MRI-aided attenuation correction for PET imaging with deep learning

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

doi:10.1117/12.2549388

28 February 2020 MRI-aided attenuation correction for PET imaging with deep learning

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

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1131723 (2020) https://doi.org/10.1117/12.2549388
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

We propose to integrate multi-modality images and self-attention strategy into cycle-consistent adversarial networks (CycleGAN) to predict attenuation correction (AC) positron emission tomography (PET) image from non-AC (NAC) PET and MRI. During the training stage, deep features are extracted by 3D patch fashion from NAC PET and MRI images, and are automatically highlighted with the most informative features by self-attention strategy. Then, the deep features are mapped to the AC PET image by 3D CycleGAN. During the correction stage, the paired patches are extracted from a new arrival patient’s NAC PET and MRI images, and are fed into the trained networks to obtain the AC PET image. This proposed algorithm was evaluated using 18 patients’ datasets. Six-fold cross-validation was used to test the performance of the proposed method. The AC PET images generated with the proposed method show great resemblance with the reference AC PET images. The profile comparison also indicates the excellent matching between the reference and the proposed. The proposed method obtains the mean error ranging from -1.61% to 3.67% for all contoured volumes of interest. The whole-brain ME is less than 0.10%. These experimental studies demonstrate the clinical feasibility and accuracy of our proposed method.

Citation Download Citation

Yang Lei, Xue Dong, Tonghe Wang, Kristin Higgins, Tian Liu, Walter J. Curran, Jonathan A. Nye, Hui Mao, and Xiaofeng Yang "MRI-aided attenuation correction for PET imaging with deep learning", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131723 (28 February 2020); https://doi.org/10.1117/12.2549388

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KEYWORDS

Positron emission tomography

Magnetic resonance imaging

Signal attenuation

Computed tomography

Computer programming

3D image processing

Cancer

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