Deep learning framework to synthesize high-count preclinical PET images from low-count preclinical PET images-Reference-Cited by-同舟云学术

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Deep learning framework to synthesize high-count preclinical PET images from low-count preclinical PET images

Published:2022-04-04 Issue: Volume: Page:
ISSN:
Container-title:Medical Imaging 2022: Physics of Medical Imaging
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Author:

Dutta Kaushik,Liu Ziping,Laforest Richard,Jha Abhinav,Shoghi Kooresh I.

Publisher

SPIE

Reference47 articles.

1. A Positron-Emission Transaxial Tomograph for Nuclear Imaging (PETT)

2. Metabolic Positron Emission Tomography Imaging in Cancer Detection and Therapy Response

3. Low-Dose Imaging in a New Preclinical Total-Body PET/CT Scanner

4. Low dose PET reconstruction with total variation regularization;Chenye;Annu Int Conf IEEE Eng Med Biol Soc,2014

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Self-Supervised learning framework to generate preclinical Standard-Count PET from Low-Count PET and multi-objective task-based performance evaluation;2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD);2023-11-04

2. Image Denoising of Low-Dose PET Mouse Scans with Deep Learning: Validation Study for Preclinical Imaging Applicability;Molecular Imaging and Biology;2023-10-24

3. Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images;Physics in Medicine & Biology;2023-03-21

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