AIMC Topic: Radiation Dosage
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Abdominal computed tomography localizer image generation: A deep learning approach.
Computer methods and programs in biomedicine
Dec 5, 2021
BACKGROUND AND OBJECTIVE: Computed Tomography (CT) has become an important clinical imaging modality, as well as the leading source of radiation dose from medical imaging procedures. Modern CT exams are usually led by two quick orthogonal localizatio...
Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography.
Medical physics
Dec 1, 2021
PURPOSE: Conventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict hu...
Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions.
European radiology
Nov 25, 2021
OBJECTIVES: To compare the overall image quality and detectability of significant (malignant and pre-malignant) liver lesions of low-dose liver CT (LDCT, 33.3% dose) using deep learning denoising (DLD) to standard-dose CT (SDCT, 100% dose) using mode...
Complex Relationship Between Artificial Intelligence and CT Radiation Dose.
Academic radiology
Nov 24, 2021
Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction techniques and image processing-based software. The latest technologies use artificial intelligence (AI)...
A deep-learning reconstruction algorithm that improves the image quality of low-tube-voltage coronary CT angiography.
European journal of radiology
Nov 24, 2021
PURPOSE: To assess the image quality (IQ) of low tube voltage coronary CT angiography (CCTA) images reconstructed with deep learning image reconstruction (DLIR).
Assessment of gastric wall structure using ultra-high-resolution computed tomography.
European journal of radiology
Nov 24, 2021
PURPOSE: To evaluate the image quality of ultra-high-resolution CT (U-HRCT) in the comparison among four different reconstruction methods, focusing on the gastric wall structure, and to compare the conspicuity of a three-layered structure of the gast...
Deep learning-based reconstruction of chest ultra-high-resolution computed tomography and quantitative evaluations of smaller airways.
Respiratory investigation
Nov 22, 2021
The full-iterative model reconstruction generates ultra-high-resolution computed tomography (U-HRCT) images comprising a 1024 × 1024 matrix and 0.25 mm thickness while suppressing image noises, allowing evaluating small airways 1-2 mm in diameter. Ho...
Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography.
European radiology
Nov 18, 2021
OBJECTIVES: Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilitie...
Computed Tomography Perfusion-Based Prediction of Core Infarct and Tissue at Risk: Can Artificial Intelligence Help Reduce Radiation Exposure?
Stroke
Oct 21, 2021
BACKGROUND AND PURPOSE: We explored the feasibility of automated, arterial input function independent, vendor neutral prediction of core infarct, and penumbral tissue using complete and partial computed tomographic perfusion data sets through neural ...
Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors.
BMC medical imaging
Oct 19, 2021
BACKGROUND: Motion artifacts affect the images of coronary calcified plaques. This study utilized convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential fa...
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