AIMC Topic: Tomography, X-Ray Computed

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A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation.

Radiation oncology (London, England)
BACKGROUND: We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.

Great debates in cardiac computed tomography: OPINION: "Artificial intelligence and the future of cardiovascular CT - Managing expectation and challenging hype".

Journal of cardiovascular computed tomography
This manuscript has been written as a follow-up to the "AI/ML great debate" featured at the 2021 Society of Cardiovascular Computed Tomography (SCCT) Annual Scientific Meeting. In debate style, we highlighti the need for expectation management of AI/...

Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study.

European radiology
OBJECTIVES: To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indicati...

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.

BMC infectious diseases
BACKGROUND: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interp...

Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury.

Scientific reports
The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for pred...

Effectiveness of temporal subtraction computed tomography images using deep learning in detecting vertebral bone metastases.

European journal of radiology
PURPOSE: To assess the clinical effectiveness of temporal subtraction computed tomography (TS CT) using deep learning to improve vertebral bone metastasis detection.

Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models.

Sensors (Basel, Switzerland)
Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine...

The evaluation of the reduction of radiation dose via deep learning-based reconstruction for cadaveric human lung CT images.

Scientific reports
To compare the quality of CT images of the lung reconstructed using deep learning-based reconstruction (True Fidelity Image: TFI ™; GE Healthcare) to filtered back projection (FBP), and to determine the minimum tube current-time product in TFI withou...

Virtual computed-tomography system for deep-learning-based material decomposition.

Physics in medicine and biology
Material decomposition (MD) evaluates the elemental composition of human tissues and organs via computed tomography (CT) and is indispensable in correlating anatomical images with functional ones. A major issue in MD is inaccurate elemental informati...

Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model.

Theranostics
Although non-contrast computed tomography (NCCT) is the recommended examination for the suspected acute ischemic stroke (AIS), it cannot detect significant changes in the early infarction. We aimed to develop a deep-learning model to identify early ...