2021
DOI: 10.1002/mp.15089
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Technical Note: A preliminary study of dual‐tracer PET image reconstruction guided by FDG and/or MR kernels

Abstract: Purpose Clinically, single radiotracer positron emission tomography (PET) imaging is a commonly used examination method; however, since each radioactive tracer reflects the information of only one kind of cell, it easily causes false negatives or false positives in disease diagnosis. Therefore, reasonably combining two or more radiotracers is recommended to improve the accuracy of diagnosis and the sensitivity and specificity of the disease when conditions permit. Methods This paper proposes incorporating 18F‐… Show more

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Cited by 3 publications
(2 citation statements)
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“…With the development of artifact intelligence, the excellent performance of deep learning methods in the medical image domain has received increasing attention in recent years. [22][23][24][25] Convolutional neural networks (CNN) are one of the most representative deep learning algorithms. [26][27][28][29] CNN can automatically extract features by learning a large amount of data and have thus been widely applied in the field of image segmentation.…”
Section: Segmentation Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…With the development of artifact intelligence, the excellent performance of deep learning methods in the medical image domain has received increasing attention in recent years. [22][23][24][25] Convolutional neural networks (CNN) are one of the most representative deep learning algorithms. [26][27][28][29] CNN can automatically extract features by learning a large amount of data and have thus been widely applied in the field of image segmentation.…”
Section: Segmentation Methodsmentioning
confidence: 99%
“…With the development of artifact intelligence, the excellent performance of deep learning methods in the medical image domain has received increasing attention in recent years 22–25 . Convolutional neural networks (CNN) are one of the most representative deep learning algorithms 26–29 .…”
Section: Introductionmentioning
confidence: 99%