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Accuracy of deep learning-based attenuation correction in Tc-GSA SPECT/CT hepatic imaging.
Radiography (London, England : 1995)
Nov 15, 2024
INTRODUCTION: Attenuation correction (AC) is necessary for accurate assessment of radioactive distribution in single photon emission computed tomography (SPECT). The method of computed tomography-based AC (CTAC) is widely used because of its accuracy...
Deep learning-based attenuation correction method in Tc-GSA SPECT/CT hepatic imaging: a phantom study.
Radiological physics and technology
Nov 30, 2023
This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECT) for AC in Tc-galactosyl human albumin dieth...
A deep-learning-based prediction model for the biodistribution of Y microspheres in liver radioembolization.
Medical physics
Oct 21, 2021
BACKGROUND: Radioembolization with Y microspheres is a treatment approach for liver cancer. Currently, employed dosimetric calculations exhibit low accuracy, lacking consideration of individual patient, and tissue characteristics.
Deep Learning-Based Computed Tomography Perfusion Mapping (DL-CTPM) for Pulmonary CT-to-Perfusion Translation.
International journal of radiation oncology, biology, physics
Mar 6, 2021
PURPOSE: Our purpose was to develop a deep learning-based computed tomography (CT) perfusion mapping (DL-CTPM) method that synthesizes lung perfusion images from CT images.
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