A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET‐CT scans-Reference-Cited by-同舟云学术

A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET‐CT scans

Published:2020-01-06 Issue:3 Volume:47 Page:1058-1066
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Xiong Xiaofan¹,Linhardt Timothy J.²,Liu Weiren³,Smith Brian J.⁴,Sun Wenqing⁵,Bauer Christian²,Sunderland John J.⁶,Graham Michael M.⁶,Buatti John M.⁵,Beichel Reinhard R.²

Affiliation:

1. Department of Biomedical Engineering The University of Iowa Iowa City IA 52242USA

2. Department of Electrical and Computer Engineering The University of Iowa Iowa City IA 52242USA

3. Roy J. and Lucille A. Carver College of Medicine The University of Iowa Iowa City IA 52242USA

4. Department of Biostatistics The University of Iowa Iowa City IA 52242USA

5. Department of Radiation Oncology University of Iowa Hospitals and Clinics Iowa City IA 52242USA

6. Department of Radiology The University of Iowa Iowa City IA 52242USA

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13970

Reference29 articles.

1. FDG PET based prediction of response in head and neck cancer treatment: Assessment of new quantitative imaging features

2. FLT PET Radiomics for Response Prediction to Chemoradiation Therapy in Head and Neck Squamous Cell Cancer

3. Quantification of tumour 18 F-FDG uptake: Normalise to blood glucose or scale to liver uptake?

4. [18F]FMISO PET/CT as a preoperative prognostic factor in patients with pancreatic cancer

5. FDG-PET reproducibility in tumor-bearing mice: comparing a traditional SUV approach with a tumor-to-brain tissue ratio approach

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