CT-guided PET parametric image reconstruction using deep neural network without prior training data

Jianan Cui; Kuang Gong; Ning Guo; Kyungsang Kim; Huafeng Liu; Quanzheng Li

doi:10.1117/12.2513077

1 March 2019 CT-guided PET parametric image reconstruction using deep neural network without prior training data

Jianan Cui, Kuang Gong, Ning Guo, Kyungsang Kim, Huafeng Liu, Quanzheng Li

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109480Z (2019) https://doi.org/10.1117/12.2513077
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Deep neural networks have attracted growing interests in medical image due to its success in computer vision tasks. One barrier for the application of deep neural networks is the need of large amounts of prior training pairs, which is not always feasible in clinical practice. Recently, the deep image prior framework shows that the convolutional neural network (CNN) can learn intrinsic structure information from the corrupted image. In this work, an iterative parametric reconstruction framework is proposed using deep neural network as constraint. The network does not need prior training pairs, but only the patient’s own CT image. The training is based on Logan plot derived from multi-bed-position dynamic positron emission tomography (PET) images using ⁶⁸Ga-PRGD2 tracer. We formulated the estimation of the slope of Logan plot as a constraint optimization problem and solved it using the alternating direction method of multipliers (ADMM) algorithm. Quantification results based on real patient dataset shows that the proposed parametric reconstruction method is better than the Gaussian denoising and non-local mean denoising methods.

Conference Presentation

Citation Download Citation

Jianan Cui, Kuang Gong, Ning Guo, Kyungsang Kim, Huafeng Liu, and Quanzheng Li "CT-guided PET parametric image reconstruction using deep neural network without prior training data", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109480Z (1 March 2019); https://doi.org/10.1117/12.2513077

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