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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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

Imaging Studies for Cardiovascular System V: CT

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

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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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
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Published on: February 21, 2025

LPGNet: Learnable Prompt-Guided Network for Low-dose Contrast Computed Tomography Angiography Imaging.

Yuehuai Zhang, Zhan Wu, Tianling Lyu

    IEEE Transactions on Bio-Medical Engineering
    |May 25, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Low-dose contrast agent computed tomography angiography (LDCTA) improves safety but struggles with image quality. LPGNet enhances LDCTA vascular imaging by integrating CLIP and intensity constraints, improving vessel visualization and detail preservation for better diagnostics.

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    High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

    Published on: December 16, 2022

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Radiology

    Background:

    • Iodinated contrast agents (ICAs) are crucial for computed tomography angiography (CTA) but pose risks like contrast-induced nephropathy.
    • Low-dose contrast agent CTA (LDCTA) offers a safer alternative but faces challenges in maintaining vessel intensity and boundary definition.

    Purpose of the Study:

    • To develop an advanced framework, LPGNet, for unpaired vascular reconstruction in LDCTA.
    • To improve the quality and diagnostic utility of low-dose contrast vascular imaging.

    Main Methods:

    • LPGNet integrates Contrastive Language-Image Pretraining (CLIP) with anatomical intensity consistency constraints.
    • Learnable prompts guide CLIP for contrast-specific semantics, while intensity loss ensures optimal aortic enhancement.
    • An iterative optimization strategy refines the network and prompts via semantic alignment.

    Main Results:

    • LPGNet effectively differentiates low-contrast vessels and identifies fine-grained structural details.
    • The framework enhances vascular contrast and preserves anatomical details in LDCTA.
    • Experiments show LPGNet outperforms existing paired and unpaired methods on simulated and clinical data.

    Conclusions:

    • LPGNet provides a robust solution for contrast-limited vascular imaging.
    • The method significantly improves diagnostic utility in LDCTA.
    • LPGNet demonstrates potential for safer and more effective vascular imaging.