
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Imaging Studies for Cardiovascular System V: CT01:28

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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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Radiological Investigation I: X-ray and CT01:30

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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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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Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

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Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
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Related Experiment Video

Updated: Mar 19, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
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Transformer-based Fusion of Longitudinal Multimodal Radiomic Features from Chest Radiography and CT in COVID-19.

Chunrui Zou1, Walter Mankowski1, Lauren Pantalone2

  • 1Department of Radiology, Columbia University, 622 W. 168th St, Alianza Dominicana Cultural Center, 5th Fl, New York, NY 10032.

Radiology. Artificial Intelligence
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

A transformer model effectively fused longitudinal radiomic data from chest X-rays and CT scans to predict COVID-19 patient outcomes, including mortality and ICU admission. This approach demonstrated superior performance compared to single-modality or cross-sectional data models.

Keywords:
COVID-19CTChest RadiographyLongitudinalLungMulti-Head AttentionMultimodalRadiomics

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Area of Science:

  • Radiomics and Artificial Intelligence
  • Medical Imaging Analysis
  • Computational Pathology

Background:

  • COVID-19 presents significant challenges in predicting patient outcomes.
  • Longitudinal imaging data offers potential for improved prognostic models.
  • Integrating multimodal imaging features can enhance predictive accuracy.

Purpose of the Study:

  • To assess the feasibility of a transformer model for fusing longitudinal multimodal radiomic features.
  • To predict clinical outcomes and identify associated events in COVID-19 patients.
  • To leverage chest radiographs (CXRs) and CT images for enhanced prediction.

Main Methods:

  • Retrospective analysis of longitudinal CXRs and CT images from COVID-19 patients.
  • Extraction of radiomic features using the Cancer Imaging Phenomics Toolkit.
  • Integration of features via a transformer-based model for outcome prediction.
  • Validation using training, validation, and test sets (65:15:20 split).

Main Results:

  • The transformer model achieved high weighted testing AUCs: 0.86 for mortality, 0.82 for ICU admission, and 0.86 for ventilator usage.
  • Performance significantly outperformed models using only cross-sectional or single-modality data (P < .05).
  • The study included 2274 patients, with data from two distinct sites.

Conclusions:

  • Transformer-based fusion of longitudinal multimodal radiomic data is feasible and effective.
  • This approach accurately predicts clinical outcomes and events in COVID-19 patients.
  • The findings highlight the potential of advanced AI models in managing COVID-19.