Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction

Gong, Kuang; Berg, Eric; Cherry, Simon R.; Qi, Jinyi

doi:10.1109/jproc.2019.2936809

Cited by 92 publications

(57 citation statements)

References 139 publications

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“…DL has greatly improved the accuracy of machine learning methods in image recognition, demonstrating many achievements 26 – 28 in the field of artificial intelligence and triggering an upsurge in research and development. However, few studies have focused on DL technology for the intelligent recognition of thyroid scintigraphy images.…”

Section: Discussionmentioning

confidence: 99%

Deep learning for intelligent diagnosis in thyroid scintigraphy

et al. 2021

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Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

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Section: Discussionmentioning

confidence: 99%

Deep learning for intelligent diagnosis in thyroid scintigraphy

et al. 2021

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“…Machine learning and deep learning (DL) and techniques have recently shown promise in many aspects of PET imaging from photon detection to image reconstruction and quantification [6,7]. In particular, deep convolutional neural networks (CNNs) have an immense potential to learn most representative image features from a multi-modal training space and hence give rise to data-driven priors which can surpass hypothesis-driven ones.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Deep Learning PET Image Reconstruction Using Forward–Backward Splitting Expectation–Maximization

Mehranian

Reader

2021

IEEE Trans. Radiat. Plasma Med. Sci.

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“…Nowadays deep learning is the most active research area, and much work has been reported in PET denoising [13][14][15][16][17][18][19]. Deep learning denoising is effective and has better results than traditional methods.…”

Section: Discussionmentioning

confidence: 99%

Poisson-noise weighted filter for time-of-flight positron emission tomography

Zeng

Huang

2020

Vis. Comput. Ind. Biomed. Art

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Image reconstruction for list-mode time-of-flight (TOF) positron emission tomography (PET) can be achieved by analytic algorithms. The backprojection filtering (BPF) algorithm is an efficient algorithm for this task. The conventional noise control method for analytic image reconstruction is the use of a stationary lowpass filter, which does not model the Poisson noise properly. This study proposes a nonstationary filter for Poisson noise control. The filter is implemented in the spatial domain in a form similar to convolution.

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Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction

Cited by 92 publications

References 139 publications

Deep learning for intelligent diagnosis in thyroid scintigraphy

Deep learning for intelligent diagnosis in thyroid scintigraphy

Model-Based Deep Learning PET Image Reconstruction Using Forward–Backward Splitting Expectation–Maximization

Poisson-noise weighted filter for time-of-flight positron emission tomography

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