scholarly journals Systematic infrared image quality improvement using deep learning based techniques

2016 ◽  
Author(s):  
Huaizhong Zhang ◽  
Pablo Casaseca-de-la-Higuera ◽  
Chunbo Luo ◽  
Qi Wang ◽  
Matthew Kitchin ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Deep Learning ◽  
Image Quality ◽  
Infrared Image ◽  
Image Quality Improvement

Image quality improvement in cone-beam CT using deep learning

2019 ◽  
Author(s):  
Yang Lei ◽  
Tonghe Wang ◽  
Joseph Harms ◽  
Ghazal Shafai-Erfani ◽  
Xue Dong ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Deep Learning ◽  
Image Quality ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Image Quality Improvement

scholarly journals Deep learning-based image quality improvement of 18F-fluorodeoxyglucose positron emission tomography: a retrospective observational study

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Junichi Tsuchiya ◽  
Kota Yokoyama ◽  
Ken Yamagiwa ◽  
Ryosuke Watanabe ◽  
Koichiro Kimura ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Emission Tomography ◽  
Gaussian Filter ◽  
Fluorodeoxyglucose Positron Emission Tomography ◽  
Tumor Delineation ◽  
Standardized Uptake Values ◽  
Positron Emission ◽  
Image Quality Improvement ◽  
Fluorodeoxyglucose Positron Emission

Abstract Background Deep learning (DL)-based image quality improvement is a novel technique based on convolutional neural networks. The aim of this study was to compare the clinical value of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images obtained with the DL method with those obtained using a Gaussian filter. Methods Fifty patients with a mean age of 64.4 (range, 19–88) years who underwent 18F-FDG PET/CT between April 2019 and May 2019 were included in the study. PET images were obtained with the DL method in addition to conventional images reconstructed with three-dimensional time of flight-ordered subset expectation maximization and filtered with a Gaussian filter as a baseline for comparison. The reconstructed images were reviewed by two nuclear medicine physicians and scored from 1 (poor) to 5 (excellent) for tumor delineation, overall image quality, and image noise. For the semi-quantitative analysis, standardized uptake values in tumors and healthy tissues were compared between images obtained using the DL method and those obtained with a Gaussian filter. Results Images acquired using the DL method scored significantly higher for tumor delineation, overall image quality, and image noise compared to baseline (P < 0.001). The Fleiss’ kappa value for overall inter-reader agreement was 0.78. The standardized uptake values in tumor obtained by DL were significantly higher than those acquired using a Gaussian filter (P < 0.001). Conclusions Deep learning method improves the quality of PET images.

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