Attenuation correction without structural images for PET imaging

Yang Lei; Tonghe Wang; Xue Dong; Kristin Higgins; Tian Liu; Walter J. Curran; Hui Mao; Jonathon A. Nye; Xiaofeng Yang

doi:10.1117/12.2548455

28 February 2020 Attenuation correction without structural images for PET imaging

Yang Lei, Tonghe Wang, Xue Dong, Kristin Higgins, Tian Liu, Walter J. Curran, Hui Mao, Jonathon A. Nye, Xiaofeng Yang

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113171R (2020) https://doi.org/10.1117/12.2548455
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Deriving accurate attenuation correction factors for whole-body positron emission tomography (PET) images is challenging due to issues such as truncation, inter-scan motion, and erroneous transformation of structural voxelintensities to PET μ-map values. In this work, we proposed a deep-learning-based attenuation correction (DL-AC) method to derive the nonlinear mapping between attenuation corrected PET (AC PET) and non-attenuation corrected PET (NAC PET) images for whole-body PET imaging. A 3D cycle-consistent generative adversarial networks (cycle GAN) framework was employed to synthesize AC PET from NAC PET. The method learns a transformation that minimizes the difference between DL-AC PET, generated from NAC PET, and AC PET images. It also learns an inverse transformation such that cycle NAC PET image generated from the DL-AC PET is close to real NAC PET image. Both transformation network architectures are implemented by a residual network and outputs are judged by a fully convolutional network. A retrospective study was performed on 23 sets of whole-body PET/CT with leave-one-out cross validation. The proposed DL-AC method obtained the average mean error and normalized mean square error of the whole-body of -0.01%±2.91% and 1.21%±1.73%. We proposed a deep-learning-based approach to perform wholebody PET attenuation correction from NAC PET. The method demonstrates excellent quantification accuracy and reliability.

Citation Download Citation

Yang Lei, Tonghe Wang, Xue Dong, Kristin Higgins, Tian Liu, Walter J. Curran, Hui Mao, Jonathon A. Nye, and Xiaofeng Yang "Attenuation correction without structural images for PET imaging", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113171R (28 February 2020); https://doi.org/10.1117/12.2548455

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KEYWORDS

Positron emission tomography

Signal attenuation

Computed tomography

Magnetic resonance imaging

Cancer

Computer programming

Feature extraction

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