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Computed Tomography

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: CT

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 for Cardiovascular System VI: Calcium -Scoring CT

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...

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関連する実験動画

Updated: Sep 9, 2025

Quantitative Micro-CT Analysis of Aortopathy in a Mouse Model of β-aminopropionitrile-induced Aortic Aneurysm and Dissection

Quantitative Micro-CT Analysis of Aortopathy in a Mouse Model of β-aminopropionitrile-induced Aortic Aneurysm and Dissection

Published on: July 16, 2018

多中心研究:大動脈解剖を予測するための革新的な双方向コンピュータトモグラフィのデータ駆動型深層学習モデル

Ziqian Li¹, Lintao Chen¹, Shengxuming Zhang²

¹Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Quantitative imaging in medicine and surgery

|September 2, 2025

まとめ

この要約は機械生成です。

新しいバイモダル深層学習モデルは,非コントラストCTとCTAの両方を用いて,大動脈解剖 (AD) を正確に診断します. このフレームワークはこの致命的な緊急事態の診断効率を向上させ治療を迅速に可能にします

キーワード:

大動脈大動脈解剖 (AD)コンピュータトモグラフィー (CT)ディープラーニング (DL)

さらに関連する動画

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Author Spotlight: Using Point-of-Care Ultrasound for Comprehensive Evaluation of the Abdominal Aorta

Author Spotlight: Using Point-of-Care Ultrasound for Comprehensive Evaluation of the Abdominal Aorta

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関連する実験動画

Last Updated: Sep 9, 2025

Quantitative Micro-CT Analysis of Aortopathy in a Mouse Model of β-aminopropionitrile-induced Aortic Aneurysm and Dissection

Quantitative Micro-CT Analysis of Aortopathy in a Mouse Model of β-aminopropionitrile-induced Aortic Aneurysm and Dissection

Published on: July 16, 2018

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

Author Spotlight: Using Point-of-Care Ultrasound for Comprehensive Evaluation of the Abdominal Aorta

Author Spotlight: Using Point-of-Care Ultrasound for Comprehensive Evaluation of the Abdominal Aorta

Published on: September 8, 2023

科学分野:

医療用イメージング
人工知能
心血管疾患

背景:

大動脈解剖 (AD) は生命を脅かす状態で,迅速な診断が必要です.
現在の計算機断層撮影 (CTA) 診断は,コントラスト剤により時間がかかります.
既存のディープラーニング (DL) モデルは,単一モダリティの入力 (非コントラストCTまたはCTA) に限定されています.

研究の目的:

改善された大動脈解剖 (AD) 診断のためのバイモダル深層学習 (DL) フレームワークを開発する.
デュアルパス検出のための非コントラストCTとCTAデータの独立した処理を可能にします.
アルツハイマー病の診断の効率を高めるため

主な方法:

2016年2月から2021年9月の間にコントラストのないCTとCTAを受けた患者を含めた遡及研究です.
2段階のDLモデルが開発されました:大動脈検知ネットワーク (AoDN) と大動脈解剖診断ネットワーク (ADDiN).
性能は,大動脈検出のための結合値0. 5 (AP@0. 5) 以上の交差点での平均精度と,診断のための受容器操作特性曲線 (AUC) 以下の面積を使用して評価されました.

主要な成果:

AoDNは,非コントラストCTでは99. 14%,CTAでは99. 34%のAP@0. 5で,大動脈検出において高い性能を達成した.
ADDiNは優れた診断精度を示し,非コントラストCTでは0. 98のAUC,CTAでは0. 99のAUCを達成した.
このモデルは3つの機関で検証されました.

結論:

提案されたバイモダルCTデータ駆動DLモデルはADを正確に診断します.
このフレームワークは,AD患者の迅速な病院診断と治療開始を促進します.
この研究は,心血管の緊急診断を改善するマルチモダルのDLの可能性を強調しています.