Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI

By Lei Xiang, Yu Qiao, Dong Nie, Le An, Weili Lin, Qian Wang, Dinggang Shen. 2017

289 citations recorded by the source corpus.

Positron emission tomography (PET) is an essential technique in many clinical applications such as tumor detection and brain disorder diagnosis. In order to obtain high-quality PET images, a standard-dose radioactive tracer is needed, which inevitably causes the risk of radiation exposure damage. For reducing the patient’s exposure to radiation and maintaining the high quality of PET images, in this paper, we propose a deep learning architecture to estimate the high-quality standard-dose PET (SPET) image from the combination of the low-quality low-dose PET (LPET) image and the accompanying T1-weighted acquisition from magnetic resonance imaging (MRI). Specifically, we adapt the convolutional neural network (CNN) to account for the two channel inputs of LPET and T1, and directly learn the end-to-end mapping between the inputs and the SPET output. Then, we integrate multiple CNN modules following the auto-context strategy, such that the tentatively estimated SPET of an early CNN can be iteratively refined by subsequent CNNs. Validations on real human brain PET/MRI data show that our proposed method can provide competitive estimation quality of the PET images, compared to the state-of-the-art methods. Meanwhile, our method is highly efficient to test on a new subject, e.g., spending ~2 seconds for estimating an entire SPET image in contrast to ~16 minutes by the state-of-the-art method. The results above demonstrate the potential of our method in real clinical applications.

Subfields: Radiation, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “For example: deformable image registration [97,239]; model-to-image registration [240,241]; MRI-based attenuation correction for PET [242,243]; PET/MRI dose calculation [244]; unsupervised end-to-end learning for deformable registration of 2D CT/MR images [245]; an unsupervised learning model for de”
3D conditional generative adversarial networks for high-quality PET image estimation at low dose (2018, methodology): “In this section, we compare our method with the existing PET estimation methods, including: 1) mapping based sparse representation method (m-SR) (Wang et al., 2016), 2) semi-supervised tripled dictionary learning method (t-DL) (Wang et al., 2017), and 3) convolutional neural networks based method (C”
Deep learning in medical imaging and radiation therapy. (2018, methodology): “This is important in modalities with ionizing radiation such as CT or PET for limiting patient dose, 237 – 239 , 241 or for limiting damage to samples in synchrotron-based After training with a limited number of high-dose training examples, they used the trained network to predict high-dose…”