AIMC Topic: Radiation Dosage
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Estimating subjective evaluation of low-contrast resolution using convolutional neural networks.
Physical and engineering sciences in medicine
Oct 11, 2021
To develop a convolutional neural network-based method for the subjective evaluation of computed tomography (CT) images having low-contrast resolution due to imaging conditions and nonlinear image processing. Four radiological technologists visually ...
Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction.
BMC medical imaging
Oct 10, 2021
BACKGROUND: Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT im...
Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms.
Abdominal radiology (New York)
Oct 4, 2021
BACKGROUND: Deep learning Computed Tomography (CT) reconstruction (DLR) algorithms promise to improve image quality but the impact on clinical diagnostic performance remains to be demonstrated. We aimed to compare DLR to standard iterative reconstruc...
Deep Learning-based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations.
Radiographics : a review publication of the Radiological Society of North America, Inc
Oct 1, 2021
Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest possible radiation dose is crucial in the radiosensitive pediatric population. The image quality of low-dose CT can be severely degraded by increased image nois...
Comparison of visibility of in-stent restenosis between conventional- and ultra-high spatial resolution computed tomography: coronary arterial phantom study.
Japanese journal of radiology
Sep 29, 2021
PURPOSE: The purposes of this experimental study were to compare the quantitative and qualitative visibility of in-stent restenosis between conventional-resolution CT (CRCT) and ultra-high-resolution CT (U-HRCT) and to investigate the effects of the ...
Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data.
Diagnostic and interventional imaging
Sep 5, 2021
PURPOSE: The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications.
Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study.
Medical physics
Sep 1, 2021
PURPOSE: To compare the impact on CT image quality and dose reduction of two versions of a Deep Learning Image Reconstruction algorithm.
Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT.
Korean journal of radiology
Aug 19, 2021
OBJECTIVE: The purpose of this study was to assess whether a deep learning (DL) algorithm could enable simultaneous noise reduction and edge sharpening in low-dose lumbar spine CT.
Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning-based image reconstruction algorithm on CT: a phantom study.
European radiology
Aug 14, 2021
OBJECTIVES: To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep learning-based image reconstruction algorithm (DLR, TrueFidelity™).
Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis.
Acta radiologica (Stockholm, Sweden : 1987)
Aug 9, 2021
BACKGROUND: Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction.
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