Computed tomography (CT) image generation method used for attenuation correction of positron emission tomography (PET) images
A CT image, attenuation correction technology, applied in the field of medical image reconstruction, can solve problems such as the increase of correction errors, and achieve the effect of reducing pressure, reducing costs, and enhancing costs
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Embodiment 1
[0021] Such as figure 1 It is a flowchart of a CT image generation method for PET image attenuation correction of the present invention, the method includes the following steps:
[0022] Step 1: Use PET / CT equipment to collect several patients at T 1 The CT image and PET image at any time, the pixels with the same coordinates in the two images correspond to the same position in the body; then collect the patient at T 2 The CT image and the PET image at each moment, the pixels with the same coordinates in the two images correspond to the same position in the body. This is because the same anatomical information corresponds to the same person's CT image and PET image acquired at the same time.
[0023] Step 2: Collect the T obtained in Step 1 1 CT image and PET image at time, T 2The CT images and PET images at each moment are input into the deep learning network for training. The deep learning network used here is selected from UNet and GAN (General Adversarial Network). Su...
Embodiment 2
[0027] The present invention is a kind of CT image generation method that is used for PET image attenuation correction, and this method comprises the following steps:
[0028] Step 1: Use PET / CT equipment to collect several patients at T 1 The CT image and PET image at the moment, the collection T 1 The moment of CT image acquisition with T 1 The pixels with the same coordinates of the PET image at any time correspond to the same position in the body; non-rigid deformation models such as thin-plate spline curves or B-spline curves are used for T 1 The CT images and PET images collected at all times are added with a deformation to generate T 2 PET images and CT images at the moment.
[0029] Step 2: Collect the T obtained in Step 1 1 CT image and PET image at time, T 2 The CT images and PET images at each moment are input into the deep learning network for training. The deep learning network used here is selected from UNet and GAN (General Adversarial Network). to T 1 CT...
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